fix

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Welcome to the mostly complete pandas cheatsheet of fast data manipulation methods!\n",
    "Apart from aspiring to be an extensive glossary of your go-to book for fastest methods in Pandas, this is also a gentle introduction to Jupyter notebooks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import time\n",
    "import random"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas reads and writes to a variety of formats. Some of the most popular being CSV, HDF5, SQL tables, Excel etc (in the order of my liking) and CSV is one of the most simplest ones of the lot. \n",
    "\n",
    "So, let's begin with a CSV file which contains 14 million NYC taxi cab rides.\n",
    "At the moment, we care about just about learning enough of pandas to write efficient code, so we will be dwelling on data analysis lesser.\n",
    "\n",
    "Data source - https://nyctaxitrips.blob.core.windows.net/data/trip_data_9.csv.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fibinse/anaconda/lib/python2.7/site-packages/IPython/core/interactiveshell.py:2705: DtypeWarning: Columns (4) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "DATAFILE = \"trip_data_9.csv\"\n",
    "df = pd.read_csv(DATAFILE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Headsup: A \"?\" after a python function is a simple way to look up its documentation. Why don't you run the next cell and see for yourself ? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "global name 'time' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-523bce363aa6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m# this function takes a string and executes it. So we can pass any pandas command and see the time elapsed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mtimer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'df[\"medallion\"].unique()'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-2-523bce363aa6>\u001b[0m in \u001b[0;36mtimer\u001b[0;34m(fn)\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtimer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;34m'''Takes a function, executes it and returns the time taken to exceute it in seconds'''\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0mstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m     \u001b[0mtime_taken\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mu'timeit -oq $fn'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0;34m'%.2f seconds'\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0mtime_taken\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: global name 'time' is not defined"
     ]
    }
   ],
   "source": [
    "# let's write a quick timer function. We want it to run a command and then tell us how much time it takes to run it\n",
    "\n",
    "def timer(fn):\n",
    "    '''Takes a function, executes it and returns the time taken to exceute it in seconds'''\n",
    "    start = time.time()\n",
    "    time_taken = %timeit -oq $fn\n",
    "    return '%.2f seconds' %time_taken.best\n",
    "\n",
    "# this function takes a string and executes it. So we can pass any pandas command and see the time elapsed\n",
    "timer('df[\"medallion\"].unique()')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. know your data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# What can you learn about your data set and how do you use Pandas to do that.\n",
    "\n",
    "print \"Data has\", df.shape[0], 'rows and ', df.shape[1], ' columns'\n",
    "\n",
    "print \"\\nThe unqiue columns in the data are : \", df.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# each column name seems to have a white spaces in front of it, let's clean that up\n",
    "df.columns = map(str.strip, df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`df.columns` gives us the column names in a Python data structure called a `list`. The ideal way to do a common action against all the elements of the list is to use the `map` function. `map` allows a function as the first parameter, which it then applies on each element of the list passed to it as the second parameter.\n",
    "\n",
    "Try executing `map?` in the next code block."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "map?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['medallion', 'hack_license', 'vendor_id', 'rate_code', 'store_and_fwd_flag', 'pickup_datetime', 'dropoff_datetime', 'passenger_count', 'trip_time_in_secs', 'trip_distance', 'pickup_longitude', 'pickup_latitude', 'dropoff_longitude', 'dropoff_latitude']\n"
     ]
    }
   ],
   "source": [
    "# lets see the column names now\n",
    "print df.columns.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We seem to have got the column names rectified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df.columns.tolist() takes  0.00 seconds\n",
      "list(df) takes  0.00 seconds\n"
     ]
    }
   ],
   "source": [
    "# We seem to using `df.columns.tolist()` a lot. Let's see how much time it takes. And if there are faster ways to do it.\n",
    "\n",
    "print \"df.columns.tolist() takes \", timer(\"df.columns.tolist()\") \n",
    "print \"list(df) takes \", timer(\"list(df)\") \n",
    "\n",
    "# looks like they take the same amount of time, so let's just go with the shorter `list(df)`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "You should try the following commands to view different subsets of your data - `df.head()`, `df.tail()`, `df.index` and `df.describe()`. Again: `?` after a function will tell you give the available function documentation, please make the most of it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>medallion</th>\n",
       "      <th>hack_license</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>rate_code</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_time_in_secs</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0CF8B9F42FED24FA1CA8AACA36D1A25B</td>\n",
       "      <td>A04B37232EB2478C7A831A6C587C15B4</td>\n",
       "      <td>CMT</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>2013-09-01 16:35:05</td>\n",
       "      <td>2013-09-01 16:47:53</td>\n",
       "      <td>2</td>\n",
       "      <td>767</td>\n",
       "      <td>2.6</td>\n",
       "      <td>-73.987900</td>\n",
       "      <td>40.724079</td>\n",
       "      <td>-73.994598</td>\n",
       "      <td>40.750580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4D3E527682E42F1FACDFBF2D56757AC6</td>\n",
       "      <td>8C1A6E14ED1D019FBD227970CC619496</td>\n",
       "      <td>CMT</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>2013-09-01 17:44:05</td>\n",
       "      <td>2013-09-01 17:58:37</td>\n",
       "      <td>1</td>\n",
       "      <td>871</td>\n",
       "      <td>3.5</td>\n",
       "      <td>-74.007866</td>\n",
       "      <td>40.710232</td>\n",
       "      <td>-74.003975</td>\n",
       "      <td>40.756569</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          medallion                      hack_license  \\\n",
       "0  0CF8B9F42FED24FA1CA8AACA36D1A25B  A04B37232EB2478C7A831A6C587C15B4   \n",
       "1  4D3E527682E42F1FACDFBF2D56757AC6  8C1A6E14ED1D019FBD227970CC619496   \n",
       "\n",
       "  vendor_id  rate_code store_and_fwd_flag      pickup_datetime  \\\n",
       "0       CMT          1                  N  2013-09-01 16:35:05   \n",
       "1       CMT          1                  N  2013-09-01 17:44:05   \n",
       "\n",
       "      dropoff_datetime  passenger_count  trip_time_in_secs  trip_distance  \\\n",
       "0  2013-09-01 16:47:53                2                767            2.6   \n",
       "1  2013-09-01 17:58:37                1                871            3.5   \n",
       "\n",
       "   pickup_longitude  pickup_latitude  dropoff_longitude  dropoff_latitude  \n",
       "0        -73.987900        40.724079         -73.994598         40.750580  \n",
       "1        -74.007866        40.710232         -74.003975         40.756569  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets introspect the data a bit more\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df['medallion'].nunique() takes : 1.41 seconds\n",
      "\n",
      "len(df['medallion'].unique()) takes: 1.47 seconds\n",
      "\n",
      "df['medallion'].drop_duplicates().size  takes:  0.80 seconds\n",
      "\n",
      "df['medallion'].value_counts() takes : 1.57 seconds\n"
     ]
    }
   ],
   "source": [
    "# how many unique medallions do we have here\n",
    "print \"df['medallion'].nunique() takes :\", timer(\"df['medallion'].nunique()\")\n",
    "\n",
    "print \"\\nlen(df['medallion'].unique()) takes:\", timer(\"len(df['medallion'].unique())\")\n",
    "\n",
    "print \"\\ndf['medallion'].drop_duplicates().size  takes: \", timer(\"df['medallion'].drop_duplicates().size\")\n",
    "\n",
    "print \"\\ndf['medallion'].value_counts() takes :\", timer(\"df['medallion'].value_counts().index\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "looks like `.drop_duplicates()` is the fastest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total medallions :  13438\n"
     ]
    }
   ],
   "source": [
    "# so that data has 13438 medallions\n",
    "\n",
    "print \"Total medallions : \", df[\"medallion\"].drop_duplicates().size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df['medallion'].value_counts() takes :  1.81 seconds\n",
      "\n",
      "df.groupby('medallion').sum()['Count'] takes :  4.72 seconds\n"
     ]
    }
   ],
   "source": [
    "# To see most commonly occuring medallion numbers, we could use one of these options. \n",
    "\n",
    "print \"df['medallion'].value_counts() takes : \", timer(\"df['medallion'].value_counts()\")\n",
    "\n",
    "# Let's try adding a new column and then sum it up according to that\n",
    "df['Count'] = 1\n",
    "print \"\\ndf.groupby('medallion').sum()['Count'] takes : \", timer(\"df.groupby('medallion').sum()['Count']\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 2. Dicing the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df[['trip_distance', 'passenger_count']] took  0.08 seconds\n",
      "df.loc[:,'trip_distance': 'passenger_count'] took  0.00 seconds\n"
     ]
    }
   ],
   "source": [
    "# How would you select a single column ? Something like df[column_name]\n",
    "\n",
    "# Now if you wanted to select multiple columns, you could : \n",
    "print \"df[['trip_distance', 'passenger_count']] took \", timer(\"df[['trip_distance', 'passenger_count']]\")\n",
    "\n",
    "# or, you could:\n",
    "print \"df.loc[:,'trip_distance': 'passenger_count'] took \", timer(\"df.loc[:,'trip_distance': 'passenger_count']\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtering data based on a list of values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df[df['passenger_count'] == 2] 0.45 seconds\n",
      "\n",
      "df[df['passenger_count'].isin([2])] 0.47 seconds\n"
     ]
    }
   ],
   "source": [
    "# This section will demonstrate ways to slice and dice your data. \n",
    "# For example - if we want to get all the rows of the data which have 2 passengers, you could do something like\n",
    "\n",
    "print \"df[df['passenger_count'] == 2]\", timer(\"df[df['passenger_count'] == 2]\")\n",
    "\n",
    "# if we wanted to look for rows which may have one values of a list of values. Let's just pass a single value \n",
    "# and see how it times.\n",
    "\n",
    "print \"\\ndf[df['passenger_count'].isin([2])]\", timer(\"df[df['passenger_count'].isin([2])]\")\n",
    "\n",
    "# Since, they time close enough, I usually prefer to go the .isin route."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtering based on a range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df[(df['trip_distance'] > 2) & (df['trip_distance'] <=5)] took 0.96 seconds\n",
      "\n",
      "df.trip_distance.between(2, 5, inclusive=True) took  0.06 seconds\n",
      "\n",
      "df.trip_distance.between(2, 5, inclusive=False) took  0.06 seconds\n",
      "\n",
      "df.query('2 < trip_distance < 5') took  1.02 seconds\n"
     ]
    }
   ],
   "source": [
    "# This is the equivalent of the SQL opertions that give you a bunch of rows between two values\n",
    "print \"df[(df['trip_distance'] > 2) & (df['trip_distance'] <=5)] took\", timer(\"df[(df['trip_distance'] > 2) & (df['trip_distance'] <=5)]\")\n",
    "\n",
    "# Then there is the between function\n",
    "print \"\\ndf.trip_distance.between(2, 5, inclusive=True) took \", timer(\"df.trip_distance.between(2, 5, inclusive=True)\")\n",
    "\n",
    "# Same function as above when inclusive is set to False\n",
    "print \"\\ndf.trip_distance.between(2, 5, inclusive=False) took \", timer(\"df.trip_distance.between(2, 5, inclusive=False)\")\n",
    "\n",
    "# And a query function took\n",
    "print \"\\ndf.query('2 < trip_distance < 5') took \", timer(\"df.query('2 < trip_distance < 5')\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting to datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pd.to_datetime(df['pickup_datetime']) took  2.56 seconds\n",
      "pd.to_datetime(df['pickup_datetime'], format='%Y-%m-%d %H:%M:%S') took  2.54 seconds\n",
      "df['pickup_datetime'].apply(pd.Timestamp) took  65.83 seconds\n",
      "df['pickup_datetime'].map(pd.Timestamp) took  65.92 seconds\n"
     ]
    }
   ],
   "source": [
    "# Let's time the ways to convert datetime columns into timestamps, so that we can then look at ways to slice them\n",
    "# There are two columns which contain date & time - pickup_datetime, dropoff_datetime\n",
    "# NOTE: The author would prefer to parse dates while file is being read itself. Pandas provides the parse_date function\n",
    "# which takes a list of column names which parses columns on data-read. But, since the process cannot be timed, this has not been\n",
    "# included below.\n",
    "\n",
    "# Since we would be unable to time it, we have not included another way to conve\n",
    "\n",
    "print \"pd.to_datetime(df['pickup_datetime']) took \", timer(\"pd.to_datetime(df['pickup_datetime'])\")\n",
    "\n",
    "# Specifying a format can make things even faster\n",
    "print \"pd.to_datetime(df['pickup_datetime'], format='%Y-%m-%d %H:%M:%S') took \", timer(\"pd.to_datetime(df['pickup_datetime'], format='%Y-%m-%d %H:%M:%S')\")\n",
    "\n",
    "# Following commented out because they took longer than 15 seconds\n",
    "print \"df['pickup_datetime'].apply(pd.Timestamp) took  65.83 seconds\"\n",
    "print \"df['pickup_datetime'].map(pd.Timestamp) took  65.92 seconds\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets use the fastest of the three methods to convert columns into Datetime format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['pickup_datetime'] = pd.to_datetime(df['pickup_datetime'], format='%Y-%m-%d %H:%M:%S')\n",
    "df['dropoff_datetime'] = pd.to_datetime(df['dropoff_datetime'], format='%Y-%m-%d %H:%M:%S')\n",
    "df_timeindex = df.set_index('pickup_datetime').sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Slice by datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, you can use `.loc` to slice based on the datetime string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df_timeindex.loc['2013-09-01': '2013-10-01', :] took  0.00 seconds\n",
      "df_timeindex.ix['2013-09-01': '2013-10-01'] took  0.00 seconds\n",
      "df_timeindex.truncate(before=pd.to_datetime('2013-09-02'), after=pd.to_datetime('2013-10-01')) took  1.82 seconds\n"
     ]
    }
   ],
   "source": [
    "print \"df_timeindex.loc['2013-09-01': '2013-10-01', :] took \", timer(\"df_timeindex.loc['2013-09-01': '2013-10-01', :]\")\n",
    "print \"df_timeindex.ix['2013-09-01': '2013-10-01'] took \", timer(\"df_timeindex.ix['2013-09-01': '2013-10-01']\")\n",
    "\n",
    "# The truncate function below removes data from before and after limits specified\n",
    "# returns a copy of the data by default but needs a sorted index to work from.\n",
    "\n",
    "print \"df_timeindex.truncate(before=pd.to_datetime('2013-09-02'), after=pd.to_datetime('2013-10-01')) took \", timer(\"df_timeindex.truncate(before=pd.to_datetime('2013-09-02'), after=pd.to_datetime('2013-10-01'))\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting by row-column index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " `.iloc` takes slice of row integer index and a column integer index. In the line below, we are selecting 8 rows \n",
    " (from the second till the 10) and the first four columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>medallion</th>\n",
       "      <th>hack_license</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>rate_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>D28DD94BE7682B7434EC5CA4D523A788</td>\n",
       "      <td>96F7564919171F55F551F0F9B96B5199</td>\n",
       "      <td>CMT</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CFF1FDD049E5433E6FBCC96EDA9E66A5</td>\n",
       "      <td>9BF57AD72288939D6385149524149269</td>\n",
       "      <td>CMT</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F9B4C49F95496C0EFA6674364F4B54AE</td>\n",
       "      <td>2F8F14718163E0F86E3E4F718A128F2B</td>\n",
       "      <td>CMT</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>F2967DBF707C06314CEB745A83332D62</td>\n",
       "      <td>217B9B441D592C5F8AAA930264BE7F66</td>\n",
       "      <td>CMT</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9BA35AC8B9018EBCF66913620278FF0E</td>\n",
       "      <td>7769D50E741629E74CEF55CCC14CE09B</td>\n",
       "      <td>CMT</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DB0D656FD074AC9E5B039E9A5A17F408</td>\n",
       "      <td>13C91816AF95F6514447B72559BEA179</td>\n",
       "      <td>CMT</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8F832FF1330148CD9C4FBA7EC1D3D99A</td>\n",
       "      <td>ADADC8964B0550C8C240454314A1AB09</td>\n",
       "      <td>CMT</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6B3D1A9F93C3769EF8F8446DE7CCB9F4</td>\n",
       "      <td>1BD12874047104377287751241BCDA3A</td>\n",
       "      <td>CMT</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          medallion                      hack_license  \\\n",
       "2  D28DD94BE7682B7434EC5CA4D523A788  96F7564919171F55F551F0F9B96B5199   \n",
       "3  CFF1FDD049E5433E6FBCC96EDA9E66A5  9BF57AD72288939D6385149524149269   \n",
       "4  F9B4C49F95496C0EFA6674364F4B54AE  2F8F14718163E0F86E3E4F718A128F2B   \n",
       "5  F2967DBF707C06314CEB745A83332D62  217B9B441D592C5F8AAA930264BE7F66   \n",
       "6  9BA35AC8B9018EBCF66913620278FF0E  7769D50E741629E74CEF55CCC14CE09B   \n",
       "7  DB0D656FD074AC9E5B039E9A5A17F408  13C91816AF95F6514447B72559BEA179   \n",
       "8  8F832FF1330148CD9C4FBA7EC1D3D99A  ADADC8964B0550C8C240454314A1AB09   \n",
       "9  6B3D1A9F93C3769EF8F8446DE7CCB9F4  1BD12874047104377287751241BCDA3A   \n",
       "\n",
       "  vendor_id  rate_code  \n",
       "2       CMT          1  \n",
       "3       CMT          1  \n",
       "4       CMT          1  \n",
       "5       CMT          1  \n",
       "6       CMT          1  \n",
       "7       CMT          1  \n",
       "8       CMT          1  \n",
       "9       CMT          1  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[2:10, :4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Visualisation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualisation is a great way to find and convey insights. So, as our next step we will introduce ourselves to the in-the-box methods available to Pandas and use them as we learn & time other facets of Pandas.\n",
    "\n",
    "We will also look at how we can use the Seaborn package to improve visual appeal and use charts which are not _yet_ available in Pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's ask some questions about the data set and visualise the answers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which is the most \"popular\" taxi medallion in the data set ? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10237a050>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xFXezXSEplpVS+ZgK16KO+2JzfFj+DG4sXCHn9yqc07OSZrDnz8XANml/PHBD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\nZlkffBZLdDvv7vns3yIZTyAmoKJViLt7JEs4K28oQj7cEcFE3Oru2wb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C/oe7719Sf2yX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1099cdcd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we will groupby the medallion column and sum up the Count column to get a Pandas series\n",
    "temp = df.groupby('medallion').sum()['Count']\n",
    "# let's sort the most medallion numbers by Count. Since Count is the only column we have taken\n",
    "# we don't need to mention the column name explicitly.\n",
    "temp = temp.sort_values(ascending=False).head(20)\n",
    "temp.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You could also make a horizontal bar chart to make the medallion numbers more readable and potentially save your neck."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Vwcfxid4L1szAq/sOLyhv+RVjkGzVFd9Nu3HeNyaN83DY81VJ3B6xVXlp/E9G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\njDHGtOR/gVw8nKrUqX0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10238bb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp.plot(kind='barh')\n",
    "top_medallions = temp.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay! We are able to not have to crane the neck to read the numbers. But the plot in itself could be way better. \n",
    "Let's see how we can improve this chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "sns.set(context=\"talk\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10fbfe790>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1NRWAXr16ce3aNWJjYykoKCAuLo6EhATevHkDgL6+PpaWlhw5coSdO3dy8eJF\n1q9fL+V98uRJunTpIv3p3LmzNNLToUMHcnJyCAsLIz8/n+vXr3P06FEp74raIy8vj06dOhEbG0ti\nYiI9e/ZUefBXKpVYWFiofG6XLl24efPme/vybb/88guurq64urqip6cHgKGhYZlpAXJzczl8+LDU\nng4ODhw+fJgXL14AkJmZKa2Dq4yjR48SGxvLv//9b4KCglTOLV68mB9++AFjY2OmTp0qHX/+/LlU\nbzMzM2bPno29vb20Lu/169cqAZmGhgZFRUXk5eUBqm1X0m5RUVFS+n379tG3b190dXUxNDTEwsKC\nXbt2AcWjlV27dmX16tXk5OSQlpbG9u3bpT4tj66uLkqlkqysLLKysrh48SL6+vqcOXOG5cuXs3Tp\nUi5fvqxyzebNm5kwYUKp72VBEARBEAThz/dBTemsUaNGqSDu9evXdOrUiatXr6ocz83NVdnAAopH\nxL7++muVYwqFgj59+tC9e3cARo0axZYtW0hKSsLQ0JCsrCwKCwtRU1NTuS47OxstLS2USiWzZ89m\n3759aGtro62tzaxZs3Bzc8PNzU3lcwDmzJnDrl27+Omnn6QplcnJyTg7O2NpacnChQtVPicxMZHs\n7GwGDBigcvzSpUvcuXNHpT4FBQVEREQwf/58GjVqxJo1awgICGDhwoX07NmTQYMGUbNmTaB445QS\n9evXZ8qUKQQGBkpltrKyKncNX82aNdm0aRO+vr4EBwfTtm1bbGxsSEtLo6CgoML2WLZsGf3796dB\ngwYAzJ8/nw4dOvDzzz+jqamJTCYjLi6uwnVoUHZfloiPj8fNzY2JEycyadKkCvMpcfToUXJycvji\niy+kY2/evCE6Oprx48djaGhIenp6mddmZmaip6enEpArFAoaNGjApEmT2LFjhzT9teScQqHAw8MD\na2trsrOzAdDT01NZw3fz5k2mT5+Ojo4O48aNK/VLjNzcXNTU1KR7631tFxUVxfPnz+nVq5d0/aVL\nl5g6dSoKhYJVq1axdOlS+vXrR926dbGxseHevXsVtltmZiYymYxatWqhUCjQ09OTRrDbt29P//79\nOXnyJB07dgTg8ePH/PDDDwQEBFSYryAIgiD80eRyGWpqsvcn/BuTy2Uqfwt/b39UP35QAZ+JiQkR\nEREqx1JTU7G0tCQ/P5/Hjx/zySefSMffnv6ZkpJCeno6vXv3Vrne2Ni41ENtUVERSqWS9u3bo66u\nTlxcnLSaZfXmAAAgAElEQVQ2rsS8efPQ0dHh66+/Jjs7WxplgeLpjSWbgsybN482bdpIo2MFBQUo\nlUp0dHSA4rV2bm5uTJs2jXHjxpWq85kzZ7C2ti41yrhnzx6++OILpkyZIh1LSkpi7ty5uLm5oVQq\nqVu3LgcOHJDOOzg40Lt3b7Kzs9m4cSPTp0+XguLc3NxSo6TlycvLo1q1auzevVs65ubmRsuWLXn5\n8mWF7fHw4UOVczKZDJlMphJQv29KZ3l9CcXTX5cvX86SJUsYPHhwpeoDxdM5PTw8GDp0qHTsyJEj\nhIWFMX78eHr27Im3tze3bt0qNQI7ceJELC0tGTVqFPb29uzbt08KrPPy8qR/T5w4kbFjx0rrQUva\nsbyRrhYtWmBtbU1CQgLjxo2jSZMmpKamSutSy5riXF7bxcfHk5uby/fff69yfMSIERw9epRhw4aR\nkZGBn5+fFDDu3r37ves8S3ZHbdy4McbGxtL9XRL8lnwvlTh9+jRdunSRRl0FQRAE4a+iq6uJvr72\nX12MP4WentZfXQThA/ZBBXzdunUjLy+PiIgIHBwc2L9/PxkZGQwcOJBTp07h7+/P0qVL+emnnzh8\n+DChoaHStdeuXaNVq1aldme0sbHB0dGRf/3rX/Tu3Zvw8HDy8vLo2rUrCoWCWbNmsWDBAmmXztzc\nXLZt28aFCxfYs2cPurq6tGvXjlWrVuHv709ubi7BwcH885//BIo3NNm6dSu9e/dGX1+fZcuW0blz\nZ+rXr8/PP/+Mq6sry5YtKzc4uXbtGra2tirHMjMzOX78OJGRkdIGHlC8bszHx4eYmBgsLCxwcHAg\nIiKCpk2bEhUVxePHj7G0tKR69eqcOHECgNmzZ/PgwQM2bdpU7nqydxUVFfHFF18QGBhIr169OHHi\nBPHx8cydO/e97WFhYcGWLVvo1asXRkZG+Pv707x5c0xMTHjw4AFKpfK9AV95fZmQkMCSJUvYunWr\nNKJUGT/99BP//ve/2bhxo8oGI7a2tvj7+3PmzBksLCwYN26c1F8dOnQgMzOTtWvXkp6ezsiRI9HX\n18fQ0JDAwEDmzZvH3bt32bJlizS616pVKzZu3EibNm1QU1PDz88PGxsbaUvod+t99+5dTp06JfX/\n0KFD2bJlC926dUNNTY3Q0FCGDRsmpa+o3SIjIxk8eLDK/VKSZ3h4uLTBjZmZGbNmzeLWrVuEhobi\n5eVVZn5FRUWcPXuWwMBAZs+eDRSvSa1RowZBQUG4uLhw7do1Tpw4wbZt26Trrl27Rvv27d/bJ4Ig\nCILwR8vKekVGRs5fXYw/lFwuQ09Pi+fPX1b512x8DEr68/f2QQV8CoWCzZs34+3tTUBAAI0aNWLj\nxo1oaGiwdOlSFi5cSJ8+fdDS0sLT01Nlh84HDx5gZGRUKs+WLVuyceNGVq1ahZubG40bNyYkJEQa\ndRk1ahS6uroEBQXh4eGBXC6nbdu2hIeHS6Mra9euxdfXF0tLSxQKBYMGDZIegh0dHcnIyGDkyJEU\nFBTQs2dPaT1bWFgYb968Yf78+dKDtUwmY+7cudK7zR48eEDt2rVVynzgwAEaNGhQanMXmUyGjY0N\nERERjB49miVLljB9+nSysrJo1aoVW7dulUZvQkJC8PHxoVu3bmhoaODo6KgynfHkyZPSZiPw33e6\neXt7M2zYMNatW8fy5cuZNWsWJiYmhISESOWsqD2mTZtGYWEho0aNIi8vj44dO6pML5XJZNKUw7e1\nb99eev9geX35zTffUFBQIE0pLCnzunXrysyzRFRUFD169Ci1m6S2tjbW1taEh4djYWGBu7s7devW\nZdGiRTx69AgNDQ26dOlCeHi4FEitXbtWmkKrp6fH+PHjsbGxAWD69OmsWrWKIUOGoKamRv/+/XF3\nd5c+LysrS2pzmUyGtrY2Q4YMwcnJCSi+F9PT0xkxYgT5+fnY2NiojAq/u8azREZGBqdPny41Og7F\nO5mGhoZy7do1fHx8mDdvHp06dUJfXx9nZ2esrKyktDdu3JDKp66uTqNGjfDy8mLQoEEAVK9enbCw\nMBYvXkyPHj3Q1tZmwYIFpb4PRcAnCIIgfAiKipQUFn4cQdDHVFfht5MpK7tHviAIwh+o95gA8VoG\nQRAE4XeRlZbCwvGdq/x7+NTUZOjra5ORkSMCviqgpD9/bx/UCJ8gCB+vFxn3/+oiCIIgCFVE8c+U\nzn91MQThgyBG+ARB+CAkJSWRlfVKrEGoAuRyGbq6mqI/qwjRn1XLx9SfLVu2ltbRV1VihK9qESN8\ngiBUaR06dBA/sKoI8QBStYj+rFpEfwrCx+eDevG6IAiCIAiCIAiC8PsRAZ8gCIIgCIIgCEIVJQI+\nQRAEQRAEQRCEKkoEfIIgCIIgCIIgCFWUCPgEQRAEQRAEQRCqKBHwCYIgCIIgCIIgVFEi4BMEQRAE\nQRAEQaiiPoj38FlaWpKeno6amhoASqUSmUzGihUrmDFjBjVq1EAmk6FUKtHW1qZv3764u7tTs2ZN\nKY+goCB2795NXl4e3bp1Y9myZejo6Ejnc3Jy6N27N507d2bTpk1lliMzMxM7OztCQkJo2rSpyrmi\noiKmTp2KhYUFDg4O0vHdu3ezefNmsrOzad68OQsWLKBFixYq1165coVZs2Zx5swZ6djIkSP5z3/+\nQ7Vq/+0CpVJJ7dq1iY2NBeDs2bOsWLGChw8f8umnn7Js2TIaNmzIpUuX+Oqrr5DJZNJ1b968wdzc\nnI0bN9KxY0fpHEBeXh7q6upcuXIFgIEDB5KWlia1aaNGjdi/fz93796lf//+aGpqSvnWqVOHr776\nis8//1ylTi9evKB37950796dDRs2qJx7u25KpRINDQ0sLS1ZsGABGhoaABw5coSgoCAeP35M/fr1\nmTVrFpaWllJ5ly9fznfffQdA//798fb2VmkrgICAAC5evMiePXsASEhIYMKECdSoUUMqf8OGDXFz\nc6NPnz4qZQ8ODub48eM8f/4cQ0NDbGxsmDJlCnL5f38Hsn//fnbt2sXt27dRKBR07tyZ2bNn06BB\nAwAePXrEkiVLSEpKQqFQMGjQIObMmUO1atWIiopi4cKFUn1LytOsWTMiIyMpKChg2bJlxMbGUlhY\nSPfu3Vm0aBG6uroATJw4kcTERNTU1FAqlairq3Pp0iUKCwtp3bq19D1Rkq9MJmPAgAEsX74cgGPH\njhEYGMizZ89o1qwZS5YsoXnz5gAcP36cwMBAnjx5QsOGDfHw8KB79+4A3Lp1i2XLlnHjxg10dHSw\ns7PD2dlZ6peFCxdy8uRJFAoFY8eOxcnJSapfUFAQ0dHRvHr1ilatWrFgwQKaNGlCZYkXr1cdH9OL\nnT8Goj+rlo+pPz+GF68LQmV8EAEfwLp161QeykvIZDKio6OlB8e0tDQWLlyIk5MTu3fvBiAsLIzv\nv/+effv2oauri7u7O6tWrWLJkiVSPocOHaJPnz6cO3eOe/fuSQ/tJRITE/H29ubBgwelynD//n0W\nLVrEuXPnsLCwkI4nJyezbt069uzZQ4MGDdi4cSOurq58//33UprIyEj8/PxUHvxL6jV//nzs7e3L\nbI+nT5/i6urK2rVr6d69O8HBwUyfPp0DBw7QpUsXKXiD4of08ePH4+HhgVwuVzn36tUrbG1tmTJl\nCgCvX7/m3r17XLx4EW1t7TLb++LFi9J/kNeuXWPMmDG0atUKU1NTKd2BAwewtLQkLi6Ohw8f8o9/\n/KPcur148QJnZ2fWrl2Lp6cnKSkpLFiwgB07dtCmTRvOnj3L1KlTOXfuHDo6Ovj5+XH37l1OnDhB\nQUEBTk5O7Nixg4kTJ6r019atW/n0009Vym9gYEB8fLz09fHjx5kxYwanT59GX1+fnJwc7Ozs6Ny5\nM7t27cLIyIiUlBTc3Nx4/PixdM+sXr2aU6dO4evrS9u2bXn58iVBQUGMGjWKQ4cOoaenx+zZs2nT\npg3r16/n+fPnTJkyhZCQEKZNmwaAmZmZdI++Kzw8nF9++YXY2Fjkcjlubm74+/tLn3/z5k0iIyNV\n2vzt9o2JiaFx48Zl5v3jjz/i7e1NaGgo7dq1Y9OmTbi5uXH48GGePn3KnDlzCA8Pp3Xr1hw4cICp\nU6dy6dIl5HI5zs7OjB07lu3bt3Pv3j0mTJhA3bp1GTZsGKtWreLp06ecOXOGtLQ0Jk6ciImJCdbW\n1kRFRfHdd9+xa9cu6tSpw4YNG/jqq684ceJEmWUsy8T54ejo1690ekEQBEEoz4uM+6yeDWZm7f7q\nogjCX+6DCfjKo1QqUSr/+xuoOnXqEBAQgLm5OWfOnMHCwoJvv/2WefPmYWRkBMDSpUvJyspSyScq\nKoqpU6eio6NDREQEX3/9tXTu8uXLzJw5kzlz5uDp6aly3Zs3bxg+fDiOjo7k5OSonLtz5w5FRUXk\n5+dTWFiIXC6XRpegeMTj1KlTTJkyhe3bt5dZt/J8//33mJmZYW5uDsC0adPYuXMnN27coGXLllK6\noqIi5syZw7Rp00qNSkJx4GJqasqwYcOA4kDik08+KTPYK6tcbdu2pUmTJty8eVMl+IiMjGT27Nlo\naGgQERGBh4dHuXno6OjQr18/Tp8+DUCTJk2Ij49HU1OT/Px80tPT0dHRoVq1auTl5bF37172798v\nlTEoKIiioiIpv5cvX+Lt7c3o0aO5du1aufUA6NevHwqFgtu3b6Ovr8/WrVvR09Nj6dKlUpomTZrg\n5+dHaGgo+fn5PH78mK1bt3L48GFMTEwA0NbW5uuvvyY7O5uUlBTatGmDjo4OU6ZMoVq1ahgaGvLZ\nZ59x9uzZCstTouTeKSgooFq1air3TlpaGtnZ2eWOjr37PfGuPXv2MHLkSNq1K/4hN2HCBHr37g0U\nj0rm5+dTUFAAFAePNWrUQKlU8vTpU5o3b864ceMAaNSoEZaWliQlJTFs2DAOHTpEcHAwmpqaGBsb\nM3LkSPbt24e1tTVZWVlMmTKFunXrAjB27FiCgoJ4+vQptWvXrlSb6OjXR7dO5UcEBUEQBEEQhPf7\nW67h09TUpEOHDly+fJnXr1+TmppKWloaQ4YMoVevXvj5+ak8ZF6/fp0nT55I0zFjYmLIzc2Vzjdv\n3pxTp04xdOjQUg/S6urqfPfdd8yaNUtluh9Anz59qF+/PoMHD8bMzIxt27bh5+cnnXd0dGTfvn20\nbt36N9fx9u3bKgGcmpoaDRo04Pbt2yrpoqKikMlkjBo1qlQeKSkp7N+/n/nz50vHkpOTkclkODg4\n0KNHDyZPnkxqamq55UhISODp06d07dpVOnblyhWysrIwNzfHwcGBvXv3kpeXV24eT58+JTY2lr59\n+0rHNDU1+fXXX2nXrh3z5s1j1qxZ1KhRg9TUVORyOZcuXaJ///706dOHnTt3SsE8gI+PDyNGjCgz\nwH2bUqnk8OHDaGpqSn0QHx9Pv379SqU1NTXF398fdXV14uPjMTExkYK9t/n6+tKxY0cUCgWbNm2i\nVq1a0rkzZ86oBOMVcXR05M6dO3Tv3p3OnTvz6NEjXF1dgeKgXEtLi0mTJtG9e3dGjx7N9evXK5Uv\nFPexhoYGY8eOpVu3bkyZMgUtLS0APv30U3r06IGDgwOtW7fG29ub1atXo66uTp06dQgJCZHyycvL\nIz4+npYtW5KRkcHz589VglBjY2Ppfpw0aRJDhgyRzp08eRJDQ8NKB3uCIAiCIAjCH+ODCfhmzZpF\nly5d6Ny5M126dGHu3LkVptfV1SUrK4vs7GygeIrh9u3bOXLkCI8fP8bX11dKGx0dja2tLWpqarRu\n3ZqGDRty8OBB6byOjg4KhaLMz5HL5ejr65d57s2bN5iamrJ//36uXLnCyJEjmT59Ovn5+QAYGhpW\nWIcVK1bQpUsXlXoHBgYCxVMv350GqqGhwevXr6WvlUolW7ZsYerUqWXm/80332Bra6vy0C2TyWjb\nti3r1q3j9OnTmJqa4uTkJJVZqVTSq1cvunTpgpmZGRMmTMDKyoo6depIeURFRWFra4tcLsfMzIx/\n/OMfHDp0qMy6dezYEXNzc548eVIq0GrQoAHXr1/nm2++YdmyZSQmJpKVlcXr1685d+4cMTEx7N69\nm3/9619s2bIFgNjYWO7fv8+ECRPKrHN6errUpm3atMHDwwN7e3tp9CwzM7Pc/iyRmZmpEsi9j1Kp\nZPHixdy7d49JkyZJx3/88cdS/Xv37l2gOJjq168f8fHxnD9/HkNDQxYtWiSda9euHd7e3sTFxTFo\n0CAmT55MRkaGlLednV2pvP/1r38B8Pz5c3bv3s28efM4e/YspqamuLi4SGs9P/nkE8LCwrh27Rrz\n5s3Dzc1NJe+SMsycORNtbW2GDx/O69evpdHAEjVq1FD5xUmJCxcusGTJEhYsWFDpNhQEQRAEQRD+\nGB/MlM7AwMAy1/CVJzMzk3r16klrzZycnDAwMADA2dmZGTNm4OPjw6tXrzh8+DDq6urs27cPKJ4S\nGB4eXu76ucpau3Yt9erVkzZpcXV1JSoqigsXLkhTMSvy9ddfq2wA8zYNDY1SD9O5ubnShioAly5d\n4tWrV1hbW5e6Pjc3l2PHjhEdHa1yfNSoUSqjgbNnzyYiIoJbt25Rs2ZNZDIZ586dk9r17t27zJw5\nEz8/Pzw9PcnJyeG7775DoVAQFRUFFLdnREQEw4cPL7Nuubm5BAcHM2rUKL7//nuqV68OII2Y9ujR\nAysrK06ePMmAAQMoKirCzc0NLS0ttLS0+PLLL4mOjmbYsGGsXLmSnTt3ltum767hS05OZvr06ejq\n6jJmzBgMDQ159uxZmddmZGSgr69P7dq1SU9PLzNNZmYmenp60oYpr1+/xt3dnTt37hAREYGenp6U\ntk2bNuWu4fP09GTp0qVS8Onp6clnn33GkiVL6Nevn0pwPGbMGL799lt++OEHqa+jo6PLXcOnUCgY\nMGCAdF/OnDmTHTt2cOfOHY4fP05hYSGdO3cGwN7enujoaGJjY3F0dJTawcXFBblczpYtW1AoFFKg\nl5ubK/Xf69evVe5HgH379rF06VKWLFnCgAEDyiyfIAiCIPwZ5HIZamqy9yf8G5PLZSp/C39vf1Q/\nfjAB32+Rk5PDlStXmDhxIvr6+ujq6vLmzRvpfEFBgTQ189ChQ5iYmBAaGiode/XqFUOHDuWHH36Q\nHnz/F48ePSo1EqSmplZqN8n/RZMmTaQ1bwCFhYXcu3dPZRrjmTNn6N+/v8qOnCUSEhKoV69eqXVg\nu3fvpnHjxnTr1g1AWs9V8hAPquvvGjZsiI2NjRQsHzhwAFNTUzZs2CCle/nyJUOGDCEpKYkOHTqU\nKouGhgZOTk5s3ryZ27dv8/jxYyIiIvjmm2+kNPn5+ejo6NCoUSNkMplKfxYVFaFUKjl37hwZGRnS\njqH5+fnk5eXRvXt3EhISymzHVq1aYWVlxfnz5xkzZgzm5uacOHGCyZMnq6T7z3/+g52dHSdPnqRn\nz54sWrSIlJSUUu03fvx4BgwYgLOzM8+fP2fChAnUqlWLPXv2VLgu8l2PHz+WRlWh+L5RU1NDLpfz\n3XffIZfLVQKmN2/elNrxszzGxsYqU2xL1j8qlUoePXpUavptyWcD3Lt3j/Hjx9OxY0eWLVsm3cv6\n+vro6emRmpoqrQ1MTU1VuR/XrVvHrl272Lx5M506dap0WwiCIAjCH0FXVxN9/cr/bP4709PT+quL\nIHzA/nYB371791i2bBlmZmb06NEDAFtbWzZu3Ejbtm1RKBSEhIQwePBgoHhzkaFDh6pM4zMwMMDS\n0pKwsLD/U8DXp08fgoKCGDhwIM2aNWPbtm2oqanRvn37/1slKd5sZM2aNZw6dQpzc3OCg4Np2LCh\ntLU+wNWrVxk5cmSZ11+9elV6MH/b48eP+fbbb9m8eTO6urqsXLmSli1b0qxZM+7evVsqkHjy5AlH\njhyRArno6GhGjBhRqj379u1LeHh4mQFffn4+YWFh6Ovr07hxYwwMDLh27RpHjx5l0KBBnD59mvPn\nz+Pu7k6tWrWwtLTE39+f1atX8+LFC3bs2IGdnR3Dhg2TNp+B4qmle/fuLXcUDeDXX3/l9OnT0mjj\n2LFj2bdvH97e3kyfPp3atWtz/fp1vv76a+zs7FQ2HSkZJW7fvj0ZGRkEBgaSlZUl5eXi4kK9evVY\nu3ZtqfWd79O7d2/Wrl1LixYtUFdXJyAgACsrKxQKBTk5Oaxfv55mzZrRsGFDtmzZglKplF6d8D7D\nhw/Hy8uLzz77jObNmxMYGEjTpk0xMTGhT58+zJw5k4SEBLp168aRI0dISUnBwsKC3NxcJk2aRN++\nffHy8iqV72effcb69etZs2YNz549Y9euXVK6yMhIvv32W/bs2UOjRo1+U1sIgiAIwh8hK+sVGRk5\n70/4NyaXy9DT0+L585dV/jUbH4OS/vy9fRABX1kjVG+fs7OzQyaTIZfL0dPTo1+/ftIGF1A8LXH9\n+vXY29vz8uVLrKys8PDw4MaNG9y8eVNlI4oSn3/+OVOmTCEtLU1lfdr7yvK20aNHk5OTg4uLCy9f\nvqRVq1Zs3ry51Nq78vj6+rJy5Urp65L3qR09epRPPvmEoKAgfH198fDwoFWrVqxbt07l+ocPH5a7\nKcaDBw9KvXoCinf7fP36tbQuq0uXLqxfv16ljiWjfyVrtqysrPD09OTHH3/k559/ZtCgQaXy/fzz\nz5k2bRpPnz5VqVtJv7Vs2ZKQkBBq1KhBjRo12LBhA8uXL2fhwoU0btyYkJAQqbx+fn6sWLGCgQMH\nUlhYiK2tLV9++WWl2jQjI0MKOmUyGTo6OgwdOlR6pYO2tja7d+/G398fW1tbXr58iZGREcOHD1dZ\nf+fp6ck//vEPvL29efToERoaGnTt2lUKXBMTE7ly5QrVq1dXee+hmZlZmTuyvsvHx4fly5fz2Wef\nIZPJ6N27t7Ru1c7OjvT0dCZMmEB2djatW7dm8+bNKBQKCgsLkclk2NraqtyPSqWS+vXrc+jQIayt\nrcnJycHd3Z0nT57w6aefEhQUBICFhQXz589n0aJFZGRkSKPftWvXlt7FGB0dLU0Ffvv9frNnz8bX\n15cBAwYgl8sZP348VlZWAISGhpKTkyONvpbcyzExMSIAFARBEP4SRUVKCgs/jiDoY6qr8NvJlBXN\nDRMEQfiT9B4TIF7LIAiCIPwustJSWDi+c5V/D5+amgx9fW0yMnJEwFcFlPTn7+2DGOETBEF4kXH/\nry6CIAiCUEUU/0z535ftCEJVIkb4BEH4ICQlJZGV9UqsQagC5HIZurqaoj+rCNGfVcvH1J8tW7aW\ndh2vqsQIX9UiRvgEQajSOnToIH5gVRHiAaRqEf1ZtYj+FISPzwfz4nVBEARBEARBEATh9yUCPkEQ\nBEEQBEEQhCpKBHyCIAiCIAiCIAhVlAj4BEEQBEEQBEEQqigR8AmCIAiCIAiCIFRRIuATBEEQBEEQ\nBEGookTAJwiCIAiCIAiCUEV9MO/he/bsGUOHDmX58uX06dOHr7/+msOHD6NQKABQKpXIZDLs7OwY\nN24cgwcPRiaTSdfn5eVRv359jh07BkBOTg7BwcHExsby/PlzDAwMGDp0KM7Ozqipqal89vHjx9m0\naRPR0dHSsQsXLuDn58edO3cwMDBg8uTJ2NvbA/DixQt8fHyIj49HqVRibm7O/Pnz0dHRISYmBi8v\nLzQ0NKRyN2zYkDFjxmBnZyflf/ToUYKCgnj06BH169fH1dUVa2trlXJlZmZiZ2dHSEgITZs2BSA7\nO5uFCxdy/vx5ACwsLFiwYAHa2sUvaTx8+DBr1qwhPT2drl27smzZMgwMDABISEjAz8+Pu3fv0qxZ\nM+bNm4eZmRmPHj16b3v6+/sTHR1NUVERNjY2zJ07V0qvVCoJDw9n79693Lt3D01NTczNzXFzc8PQ\n0BCAL774gqtXr0ovQC3pzxkzZjBu3Djpc9esWUNISAhRUVG0adNGpT2ioqLYtGkTWVlZNGvWDC8v\nL1q3bl1h3QDS0tJYsmQJiYmJqKurM3DgQDw9PaWyVPZeOXjwIN9++y0pKSkoFAo6deqEm5sbjRo1\nAuDJkyd4e3uTlJRE9erVsbW1ZdasWdL1HTp0UKl7p06dCA0NfW/dLC0tSU9Pl8pScv3KlSvp168f\nW7ZsITAwEIVCIZ3bvHkzHTt2LNXuJdfr6+tz8uTJStft7XyUSiUaGhrSvaepqQnA1q1bCQgIKLMc\nlSFevF51fEwvdv4YiP6sWj62/vwYXr4uCO/zwQR8Xl5eZGVlSV/LZDLGjh3LnDlzykx/5coV6d/P\nnj3D1taWBQsWAPDy5UscHBxo27Ytu3btwsjIiJSUFNzd3Xn48CG+vr4AFBQUsG3bNtavX0/z5s2l\n/HJycnB2dmb16tVYWVnx008/YW9vT7t27WjevDm+vr68fv2a48ePU1RUhIeHB0uXLsXPzw+AVq1a\nqQSPCQkJuLm5UVhYiKOjI3fu3MHLy4vt27fTtm1bEhIScHJy4uzZs+jp6QGQmJiIt7c3Dx48UKn3\n0qVLkcvlxMXFUVRUxPTp0wkODsbT05ObN2+yaNEitm3bhqmpKUuWLGHu3LmEhoZy//59XFxc8PLy\nwtbWlrNnzzJ58mSOHj1K3bp1K2zP8PBw4uLiOHz4MABOTk5s3bqViRMnAuDh4cH9+/dZsWIFLVq0\nIDMzE19fX8aOHcuBAwek/2jnzp3LqFGjyr0HioqKiImJwc7OjvDwcFauXCmdu3XrFv7+/kRGRtKw\nYUNCQ0NxdXXlxIkTFdbNwMAAd3d3TE1NiY+PJzs7GxcXFzZs2ICrq2ul75XAwECOHTvG8uXLad++\nPa9evSI4OJjRo0dz8OBB9PX18fHxoXHjxmzcuJEnT54wevRoTExMsLGx4ddff0Uul5OYmFiq3hXV\nrcS6devo06dPme1248YN3N3dVQLnt72v3StTt3fzycnJwcXFhTVr1jBv3jz4f+zdeVyNef/48dc5\nR3aG3vYAACAASURBVKddyX6PrQlZxlIaeyRxGyYxQvZBskVI0qAUIUvIlpAty1SGyRZZZrKUhmxD\n5nbbxjJjqZSkTnXO748eXbejVN/7N3Mzzef5eHiYcy2f81muxvXu/flcF3Dz5s1S61GWsXMjMDar\n81+dKwiCIAjvepX2iOWe0LJl6w9dFUH4oD6KgG/v3r0YGhpSq1at/+p8X19fevfuTadOnQDYtm0b\n+vr60s06gIWFBcuWLWPDhg2oVCqUSiX+/v7cv3+fMWPGcPbsWelYIyMjzp07h4GBARqNRsquFGUy\n1Go1kyZNkj4PGjRI67ve1aFDB7y9vVm6dCkuLi40aNCA8+fPo6+vT35+Ps+fP8fIyEgKjC5dusS0\nadOYNWsW3t7eWmUtWbIEtVqNjo4OT58+JTs7mypVqgCF2T0HBwcpMzZz5kw6duxIWloaZ86cwdLS\nEmdnZwC6du1Kq1atiI2NZdiwYaX2Z0xMDKNGjZIyhePHjyckJISxY8dy8eJFTp48ycmTJ6XAoEqV\nKgQGBuLt7c2vv/6KhYUFUJhZKs2pU6cwMzPD3d2dXr16MXv2bKltDx48QKPRkJeXR0FBAXK5HH19\nfYBS2zZo0CAMDQ2ZOHEiOjo6VK1aFUdHRymYKs+18vz5czZt2kRMTIyUaTU0NGTWrFlkZmZy9+5d\nzMzMuHfvHjVq1CA/Px+NRoNCoZDqePPmTSwtLUtsd2ltK4+UlBQGDBjw3v2l9fuTJ0/K1bZ3yzEy\nMuKf//ynlAEuTz3KYmxWB5OaFv/1+YIgCIIgCEJxH3wN371799i6dSvz588vMyAoSUJCAleuXMHD\nw0PadvbsWXr27Fns2IYNG7JixQppmujUqVPZuXOnNG3tbQYGBhQUFNCyZUvGjBnD8OHDqVOnMPsQ\nFBREkyZNpGNPnjyp9bkktra2pKWlcffuXQD09fV59OgRrVq1Yvbs2UyfPh1DQ0MAGjduzKlTp+jb\nt2+xPlEoFOjo6ODj44OdnR1ZWVm4uLgAcPfuXSm4AjA1NcXExIS7d++iVqulaaZF5HI59+/fL7M/\n7969KwUDAObm5ty7dw8oDLasra2loKCIUqlk5cqVWvUpS1RUFM7OztSsWZP27dsTGRkp7evcuTP1\n69enT58+tGzZkk2bNrFs2TKAUtumo6NDaGioFKwCnD59mqZNmwLlu1bOnz9PvXr1tPqgyMKFC7Gx\nsQHA1dWVyMhIrKys6NatG9bW1lLZKSkpZGZm0q9fPzp27IiHhwdPnz4ts21lycnJ4d69e+zYsYPO\nnTvTp08f9u3bV65zAc6dO1eutr3rxYsXxMbG0q1btz+kHoIgCIIgCMKf44MGfAUFBXh7ezNv3jwq\nV65cbH9ERARt27albdu2fP755yXemG/atIkxY8ZoZUTS09OlzFBpqlevXup+hUJBcnIy+/fvZ9++\nfRw4cKDYMeHh4Rw/fhxPT89SyzIxMQHQmrb6j3/8g2vXrhEeHs7ixYu5cOECAMbGxlJQ+j7+/v78\n9NNPmJub4+7uDsCbN2+KZYb09PTIycmhc+fOXL16lePHj5Ofn098fDwJCQnk5uZqHV9Sf75580Yr\noNLT00OtVqNSqUhPTy8W7L3PsmXLpPFs27Yto0aNkvb99ttvJCUl0bdvXwBcXFzYs2cParUagNzc\nXBo1asR3333H5cuXGTFiBO7u7qhUqnK3DQqDmHv37uHm5gaU71opbxs1Gg0TJkwgOTmZQ4cOcfHi\nRSloVSqVWFlZSdeLgYEBU6dOLbNtRaZPny79HLRt2xYfHx+gMPBq06YNQ4cO5YcffsDf358lS5Zw\n5syZEvu96Pzly5f/n9r2djlt2rShc+fOPHnyRPqZLE89BEEQBEEQhP+9Dzqlc926dTRt2pTOnTuX\nuH/48OHvXcMH8Pvvv/PTTz8RHBystb169eqkpqaWeE5aWlq5b3ABdHR0aNKkCYMHD+b48eP069cP\nKMwqBQYGcuzYMbZv306DBg1KLSc9PR1AK7iQywvj7fbt2/PPf/6TEydO0K5du3LVS6lUolQq8fLy\nokePHmRmZkrB3dvevHmDgYEB9evXZ9WqVQQHB+Pn50enTp344osvtALt9/Xnu+Xm5OSgUChQKpVU\nr15da/3f297tay8vr2LTR4tER0eTl5fHF198ARQGT2lpaZw4cYKePXuydu1aatWqRbNmzQBwd3cn\nKiqK8+fPY2dnV2bbcnNz8fLy4vbt20REREjjUJ5rpVq1au89Jj09HVNTU168eMH8+fP56aef0NHR\nwcLCAjc3N/bu3cugQYOkoLyIt7c3HTp04MWLF2zYsKHUtkHhOruS1vDVqVOHnTt3Sp9tbGxwcnLi\nxIkT2Nraltnv5Wlb0cN53i4nNzeXDRs2MGTIEOLi4spVD0EQBEH4X5PLZSgUsrIP/IuSy2Vafwt/\nbX/WOH7QgO/o0aO8ePGCo0ePAoVPv5w+fToTJ04s1/mnT5+mbdu20oNOitja2nLs2DEmTJigtf3W\nrVv0799fukF9n1u3buHl5cXBgwelbXl5eVIAoVKpcHd35/nz50RHR5dr7WF8fDw1atSgQYMG/Pjj\nj2zbto2tW7eWWH5pxo4dy8iRI6Wbf5VKJa0Vs7CwkKZaQmHAkpmZiYWFBa9fv6Z27dp8//330v7B\ngwfTpUsX6fP7+rOo3KKnXr49ddTW1pbw8PBiwZ1KpaJv3754enrSv3//UtukVqv57rvvpAxSkc2b\nNxMREUHPnj158uSJtGayiEKhQKFQlNm2jIwMXF1dMTIyIjIyEmNjY+m48lwrnTp1wtfXl19++aXY\nOryxY8dib2+Pvb09+fn55OfnS2sx5XI5lSoV/oiFhYXRuXNnKagryj7q6uq+t21F55bm5s2bnD17\nVspYFpVd3jWA5Wnbu8FqUb3d3NwIDQ3l9u3bKBSK/696CIIgCMKfwcTEADMzow9djT+dqanhh66C\n8BH74AHf2+zt7fHz86Nr167SlLXSXL16FSsrq2Lbhw8fzr59+5g7dy5Tp06lRo0aXL9+HR8fHwYM\nGFBqsAfw6aefkp2dTVhYGK6urly/fp2oqChCQkIAmDdvHi9fvmTXrl3FbtTfpVarOXPmDCtXrpSm\nfTZv3pwbN24QExODo6Mj8fHxxMfHl3hj/a5mzZqxYcMGWrRogUKhYOnSpTg5OaGjo8OXX37JiBEj\nGDBgAM2bNyc4OJguXbpgYmLC48ePGTx4MLt27aJhw4ZERUXx+++/Y29vX2Z/9u3bly1bttC+fXsU\nCgVhYWFSprN169Z069aNSZMm4e/vj6WlJb/99huBgYGYmZnRu3fvMtv0448/kpOTQ8+ePbVegzB4\n8GB69erF7du3sbOzY+XKlXzxxRdYWlqyY8cO1Go1bdq0IT09vdS2ubu7U716ddasWVPslRzlvVZG\njx6Nh4cHgYGBWFtbk56ezurVq0lNTWXIkCEYGxtTs2ZNlixZwpw5c3j27Blbt26VXuVx7949zp07\nx+rVq1EoFCxatAgHBweMjY3f27ai1ziUxsDAgHXr1tGgQQN69OhBYmIiR44cYdeuXWWeC1CzZs0y\n21aSvLw8du7ciampKZ9++inPnz///6qHIAiCIPwZMjKySUvL+tDV+NPI5TJMTQ15+fL13+I1GxVd\n0Xj+0T6Kp3QWefs9cOXx+PHjEgMUAwMD9uzZw4oVK3B2diYrK4vq1aszYMAAXF1dyyxXqVSyceNG\n/P39CQsLo3bt2vj7+/P555/z9OlTvv/+e3R1denUqRMymazYe81SUlKkm3UdHR3q16/PnDlzpOmK\n1apVY8OGDSxatIiAgAAaNGjA+vXrMTc3L7NPpkyZwrJly3B0dEShUNCzZ09mzpwJQJMmTViwYAE+\nPj6kpqZiY2MjPX3yk08+ISAggClTppCRkUGzZs0IDw/XWpv3vv4cOnQoqampODs7k5eXh5OTk9aj\n95ctW0ZoaChTp07lxYsXGBkZ0bVrVwICAtDV1S2xHW+LioqiV69exYKxBg0a0Lp1ayIiIvD39ycz\nM5MpU6bw6tUrmjZtyubNmzEwMMDAwOC9bbt8+TIXL15EV1cXGxsbqR7Nmzdn586d5b5WPD09qVWr\nFvPnz+e3335DT0+Ptm3bEhERIT0QJiwsjEWLFmFra4uhoSGDBg1i5MiRAMydO5fAwEC++OIL8vPz\nsbOzw9fXFygMbN/XtrL6rkGDBqxevZrg4GC8vb2pVauW9HqMIkFBQaxYsUL6XPSOvJiYGOrUqVOu\ntr1djkwmQy6X06RJE0JDQzE0NMTQ0LDMegiCIAjC/5paraGgoOIHQn+Xdgr/HZnmv3k0piAIwh+s\ny/Bg8VoGQRAE4Q+T8fQOfqM/r9Dv4VMoZJiZGZGWliUCvgqgaDz/aB9Vhk8QhL+vV2mPPnQVBEEQ\nhAqk8N+Vzz90NQThgxMZPkEQPgrJyclkZGSLNQgVgFwuw8TEQIxnBSHGs2L5u41n06bNpYepVUQi\nw1exiAyfIAgVmrW1tfgHq4IQNyAVixjPikWMpyD8/XzQF68LgiAIgiAIgiAIfx4R8AmCIAiCIAiC\nIFRQIuATBEEQBEEQBEGooETAJwiCIAiCIAiCUEGJgE8QBEEQBEEQBKGCEgGfIAiCIAiCIAhCBSUC\nPkEQBEEQBEEQhArqo3wP39OnT/Hz8+Onn37C2NiYsWPHMmLECGm/RqNh5MiRtGjRglmzZknbV6xY\nQXR0NGq1GicnJ3x8fJDJZACsXr2a6OhosrOz+eyzz5g3bx4NGzYEoEmTJujr6yOTydBoNFSpUoXB\ngwczfvx4AB4/fkz37t0xMDDQqoNMJuPEiROYmZmxfft2duzYwatXr2jfvj1+fn5UrVoVKHyhdGBg\nIPfv36dGjRpMnjyZL7/8EoARI0Zw5coVrZeCajQazMzMOHnypFa//Pvf/2bAgAHs27dPqntWVhZ+\nfn6cOXMGHR0dnJ2dmT59utZ5ubm5jBw5kkmTJtG1a1dp+/r169m7dy85OTm0bNkSPz8/6tatW2p7\nR44cybRp08jLy2Pp0qUcPXqUvLw8rK2t8fX1pXbt2lrfvW3bNpKTkwkJCdHaHh8fT3h4OCkpKQC0\naNGCadOm8dlnnwFgZWUljR1AXl4eANevX6egoICQkBD279+PSqWiR48e+Pj4SPUNCAggKioKHR0d\nqd5HjhyhVq1aAFy9epXQ0FCuXLlCQUEBjRo1YvLkyXTs2BGAzMxM/Pz8OH/+PAB2dnbMmzcPIyMj\nkpKSGDlypPRdGo2GunXrMmPGDOzs7HjX6tWriY+PZ9++fdK28ePHk5iYiEKhkOqXnJysdZ6XlxdH\njx7l9OnTVK9eXWtfXFwcwcHBPHv2jEaNGjF//nyaNGkCQEREBOHh4bx8+RILCwu8vb2xsbEBSr9W\nfHx8OHjwIEqlEgCFQkHTpk3x8PCgTZs2APj5+RETEyONi0aj4c2bN6xYsQK1Wo2vr6/WvpycHAYO\nHEhAQECxfimJePF6xfF3e7FzRSfGs2L5u45nRX8BuyCU5qMM+CZNmkSHDh1Yv3499+7dY+jQobRo\n0YLWrVsDsGXLFpKTk2nRooV0TkREBPHx8Rw6dAgANzc3wsPDGTt2LFFRUcTFxfHdd99RvXp1QkJC\nmDVrFt999x0AMpmM6OhoLCwsAHjw4AFDhgzBwsICBwcH6Zjz58+jp6dXrL5Hjhxh/fr1bNq0iebN\nm7Nu3TomTpxIZGQkarUad3d3/P396dGjBxcvXuTrr7/G2tqaf/zjH0DhzfbQoUNL7ZO8vDxmzZqF\nSqXS2u7j44O+vj5nz54lPT2dESNG0LhxY/r06QPAv/71L3x9fbl27ZrWeadOneL7779n//79mJqa\nEhgYyNy5c9m+fXuZ7QUIDQ3lxo0bxMTEYGRkRGBgIJ6enuzevRuAN2/esGbNGrZu3UrPnj21zo2M\njCQkJITAwEA6d+5MQUEBu3btYtSoUURGRmJhYcHly5el49+8eYOzszOjR48GIDw8nMOHD7N9+3Y+\n+eQT5s+fzzfffMOqVasASElJITg4mB49ehSrd3x8PJ6ensybN4/Vq1dTqVIlDh06xOTJk9mwYQPt\n27dnwYIFyOVy4uPjUavVTJkyhXXr1uHt7Q1AlSpVSEhI0OrLqVOncurUKapVqyZtv3LlCps3b8bS\n0lKrDikpKezZs4dmzZqV2LeZmZnEx8fzxRdfsGfPHqZOnSrtu3nzJnPmzCE0NBRra2s2bdrEtGnT\niI2NJSEhgdDQUCIiImjQoAFRUVG4u7uTmJgIlH2tjBw5UvoFikqlIjo6GldXV3bv3k3Tpk3x9/fH\n399fqktISAiXLl2iV69eKBQKHB0dpX0JCQl4e3vj7u5eYhtLMnZuBMZmdcp9vCAIgiCUx6u0Ryz3\nhJYtW3/oqgjCB/HRBXxXr17l+fPneHp6IpPJsLCw4Ntvv6VKlSoA3Lp1i/3790uBWJGYmBhGjRol\nZdXGjx/P6tWrGTt2LAMHDsTR0RE9PT2ysrLIzMyUyoPCbIRG85/fctWvXx8bGxtSUlK0vuftY94W\nFxfH4MGDadmyJQBTpkxh27Zt3L59m+rVq5Oeni5lqGQyGTo6OigUijLLfVtISAidOnWSMmIAz549\nIz4+nnPnzqFUKqlZsybbtm2TsjRPnjxh1KhRjB8/nmfPnmmV9+DBAzQaDfn5+RQUFCCXy9HX19c6\nprR65eTkMGnSJMzMzAAYNmwYX331lbTf3d0dAwMDXFxcSEtL0zovKCiI4OBgKduoUCgYPXo06enp\n3LlzRwq8iwQHB2Nubo6zszNQ2N9ubm6Ym5sDMHPmTGxtbcnKysLQ0JBbt25JGa93LVy4kOnTp9O3\nb19pW9++fUlLS+PevXu0b9+eJUuWoFar0dHR4enTp2RnZ2tdL++yt7fHwMCAO3fuSAFfdnY2c+bM\nYdiwYVy8eFE6Ni0tjbS0NClDW5IDBw7w+eefM2zYMNzd3Zk0aRKVKhX+qH777bcMGjQIa2trAEaP\nHk2nTp0A6NChA3Fxcejr65Obm0t6erpU76dPn5Z6rbxLqVQydOhQrl+/zoYNG4plaH/++Wd27tzJ\noUOHtK5lgNevXzN79mzmz59PjRo13tvOdxmb1cGkpkXZBwqCIAiCIAjl9tGt4btx4wYNGzZk6dKl\ndO7cmV69enHlyhVMTExQqVTMnj2bhQsXak03BLh7967WTbS5uTn379+XPuvp6bF//34+//xzYmJi\nik17fFtKSgrXrl3Tmv4I7w+ACgoKimXCZDIZDx48wNTUlCFDhjBjxgyaN2/OiBEj8PX1pWbNmuXt\nEi5evMi5c+fw8PDQqkNKSgqffPIJu3fvplu3bnTv3p3Dhw9LQYeZmRlxcXF8/fXXxcrs3bs3MpkM\nOzs7rKysOHXqlFb2prT2QuGUw86dO0ufT548SePGjaXPS5YsYc2aNVIAXiQ5ORm1Wo2trW2xMmfM\nmFEsG3jv3j2io6OZN2+etK2goABdXV2t4woKCnj48CH3798nNzeXoKAgOnTowFdffcUPP/wAFAa5\nDx8+LDHz9/XXXzNkyBCgMADV0dHBx8cHOzs7srKycHFxKbEfNBoNR44cQUdHR5qOCrB48WKcnJyK\nZfdu3ryJoaEh48ePp0OHDgwdOpQrV65oHRMVFYWzszOtW7fGzMyM2NhYrfP19fUZNWoU7du3Z/z4\n8Vo/C/r6+ly4cAErKyvWrl3L7NmzgcJflJR2rbyPra1tsemmUDi+EyZMKPE6Lspq2tvbl1q2IAiC\nIAiC8Of76DJ8GRkZXLhwgQ4dOvDDDz9w/fp1XF1dqVOnDidOnKBLly5YWVkRGRmpdd6bN2+0gi49\nPT3UajUqlUrKYnz55Zc4OjqyY8cOxo4dS1xcHJUrVwbAxcUFuVyOSqUiNzcXW1tbrQBGo9FIa7SK\n1l15eXkxcOBA7O3tWbVqFfb29lhYWLBx40Zyc3PJzc1Fo9Ggp6fHmjVr6NatG+fOncPT05NmzZpJ\nwcCyZctYvXq1VtmDBg1i5syZZGVlMXfuXEJCQqQsz9t99eDBA54+fUpsbCyPHj1izJgx1KpVS8po\nvo9KpcLGxobNmzdTrVo1Fi1ahIeHB3v37i3W3rfrtWPHjmLZsyNHjhAWFsamTZukbe+uOyuSnp5O\n5cqVkcvL97uG8PBwnJyctAILe3t7wsPDsba2plq1aqxatYpKlSqRm5uLSqWiXbt2jBs3jtWrV3P6\n9GmmTZtGVFQUr1+/BpCykmXx9/dnzpw5zJkzh8mTJ7Nz504AXr58Sdu2bYHC6y4/P59JkyZhaGgI\nFAa/d+7cISAggAMHDmiVmZubi5WVFV5eXtSrV4/o6GjGjRtHbGwsVatWJTk5mVevXkm/bHBxcSEi\nIkJa85mRkcHevXvZuHEjjRo1IiQkhIkTJ3L48GGpT9u0acP169eJjY3Fw8OD/fv3l3mtvI+pqSkv\nX77U2nbp0iXu3LmjNd5FsrOz2bVrF5s3by5XHwuCIAjC/4JcLkOhkJV94F+MXC7T+lv4a/uzxvGj\nC/iUSiWmpqaMGzcOKHx4R8+ePVm0aBEajYaoqKgSz9PT0yMnJ0f6nJOTg0Kh0JqyVrRYd8yYMURE\nRJCUlCRN2fz222+lqYSpqan4+Pgwffp0NmzYABRm7OLj40sMovr168fz58+ZNGkSBQUFODs7Y2Fh\ngbGxMcePH+f69evS2qiuXbtiZ2fHgQMHpDVhXl5eDBs2rMR2LViwgK+++kor+Hy7rzQaDV5eXujq\n6mJhYcHAgQM5ceJEqTfxAIGBgfTs2ZO6desCMHfuXKytrbl9+zYGBgaltvdtRYHe2rVrpYeDlKZa\ntWpkZGRQUFBQbCpgZmYmhoaG0naVSsXhw4fZs2eP1nFubm68fv2aYcOGoaury+jRozEwMMDY2BgL\nCwu2bt0qHevg4ED79u05ffo0vXv3RqPR8OLFi2KZqdevX6Ojo6N1vSiVSpRKJV5eXjg4OJCZmQkU\nBkFvr+G7desWU6ZMwdjYGEdHRxYtWsS2bdukhwC9rXv37nTv3l36PGTIEHbv3s2FCxfo3bs3kZGR\npKenSxnQ/Px8MjIyuHnzJs2aNUOpVNKzZ09p/Z+Hhwdbt27VynAX/WKgT58+7N27lx9//JFatWr9\nV9fK29NCi+zfv5++ffsWmwIMcOLECT755BNperMgCIIgfAxMTAwwMzP60NX405iaGn7oKggfsY8u\n4DM3Nyc/P1/KKAGo1WqaNGnCsWPHpCcpZmdno1AouHv3LqGhoVhYWHDv3j3pRvPu3btSALdmzRry\n8/O1pnHm5eVJ2T3Qnr5YtWpVhg4dWmza5/umOD5//pzevXtLQeqrV6/YsmULzZo149ChQ8UetFKp\nUqVi2br3iY2NRVdXVytj4uLigr+/Pw0bNkSj0aBSqaTATK1Wl2tN4JMnT7TqJZPJkMlk5V5bqNFo\nmDdvHufPn2fXrl0lBqQlsbKyQkdHh/j4eLp166a175tvvsHY2JjFixcDkJiYSI0aNYpNi3z27Bmj\nR4+Wgug7d+6Qn5+Pubk5CQkJ/PrrrwwePFg6XqVSoaurS506dTA3NycuLo7hw4drlRkSEsLNmzfZ\nuXMnY8eOZeTIkVKWTaVSUalSpRIDHCh8yquDgwMJCQmYmZmRlpbGgAEDgMLrTKVS0bZtW5KSkjh2\n7BhqtZovvvhCq35KpZKsrCxiY2PZvn27FIhD4brDnTt3snjxYszNzbXGTa1WA0i/DLl06RJLliyR\n9hdd5+bm5v/VtXLmzBkpm1nk9OnTrFu3rsTjT58+rdU2QRAEQfgYZGRkk5aW9aGr8YeTy2WYmhry\n8uXrv9VTVyuqovH8w8v9w0v8/9SpUyf09fVZu3YtBQUFJCcnc+LECVxcXLh06RJJSUkkJSXh6OjI\nsGHDCA0NBQofvLFlyxaePn3KixcvCAsLo1+/fgC0atWKvXv38q9//Yu8vDzWrFmDsbGx9NTPd2Vm\nZrJv3z7pwRhQevBz/vx5xo8fT3p6OllZWSxYsIDOnTtTrVo1OnbsSEpKCvv37wcgKSmJEydOlPum\n+OrVq1Kbk5KSgMJsZJ8+fbC0tKRZs2YsXbqU3Nxc7ty5Q1RUFL179y6zXDs7O7Zs2cKjR49QqVSs\nWLGCxo0b8+mnn0rtLa3Na9asITExkaioqHIHe1CYNZs+fTrz5s3jxx9/pKCggNevX7N27VoSExNx\ndXXVaruVlVWxMr7//nu8vLzIzs4mLS2NRYsWMXDgQORyOXK5nKCgIC5duoRarebgwYNcu3ZN6m9v\nb29CQkKIiYlBpVKhUqnYu3cvkZGR0hMlmzVrxoYNG0hLSyMjI4OlS5fi5OQkZYjf7Zdff/2VU6dO\nYW1tTd++fbl8+bI0Xr6+vjRt2lQau+zsbAIDA6UgdfPmzeTm5tK5c2cOHDhAgwYNaN26NVWrVpX+\nODs7c/jwYV6+fEn//v05cOAA169fJy8vj1WrVmFubk6jRo1o1aoVx44dIzExEbVaTVRUFA8fPqRb\nt27/52slJyeHHTt2cPLkSSZOnChtf/ToERkZGVrrFd929erV9/5cCYIgCMKHolZrKCioeH+KgryK\n2r6/258/K2j/6DJ8urq67Ny5E39/fzp27IiRkRHz5s0rc4rY0KFDSU1NxdnZmby8PJycnKSHlXTp\n0gVPT08mTZrEq1evsLKyYvPmzdL0PZlMxsCBA6Usl46ODh06dCAoKEgq/+13wr3LycmJX375hd69\ne6NWq+nWrZuUZWncuDEhISGsWrWKwMBAateuTVBQkNYj+YOCglixYoX0uSi7GRMTQ5062o+pf3ea\n4KZNmwgICMDOzo5KlSoxcuRIevXqVayO79bf3d2dgoIChg4dikqlok2bNqxfv17r+LcfylLEysqK\nsLAwtm7dSn5+vvQAlKI6l/YqhyJDhw7FxMSEtWvX4uXlhVwup1WrVkRERGg9ofPx48clPuXRgxmF\nzgAAIABJREFU1dVVCmSKXgfg5eUFQLt27ZgzZw7ffPMNz549w9zcnNDQUKkcOzs7Vq5cSWhoKIGB\ngWg0GiwtLdm4caOUyZoyZQrLli3D0dERhUJBz549mTlzpvT9GRkZ0i8DZDIZRkZGODo64ubmVmq7\nAfr378/z589xdXXl5cuXNG/enM2bN6Onp0dUVFSJ0ys7duyImZkZkZGRuLm5MW/ePLy9vXn69CnN\nmjWTsm2NGzdm2bJlLFiwgOfPn2NpacnWrVulKZllXSs7d+6U1nAaGBjw2WefsX37dq2HIT1+/BhT\nU9MSM9RqtZrff//9ves3BUEQBEEQhP89maY88/8EQRD+ZFa9Z4j38AmCIAh/uML38PWvkO/hUyhk\nmJkZkZaWRUGBuKX/qysazz+aCPgEQfgoJCcnk5GRLdYgVAByuQwTEwMxnhWEGM+K5e86nk2bNpeW\nZlQkIuCrWP6sgO+jm9IpCMLfk7W1tfgHq4IQNyAVixjPikWMpyD8/Xx0D20RBEEQBEEQBEEQ/hgi\n4BMEQRAEQRAEQaigRMAnCIIgCIIgCIJQQYmATxAEQRAEQRAEoYISAZ8gCIIgCIIgCEIFJQI+QRAE\nQRAEQRCECkoEfIIgCIIgCIIgCBWUeA+fIAgfBfHi9Yrj7/pi54pKjGfF8ncez4r68nVBKMsHD/js\n7e1JTU1FoVAAoNFokMlkLFmyhKlTp6Kvr49MJkOj0VClShUGDx7M+PHjpfMPHTrEqlWrSE1NpV27\ndgQGBlK1alUAmjRpIp3/dtk9evQgKCiIgoICQkJC2L9/PyqVih49euDj44OBgYFUfkxMDLt37+bO\nnTsolUpsbGyYMWMG9evXByAvL48lS5Zw+PBhABwcHPDz8yv2P5TVq1cTHx/Pvn37pG3bt29nx44d\nvHr1ivbt2+Pn5yfVPTk5mcDAQO7fv0+NGjWYPHkyX375pVaZ6enpDBw4kNDQUBo2bCjVZ+nSpRw9\nepS8vDysra3x9fWldu3aAERGRrJlyxZSU1MxNzfH29sbGxubcvWXSqVi0aJFHDt2jPz8fNq2bYuv\nry81a9YE4OTJkwQHB/P7779Tr149vLy86NixIwCJiYksXbqU+/fvU7VqVcaNG8egQYO02pOQkMDo\n0aPx8vJi7NixWvsuXrzI0qVLuXv3LmZmZowdO5bBgwcDMGLECK5cuYKOjg4ajQalUomVlRUzZ86U\n+uVt0dHRLF++nMTERAAeP35M9+7dtca9qO0jR45k2rRppfZrUlISI0eOlM7XaDTUqlWL/v37M27c\nOKk/izx8+JABAwbw448/oq+vL9XB39+fy5cvo1Qq6dOnD7NmzaJSpf/8iGZlZdGlSxc+//xzNm7c\nqFXmzz//zKBBg9DT05PqPmHCBNzc3Fi7di0bNmxAV1cXAJlMRsOGDXFzc6N79+5SGREREYSHh/Py\n5UssLCy0ro23+/jt/pk6dSpff/11mfvLY+zcCIzN6pTrWEEQBEH4v3iV9ojlntCyZesPXRVB+J/7\n4AEfQEhICF27di22XSaTER0djYWFBQAPHjxgyJAhWFhY4ODgwK1bt5g/fz5bt27F0tKSgIAAfHx8\nCAsLK/H8d4WHh3P48GG2b9/OJ598wvz58/nmm29YtWoVACtXriQ2NpbFixdjZWVFdnY269atY9iw\nYcTExGBmZsaKFSu4c+cOcXFxaDQa3Nzc2Lp1K25ubtL3XLlyhc2bN2NpaSltO3LkCOvXr2fTpk00\nb96cdevWMXHiRCIjI1Gr1bi7u+Pv70+PHj24ePEiX3/9NdbW1vzjH/8ACgMgX19fHj9+rNWm0NBQ\nbty4QUxMDEZGRgQGBuLp6cnu3btJTExk5cqVbNu2DUtLSw4cOMDEiRM5ceIEJiYmZfbX+vXruXv3\nLsePH0dfXx9fX18CAwMJCQkhLS2NmTNnsn37dlq2bMmhQ4eYPHkyFy5cQKVSMXHiRJYvX0737t35\n17/+xaBBg2jdujWNGzeWyo+MjGTgwIHs2bNHK+DLzMxk8uTJ+Pn50bt3b27evMno0aOpV68eHTp0\nAMDHx4ehQ4cCkJ2dzaZNmxg+fDjff/+9FJBCYbAVFBSkFUgVXSvnz59HT0+vxLaX1q8AVapUISEh\nQTr+559/xtPTk1evXuHp6SltP3HiBAEBAbx69UqrfC8vL6ysrAgNDeXVq1eMHDmSvXv3Mnz4cOmY\ngwcP0rVrV86dO8fDhw+pW7eutC8lJYUuXboQGhpaYv0dHBxYvXo1AAUFBcTFxTFz5kxWrVpF165d\nSUhIIDQ0lIiICBo0aEBUVBTu7u5SUPxuH5ekrP1lMTarg0nNkq89QRAEQRAE4b/zUa/h02g0aDT/\nmW5Qv359bGxsSElJAQqzew4ODrRo0QKlUsnMmTM5c+YMaWlpJZ7/rri4ONzc3DA3N5fOj4uLIysr\ni8ePH7Np0ybWrVuHtbU1MpkMQ0NDZs2ahZ2dHXfv3iU/P5/IyEh8fX0xNjamcuXKrFmzBkdHR+k7\nsrOzmTNnDsOGDSv23YMHD6Zly5YoFAqmTJnCv//9b27fvk1mZibp6enk5eUBhcGIjo6OlAW9dOkS\n06ZNY8KECcXalJOTw6RJkzAzM0OpVDJs2DCuXbsGwNOnT3F1dZUCz379+iGXy7l9+3a5+svDw4PN\nmzdjbGzMq1evyMrKokqVKgD89ttvqFQq8vPzAZDL5VLwZGRkxLlz5+jevTsajUbK6L6dUUtLS+OH\nH35g+vTpVKpUidOnT0v7njx5gp2dHb179wagWbNmtGvXjsuXL0vHvF1vAwMDPDw8aNy4Mdu2bZO2\nq9VqvL29cXFxKbF9pbW9tH4tyWeffcbChQvZunUrmZmZQGHAFhQUhLu7e7Hjt23bhqenJ3K5nPT0\ndHJzczEzM9M6Jioqii+//JJevXqxa9curX03b96kadOm763P2xQKBb169WLs2LFSENihQwfi4uJo\n0KABubm5pKenS2NbpLT+Kc9+QRAEQRAE4X/vo8jwlVdKSgrXrl3D1dUVgLt372JlZSXtNzU1xcTE\nRJr2V5aCggJpmlsRtVrNw4cP+fnnn6lXr16JUwIXLlwIwJ07d1Cr1Vy5coWJEyeSk5NDnz59tDI6\nixcvxsnJierVq3Px4kWt7343mySTyXjw4AGNGjViyJAhzJgxAy8vLzQaDYGBgVKmqnHjxpw6dQql\nUsmsWbO0yvDy8tL6fPLkSSmL5uTkpLXv0qVLZGdn06hRozL7qqh+SqWStWvXsm7dOmrWrElERARQ\nGIR16dKFoUOHolAoqFSpEuvXr0epVAKFQVhBQQGtW7cmPz8fNzc36tT5z/S9/fv3Y2tri5mZGYMH\nDyYiIoJu3boBhVNNg4KCpGMzMjK4ePEi/fv3L7W+tra2nDhxQvq8ceNGGjVqhK2tLdHR0cWOLy1g\nKa1f3+fzzz+nUqVKXL16FVtbWzp16kSfPn347bffih1b1E+jRo0iKSmJdu3a4eDgIO2/du0az549\nw87Ojlq1ajFmzBimTZsmXUMpKSkolUopqP7nP//JjBkzSl2r0KVLF9avX09OTg56enro6+tz4cIF\nRo8eTaVKlVizZk2p7RMEQRAEQRA+fh9FwFeU1Sla99O9e3cWL14MgIuLC3K5HJVKRW5uLra2ttKN\n9ps3b6Q1UEX09fXJycmRPhedD/9ZVxQUFES3bt2wt7cnPDwca2trqlWrxsqVK1EoFFKGo6yg8eXL\nl6hUKn744Qf27dvH69evcXNzo3LlykyYMIGTJ09y584dAgICOHDggNa59vb2rFq1Cnt7eywsLNi4\ncSO5ubnk5uai0WjQ09NjzZo1dOvWjXPnzuHp6UmzZs2wtLTE2Ni4XP165MgRwsLC2LRpU7F9//73\nv/Hw8MDDwwMTE5Ny9VcRNzc33NzcWLZsGWPHjuXIkSPk5+dTo0YNtm/fTps2bThw4AAzZszg0KFD\nVK9eHSjMLCUnJ3Pnzh1cXV0xNzenX79+QGH2au7cuQD079+f1atXc+/ePczNzbXq/erVKyZMmECL\nFi206lQSU1NTXr58CRROsTx06BD79u0rMTOn0Wiws7PT+iyTydixYwdNmjQpd7++q3LlymRkZACU\n65cQmzZtIisri6lTp+Ln5yf9HERHR/PVV1+hUCho3rw59erVIyYmRloHaWZmRtu2bXFxceHFixdM\nnTqVNWvWMGPGjPd+l4mJCRqNhszMTClwbNOmDdevXyc2NhYPDw/2798vjcGyZcukjCBA06ZN2b59\nu/S5rP2CIAiC8CHJ5TIUClnZB/6FyOUyrb+Fv7Y/axw/ioBv5cqVJa7hA/j222+lNWWpqan4+Pgw\nY8YM1q9fj56enlZwB4VBoKGhYYnnv8vNzY3Xr18zbNgwdHV1GT16NMeOHcPY2Jhq1aqRmppa4nnp\n6emYmpqiVCrRaDRMmzYNIyMjjIyMGD16NBEREQwcOJBFixaxbds26aEzb+vXrx/Pnz9n0qRJFBQU\n4OzsjIWFBcbGxhw/fpzr169L2buuXbtiZ2fHgQMH8Pb2LlefFgUka9eulR68UeTs2bPMmDGDsWPH\nStnS8vRXkaJs1KxZs9izZw//+te/SEhIIDc3l3bt2gHg7OzMvn37OH78uNZ0Vh0dHZo0acLgwYM5\nfvw4/fr148KFC9y/f5/Zs2dLx+Xn57Nr1y4pCITC9XcTJ06kfv36rFy5ssw+KJqWmJubi4+PDwsX\nLpQeavIumUxGfHz8e9fwFSmtX9+lVqvJzMwsNjWyNEqlEjMzM6ZMmcLkyZNZvHgx2dnZHDp0CB0d\nHb777jsAXr9+TUREhBTwrV+/XiqjTp06TJgwgZUrV5Ya8KWnpyOXy7UC/qK1jX369GHv3r38+OOP\nUsDn5eVVbGry28raLwiCIAgfkomJAWZmRh+6Gn8KU1PDsg8S/rY+ioCvNG/fnFetWpWhQ4cyffp0\nACwsLLh37560Py0tjczMTK2ApbRpes+ePWP06NFSYHXnzh3y8/MxNzfHyMgIX19ffvnlF62HrQCM\nHTsWe3t7Ro0ahUwmQ6VSSfsKCgrQaDScP3+etLQ0BgwYABQ+PVOlUtG2bVuSkpJ4/vw5vXv3Zty4\ncUBh5mrLli00a9aMQ4cOaZUJhTfi7z5o5H39NW/ePM6fP8+uXbuKTTvct28fixcvJiAgQFoT9+75\n7/PNN9/QokULhgwZAiCt1zM2NubJkycl1lmhUHDr1i28vLw4ePCgtC8vL4/KlSsDhQ9rGTFihNaa\nxOTkZCm4NzAw4MaNG4wbNw4nJ6dyB71nzpyhXbt2XL9+nUePHklPd83Pz+fNmze0bduWmJiYcrW9\nrH4tSVJSEhqNhlatWpV6nFqtxsnJiRUrVkjlqlQqqX8OHjzIp59+SlhYmFTH7Oxs+vbty08//YSl\npSUbNmxgypQp0rrInJycYtOV3xUfH0+LFi3Q1dUlKiqKS5cusWTJEmn/22MkCIIgCH91GRnZpKVl\nfehq/KHkchmmpoa8fPn6b/eajYqoaDz/8HL/8BL/RJmZmezbtw9ra2sAvvzyS44fP05ycjK5ubkE\nBwfTpUuXct+kfv/993h5eZGdnU1aWhqLFi1i4MCByOVyatasyejRo/Hw8ODSpUtoNBrS0tLw8/Mj\nNTWVIUOGYGxsjIODA8HBwbx69YqnT5+ybds2evfujaOjI5cvXyYpKYmkpCR8fX1p2rQpSUlJAJw/\nf57x48eTnp5OVlYWCxYsoHPnzlSrVo2OHTuSkpLC/v37gcLA4cSJE3zxxRdltmnNmjUkJiYSFRVV\nLChJSEggICCAjRs3lhjslaVly5Zs3bqVx48f8+bNGwIDA7GxsaFOnTp07dqVU6dOcfbsWTQaDUeP\nHuXWrVt069aNTz/9lOzsbMLCwlCr1Vy9epWoqCgGDBhAeno6cXFxDBgwgKpVq0p/HBwcMDQ0ZP/+\n/aSmpjJu3DjGjBlTrmAvKyuL4OBg7t27x4gRI7CxsdEai9DQUExNTUlKSqJWrVpA2Q+sKa1fi85/\nW3JyMvPnz2fcuHEYGRX/beLbx8vlcho3bszq1avJzs7m6dOnhISE4OzsDBQGxI6OjpiZmUn9U7du\nXezt7dm5cyfGxsacOHGCNWvWkJ+fz4MHD9i4caP0y4Z35eXlcfDgQXbu3ImHhwcArVq14tixYyQm\nJqJWq4mKiuLhw4dlTpsVBEEQhL8KtVpDQUHF+lMU5FXEtv0d//xZQfsHz/C9+46yd/cNHDgQmUwm\nPamyQ4cO0gM8mjRpwoIFC/Dx8SE1NRUbGxsWLVpU4vlFit6RdvToUVxdXaWbWoVCgaOjo9bDOTw9\nPalVqxbz58/nt99+Q09Pj7Zt2xIRESG9L2/JkiUsWbKE3r17k5eXR//+/Rk9enSZ7XZycuKXX36h\nd+/eqNVqunXrJmVXGjduTEhICKtWrSIwMJDatWsTFBREs2bNSu2/goICtm7dSn5+Pj169JDaW/TK\ngc2bN5Ofny9lFYv2hYSE0Llz5zL7y8XFhbS0NIYMGUJ+fj6dOnWSXmFha2uLr68vCxculN7xt3Hj\nRulBMxs3bsTf35+wsDBq166Nv78/n3/+Odu2baNu3brF1snJZDKcnJzYtWsXWVlZpKens379etat\nWyftL3pHHkBQUBArVqyQnqZqY2PD7t27qVatWpljUVRe586di223srIiLCys1H6FwgfJFP0iolKl\nStSuXZuRI0e+9zUF71738+fPJyAgAHt7ewwMDBgwYAATJkwgJSWFW7dulfi6hf79+zNhwgSePXtG\naGgoCxcupH379ujp6eHi4sKIESOkY0+ePCnVT1dXl0aNGhESEiK91qJx48YsW7aMBQsW8Pz5cywt\nLQkPD5emo5b2c1qe/YIgCIIgCMKHIdOIZ6kLgvARsOo9Q7x4XRAEQfhTFL54vX+Fe/G6QiHDzMyI\ntLQsCgrELf1fXdF4/tFEwCcIwkchOTmZjIxssQahApDLZZiYGIjxrCDEeFYsf+fxbNq0eamvK/or\nEgFfxfJnBXwffEqnIAgCgLW1tfgHq4IQNyAVixjPikWMpyD8/fylHtoiCIIgCIIgCIIglJ8I+ARB\nEARBEARBECooEfAJgiAIgiAIgiBUUCLgEwRBEARBEARBqKBEwCcIgiAIgiAIglBBiYBPEARBEARB\nEAShghIBnyAIgiAIgiAIQgUl3sMnCMJHQbx4veL4O7/YuSIS41mx/J3HsyK+eF0QyuOjDfiuXbvG\n5MmTOXPmDAA+Pj4cPHgQpVIJgEajQSaTMXDgQHx8fJg9ezaHDh167/4RI0Zw5coVrR90jUaDmZkZ\nJ0+eBOD27dv4+/tz48YNqlevzrRp0+jduzcA9vb2pKamolAotOrZvHlzdu7cCcDFixdZtGgR9+7d\no27dunzzzTe0b98egISEBJYuXcqvv/5Ko0aN+Oabb2jZsqVUzt27d1m3bh2JiYmoVCrq1q2Lq6ur\n9P0A27ZtIzw8nOzsbOzt7QkICEBPT4/Hjx/TvXt3DAwMpHbVqFGDcePG4ezsXKxvo6OjWb58OYmJ\niQBs3LiR0NBQZDKZdP6bN2+YMWMGbm5uZGVl4efnx5kzZ9DR0cHZ2Znp06cD8OrVKxYuXMjZs2fR\naDTY2toyZ84cKleuDMD69euJiori9evXNGnShHnz5tGoUSMAIiIiCA8P5+XLl1hYWODt7Y2NjQ0A\nx44dY8aMGejq6kpjGRAQwJdffllsrN8e7/Pnz6Onp1dqfz979gxfX1+Sk5PR1dXlq6++ktpTUFDA\n4sWLiY2NJT8/n/bt2+Pn50eVKlUAGD9+PImJiSgUCuk7k5OTAWjSpAn6+vrIZDI0Gg1VqlRh8ODB\njB8/HgA/Pz9iYmKK9fOKFSvo06dPsWusqPygoCB69OhRZvkAhw4dYtWqVaSmptKuXTsWLVqEmZkZ\nBw8exNfXV+u7c3JyGDhwIAEBAVr9OHLkSFq0aMGsWbMAePPmDUuWLCEuLg65XE7//v2ZNm2aVj0j\nIiLYt28fDx8+xMDAAFtbW2bMmEG1atWKXX/vM3ZuBMZmdcp9vCAIgiCU16u0Ryz3hJYtW3/oqgjC\n/9xHGfBFR0cTFBREpUra1Rs5cqR0E/oumUxW6n4oDBqHDh1a4r6cnBzc3NxwdXUlIiKCixcvMm7c\nOKytralVqxYAISEhdO3atcTznz17xqRJk1i0aBEODg4cPnyYqVOncvbsWWnfnDlz+Oqrrzhz5gzj\nxo3jyJEjVK1alVu3bjFixAjc3d1ZsGABBgYGnD17Fk9PT1QqFf369eP06dNs3bqViIgIzMzMmDFj\nBkFBQfj5+UntLwp2AK5fv86wYcNo3rw5TZs2ler58OHDYn07fvx4raBh3759bN26leHDh0v9pq+v\nz9mzZ0lPT2fEiBE0btyYPn36EBgYyJs3b4iLi0OtVuPl5cXChQtZunQp3333HTExMURERFC7dm02\nbtzI+PHjOXXqFAkJCYSGhhIREUGDBg2IiorC3d1dCkJv3rzJkCFDmDt37v95rB8/flxqfy9cuJAG\nDRqwYcMGnj17xrBhw/j0009xcnJi9+7dpKSkEBsbS6VKlZg5cybLly8nMDAQgJSUFPbs2UOzZs1K\nrFd0dDQWFhYAPHjwgCFDhmBhYYGDgwP+/v74+/tLx4eEhHDp0iV69eqlte1911hZ5d+6dYv58+ez\ndetWLC0tCQgIYPbs2YSFheHo6Iijo6NUVkJCAt7e3ri7u2t9x5YtW0hOTqZFixbStqCgIG7cuMGB\nAwcwMDBg+vTpBAcH4+XlBYCXlxePHj1iyZIlNGnShPT0dBYtWsSoUaM4cOBAuX+bamxWB5OaFuU6\nVhAEQRAEQSifj24NX1EQMHHixD+8bI3m/VMXTp06RfXq1Rk2bBgANjY2REVFSZmqshw4cIBOnTrh\n4OAAQJ8+fdi+fTsymYwzZ85gaWmJs7Mzcrmcrl270qpVK2JjYwFYsmQJgwYNYtSoUVKWrnPnzsyd\nO5dHjx4BEBMTg7OzM/Xq1cPIyAgPDw++//57rTa9/d8tWrSgUaNGpKSkSNvUajXe3t64uLi8tx2/\n//47ixcvZunSpRgYGPDs2TPi4+Px9fVFqVRSs2ZNtm3bRrt27aTvnDRpEgYGBhgZGTFo0CAuX74M\nwMuXL5kwYQKffPIJcrmckSNH8uTJE37//Xc6dOhAXFwcDRo0IDc3l/T0dCmLBoWBVZMmTcrV9++K\nj48vtb/v3btHfn4++fn5aDQaFAoF+vr6QGEQVVBQQH5+Pmq1GrlcLu1LTU0lLS2Nhg0blvi9Go1G\nawzq16+PjY2N1hgU+fnnn9m5cydLly4tljV+n7LKP3ToEA4ODrRo0QKlUsnMmTM5c+YMaWlpWuW8\nfv2a2bNnM3/+fGrUqCFtv3XrFvv375eu4SJxcXFMnz6dGjVqYGRkxJQpU/juu++Awqz2yZMnWb9+\nvTReVapUITAwkMaNG/Prr7+Wq22CIAiCIAjCn+OjC/icnZ05cOAAn3322f/0e2/cuEH9+vXx8fGh\nffv2ODk58eTJEykAK8vNmzepUaMG7u7utGvXDhcXF/Ly8tDR0UGtVkuZtyJyuZz79++jUqm4cOEC\nPXr0KFamo6OjlIG5e/eulNkBMDc3Jzs7m6dPn0rb3g4GEhMT+f3336XADAqnbjZq1AhbW9v3tiM4\nOJi+fftKGayUlBQ++eQTdu/eTbdu3ejevTuHDx+WpuoFBQVpBWYnT56UPo8ZM4Z+/fpp7atSpYqU\nMdXX1+fChQtYWVmxdu1aZs+erdWfx44do0uXLvTs2ZOwsLD31vldpfU3gKurK5GRkVhZWdGtWzes\nra3p2bMnAIMGDeLRo0d06NABGxsbfv31V2m6Z0pKCoaGhowfP54OHTowdOhQrly58t56pKSkcO3a\ntRIzdkuWLGHChAnUrFmz3O0qq/x3rxFTU1NMTEy4e/eu1nmbN2/G0tISe3t7aZtKpWL27NksXLiw\n2DVfUFCArq6u9Fkmk/Hy5UsyMzM5c+YM1tbWmJmZaZ2jVCpZuXKlVn0EQRAEQRCE/72PbkpnaWt+\nIiIiiI6OBpDWMB0/frzc+5ctW8bq1aul/TKZjEGDBjFz5kwyMjI4evQoixcvZuHChZw+fRoPDw9i\nYmKoW7cuANOnT5emQhad7+HhwbBhw8jIyCA+Pp5169axevVqvv32W8aPH8/x48fp3Lkzy5cv5/jx\n49jb23P+/HkSEhKoUaMGGRkZ0lrC0rx580bKNAHSf7958walUolGo8HOzg4onJ6al5dH//79qV27\nNlCYUTp06BD79u3j2rVrJX7H48ePiYuLkzJhABkZGTx48ICnT58SGxvLo0ePGDNmDLVq1dKaIggQ\nHh7O8ePHiYyMLFZ2UlIS8+fPZ+HChVrb27Rpw/Xr14mNjcXDw4P9+/dTq1YtzM3NcXR0ZN26ddy7\nd4+JEydiYmLC4MGDAe2xLmJra8uKFStK7e+isZswYQKurq48fPiQCRMmEBkZyaBBg1CpVHTv3p2p\nU6dSqVIlvL298fX1ZcWKFeTm5mJlZYWXlxf16tUjOjqacePGERsbS9WqVQFwcXFBLpejUqnIzc3F\n1taWxo0ba9Xz0qVL3Llzh02bNhXrp6JrrOj66t69O4sXL5b2l1b+u9cIFF4nOTk50ufs7Gx27drF\n5s2btY4LDg6mS5cuWFlZFRs/e3t71q1bx/Lly6lUqRKhoaEAUma2rGtXEARBED4GcrkMhUL2oavx\nh5LLZVp/C39tf9Y4fnQBX2mGDx9e6hq9svZ7eXlJUzbfpVQqadasmRTEFE2Ni4+Pl85ZuXLle9dX\nKZVKunbtSocOHQAYOnSotB6qa9eurFq1iuDgYPz8/Oj0/9i777iqq/+B46972ZvAmZYiuRNH5h4s\nS3ESONBcKSiKC9cXByiKgiUqLlQSZ8owNdNUMLcouSuxEpEcicgeci9w7+8PHnx+XhkujJidAAAg\nAElEQVRSX/1adJ6Px30En3HO+XzOh/y871ldu9KnTx9MTU0xNzdHW1ubp0+f8u6772qkqVAoKCoq\nwsjICH19fY0X92fPngFgaGhIUVERMpmMM2fOSC1b9+/fZ8aMGSxfvpxZs2bh4+PD0qVL0dfXr7Br\n6zfffEPXrl01Wp1Kg8nZs2ejp6eHtbU1gwcPJjY2VrpXKpWKgIAAjh07xvbt22nYsKFGugcOHMDf\n3x9fX1+NSWgAKYDu27cve/fu5fTp04wZM0aaCAegadOmjBw5kpiYGCngq6yuGzRoUOH9Tk1NZdGi\nRfzwww/o6OhgbW2Nh4cHe/fuZciQIfj4+LBgwQIpgPPx8aF37974+/vj4OCAg4ODlI+bmxtfffUV\nly5dkq4rIiJCatVKS0vDx8eHGTNmsHHjRum8/fv3M2DAgDLBGVT+jFWUvre3Nxs2bCjzjEDJc/J8\ni11sbCz16tXTmDAoLi6OixcvlgmgS82bN4/ly5czYMAAzM3NGTNmDN9//z2mpqbUrFlT6sL7ovT0\ndBEMCoIgCH8bZmaGWFgYv+livBbm5kZvugjC39g/KuB7naysrKTZFkupVKo/df79+/fLnK9Wq8nL\ny6Nu3bocPHhQ2jd06FB69OiBjo4OHTt25Pjx47Rr107j/IiICHbs2EFsbCzW1tYkJSVJ++7evYuZ\nmRm1a9fm4cOHgGaXznfeeQdnZ2f27NnDjz/+yIMHD6SJWYqKinj27BkdOnTgm2++kbpYnjx5ktGj\nR5e5LrVajVKplILJ0uuCkq6AXl5epKamEh0dLaVVav369ezcuZPQ0FA6dOggbY+KiuLKlSsEBgZK\n2woLCzE1NeXBgwdEREQwc+ZMaZ9CodDoVliZyu53amqqNH6vdDIRuVwuBZ6PHj1CqVRK58nlcuRy\nOVpaWhw7dgyVSkWfPn2k/UqlUqNcz9eBpaUlw4cPl7qEljp58iTr16+v0rW8qLL0X3xG0tPTyc7O\n1uhWefLkSY3yA3z33Xfcv3+fLl26ACWtgFpaWty9e5fQ0FBSU1OZO3euNHHNmTNnaNiwIXp6enTv\n3p2tW7eWCe6USiUDBgxg5syZODs7/6VrFQRBEIRXKSsrn/T03DddjFdKLpdhbm5EZmbev26Zjeqo\ntD5febqvPMV/qI8//pj79+8TFRWFWq0mNjaWn3/+WaNFpzIDBw7k3LlznD59GrVazc6dO1EqlXTs\n2JHMzEyGDh3KrVu3UCqV7N69m8ePH0tjqGbOnEl0dDQ7duwgPz+foqIijh8/TkhICFOmTAFgwIAB\nREREcOfOHXJzc1m7dq1Gl8oXJ/RITU3l0KFDtGvXjvbt23Pt2jXi4+OJj48nNDQUc3Nz4uPjpQBN\nqVRy69Yt2rTRnK64adOmtGjRghUrVqBQKEhMTCQqKkpq0Vq4cCGZmZns3r27TLC3b98+duzYwZ49\nezSCPYDWrVtz7NgxLl68iEqlIioqivv372NnZ4eZmZkU7KrVan7++Wd27dqFi4tLleqisvv93nvv\nUbt2bQIDA1EqlTx48IDw8HD69u0LgK2tLSEhIaSnp5Obm0twcDB2dnbo6+uTn59PQEAAiYmJFBUV\nERYWhkKhoGvXruWWIzs7m3379mkE8g8ePCArK+uVjFF9Mf1+/fpx/Phxrl69ikKhkLppmpmZSefc\nuHGjTB37+/tz5coV6fno378/I0aMkLpuhoWFsXTpUgoLC3nw4AHBwcG4ubkB0KZNG+zs7Jg0aRK/\n/PILAH/88Qfe3t5YWFiUadEVBEEQhDdFpVJTXFy9PqVBXnW8tn/j53UF7f+qFr6goCBWrlwp/V46\nTuqbb76hfv367Nixg6VLlxIUFETt2rVZs2aNRhAzbdo05HJ5mfOvXLlC8+bN2bhxI59//jne3t40\nbNiQ0NBQDAwMqFevHv7+/kyZMoWsrCxatGjB1q1bpRazFi1asG3bNkJCQti4cSOFhYVYWVmxbNky\naTIROzs7Hj58KK2LZ2trK02LDyUTaXTr1k36WV9fHwcHB3x8fKp0b548eUJxcTE1a9Yss2/Lli34\n+/tja2uLtrY2o0aNonfv3qSkpHDw4EH09PTo2rWrtD5c6dqGmzdvJi8vTwrUSu9XdHQ0TZo04fPP\nP2fJkiWkpqbStGlTwsPDpZk6N2/ezPLly1m9ejXm5uZ4eXlpTDKyc+dO9u7dW6YuNm3axIcffljp\n/d68eTPLli2je/fuGBkZMWTIEEaNGgXA4sWLCQwMlILpHj16SEspODs7k5qayvjx48nMzKRly5Zs\n2bJFSrd03UeZTIZMJkNHR4fOnTsTFBQklfPhw4dSN94Xla6RV5GXpd+sWTOWLFmCj48PaWlptG/f\nnmXLlknnq1QqHj9+XG4dV2bOnDn4+PjQpUsXDA0NGT58uHS/oGRsbGhoKFOnTuXp06cYGxvTs2dP\n/P39q9wqKwiCIAiCILweMnVlaxUIgiD8j7R18hYLrwuCIAivRcnC687VbuF1LS0ZFhbGpKfnUlws\nXun/6Urr81UTAZ8gCH8LV69eJSsrX4xBqAbkchlmZoaiPqsJUZ/Vy7+5Pps3bymN368uRMBXvbyu\ngO9f1aVTEIS/r3bt2ol/sKoJ8QJSvYj6rF5EfQrCv4+YtEUQBEEQBEEQBKGaEgGfIAiCIAiCIAhC\nNSUCPkEQBEEQBEEQhGpKBHyCIAiCIAiCIAjVlAj4BEEQBEEQBEEQqikR8AmCIAiCIAiCIFRTIuAT\nBEEQBEEQBEGopsQ6fIIg/C2Ihderj3/zws7VkajP6uXfXp/VcfF1QXiZNx7w3bp1Cz8/P+7cuUPD\nhg1ZtGgRrVu3prCwkMDAQA4fPgyAo6Mjfn5+0h9pbm4u69ev5/jx42RmZmJpacmAAQPw9PRES0sL\ngMOHD7Nu3TpSU1OxsbHBz8+PBg0aSOf7+flx9uxZdHR0cHV1ZcaMGRplUygUjBo1ikmTJtGzZ08A\n9u/fz/z589HX15eOU6vVyGQyQkJC6NatGyNHjuT69evo6OigVqvR19fH1taWBQsWYGRkBECzZs0w\nMDBAJpNppNGrVy+CgoJwd3fn8uXL0n6VSkVBQQF79+6lZs2aODg4YGhoWKYMo0aNYvr06RWWs1Gj\nRuzbt0/jOtesWcOZM2ek7fHx8YwaNUpKX61WU6dOHZydnXF3d5fKVOr+/fu4uLhw+vRpDAwMAHj4\n8CGLFy/m2rVr6Orq0rdvX+bMmYO2tnaZ9Evz0NfXJy4uDqVSybJlyzh27BhFRUV06NABX19fateu\nrZHv8OHDSUpK4vTp0+jq6krb161bx8aNG9HT0ytzf6Kjo2nUqNFL6//JkyesW7eO06dPk5eXR506\ndXBzc2PEiBHSMd9++y2rV68mLS2Njh07EhAQgKWlJVDxcw3w008/MWTIEPT19aVyTZw4EQ8PjyqV\n3d7enrS0NOk5L9WyZUt27tz50rK9eH5p+kFBQfTq1Ysvv/ySVatWoaurK+3bsmULH3zwAdnZ2cyb\nN4+LFy9iamrKpEmTcHV11SiHWq1m6tSpdOrUSeN+vcy4Bbswsahf5eMFQRAE4c/ISX/AFzPBxqbN\nmy6KIPxPvdGAT6lU4unpKb00HjhwAE9PT06cOMGaNWtITEwkJiYGtVqNh4cH4eHheHh4kJeXx9Ch\nQ2ndujV79uyhVq1aJCYmMmvWLB49esSyZcu4fv06Pj4+rF27lu7du7Nv3z7Gjh3L0aNH0dXVxcfH\nBwMDA86dO0dGRgYjR46kSZMm9O3bF4Bff/0VX19fbt68WabcLVq0IDo6utJr8/HxYfjw4UBJcDlp\n0iTWrFnDvHnzAKQXeGtr63LP37Jli8bv//nPf1CpVLRp04aHDx8ik8m4cOGCRkD3V8p5/fp1wsLC\naNq0qcb2t956i7i4OOn3n376iZkzZ5KTk8PMmTOl7bGxsfj7+5OTk6Nx/uzZs2nbti2hoaHk5OQw\natQo9u7dy6efflpu+s/bsGEDd+/e5fjx4xgYGODr60tAQAAhISHSMYmJiTx+/JgWLVpw6NAhXFxc\nNNJwdHRkzZo1FV53ZfWfkpKCi4sLLi4uHDx4EHNzc27evMn06dPJzMxk8uTJ3L59m0WLFhEeHk7T\npk3x9/fHx8eHzZs3V/pcGxgYkJCQQI8ePQgNDS23bC8rO0BISIj0JcSLKitbVc5PSEhg1qxZjBkz\npsy+0i8t4uLiSEhIwN3dnSZNmmBjYwP8f6B/9uxZOnXqVOk1vMjEoj5mtcv/exAEQRAEQRD+mjc6\nhu/ixYtoaWkxdOhQtLS0cHFxoUaNGpw8eZLIyEgWLlyIiYkJpqamrF27lv79+wOwbds2DAwMWLZs\nGbVq1QLA2tqaFStWoFAoUCqVxMbG0qtXL3r27IlcLmfw4MEYGBhw4cIFnjx5wpkzZ/D19UVXV5fa\ntWuzbds2OnbsCMCjR48YPXo0vXv3pm7dun/p2tTq/+8mYWxszMcff0xCQoLG/uePqUxsbCyXLl1i\n0aJFFebxV+Tn5zN//vwqtcK8//77LF26lPDwcLKzswE4dOgQQUFBeHl5lTl+27ZtzJw5E7lcTkZG\nBgqFAgsLiyqVa9q0aYSFhWFiYkJOTg65ubm89dZbGsdERkbSq1cvPvnkE3bv3l2ldEu9rP5DQkL4\n4IMPmDFjBubm5gDY2NgQEBBAamoqUNKC5ujoSKtWrdDV1WXWrFmcPXuW9PR04uLiyjzXlpaWnD59\nGihp/WvevPmfKvOfUVnZqiIhIaHMFwBQ8rycOHGCqVOnoqOjg42NDf379+fAgQMAFBYW8sknn9Cs\nWTPatm37Sq9JEARBEARB+GveaMB39+7dMi1cDRs2JCkpCZVKxY0bN/j444/p2bMn4eHhUnB37tw5\nPvroozLpNW7cmJUrV6Krq0txcXGZ1i+5XM69e/dISEjg7bff5quvvsLOzg4HBwcOHz5MjRo1ALCw\nsCAmJqbcFo6/4unTpxw9ehQ7O7s/fW5xcTGBgYHMnTtXowsk/PcB3/Llyxk4cGC5L/fl+fDDD9HW\n1ubGjRsAdO3alWPHjtG1a9cyx+rq6iKXyxk9ejR9+vShTp06ODo6VikfmUyGrq4u69ato0uXLty8\neRN3d3dpv1Kp5ODBg7i6utKrVy8eP37MtWvXqpQ2lAQ09erVq7D+z549W+7z1blzZynofvHZNTc3\nx9zcnLt375KUlFTmubaysuLu3btS/leuXMHBwQF7e3uCgoIoLCyscvlfpryymZmZSflXpqCggKSk\nJHbs2EG3bt3o27ev1NU3OTkZHR0d6tWrV+51aWtrc+TIEby9vct0NxUEQRAEQRDejDfapfPZs2fS\nmK9SBgYGXL58GaVSyalTp9i3bx95eXl4eHhgamrKxIkTycjIKNPi8yIHBwcmTpyIs7Mzbdq04eDB\ngyQlJaFUKsnKyuL3338nJSWFo0eP8uDBAz777DPq1KlD//79K+0mCSUv7B06dJB+V6vVGBkZcerU\nKWnb559/zpo1ayguLiYvL4969eqVCSKGDRuGXC6X0igdR/V8YHj48GH09fXp3bu3xrlqtRpbW1uN\n32UyGTt27KBZs2Zlylm6f+/evTRq1IgTJ06QmJiIv7+/1EJTFaampmRlZQFUqcVuy5Yt5ObmMnXq\nVPz8/Fi+fDkAmZmZZcq2atUqjeDRw8MDDw8PPv/8c8aNG8eRI0fQ0tLi2LFjNGzYkMaNGwPg7OzM\nrl27NFqVTpw4UaaO3nvvPfbs2UNWVhbJyckV1n9GRsZLr628Z1dfX5+CgoIKn+uCggLpvnXo0IFh\nw4bx9OlTpk6dytq1a/H29i637FDSgr1nzx7p9xkzZqCtra1x/6ZNm8aIESNemv/z55ee6+DgwPLl\ny3n69CkffPABw4cPp3Pnzly/fh1PT09q1aqFoaGhxtjC568ZSgL10nGCgiAIgvB3JJfL0NKSvfzA\nfwi5XKbxX+Gf7XXV4xsN+F58CYWSF+kPP/yQixcvMn36dIyNjTE2Nmbs2LHs2rWLiRMnUrNmTdLS\n0spNMz09HQsLC9q3b8/8+fNZsGABOTk59O7dm86dO2NiYiJNRjF79mz09PSwtrZm8ODBxMbGSt1G\nK9O8efOXjo2bPXu21FVSoVCwceNG3NzciImJkQLKiIiICsfwldq/fz9Dhgwps10mk3HmzJlKg9OK\nypmWlsayZcvYtm0bMpmsyi2FKpWK7Ozslwbbz9PV1cXCwoIpU6YwefJkKeAzNzevcAzf8+cCzJkz\nhz179vDrr7/SvHlzIiMj+eWXX+jWrRtQ0pUwPz+fp0+fSq10Dg4OFY6De1n916xZk6dPn5Z7/Tk5\nOZiZmWkEOqWePXuGoaFhhc91aQvthg0bpO3169dn4sSJrFq1Sgr4Kit7qVWrVlU4Bq+ysr3s/Pr1\n60sTvwC0b9+egQMHEhsby9ChQ1EqlRrHFxQUlGl5FgRBEIS/KzMzQywsjN90MV45c3OjN10E4W/s\njQZ8jRo1KjP+KikpiQEDBiCXyzVeLouKiqTApHv37hw7doyJEydqnHv79m2cnZ2JiYnB2NiYtm3b\ncvToUaDkZd3Ozg4vLy9pdkSlUikFTCqV6r/uIlkRPT09PDw8CA0N5bfffqNVq1bAy7tk5uXl8cMP\nP7BixYpy9//V8p4/f5709HRpopPCwkKUSiUdOnQgPj6+wvPi4+NRq9XSbJMVUalUDBw4kJUrV9Kk\nSROgpBumqalplco3b948WrVqhZubG1BS9wAmJiYkJSVx8+ZNvv32W41Aw8vLi71795Y7nvBFVlZW\nldZ/t27dOH78eJng/+TJk8yaNYvz589jbW1NUlKStC89PZ3s7Gysra3Jzc2t8LnOzs5m48aNTJky\nRSp/QUFBmZaz/0ZFZXvvvfdeeu6tW7c4d+4cHh4e0jaFQoGBgQENGjRAqVTy+PFj6tSpI13Xy760\nEARBEIS/i6ysfNLTc990MV4ZuVyGubkRmZl5/8plNqqb0vp85em+8hT/hE6dOqFUKtm9ezdFRUVE\nR0eTnp5Ot27dcHBwIDg4mJycHFJSUti+fTtOTk4AfPrpp+Tm5rJgwQKePHkCwI8//sisWbNwcXGh\nfv363Llzh08//ZSHDx9SUFDA6tWrsbS0pHXr1jRt2pQWLVpIk7wkJiYSFRUlpf+qFRYWsnPnTszN\nzWnUqFGVz/vpp5+oVasWNWvWLLPvz0z68qIBAwZw7do14uPjiY+Px9fXl+bNm2sEey+mffXqVRYt\nWoS7uzvGxmW/GXv+eLlcTpMmTVizZg35+fmkpKQQEhJSZvr+itjY2BAeHs7Dhw959uwZAQEBtG/f\nnvr16xMZGUm3bt145513sLS0lD7Ozs7s3buX4uLil6b/svqfPHkyly9fZtWqVWRlZaFSqYiLi2PR\nokV4eHhgaGhIv379OH78OFevXkWhUBAcHEyPHj0wMzOr9Lk2MTEhNjaWtWvXUlRURHJyMps2bSoz\ny+h/o6KyVSXgNjQ0lJY7UavVxMXFceTIET755BOMjIxwcHBg5cqVFBQUSIF3VVrFBUEQBOHvQKVS\nU1xcfT6lQV51u65/6+d1Be1vtIVPV1eXLVu24OvrS3BwMA0aNGDjxo3o6+sTGBhIYGAgTk5OFBYW\n4uzszNixY4GSl9I9e/awcuVKXF1dyc3NpWbNmri4uDB+/HigpCvauHHjcHNzo6CggPbt22tMg79l\nyxb8/f2xtbVFW1ubUaNGlRknB5RZcw5Kxsa1a9dO+r10HJS7uzuenp4ABAUFsXLlSmQyGXK5nGbN\nmhEaGiqtwyeTyRg8eLBG+qXr3X333XdAyRT3pRPVlFeu0i6Nz2vbti1ffvll5Te+CrKysqRr1NbW\npm7duowaNUpaaqK88jxv0aJF+Pv7Y29vj6GhIS4uLmVaZCsybNgw0tPTcXNzo6ioiK5du7J69WoK\nCws5ePAgCxcuLHNOnz59pLX7oGQcXHl15Ovry6BBgyqt/9q1axMREUFwcDBOTk4UFBTw9ttv4+Xl\nxdChQ4GSdRSXLFmCj48PaWlptG/fnmXLlgGVP9cAoaGhLF26lE6dOqGvr8+wYcMYOXKkVNaXlR1K\nZjItHf/5/DFXrlyptGzl1dXzGjZsyJo1awgODmbu3LnUqVOHwMBAaVzokiVL8PPzo2fPnhgZGTF3\n7lxpSYbnVZaHIAiCIAiC8L8jU7+ufoyCIAh/Qlsnb7HwuiAIgvDalCy87lytFl7X0pJhYWFMenou\nxcXilf6frrQ+XzUR8AmC8Ldw9epVsrLyxRiEakAul2FmZijqs5oQ9Vm9/Nvrs3nzlujo6LzpYrwy\nIuCrXl5XwPdGu3QKgiCUateunfgHq5oQLyDVi6jP6kXUpyD8+7zRSVsEQRAEQRAEQRCE10cEfIIg\nCIIgCIIgCNWUCPgEQRAEQRAEQRCqKRHwCYIgCIIgCIIgVFMi4BMEQRAEQRAEQaimRMAnCIIgCIIg\nCIJQTYmATxAEQRAEQRAEoZoS6/AJgvC3IBZerz7+7Qs7VzeiPqsXUZ//r7otwi4IFflbBHwpKSn4\n+fnxww8/YGJiwrhx4xg5ciSFhYUEBgZy+PBhABwdHVm0aBHa2tr069ePR48eSWkUFRVRWFjImTNn\nqFmzJpGRkXz55ZekpaVhZWXF3Llzad++PQDNmjXDwMAAmUyGWq3mrbfeYujQoUyYMAGA+Ph4Ro0a\nhaGhIQBqtZp33nkHb29vbG1ty5R/zZo1nDlzhn379knbKsr/0KFD+Pr6IpPJpLQLCgoYPHgw/v7+\n5V6zn58fOjo6FBcXs3z5co4ePUpRURGdOnXCz8+Pt956C4AJEyZw8eJFtLS0UKvVyGQyrl69+kqv\neeXKlURHR6NSqRg4cCA+Pj7StaxZs4bo6Gjy8/N5//33WbhwIe+995507pkzZ9i6dSsJCQkAtGrV\niunTp/P+++9r3M+4uDjGjh3L7NmzGTdunMa+q1evEhAQwL1796hVqxaTJ0+mX79+AJXeu2fPnhEY\nGEhMTAxyuRxnZ2emT5+OlpYWANu3b2fHjh3k5ORI99XS0rLKZV+3bh0bN25ET09Po7xdu3Zl7dq1\nADx58oR169Zx+vRp8vLyqFOnDm5ubowYMaLMM7Vt2zauXr1KSEgIwEufm5EjR3L9+nV0dHRQq9Xo\n6urStm1bZs2apVEHMTExBAcH8+TJExo3bsyiRYto1qwZAP7+/kRFRUlpyGQyjhw5Qp06dcjNzcXP\nz4+zZ8+io6ODq6srM2bMqHK9VcW4Bbswsaj/p88TBEEQhD8rJ/0BX8wEG5s2b7oogvDa/S0CvkmT\nJtG5c2c2bNhAUlISw4cPp1WrVhw9epTExERiYmJQq9V4eHiwdetWPDw8+PbbbzXSGD16NO3ataNm\nzZpcvHiRVatWsW3bNpo2bcqBAwfw9PQkNjYWMzMzZDIZ0dHRWFtbA5CcnIybmxvW1tY4OjoC8NZb\nbxEXFyel//333zN16lS+//57atSoIW2/fv06YWFhNG3aVNp26dKlCvPv378//fv3l46Ni4tj7ty5\neHl5ASUB1YvXHB4ejoeHB1999RUJCQkcPXoUbW1tZs2axRdffEFAQAAACQkJ7NmzhxYtWpS5x6/i\nmnft2sWZM2eke19aH+PGjSMqKoqYmBi+/vpratasSUhICHPmzOHrr78GSgLgkJAQAgIC6NatG8XF\nxezevZvRo0cTGRkplav02MGDB7Nnzx6NwEGlUuHl5cXixYvp1asXly9fZsyYMbRr146333670nsX\nFBTEzz//zIEDBzA0NGTGjBmsWrWKWbNmceTIETZs2MCWLVto2bIl69evx9PTk8jIyD9VdkdHR9as\nWVPuM56SkoKLiwsuLi4cPHgQc3Nzbt68yfTp08nMzGTy5MkAPHv2jLVr1xIeHs5HH30knf+y5wbA\nx8eH4cOHA5Cfn8+WLVv49NNPOXjwILVr1+bWrVvMnz+f0NBQ2rVrR1hYGNOnT+fo0aPS8xMcHEyv\nXr3KlN/HxwcDAwPOnTtHRkYGI0eOpEmTJvTt2/el9VZVJhb1Matt/fIDBUEQBEEQhCp742P4bty4\nQWpqKjNnzkQul2NtbU1ERATvvPMOkZGRLFy4EBMTE0xNTVm7dq3GS2+pbdu2kZuby9SpU4GSl+vx\n48dLQdigQYOQy+X89ttvQEnriFr9/90YGjRoQPv27aXWm/LY29tjaGhIYmKitC0/P5/58+eXaaF5\n/PhxpfmXysvL4z//+Q+LFi2iVq1aFBUVERkZia+vb7nXnJycTHFxMUVFRahUKuRyOQYGBgCkpaWR\nnp6u0ZrzvFdxzd988w2jR4/G0tISS0tLJkyYIAV0gwcPJjo6mpo1a5Kbm0t2drbU8lhQUEBQUBAB\nAQH07NkTLS0tdHV1GTt2LCNGjNC4p+np6Zw6dYoZM2agra3NyZMnpX3Z2dlkZGRQWFgIlASxOjo6\naGlpvfTexcTEMGPGDGrVqoWxsTFTpkyRyh4TE8PQoUOxsbFBS0uLKVOmcOfOHX777bc/VfbKhISE\n8MEHHzBjxgzMzc0BsLGxISAggNTUVOk4Ly8v7t+/z7BhwypM68XnptTz9WtoaMi0adNo0qQJ27Zt\nAyAiIoIhQ4bQrl07AMaMGUNwcLB07u3bt6XWvuc9efKEM2fO4Ovri66uLrVr12bbtm107NhROqay\nehMEQRAEQRDenDce8P3888+89957rFixgm7dutG7d2+uX79OZmYmxcXF3Lhxg48//piePXsSHh6u\n8YILJUHA+vXrWbRokdTdbeDAgRotDFeuXCE/P5/GjRuXW4aEhARu3rxJz549y92vVqs5cuQIOjo6\nGt0Ply9fzsCBAzVa9yrL/8VgrLRl0N7eHigJ6FQqFdevXy/3mocMGcKDBw/o3Lkz7du35/fff5e6\n1SUkJGBkZMSECRPo3Lkzw4cP5/r16xXe9z9zza1atQLg7t27GtdgZWXFvXv3pCwjnLcAACAASURB\nVN/19fXZv38/H374Id98841UtqtXr6JSqejevXuZfLy9vTVasvbv30/37t2xsLBg6NCh7Nq1S9pn\nbm6Om5sb3t7etGzZkpEjR+Lr60vt2rVfeu+Ki4s1ulvKZDIyMjLIzs6muLgYfX19jXLJZDKSk5P/\nVNkrc/bs2XKP7dy5M4sWLZJ+DwwMZO3atRrdSV/04nNTme7du0vdem/duoWBgQGjR4+mU6dOTJgw\nQerCe+/ePRQKBUFBQXTu3JlPPvmEU6dOASXPSr169fjqq6+ws7PDwcGBw4cPa7R0V1ZvgiAIgiAI\nwpvzxrt0ZmVlcenSJTp37sypU6f48ccfcXd3JzQ0lMLCQk6dOsW+ffvIy8vDw8MDU1NTJk6cKJ2/\ne/du2rRpIwUlL7pz5w7Tpk1j2rRpmJmZSduHDRuGXC5HqVSiUCjo3r07TZo0kfZnZmbSoUMHoKSb\nXVFREZMmTcLIyAiAEydOkJiYiL+/PwcOHKjw+p7Pv7RlB0paB3fv3k1YWJhGnkqlssJrViqVODg4\nMHXqVLS1tZk7dy6+vr6sXLkShUJB27ZtmT17Nu+++y7R0dG4u7tz9OhRKXj4q9dcGhQ8e/ZMIzDS\n19dHpVKhVCrR1dUFoF+/fvTv358dO3Ywbtw4YmJiyMjIwNTUFLn85d8vREVFsWDBAgCcnZ1Zs2YN\nSUlJWFlZoVar0dfXZ+3atdjZ2XH+/HlmzpxJixYtyM3NrfTe2dvbs379er744gu0tbUJDQ0FQKFQ\nYG9vz+rVq7G3t8fa2ppNmzahUCikT1XLfuLECen+lY6BO3PmDPr6+mRkZGBhYfHSNGrWrFnp/vKe\nm8qYm5uTmZkJlPyt7d27l02bNtG4cWNCQkLw9PTk8OHDZGdn07FjR9zd3VmzZg0nT55k+vTpREVF\nkZWVRXJyMikpKRw9epQHDx7w2WefUadOHakFtbJ6EwRBEIS/I7lchpaW7E0X478il8s0/iv8s72u\nenzjAZ+uri7m5ua4u7sD0LZtW3r16sW6detQq9VMnz4dY2NjjI2NGTt2LLt27dII+Pbv389//vOf\nctM+d+4c3t7ejBs3jvHjx2vsi4iIkMZepaWl4ePjw4wZM9i4cSNQ8qL8/Hi227dvM2XKFExMTOjf\nvz/Lli1j27Zt0iQofzb/2NhY6tWrh42Njca9qOyafXx8WLBggRTA+fj40Lt3b/z9/XFwcMDBwUFK\ny83Nja+++opLly7h5OT0X1/zmDFj0NfXp6CgQNpfUFAgdXEsVTrb1WeffcauXbuIj4+nRo0aZGVl\nUVxcLE2SUio7OxsjIyO0tLSIj4/n3r17GvVZVFTE7t27WbBgAcePH+fHH39kzpw5APTs2RNbW1sO\nHDiAk5MTKpWqwns3b948li9fzoABAzA3N2fMmDF8//33mJqaMmjQIFJTU5k0aRLFxcW4urpibW2N\niYkJenp6VSo7gIODQ4Vj+GrWrMnTp0/LbFepVOTk5Gh8GVGZ8p6bymRkZEhda3V1dfnoo4+kMZ7T\npk0jPDycu3fv0rp1a8LDw6XzHB0d6dSpEydPnuTdd99FrVYze/Zs9PT0sLa2ZvDgwdKY1EuXLlVa\nb4IgCILwd2RmZoiFhfGbLsYrYW5u9KaLIPyNvfGAz8rKiqKiIqlFBEpeguvXr88PP/yAUqmUji09\nrlRiYiJpaWn06NGjTLr79u1j+fLl+Pv7SwHP855Px9LSkuHDh5eZdfB5zZo1w9HRkbi4OCwsLEhP\nT8fFxQUomR1SqVTSoUMH4uPjq5T/yZMn6dOnj8a2hg0bSi1w5V3zo0ePNPbJ5XLkcjlaWlocO3YM\nlUqlkaZSqdToxvjfXPOYMWOwtrYmKSlJCjbu3r0rBZBr166lqKhII73CwkJMTU1p06YNOjo6nDlz\nBjs7O4085s2bh4mJCcuXLyciIoKRI0dqBPRXr17Fx8cHb29v/vjjD43rB9DW1kZbW/ul9y41NZW5\nc+dKE9ycOXOGhg0boqenR2pqKk5OTtKXDjk5OYSFhdGyZUtMTEyqVPaX6datG8ePHy8zBvXkyZPM\nmjWL8+fPSy2plSnvuanM2bNnpbF2VlZWGvdHpVIBJc9FXFwcv//+O0OHDpX2lz4/pa2rSqVSauFV\nqVTSvY2MjKy03qpyXYIgCILwv5aVlU96eu6bLsZ/RS6XYW5uRGZm3r9+mY3qoLQ+X3m6f+bgzMxM\nLl26xPnz5zl37pzG56/q2rUrBgYGrFu3juLiYq5evUpsbCxDhgzBwcGB4OBgcnJySElJYfv27RrB\n040bN2jRogXa2ppxa1xcHP7+/mzatKncYOtF2dnZ7Nu3T5rMAijTavf777/z/fff065dOwYMGMC1\na9eIj48nPj4eX19fmjdvLgV7Vcn/xo0btGmjORWwiYlJpddsa2tLSEgI6enp5ObmEhwcjJ2dHfr6\n+uTn5xMQEEBiYiJFRUWEhYWhUCjo2rXrK7lmgAEDBvDll1+SkpLC06dP2bx5M4MGDQKgdevW7N27\nl19//ZXCwkLWrl2LiYkJbdq0QVdXlxkzZrBw4UJOnz5NcXExeXl5rFu3josXLzJ+/HgyMjKIiYnB\nxcVFmhTG0tISR0dHjIyM2L9/P126dCEhIYH9+/cDJUtJxMbG0qdPH0xMTHB0dKzw3oWFhbF06VIK\nCwt58OABwcHBuLm5AXDhwgUmTJhARkYGubm5LFmyhO7du2NpaVmlslfF5MmTuXz5MqtWrSIrKwuV\nSkVcXByLFi3Cw8OjykFRec9NeUqfj6SkJEaOHAmUdLU8cOAAP/74I4WFhaxevRorKysaN26MXC4n\nKCiIK1euoFKpOHToEDdv3sTJyYmmTZvSokULVqxYgUKhIDExkaioKJycnMjMzHxpvQmCIAjC35FK\npaa4+J/9KQ3yqsO1iI/6tQXtVW7h+/rrr1m0aFGZFhYomeCistkeK6Onp8fOnTtZvHgxXbp0wdjY\nmIULF2JjY8Py5csJCgrCycmJwsJCnJ2dGTt2rHTuw4cPy0ziAiUv90VFRVKLTWnrYUhICN26dUMm\nkzF48GBkMpk002Pnzp0JCgqS0sjKypICHZlMhrGxMf3798fDw+Ol1/Sy/FUqFY8fPy53vFZgYCCB\ngYHlXvPixYsJDAyUWol69OjB4sWLgZKX+dTUVMaPH09mZiYtW7Zky5YtUovMq7jm4cOHk5aWhqur\nK4WFhQwcOJAxY8ZIZZk5cyaTJk0iJyeHtm3bEhYWJnX3HD58OGZmZqxbt47Zs2cjl8tp3bo1u3bt\nwtramm3btvHOO++UmSVSJpMxcOBAdu/ezYgRIwgJCWH16tUEBARQt25dgoKCpC6Kld27OXPm4OPj\nQ5cuXTA0NGT48OGMGjUKKJlk55dffpG6hdrZ2REYGCiV4WVlr4ratWsTERFBcHAwTk5OFBQU8Pbb\nb+Pl5aXRqlaZyp4bgKCgIFauXIlMJsPIyIj27dvz1VdfSZOr2Nvbs3DhQubOnUtKSgotWrRg/fr1\nAHTs2JH58+czb948njx5gpWVFaGhoVJeW7Zswd/fH1tbW7S1tRk1ahS9e/eucr0JgiAIgiAIb4ZM\nXdEAtBfY2tri6OgojZESBEF4ldo6eYuF1wVBEIT/iZKF153/8Quva2nJsLAwJj09l+Ji0aXzn660\nPl+1KrfwZWRkMGbMGBHsCYLwWny59FOysvLFGIRqQC6XYWZmKOqzmhD1Wb2I+iz1Ic2bt3zThRCE\n/4kqB3xdunThwoULDBky5HWWRxCEf6l27dqJbyirCfGNc/Ui6rN6EfUpCP8+VQ74WrZsSUBAAN9/\n/z1WVlbS9PulvL29X3nhBEEQBEEQBEEQhL+uygHfpUuXsLGxIS8vj59++kljX+lyCoIgCIIgCIIg\nCMLfR5UDvp07d77OcgiCIAiCIAiCIAiv2J9aeD0vL4/9+/dz584dVCoVjRo1on///lhaWr6u8gmC\nIAiCIAiCIAh/UZUXXv/111/5+OOPCQsLIy0tjadPn7J161acnJy4c+fO6yyjIAiCIAiCIAiC8BdU\nuYUvICCALl26EBAQIE3YolQqmT9/PsuXL+fLL798bYUUBEEQBEEQBEEQ/rwqt/Bdv36dCRMmaMzO\nqaury4QJE7h69eprKZwgCIIgCIIgCILw11W5hc/S0pInT55gbW2tsf3JkycYGBi88oIJgvDvcvXq\nVbEQcDUhFnauXkR9Vi+iPv9f8+YtyywzJgjVUZUDvgEDBrBw4UIWLFiAjY0NUNLqt2zZMvr37//a\nCliZmzdvMnnyZM6ePauxXa1WM2rUKFq1asWcOXOk7e3atZP2y2Qy2rdvz+bNmwGIiopi06ZNZGVl\n0bhxY+bPn0/Lli1fml9hYSGBgYEcPnwYAEdHR/z8/KT/gVSW54QJE7h48SJaWlrS/tLW0mbNmmFg\nYIBMJkOtVvPWW28xdOhQJkyYIOW9fft2duzYQU5ODp06dcLPz0+aQOfy5cusWLGCu3fvYmFhwbhx\n4xg6dCgA2dnZzJs3j4sXL2JqasqkSZNwdXUtcw+nTp1Kp06dGDFiBAAPHz7EwcEBQ0NDjeNkMhmx\nsbFYWFhI2xwcHDAyMuLQoUMa6fr4+HDo0CF0dXVRq9Xo6OjQsWNHfH19qVmzJgDHjh3D29sbPT09\nKX1/f3/69esnpaNQKBg1ahSTJk2iZ8+e0vZ+/frx4MED5HI5arWa+vXrS2WIjY1lzZo1/PHHH9St\nW5dp06bh6OgIwMWLF1mxYgX37t3D0tISd3d3hgwZAkDbtm01lh4pLCwE4McffwRg27ZtbN26lfz8\nfOzt7fH390dfXx+AEydOEBwczOPHj3n33XeZPXs2Xbp0eWme6enpLFmyhAsXLmBoaMinn37KuHHj\npDLExMQQHBzMkydPaNy4MYsWLaJZs2YA+Pv7ExUVhY6OjnT/jhw5Qp06dfjtt99YunQpt27dwtjY\nGFdXVyZPngxATk4OS5cu5dy5c6jVarp3786CBQswMTFh//79zJ8/X7qu5+s+JCSEbt268dNPPzFk\nyBD09fWlfRMnTsTDw4OqGLdgFyYW9at0rCAIgiD8N3LSH/DFTLCxafOmiyIIr12VA75Jkybx9OlT\nJk+ejEqlAkBLS4vhw4czc+bM11bAikRHRxMUFIS2dtlL+PLLL7l69SqtWrWStiUnJyOXy7l8+XKZ\n43/55RdWrlxJZGQk7777Lps3b2batGnExsa+NL+VK1eSmJhITEwMarUaDw8PwsPD8fDwqDRPgISE\nBPbs2UOLFi3K7JPJZERHR0stqsnJybi5uWFtbY2joyNHjhxhw4YNbNmyhZYtW7J+/Xo8PT2JjIwk\nOzubyZMn4+fnh5OTE7du3WLs2LG8++67dO7cmQULFmBkZERcXBwJCQm4u7vTpEkTKZB/+PAhixcv\n5uzZs3Tq1KlMuS5cuKDx4v+is2fPUq9ePZ48ecKlS5fo2LGjxv5Ro0ZJgbhCoWD+/Pn4+fmxYcMG\nAG7duoWbmxsLFiwoN/1ff/0VX19fbt68qbFdoVBw7949zp8/j5mZmca+e/fuMXfuXDZu3EiHDh04\nf/48Xl5efP3119SsWRNPT0+++OILHBwc+PXXXxkyZAht2rShSZMmXLt2TUrn2bNnuLq6MnbsWABO\nnjxJeHg4u3btwsLCAm9vb4KCgvDz8yM9PZ1Zs2axfft2bGxs+Pbbb5k8eTKXLl1CqVRWmufcuXOR\ny+XExsaiUCjw8PDA0NAQNzc3bt26xfz58wkNDaVdu3aEhYUxffp0jh49Kj1XwcHB9OrVS+MeqNVq\nPD09GTZsGOHh4Tx8+JDPPvuM2rVr4+rqSkBAAM+ePSMmJgaVSsXs2bNZsmQJK1asAKBFixZER0dX\nWO8JCQn06NGD0NDQCo+pjIlFfcxqW7/8QEEQBEEQBKHKqjyGT1dXl6VLl3Lx4kUiIiI4cOAAP/zw\nA/PmzUNXV/d1lrGM0NBQdu3ahaenZ5l9t2/fZv/+/VLLTalbt27RtGnTctNLTk5GrVZTWFhIcXEx\ncrlco5tqRfkVFRURGRmJr68vJiYmmJqasnbtWqnFs7I809PTSU9P57333it3v1qtRq3+/64WDRo0\noH379iQkJAAlLTxDhw7FxsYGLS0tpkyZwp07d/jtt9949OgRtra2ODk5ASUv6h07duTatWvk5+dz\n4sQJpk6dio6ODjY2NvTv358DBw4AJa1Xn3zyCc2aNaNt27YVlq0ykZGR9OrVCxcXF3bt2lXpsXp6\nevTr14/bt29L2xISEqTWqhc9evSI0aNH07t3b+rWraux75dffqFGjRplgj0oCWKHDBlChw4dAOja\ntStWVlbcvHkTY2Njzp8/j4ODA2q1mrS0NLS0tDRaMksFBwdjZWUltYh+8803uLq68u6772JsbMy0\nadM4ePAgarWaP/74A6VSSVFREQByuVwKlCvK08jIiGfPnnHu3Dl8fHwwMTGhRo0auLu7S8FWREQE\nQ4YMkVqPx4wZQ3BwMFBSN7dv3y73/qWmpmJtbc348eORy+W88847ODo6SgGtWq1m0qRJGBoaYmxs\nzJAhQzSC3Ze5desWzZs3r/LxgiAIgiAIwutXaQvfuXPn6NSpE9ra2pw7d67M/tTUVOnnbt26vfrS\nVcDV1ZWJEycSHx+vsV2pVPKf//yHpUuXEhkZqbEvISGB7OxsBg0axJMnT/jwww+ZN28etWvXplu3\nbjRo0IC+ffuipaWFsbEx27dvf2l+ycnJqFQqrl+/jqenJwUFBfTt21dq8awsz1u3bmFkZMSECRO4\nffs2VlZWzJkzhzZtyu9akJCQwM2bNxk/fjwAxcXFZVrZZDIZycnJODo6EhQUJG3Pysri8uXLODs7\nk5ycjI6ODvXq1ZP2W1lZERMTA4C2tjZHjhzB0tKSkSNHlluWygK+1NRULly4QEBAAIWFhWzYsIHH\njx9Tp06dco/Pzc3l4MGD2NnZSdtu3bqFWq0mJCQEfX19XF1dpW6BFhYWxMTEYGxszI4dO8rcIy0t\nLYYNG0ZycjItWrRg3rx5WFtb07VrV7p27Sode//+fe7cuSMFRoaGhhQXF9OmTRuKiorw8PCgfn3N\n7oVJSUlER0dLLWkAd+/e1WhJs7KyIj8/n5SUFFq0aEGPHj0YPnw4WlpaaGtrs2HDBukLkvLyrFev\nHnl5eQAa9Vtat6X3x9bWltGjR/PLL7/QsmVLFi5cCJS0ZCoUCoKCgrhy5Qp169Zl6tSp2NraUqtW\nLTZt2iSlWVhYyNmzZxk2bBiAxjMDJd1RKwq8y5OQkICurq4UxH788cd4e3uL8RGCIAiCIAhvUKUB\n3/jx4zl//jyWlpZSoFEemUwmtTz9L9SoUaPc7cHBwfTo0YO2bduWCfh0dXVp27Yt06dPR1dXl4CA\nAKZOnUpERAQKhUIaB/Xee++xefNmvLy8OHLkCLq6uhXml5mZiVKp5NSpU+zbt4+8vDw8PDwwNTVl\n4sSJL82zbdu2zJ49m3fffZfo6Gjc3d05evSoNA5v2LBhyOVylEolCoWC7t2706RJEwDs7e1ZvXo1\n9vb2WFtbs2nTJhQKBQqFQqOMOTk5TJw4kVatWmFnZ8eVK1fQ09PTOEZfX5+CggKgpC5L8y+PWq3G\n1tZW+lkmkzF79mwGDx4MwNdff42dnZ3UymZra8uePXuYMWOGlMauXbuIjo5GrVaTm5uLqakpW7du\nBUq6TFpZWdG/f3/Wr19PUlISnp6emJmZMXTo0Eq7kgLY2NgwZ84cLC0tWb9+PR4eHnz33XcardAp\nKSl4eHjg4uKi0QKrpaXF1atXSUxMZPz48VhZWTFo0CBp/9atWxk4cCC1a9eWtj179kyjNbj052fP\nnqFUKqlVqxbbt2/ngw8+4MCBA3h7e/Ptt99K4xUryrNjx4588cUXLFq0iLy8PLZt2ybVbVZWFnv3\n7mXTpk00btyYkJAQPD09OXz4MNnZ2XTs2BF3d3fWrFnDyZMnmT59OlFRUTRu3FgqZ2FhId7e3ujq\n6krjBp+3detWjh8/rvF3lJCQILWQlta/kZERp06dAkqC8Q4dOjBs2DCePn3K1KlTWbt2Ld7e3pXW\nmSAIgiC8CXK5DC0t2csP/BuTy2Ua/xX+2V5XPVYa8D3fze75n/+O4uLiuHjxYoVjjLy8vDR+nzt3\nLp07d+bp06ds3LiROnXqSGPpvLy8iIqK4sKFC1JwU57SiUemT5+OsbExxsbGjB07ll27djFx4sRy\n8+zUqRNPnz7FwcEBBwcHaZ+bmxtfffUVly5dkrpiRkRESGP40tLS8PHxYcaMGWzcuJFBgwaRmprK\npEmTKC4uxtXVFWtra0xMTKQ079+/j6enJw0aNGDVqlVASUCiVCo1ylVQUFBu98XyyGQyzpw5U2Hg\nFRUVRWZmptTiW1BQQHx8PJMnT5aCrk8//VQaw1dYWMi+ffv49NNPOXr0KLVr12bnzp1Sek2bNmXk\nyJFSF9bKDB06VOOYGTNmsHv3bhISEmjdujVQ0jrm6emJvb09fn5+ZdLQ0dGhWbNmDB06lOPHj0sB\nn1Kp5PDhw+zZs0fj+OeDZSgJ9GQyGYaGhuzevRuFQiGNYXR1dWXfvn0cP35cmginojxXrFjB0qVL\n6dWrF3Xr1mXgwIHcv38fKHnuPvroI+l5nTZtGuHh4dy9e5fWrVsTHh4upe3o6EinTp04efKkFPBl\nZGTg5eVFcXEx4eHhGsGwSqUiICCAY8eOsX37dho2bCjta968eaVj+ErHYALUr1+fiRMnsmrVKhHw\nCYIgCH9LZmaGWFgYv+livBLm5kZvugjC31ilAd+LgUFl/tfj+F703Xffcf/+fWkGxPz8fLS0tLh7\n9y6hoaFs3ryZbt26SS/Jpa0lenp6PHr0qEzAo6WlhZaWVqV5NmzYUGqBK1VUVCR1eSwvT5lMhp6e\nHseOHUOlUtGnTx/pXKVSqdH69nzXSUtLS4YPHy61lKWmpuLk5IS7uztQ0pIXFhYm5fXzzz/j7u7O\nwIEDmTt3rpROgwYNKCws1OhmmZSUVGa5jcpU1KXz3LlzFBQUcOzYMY3trq6uHDlyRKO1rJSOjg7D\nhg1j9erVXLt2jffff5+IiAiNiYAUCkWZVsnyREZG8s4779C5c2egpC6Kioqkc8+cOYO3tzdeXl6M\nGTNGOu/27dvMnj1bY0bRwsJCTE1Npd8vXrxIrVq1yozJtLa2JikpSfr97t27mJqaUrt2bR49elTm\nb0hbWxstLa2X5pmRkcGKFSukwHrv3r3S+DgrKyuNdEsnUVKr1cTFxfH7779rBL7PP1cPHjzgs88+\nw8bGhuXLl2t0t1QqlXh5eZGamkp0dHSF3XDLk52dzcaNG5kyZYr0t1RQUFClehMEQRCENyErK5/0\n9Nw3XYz/ilwuw9zciMzMvH/9MhvVQWl9vvJ0K9tpY2ND69atK/2UHvOm+fv7c+XKFeLj44mPj6d/\n//6MGDFCmjEwKSmJoKAgMjMzycnJYdmyZTg6OmJiYoKtrS1RUVHcunVLavVQqVR88MEHleZpYmKC\ng4MDwcHB5OTkkJKSwvbt26UWusryzM/PJyAggMTERIqKiggLC0OhUGiMM3tednY2+/btkybquHDh\nAhMmTCAjI4Pc3FyWLFlC9+7dqVGjBk+fPsXd3Z3PPvtMI9gDMDIywt7enpUrV1JQUMDNmzf59ttv\nq7y0RmXj9yIjI3FycsLS0lLjM2DAgAonb1Gr1Rw8eJBnz57x/vvvY2ZmRkREBDt27ECtVvPzzz+z\na9cuXFxcXlq2J0+esGzZMh4/fkxBQQGBgYE0atSIZs2a8dtvvzFt2jT8/f01gj2ARo0akZ+fz+bN\nm1GpVNy4cYOoqCiNPG/cuFHuJDYDBgwgIiKCO3fukJubqzFpT8+ePfn++++lZQ6+++47bt++jZ2d\n3UvzXL58ORs2bJAmYdm8eTNubm4AODs7c+DAAX788UcKCwtZvXo1VlZWNG7cGLlcLo3fU6lUHDp0\niJs3b+Lk5IRCoWD8+PF069aNL774oszYuoULF5KZmcnu3bv/VLAHJX8LsbGxrF27lqKiIpKTk9m0\naVOV6k0QBEEQ3gSVSk1x8T/7UxrkVYdrER/1awvaK23h2759u8b6Y/9kCxYsICAggD59+lBUVISt\nrS2+vr5ASVfA7OxspkyZQk5ODs2bNycsLKxK3RwDAwMJDAzEycmJwsJCnJ2dpSn7K8vT2dmZ1NRU\nxo8fT2ZmJi1btmTLli1Si45MJmPw4MHIZDJkMhk6Ojp07txZmlhj4MCB/PLLLzg5OaFSqbCzsyMw\nMBCAffv2kZGRwYYNG1i/fr2U3qhRo5g+fTpLlizBz8+Pnj17YmRkxNy5c6UlGZ5XXt1X9Dykp6dz\n8uRJdu/eXWbfoEGD2Lx5Mzdu3ABg586d7N27V7q2hg0bEhISIk2SsnnzZpYvX87q1asxNzfHy8sL\ne3v7l5bF09OTvLw8XF1defbsGR9++KHUzXDnzp0oFAoWLFjA/PnzpfN9fHwYPHgwmzZtYvHixWze\nvJm6deuyePFiPvzwQynthw8fUqtWrTJlsLOz4+HDh3h4eJCbm4utrS2zZ88GoHv37vj6+rJ06VLS\n0tKwsrJi06ZN0hjAyvJcunQp8+bNo3379lhYWODp6Sl1Aba3t2fhwoXMnTtXmhymtJ47duzI/Pnz\nmTdvHk+ePMHKyorQ0FBq1qzJt99+S3JyMikpKezfv1+6B7169cLb25uDBw+ip6dH165dpfUfLSws\nOHHiBFAyhq/0Cwf4/zGc7u7ueHp6EhoaytKlS+nUqRP6+voMGzaswol/BEEQBEEQhP8Nmfplc+wL\ngiD8D7R18hYLrwuCIAj/EyULrzv/4xde19KSYWFhTHp6LsXF4pX+n660Pl+1SgO+P7PUQnnLNgiC\nIFTV1atXycrKF2MQqgG5XIaZmaGoz2pC1Gf1Iurz/zVv3vIfv3SQCPiqaaum2AAAIABJREFUl9cV\n8FXapfP5iTMEQRBep3bt2ol/sKoJ8QJSvYj6rF5EfQrCv0+lAZ+zs3OVEnn27NkrKYwgCIIgCIIg\nCILw6lQa8D0vJSWF9evXc+fOHY1p4JVKJffu3ePatWuvrZCCIAiCIAiCIAjCn1fpsgzPmz9/PvHx\n8XTo0IGffvqJDh06UKdOHW7fvs2sWbNeZxkFQRAEQRAEQRCEv6DKLXxXrlwhLCyMDz74gP9j787j\nak7/x/8/zintm7LU2DV2E0JkT+WNGTHJHmNtJKSURFGUrEW27OZtGcQYauwxY8uaZUgzlhAj0Sra\nO78/+vV6Oyoy+I7pc91vt3O7jddyva7XdV3GeZ5rO3nyJFZWVrRq1YrVq1fz22+/MWzYsE+ZT0EQ\nBEEQBEEQBOE9lbuHr7CwEBMTEwBMTU2JjY0F4JtvvuH69eufJneCIAiCIAiCIAjC31bugK9Bgwac\nOHECgIYNG3Lx4kUAnj9/TkFBwafJnSAIgiAIgiAIgvC3lXtI56RJk3BxcUEul2NnZ8fq1asZNWoU\nd+7coUuXLp8yj4IgCIIgCIIgCMLf8NaN19/06NEjCgoKqFOnDn/88Qfh4eFUq1aNESNGoKGh8Snz\nKQhCBSc2Xq84xMbOFYuoz4pF1Of/iI3Xhc/Np9p4/b0Cvk+lcePGaGpqIpPJUCgU6OjoYGVlhYeH\nB3p6egDk5ubi5+dHVFQUlSpVwtHRkfHjxwNQUFBAaGgoe/fuJTc3F1tbW7y9vdHS0lJ6zp07d+jf\nvz979uzhyy+/BODu3bv4+fkRFxeHrq4u3333Hd999x0Ajx8/xt/fnytXrqCmpsbXX3+Np6en9D+H\nb775hkePHiGXy1EoFNSsWZOIiAgAfvnlF1asWMGzZ88wMzNj9uzZ1KlTB4Du3buTnJyMiooKCoUC\nTU1NLC0t8fT0xNjYGIAVK1awevVq1NXVUSgUqKioYGZmho+PD/Xr1wcgKSmJWbNmERMTg7q6Ovb2\n9ri5uZX7/mLh4eH4+vqydOlSevbsqXQuKiqK4OBgEhMTqV27Np6ennTo0KHEe7yuWbNmbNmyRfqz\nQqHA2toabW1tqXyKlVbG06ZNQ1VVtdR6nTFjBpqamqxZs4awsDBkMpn0jKysLNzd3XFycnpn3i5c\nuMCIESOkNqJQKKhVqxbu7u5069ZNaldBQUEcOnSI/Px82rdvz+zZs6lcubJSmtHR0YwePZqYmBg0\nNTWVzj1//hw7OzuCgoLo2rUrAMOHD+fq1atK/8goFAoMDQ2JiooC4OzZswQFBfHo0SOaNWtGQEAA\ndevWBd7eZovl5OQwYsQIJkyYID0Xiv4erVu3jsjISJKSktDT08PW1pYpU6ZIZfH06VNmz57NxYsX\n0dXVZcyYMQwfPlwp/dTUVAYMGEBYWJj0d6lYYWEhLi4udOvWjUGDBlFerXq7o2tYs9zXC4IgCMLf\n9SLlEYunfouZWct/OisfRAR8FcunCvjeOqRz0KBB0hfqd9mxY8ffzoRMJmP37t2YmpoC//vC6eTk\nJKUbEhJCYmIix48f5/nz54wePZq6devSs2dPNm7cyC+//MIPP/xAjRo18PPzY8aMGSxdulR6Rl5e\nHtOmTSM3N1fp2Z6entjZ2bFlyxbu3r3L4MGDadasGW3atMHT05NWrVoRFhbGixcvGDFiBDt37sTR\n0ZGcnBzu37/PmTNn0NfXV0rz6tWreHt7s3z5cjp37syePXsYNWoUhw4dQk1NDYDQ0FDpi3haWhqL\nFi1i+PDhRERESL2lNjY2LFu2DID8/HyWLl2Km5sb+/btA5CCgNWrV5OUlMSwYcOoX78+ffv2Ldf9\nxcLDwxkwYABbt25VCvhSUlLw8PDghx9+wMzMjMjISFxcXDh//nyp71GWU6dOUaNGDZKSkjh//jzt\n2rVTKv83y3jHjh04OjqWWq/e3t4sXbqU77//nu+//15KZ8+ePWzatAlHR0fp2LvyVrlyZaKjo6U/\nHz9+nMmTJ3P8+HGqVKnC9u3buXXrFocOHUJVVRUPDw8WL15MYGCgdE9GRgYzZ84s8xkzZ84kPT29\nxHFvb2+GDh1a6j3JyclMmjSJ4OBgOnbsSFhYGBMnTiQyMlIqs7LaLMCff/7JrFmzSiymVFBQwJgx\nY9DW1mbNmjXUrl2bJ0+e4OPjw4QJE9i8eTMAEyZMwNLSklWrVhEfH8/QoUP56quvaNmy6B/FS5cu\nMWvWLB4/flwi748ePcLPz48zZ85IgXN56RrWRL+66XvdIwiCIAiCILzdWxdt6dy5M506daJTp040\na9aMGzduYGxsjK2tLb169aJ27drExsbSunXrD8qEQqHg9Y7G6tWrExwczO3bt/n1118B2L9/P+PH\nj0dbW5s6derg6OjI3r17ATh69ChOTk7Uq1cPNTU1PDw8OHr0KJmZmVKaoaGhdOzYscSz79+/T35+\nPoWFhVJPWHEws3nzZqZOnYpcLic1NZWcnBwMDQ0B+OOPP6hSpUqJYA/g2LFj2Nra0rVrV+RyOQMG\nDEBTU5OzZ8+W+v4GBgbMnTsXmUzGnj17Sr1GVVWVvn37cvv2bWnj+/j4ePLz88nPz5fy/mYP09vu\nB4iLiyMhIYHp06fzxx9/8Oeff0rnnjx5Qm5uLvn5+QDI5fK/NXR3165d2Nra0r9/f7Zu3ap07m1l\nXJ56BUhMTCQoKIiFCxeW6NV9H927d0dLS4u7d+8C8ODBAwoKCqT2IZfLS5Svn58fX3/9danp7dix\nA21tbanX9nVv61g/cuQITZs2pWvXrqiqqjJhwgSSkpL4/fffgbe32b/++ovvvvuOnj17SqvqFouI\niCAhIYHly5dTu3ZtAExMTFi0aBF6enokJydz7do1nj17JtWJqakpO3fupF69ekDR9ixTpkyRetdf\nl5OTQ//+/WnWrBktWrQo8/0EQRAEQRCE/3feGvBNnDhR+ty/f1/qXRkzZgzfffcdCxcuxNfXl8uX\nL3/0jGlpaWFubs7ly5fJyMggOTlZ6gEEqFevHvfu3QOKei7U1dWV7i8oKCAhIQEo6pE4c+YMrq6u\nJb5ojx8/npCQEMzMzOjTpw+Ojo6YmZkBoKamhlwu57vvvqNXr14YGxtjY2MDwK1bt1BRUWHw4MFY\nWloyZswYKVAoKCgoERjJ5XLu379f5vvK5XI6dOhQZlnm5OQQHh5Oly5dkMuLqm3s2LHs2rWLVq1a\nYWVlhbm5OT169Cj3/VAUjPXr1w9tbW369u2rFJA1bdqULl26MHToUJo1a4a3tzeLFy+WgovyePbs\nGWfPnqVv377Y29tz6tQpEhMTpfNvK+N31Wux4OBg7OzsaNq0abnz9SaFQsGBAweoVKkSzZs3B2Dg\nwIE8evQIS0tL2rRpw8OHD6Uhs1D0I8SLFy8YPHhwiXYVHx/Ppk2b8PPze2twV5p79+4ptXW5XE6t\nWrWk9v62NmtoaMjRo0cZOXJkiXRPnz5Nly5dSsxXMDQ0JDQ0FCMjI27evMmXX37JwoUL6dSpEz17\n9uTq1avSDxsNGzbk+PHj2NnZlXivSpUqcfDgQdzc3JTamCAIgiAIgvDPKfe3skuXLpXaQ9a6dWvi\n4uI+aqaK6evrk56eTlZWFoBSEKWhoSEd7969Oxs3biQhIYGsrCyWLl2KqqoqOTk5ZGZm4uPjw/z5\n81FVLTmCVS6X4+vry5UrV/jxxx/Ztm0bp06dUrpm3bp1nDlzhvz8fGbPni0dNzMzIyQkhN9++43m\nzZvj5OREbm4u1tbWHD58mEuXLpGfn8+ePXu4d+9eieGkZb1vsaioKCwsLLCwsKBVq1bs2LFDaRig\nQqFg/PjxxMTEEBkZyaVLl9i1a1e578/OziYyMpKBAwcCRUN4IyIiePHiBVA036tatWr88MMPXLt2\nDV9fX9zd3Xn27JmUhpubm/SMtm3bYmFhwbZt26TzP/30E1ZWVujr61OlShW6devGjz/+WOLdSyvj\nt9VrscePH3P06FGl4Z3lzVtaWpp03szMjKlTpzJw4EC0tbWl97e2tub06dNER0djbGzMrFmzgKKe\ntOXLlxMUFASgNPS5oKAALy8vfH19pTmob1q0aFGJvC1evBiArKysEj2JmpqaZGdnA29vsxoaGujo\nlD72OzU1Veo9LUt6ejrnz5/H0NCQX3/9laCgIObOnSv9EKGrq1tmwC+Xy9+ZviAIgiB8LuRyGSoq\n/+6PXC6rMO8iPv+rz4+t3NsyNGzYkO3bt+Pt7S19uc3Pz2fDhg0f1LPyNqmpqdSoUUMK9HJycqQv\n49nZ2dJ/Ozk58fLlS4YNG4a6ujqjRo1CS0sLXV1d5s6di729PQ0bNiyR/o0bN9i2bZu0v2DLli0Z\nOHAg4eHhdO7cWbpOTU0NQ0NDaWuKoKAgBg0apLQghZubG9u2bePWrVu0adOGmTNn4uPjw4sXL+jZ\nsycdOnRAV1f3ne9rYGAg/dna2lqag1dYWEhUVBSurq5s2bKF6tWr4+fnx8WLF6lUqRKmpqbSnMfi\nAO5t9zdv3pwDBw6QmZmptCBHTk4Ou3fvZtSoUWzbto2cnBxpzp2DgwN79uzhyJEjDBs2DCiaW/m2\neXLh4eGkpaXRqVMnqd4uXLiAi4uLUuBQWhm/rV6L7d+/n44dO1K9evUSz35X3gwMDJTm8MXFxTFp\n0iR0dXUZOXIk3t7e+Pj4YGRkBBTNu+vVqxdz5szBy8sLNzc3qlSpwqNHj5TSXblyJU2aNJHeuTSe\nnp5SGb5JQ0NDCu6KZWVloaWlVe42W5qqVauSnJxc6rmUlBQMDQ1RU1PDwMCAcePGAdCqVSt69OhB\nVFTUBw/dFgRBEITPib6+1idZIOOfYGCg/U9nQfiMlTvg8/HxYdy4cRw7doyGDRuiUCi4desWCoWC\nTZs2ffSMZWZmcuXKFcaMGYO+vj5GRkbcu3dP6kGIj4+Xhr0lJSUxatQopk2bBhStYpifn0+9evU4\ndOgQ6urqrF+/Xkp78ODB+Pv7o6amRl5entJzVVVVUVVVpbCwkL59+7JkyRIpWMzNzZV6bHbt2kWt\nWrWwtLQEkObSqaurk5aWRqtWrTh06BBQFGxZWVkxadKkMt9XoVBw9uzZUnuqoKj3xNbWlvr163P+\n/HksLS2lZxYP0ZPL5aX2YpZ2f/Pmzdm1a5e0AEixX375hS1btjBq1Cj++uuvEr2SqqqqvLnyZVlO\nnz5NdnY2hw8fVjrev39/Dhw4gJ2d3VvL+G31WuzEiRMlVqj8uxo3boyNjQ3R0dGMHDmyxPvL5XJk\nMhkZGRlcv36dP/74Az8/P2kuXdeuXQkLC+PgwYM8f/6cgwcPAvDixQvc3NxwdnaWAqm3MTU1ldoO\nFLWfhw8f8uWXX/Lo0aMy2+y7dO7cmYULF5Kbm6sUbKekpNC1a1c2bNhAvXr1pDmhxT/sFL+fIAiC\nIFQk6emvSEnJfPeFnzG5XIaBgTZpaS//z2+zUREU1+dHT7e8F7Zo0YLDhw8zZswYvvjiC2rWrImL\niwsHDx4ssSz7h0pISMDDwwMzMzNpCwA7OztWrFhBeno69+/fZ+vWrfTr1w+Affv24enpyatXr0hJ\nSWHevHkMGDAAuVzOtWvXuHDhgvQB2LlzJ19//TXm5ubk5uayevVqCgsLiYuLk87J5XIaNmzIsmXL\nePXqFU+fPiU0NBQHBwegKBiZN28eiYmJZGdnM3/+fOrXr0/jxo25c+cOjo6OPH78mOzsbJYuXYqR\nkZE0z+pNz58/Z8aMGairqysFX286e/Ysd+/epVWrVnz55ZdUr16d+fPnk5uby6NHj9i0aVOZC4i8\nef+ff/7JjRs36NevH0ZGRtLH3t6epKQkfv31V7p27crx48c5ffo0CoWCgwcPEhcXh5WVVbnqcdeu\nXfTu3VspfSMjI2muoFwup0GDBmWW8dvqFYqCw9jYWGn1yPf1ZhDz8OFDjh8/jrm5OQDdunUjNDSU\nlJQUMjMzCQ4OxsrKChMTE6V2Vbzq6cmTJzE3N+fgwYNcvHhROm9iYkJISEi5gj0AW1tbbt68ybFj\nx8jLy2PVqlUYGxvTpEmTt7bZd+nVqxdffPEFkydP5uHDh0BRED1p0iRpWGnHjh3R1NRkxYoVFBQU\nEBMTw7Fjx+jVq9f7FK0gCIIgfPYKCxUUFPy7P8VBXkV4F/FRfLKgvdw9fFC0jL2NjQ2mpqa0aNGC\nly9fljlf6H3IZDIGDBiATCZDLpdjYGCAra0trq6u0jVTpkwhKCiIXr16IZfLGTFihLRAydixY0lI\nSMDKygoVFRX69OmDp6dnmc8q/qJvZGTE2rVrmT9/Phs2bMDIyIjJkydjbW0NFK3AOGfOHGn1xv79\n+0urEzo7O/Py5UscHBzIysqibdu2rFq1CoA2bdowZswYhgwZQnZ2Nm3atCEsLEwpH66urlKPkZ6e\nHh07dmTLli1Ki5RERUVJwYdMJsPExAQ/Pz/p2Nq1a5k3bx6dO3dGW1ubgQMHMmLEiHLdHxgYSIcO\nHUrsKaejo4ONjQ1bt25l/fr1zJo1i4CAAJKTk6lXrx5r1qxRGj5Z/B7FinuGjh49yokTJ5TmzBXr\n168fa9eu5dq1a/j7+5dZxu+q16SkJAoKCqhatWqpdV1W3orno6WnpyuVj46ODn369MHJyQkAf39/\n5s+fT58+fQDo0qUL/v7+pT7r9XZV2rk3LViwgCVLlpTI2/79+6lZsyarVq0iMDAQLy8vmjRpwooV\nK4B3t9m3PVcul7Nx40aWLVvGyJEjSUtLo3LlyvTq1QsXFxcA1NXV2bJlC/7+/nTo0AEdHR18fX1L\n/bHibVu2lHc7F0EQBEEQBOHTKvfG669evWLGjBkcOnQIuVzO4cOHCQoKIi0tjRUrVojFGgRB+CBi\n43VBEATh/xWx8brwOSquz4+t3AGfv78/cXFxzJs3D3t7e/bv309ubi7Tpk2jXr160gqDgiAIf0dM\nTAzp6a/EHIQKQC6Xoa+vJeqzghD1WbGI+vyfJk2aldiq6N9GBHwVy6cK+Mo9pDMqKooVK1YoLZhh\namqKv78/Y8aM+egZEwTh/xZzc3PxD1YFIb6AVCyiPisWUZ+C8H9PuRdtyczMLHW+nlwuJz8//6Nm\nShAEQRAEQRAEQfhw5Q74OnXqRFhYGAUFBdKx1NRUFi1aVOqG7IIgCIIgCIIgCMI/q9wBn4+PD/fv\n38fS0pLs7GzGjh2LlZUV6enpzJw581PmURAEQRAEQRAEQfgbyj2Hr1q1auzatYvo6Gju3btHfn4+\npqamdOzYUSzBLgiCIAiCIAiC8Bl6r334ANq3b0/r1q2lP+fl5QGgpqb28XIlCIIgCIIgCIIgfLBy\nB3wXLlzA39+f+/fvU1hYKB0v3jD61q1bnySDgiAIgiAIgiAIwt9T7oDP19eXL7/8Ei8vLzQ0ND5l\nngRBEARBEARBEISPoNwBX1JSEmFhYUr78AmCIHwsYuP1ikNs7FyxiPqsWER9llQRNmAXhLcpd8Bn\na2vLb7/99tEDvujoaBYuXMjDhw9p0KABM2bMwMzMjOHDh3P16lWlv4AKhQJDQ0OioqIA2Lp1Kxs3\nbiQtLQ1TU1O8vLxo06aNdP21a9cICwvj6tWrFBQU0KBBA1xcXOjQoQMAK1asYPXq1airqys9QyaT\nsXv3blJSUhg3bpy0KI1CoSA3N5f27duzYcMGoCgQXrFiBb/99hsvX77E2NiYIUOGMGzYMKBoKOyI\nESPQ0tKS0jA2Nubbb79VSvvOnTvMnTuX2NhYdHR0cHBwwMXFBYCUlBTmzp3L2bNn0dLSwtHRUdrs\nPjc3l3nz5nH48GHy8/OxsLBg1qxZVK9eHYBjx46xbNkynjx5gomJCa6urtjY2ABw7tw5Fi5cyP37\n9zEyMmLcuHEMHDhQStfPz4+oqCgqVaqEo6Mj48ePl8rp6NGjBAcHk5SURIMGDfDz86Nx48ZKdXvn\nzh369+/Pnj17+PLLLwHo3r07ycnJqKiooFAo0NTUxNLSEk9PT4yNjYGieaELFy7k4MGD5OXlYW5u\nzqxZszAxMSnRdkaPHk1MTAyamppK565fv46LiwunTp0q0eZSUlJwcHBgw4YNSu1527ZtbNiwgYyM\nDFq0aEFgYCDGxsYoFAqWLl3Knj17yMrKwszMDB8fH0xNTSkoKKBZs2Zoamoik8lQKBRUrlyZIUOG\nMG7cOAAePnxIjx49pDbwejs7ceIE+vr67Nixg3Xr1pGRkUHDhg3x9fWlcePGpaavq6tL9+7dmTp1\nKrq6ulKaT58+VWqLX3zxBUOGDGHo0KFK5VVcVgqFgtq1a+Pu7k7Xrl0ByM/PJzAwkCNHjlBQUICl\npSV+fn7o6+sDsG/fPkJDQ0lNTaV9+/YEBARgaGjIzz//jL+/v9LflezsbIYMGcKsWbNK1EFpxvhs\nRdewZrmuFQRBEISP4UXKIxZPBTOzlv90VgThkyl3wOfu7o6dnR2RkZHUqlULuVx5R4clS5a898Mf\nP37MhAkTmDlzJvb29pw6dQonJyciIyMBmD59uhQ4vSk6OpqwsDC2bt1K3bp1CQ8PZ+LEiZw7dw6A\nkydPMnXqVHx9fVm2bBmqqqpERkbi4uLC6tWrad++PQA2NjYsW7as1GfUr1+fK1euSH++c+cOI0aM\nwMvLCyj6gt2/f3/69+/Pvn37MDAw4Pr160yZMoW0tDQpYKtcuTLR0dFSOjdu3GDq1KlkZGTg4eGB\nQqFg/PjxDB48mE2bNvH48WNGjx5N9erVcXBwwMvLC7lczrFjx8jJycHJyQktLS2GDBnCqlWruHfv\nHkeOHEFTU5NZs2YRGBhIaGgo8fHxeHl5sXr1aiwsLDhz5gwTJ07kp59+omrVqjg7O7N48WKsra35\n888/GThwIC1btqRhw4aEhISQmJjI8ePHef78OaNHj6Zu3br07NmT2NhYZs6cSVhYGObm5qxfv54p\nU6Zw6NAh6R3z8vKYNm0aubm5Jco1NDRUCjDS0tJYtGgRw4cPJyIiAg0NDcLCwrh58yb79+9HR0eH\nwMBApk6dyvbt26U0MjIyytwOZPfu3SxYsABV1ZLN++LFi/j6+vLkyROl40ePHmX9+vVs3LiRWrVq\nERAQwOzZs1mzZg07d+7k+PHj7Nu3D0NDQ5YuXcr06dMJDw8HQCaTsXfvXurWrQtAfHw8Q4YMoUGD\nBnTr1k265vz586X+ghgbG0toaCg7d+6kVq1arF69GldXVw4fPlxq+omJifj6+vL9999LZZKYmEj/\n/v0ZOHAgERER6Ovrc/XqVaZMmcKLFy/4/vvvATAyMuL06dNK7z158mROnDiBoaEhW7du5c6dOxw5\ncgS5XI67uztLlixhzpw53Lx5k4CAADZv3kyDBg2YPXs2M2fOZPXq1fTr149+/fpJ6Z4+fRofHx8m\nTJhQah2VRtewJvrVTct9vSAIgiAIgvBu5d6Hb+bMmchkMmrWrImGhgZqampKn7/j5MmTNGrUCAcH\nB+RyOV27dqVFixZKgUNZLC0tOXr0KHXr1iUnJ4fU1FQqV64snQ8ICMDNzQ07OzvU1NSQy+XY2dnh\n6upKfHz8e+dVoVAwbdo0nJ2dadiwIVAUuLRu3Ro3NzcMDAwAMDMzIzAwkGfPnpWZVvPmzaUvzhkZ\nGTx79gxTU1PGjh2LXC6nVq1a2NjYcOXKFbKysjh9+jTe3t7o6upSpUoVxo0bx+7duwFwdXVl/fr1\n6Orq8uLFCzIzM6Vy+Ouvvxg4cCAWFhYAdOzYkXr16nH9+nV0dHQ4c+YM1tbWKBQKqddNW1sbgP37\n9zN+/Hi0tbWpU6cOjo6O7N27F4CdO3cycOBAzM3NARg5ciTBwcFK7xgaGkrHjh3fWa4GBgbMnTsX\nmUzGnj17AMjOzmbChAkYGhqipqbGsGHDuH79utJ9fn5+fP311yXSK/4RwNnZucS5ixcv4u7uXuq5\n7du3M2HCBOrVq4eqqiqenp54enoCMHjwYMLDwzEyMuLly5e8ePECQ0ND6V6FQoFC8b9hMfXq1aN1\n69bExsYqPeP1a15XvBBSXl4eBQUFyOVypR7LN9M3NjYmJCSEW7duST2YISEhtG/fHldXV6k3rmXL\nlgQGBpKUlFTqc6Go515NTY179+4p5SU/P5/CwkKlvERGRtKjRw+aNWuGmpoaHh4e/Pbbb6SlpSml\nmZmZibe3N/7+/lSpUqXMZwuCIAiCIAifXrl7+C5dusTWrVv56quvPtrDCwsLSywAI5PJuH//frn2\n9tPU1OT8+fOMGjUKVVVVli9fDsCDBw9ISEjA1ta2xD0jR478W3nds2cPeXl5ODo6SsdOnTol9fa9\nztLSEktLy7em17ZtW1RVVbl27RqdO3dmzZo10rm8vDxOnTrF4MGDpRVRXy8nmUzGgwcPpP9WU1Nj\nxYoVrFy5kurVq7N161agKMB7PehKSEjgzp070tBLLS0tCgoKaNmyJfn5+Tg5OVGjRg0yMjJITk7G\n1PR/vS316tWTepNiY2Pp1q0b3333HX/88QfNmjXD19dXuvbSpUucOXOGXbt2sW7duneULMjlcjp0\n6MDly5cZNmyYFGgVi4qKkoJsKApGX7x4weDBg0uk7+DgwPjx47lw4UKJ5zRq1IioqChUVFRK1Fts\nbCzW1tY4ODjw5MkT2rZtqzQUUUNDg/DwcGbNmoWenh6bNm0q831u3rzJjRs3Sg0sS9O1a1dq1qxJ\n7969UVFRQVdXl//+979vvUdHR4dWrVpx+fJlOnfuzOnTp5XqoNibbeB1CoWCX375BS0tLZo1awYU\nBbdjxoyR2m/jxo1ZtGgRAPfu3ZN6xqGot1BbW5v79+/TsuX/hsKsXbuW5s2bS724giAIgiAIwj+n\n3D18derUKXV43ofo1KkT165d48iRI+Tn53Py5Emio6PJzc1FoVCwaNEiLCwssLCwoG3btlhYWLB4\n8WKlNFq3bs3vv/9OUFCQ1HuXmpoKoNQLU5aoqCjpGcWfIUOGlLiwSPoLAAAgAElEQVRu/fr1TJgw\nQSkQTU1NLdczyqKnp0d6errSsby8PNzd3VFTU2PgwIFoa2vTrl07Fi9eTGZmJk+fPmXz5s3k5OQo\n3efk5MS1a9ewtbVlzJgxFBQUKJ1/+vQpTk5O9O/fn0aNGknHVVRUiImJYe/evezZs4eff/6ZrKws\nZDKZUpCpoaFBVlYWAOnp6ezYsQMvLy9OnTpF06ZNcXZ2prCwkMzMTHx8fJg/f36pQyrLoq+vX6Is\nAA4cOMDatWuZMWMGUNRruXz5coKCggBK/DDwth4lPT29MnujMzIy2LVrFyEhIRw7dgwVFRWmTZum\ndE3fvn35/fffGTNmDKNHjyYzM1M6N2DAACwsLGjZsiUODg40adKEBg0aSOcVCgWdOnVSasvFPaY5\nOTk0atSIn3/+mStXrjBkyBAmTZok7XFZltfLrLxtMTk5WWrnX331FZ6engwcOFDqxcvNzcXW1pbT\np09z9uxZqlSpgr+/PwCvXr0q8QPN6+0Cinr3tm/fzqRJk96ZF0EQBEH4HMjlMlRU/p0fuVz2r38H\n8SlZnx9bub+ROzs7M336dIYPH07t2rVLfJnv1KnTez+8Tp06LF26lODgYGbPnk3Hjh3p1auXtBCF\np6dnmXP4pBf4//Px9ddfs2PHDn777TdsbGxQKBQ8f/5cWryk2MuXL6lUqZL0xd/a2rrMOXzFLl26\nREZGBv/5z3+UjletWpXnz5+XuL6wsJAXL15IQ+tKU1hYSEZGhtIw1NTUVCZOnEhBQQGbNm2S8rho\n0SLmzp2Lra0tJiYm9O3bl4SEBKX0iq+dNm0aP/74I3/++SdNmjQBinqvnJ2d6d69O7Nnzy6Rl0qV\nKtG4cWMGDRrEkSNHsLKyQqFQkJOTIw3xzM7Olv5bTU2NHj160LRpU6BoWOnmzZu5d+8e69atw97e\nXqlHrjxSU1OlYbHF1q5dy7p161ixYgVt2rRBoVAwffp03NzcqFKlCo8ePQLKHir5PlRVVRk+fDi1\natUCYMqUKfznP/8hJydHWtSnuIydnJzYtm0bFy9epEuXLkDRvMHiOXbPnj1j+vTpeHh4SL3OMpmM\nM2fOlDqHb9myZdSoUUPqeXV1dSU8PJxz585JCwyVJjU1VVp0pkqVKqW2xYKCAjIzM6W2+OYcvtjY\nWCZNmoS+vj6Ojo54eXkxd+5cKXj08vKiT58++Pv7o6mpWeKHhtfbBSANsy5uG4IgCILwudPX18LQ\nUOefzsYHMTDQfvdFwv9Z5Q743NzcAJg3b16Jc3934/WXL19iYmLCvn37pGODBg2iS5cuJeZsvSk8\nPJzLly8zf/586VheXh56enrUrFmTevXqcfToUaUhmFA0tyw2NpYtW7aUO5+//vorNjY2JRaq6dSp\nE0eOHKFPnz5Kx0+cOIGHhwdnzpwpM80LFy6gUCho0aIFAI8ePWL06NGYmZkRFBSkFBikpKSwcOFC\nqXdlx44dUjA3Y8YMvvrqK6lXMj8/X1rJEYrmSbq7uzNx4kSl4axxcXF4enoSEREhHSsuP319fYyM\njLh37570xT8+Pl4a4lmvXj2l3t7CwkJpntmhQ4dQV1dn/fr10vnBgwfj7+9f6pw7KArYzp49i5OT\nk/RnX19fzp49y7Zt26TgMTExkevXrxMXF4efn5/03G7dukkLyPxdb75Tfn6+tCpmSEgIqqqqUq+V\nQqEgPz8fPT09pXcoVrVqVYYMGcL06dNLvGdpnjx5ohT4Q1HP69t6SDMyMrh27Zo0bLRz584cOXKE\n3r17K10XFRWFt7d3mW2xadOmWFtbc/bsWRwdHUlMTFTqWVRRUUEulyOXyzE1NZXm+gE8f/6cV69e\nUb9+fenYiRMn6NWrV5n5FgRBEITPTXr6K1JSMt994WdILpdhYKBNWtpLsc1GBVBcnx893fJeGBcX\nV+bn7wR7ULRC46BBg4iNjSU3N5dt27aRmJhI9+7d33lvixYtOHz4MOfOnaOwsJDw8HASEhKwsrIC\ninomQkND2b9/P7m5ueTm5rJjxw527drFxIkT3yuf165do1WrViWOu7i4cOnSJUJCQkhPT6ewsJDo\n6Gj8/PyklTSh5Bf9mJgY/Pz8GDduHDo6OuTk5DB27Fg6derE4sWLS/QCBQUFsWrVKhQKBXFxcaxd\nu1YK8MzMzKSVPbOysggMDKRt27bUrFmT27dv4+rqypw5c0rMXaxfvz6vXr1i7dq1FBYWcu3aNcLD\nw+nfvz8AdnZ2rFixgvT0dO7fv8/WrVulVRi//fZbfv75Z37//Xfy8vJYunQp9erVo0GDBly7do0L\nFy5IHyha5KWsYO/58+fMmDEDdXV1+vbtC8Dy5cs5d+4c4eHhSj2FJiYmXL16VUq7+IeCkydPflCw\nB2Bvb8/mzZt5+PAhWVlZ0kqiGhoatGzZkm3btnHnzh3y8vJYtmwZhoaGUrD+pvT0dH766SelPL2t\nF7Jr167s3LmTuLg4CgoKWL9+PSoqKqW2OSiaizl16lTMzc1p164dABMnTiQ6OprQ0FAyMjIoLCzk\n7NmzzJkzh++//77EUMxiDx484MSJE1Jeu3TpwrJly0hNTSUzM5Pg4GCsra1RU1Pjm2++4dChQ1y9\nepXs7GyWLFmClZUVOjr/+1X06tWrSvP5BEEQBOFzV1iooKDg3/kpDvL+ze8gPiXr82Mr/ySrT6BG\njRrMmTOHSZMmkZ6eTtOmTdm4caP05XTBggVK2z0U7122f/9+GjZsKA11fPbsGY0aNWLTpk1ST0m3\nbt0ICQkhLCyMwMBAFAoFjRo1Ys2aNdKqlVDUA/LmF3OZTMasWbOkAOfx48dUrVq1RP6rV6/Ozp07\nCQ4Opnfv3mRnZ/PFF18wceJEBg0aJF2Xnp4uPUNVVRUTExNGjBgh7Y929OhRHjx4wNOnT6V5XTKZ\nDFtbWxYsWEBAQAAzZsygTZs2GBoa4uzsjLW1NVDUe5aSksKQIUPIz8+nY8eOLF26FIAtW7aQk5OD\nj4+PtIWBTCbD29ubAQMGsGbNGvz9/Vm7di0mJib4+/vTtm1boGhIY1BQEL169UIulzNixAh69OgB\nFO2l5+vri5eXF0+fPqVp06asXLmy1Dou7iV7naurK3K5HJlMhp6eHh07dmTLli2oq6tTPJw1Pz9f\nWnSnuE7Onj1b6iI/f2dI55tz/0aOHElhYSGjR48mLS0NS0tLAgICALCyssLV1ZXvv/+ezMxMWrdu\nzdq1a1FVVaWgoACZTIa9vT0ymQyZTEalSpXo2LGj0rYRb1uEaNiwYWRmZjJhwgRevnxJ06ZNWbdu\nHRoaGiXSl8vlVK5cmR49ejB58mQpDRMTE3bu3ElISAi9evUiOzubmjVr4urqyoABA6TrUlJSpLYo\nk8nQ1dXFzs5O2tcxICCAoKAgvvnmG2QyGV26dMHb2xuAZs2a4efnh5eXF8nJybRt21aaSwlFPcTP\nnj2jWrVq710fgiAIgiAIwqchU3yMCVCCIAgfqFVvd7HxuiAIgvD/VNHG69/+azdeV1GRYWioQ0pK\nJgUF4iv9v11xfX5sIuATBOGzEBMTQ3r6KzEHoQKQy2Xo62uJ+qwgRH1WLKI+S2rSpFmpi6r9G4iA\nr2L5VAHfPzqkUxAEoZi5ubn4B6uCEF9AKhZRnxWLqE9B+L+n3Iu2CIIgCIIgCIIgCP8uIuATBEEQ\nBEEQBEGooETAJwiCIAiCIAiCUEGJgE8QBEEQBEEQBKGCEgGfIAiCIAiCIAhCBSUCPkEQBEEQBEEQ\nhApKBHyCIAiCIAiCIAgVlNiHTxCEz4LYeL3iEBs7VyyiPisWUZ/v59+8KbsgFPusAr7r16/j4uLC\nqVOnePLkCb1790Ymk0nnc3NzqVmzJocOHZKOKRQKJk+eTPv27Rk2bBgAjx8/xtraGi0tLaXrZDIZ\nx44d486dO4wYMUI6r1AoMDY25ttvv2XcuHHSM2NjYwkICOCPP/6gZs2auLu707VrV6U85+TkMGLE\nCCZMmKB0Ljc3l3Xr1hEZGUlSUhJ6enrY2toyZcoU6bmNGzdGU1MTmUyGQqGgcuXKDBo0iO+//75c\n72FoaEhkZCRLly4lOTmZdu3aERgYiJGRUanp6+joYGVlhYeHB3p6elKaK1asYMeOHeTm5tKuXTvm\nzZuHrq4uFy5cUCqn4udraGgQHR0tHcvMzKRLly60bduWNWvWKJXP8OHDuXr1KpUqVZLu7datGz4+\nPmhrawPQvXt3kpOTUVFRUXrHBQsWYGtrK6X1/Plz7OzsCAoKkso6ISGBOXPmcPXqVQwNDXF2dqZf\nv34AZGVlMX/+fI4ePYpcLufbb79lypQp0nOKn2VtbY22tjYRERFKeU9JSWHu3LmcPXsWLS0tHB0d\nGTNmDG/avXs3ixcv5ty5c9KxOXPmEB4eLr23TCbjwIEDGBsbc/v2bQICAoiNjUVHRwcHBwdcXFyU\n0oyOjmbUqFF4enqW+sy/Ux5QtLn562Xcpk0b1q5dC/DBbSk1NRVLS0u0tLSk9O3s7PDz8ys1/28a\n47MVXcOa5bpWEARBED61FymPWDwVzMxa/tNZEYQP8tkEfLt372bBggWoqhZlycTEhCtXrkjnnz9/\njr29Pb6+vtKxx48f4+/vz6lTp2jfvr1SejKZjLNnz6KhoVHq8ypXrqwUtNy4cYOpU6eSkZGBh4cH\nmZmZODk5MXjwYP773/8SGxvLuHHj2LJlCw0bNgTgzz//ZNasWVy/fl0p7YKCAsaMGYO2tjZr1qyh\ndu3aPHnyBB8fHyZMmMDmzZulPO7evRtTU1MAHjx4wJAhQzA1NcXGxuad7xEXF4efnx+bNm2iUaNG\nzJkzB29vb+kL/JvpP336lNmzZ+Pk5MSOHTsA2LJlC4cPH+ann35CX18fDw8PFi1axJw5c0otp9JE\nRETQtWtXzpw5Q0JCArVq1VI67+3tzdChQ4Gi4HDChAksW7aMGTNmSNeEhoaWCKbfNHPmTNLT06U/\nFxYW4uLigpmZGWfOnOGvv/5i9OjRGBoa0qVLFxYsWMDNmzf5+eef0dLSws3NjeDgYDw9PaU0Tp06\nRY0aNUhKSuL8+fO0a9dOOufl5YVcLufYsWPk5OTg5OSElpYWQ4YMka5JSEhQarfFbt26RXBwsFLA\nCkWBlrOzM4MHD2bTpk08fvyY0aNHU716dRwcHKTrdu3axYABA/jxxx/LDPjetzwePHiAXC7n0qVL\nJdL6GG3p1q1bNGjQoETgXF66hjXRr276t+4VBEEQBEEQSvdZzOELCwtj69atODs7l3nNrFmz6N27\nNx07dgQgLy8Pe3t7GjduTKtWrUq9R6Eo/1CF5s2bExAQwObNm8nIyCAmJgaZTMbEiRNRVVXFzMyM\n3r17s3fvXgD++usvvvvuO3r27ImJiYlSWhERESQkJLB8+XJq164NFAWwixYtQk9Pj+TkZCl/r+ex\nTp06tGnThlu3bpXrPSIjI7GxseGrr75CTU0NDw8PTp06RUpKSqnpV69eneDgYG7fvs2vv/4KwPbt\n25k2bRrVqlVDXV2duXPnlhlglCU8PJxvvvmGnj17sm3bthLnX8+Djo4O//nPf0q847vs2LEDbW1t\njI2NpWPx8fHcvXsXX19f1NTUqFu3LkOHDmX37t0AHD16FDc3N6pVq4aOjg6TJk3ip59+Ukp3165d\n2Nra0r9/f7Zu3Sodz8rK4vTp03h7e6Orq0uVKlUYN26clDYUBVheXl4MHjy4xPvGxcXRuHHjEu/x\n7NkzTE1NGTt2LHK5nFq1amFjY6P040ZKSgq//vorbm5uqKqqcuLEiY9SHrGxsTRq1KjU8v0YbSk2\nNpYmTZqUmr4gCIIgCILwz/gsAj4HBwd+/vlnmjdvXur56Ohorl69iqurq3RMVVWVAwcO4O7urjRE\n73XvE/ABtG3bFlVVVa5du0ZhYWGJXjW5XM6DBw+Aop6vo0ePMnLkyBLpnD59mi5dupQY821oaEho\naKg0TO5Nt27d4vr16yV6usp6j3v37kk9LgAGBgbo6+tz7969Mt9RS0sLc3NzLl++TFZWFvHx8Tx9\n+pQ+ffrQqVMnFi5cSNWqVcu8/03Xr18nKSmJbt26MWjQIPbu3Ut2dnaZ1z9//pxDhw5hZWVV7mfE\nx8ezadMm/Pz8lMqisLAQFRUVpXKWyWRSHRUUFKCurq50Li0tjYyMDACSkpI4e/Ysffv2xd7enlOn\nTpGYmCilDSi1gdfTBlizZg0NGjSgc+fOSvm9f/8+OTk5LFiwAEtLS+zt7aWgqFq1akrDXvPy8jh1\n6pRScLh37146d+6MoaEhgwYNUgpEP6Q8bt26RUZGBv369aNDhw64urqSlJQEfHhbKk7/wYMH9OrV\ni86dOzNz5kxevHhR5v2CIAiCIAjCp/dZBHxVqlR56/l169YxevRoNDU1pWMymazMwAmKgqRu3bph\nYWFB27ZtsbCwIDw8/J150dPTIz09HXNzczIzM9myZQt5eXlcv36dAwcOkJOTA4CmpiY6OjqlppGa\nmoqhoeE7nwUwePBgLCwsaNmyJfb29jRs2FAaMvqu98jKylIqk+J8vS3gAtDX1yc9PV0KfPbt28fm\nzZv55ZdfSExMJCgoSLo2LS0NCwsLpeefOXNGOr97927s7e1RUVGhWbNm1K5dm/379ys9b9GiRVhY\nWNC6dWs6derEX3/9RY8ePZSucXNzU3qGt7c3UBS0eXl54evrqzTvEKB+/frUqFGDJUuWkJOTQ3x8\nPLt27ZLqqHv37qxcuZLk5GTS09MJCwsDkM7v3bsXKysr9PX1qVKlCt26dePHH38EQFtbm3bt2rF4\n8WIyMzN5+vQpmzdvlu69ceMGkZGRUj5fl5GRQbt27Rg3bhynT59mwoQJTJkyhdu3bytdl5eXh7u7\nO2pqagwaNEg6Hh4ezsCBAwH49ttvuXz5MvHx8R9cHmpqarRq1YqNGzdy5MgRtLS0mDx5MvDhbQlA\nV1eX9u3bs2vXLvbt2ycN+xQEQRCEfyu5XIaKyuf7kctl/4p8is/71efH9tnM4StLYmIiFy9eJDg4\n+L3uk8lknDx5ssw5fKUpLCwkIyODypUro6enx5o1a5g3bx4rV66kRYsW9O3bl6dPn74znapVq0rD\nNt+UkpKiFAzu3LlT6llJTk7G29sbNzc3Vq9e/c730NDQKPGFPCsrS1oMpSypqanUqFFD6glycnKS\ngmdnZ2cmT57M3LlzgaKenrLm8L169YrIyEgqVaokDZV8+fIlW7dulQIWAE9PT2lBnZycHFavXs2Q\nIUM4evSo9F4hISGlzuFbuXIlTZo0oVOnTiXOqaiosGrVKubOnUvXrl358ssv6du3r9SbNmPGDIKC\ngrCzs8PAwICRI0dy/PhxKVAKDw8nLS1NSjs7O5sLFy7g4uKCmpoaixYtYu7cudja2mJiYkLfvn1J\nSEggJycHb29vAgIC0NDQKNED26JFCzZt2iT92cbGhvbt23PixAkaNGgg1cHEiRMpKChg06ZNqKmp\nAXDhwgXu37/P9OnTpfvz8/PZtm0bPj4+H1QeEydOVLrey8sLS0tLnj9//sFtCSixOIubmxuOjo5v\nvV8QBEEQPmf6+loYGpb+A//nxMDg7f9eC/+3ffYB34kTJ7CwsMDAwOC9733fIZ0XLlxAoVDQokUL\ncnNzUVVVlRakAHB3dy/XHKXOnTuzcOFCcnNzpS/yUBTsde3alQ0bNmBhYVEij0ZGRgwdOhQ3N7dy\nvYepqanU81OcfkZGhtLQvDdlZmZy5coVxowZg6GhIfr6+lIPEBQFF+Utt4iICOrXr8/atWule169\neoWdnR0XL16kbdu2Je5RV1fHycmJsLAwbt++zVdfffXWZxw8eJDnz59z8OBBAF68eIGbmxvOzs6M\nGzeOly9fsmHDBmll1SVLlkh19OzZM7y8vAgMDATg5MmT1K1bF3V1dU6fPk12djaHDx9Wep6DgwMH\nDhygX79+pKSksHDhQiko3bFjB02aNOH333/n0aNH0mqq+fn5ZGVlYWFhwf79+4mPj+fhw4dKvXa5\nubnS8NJHjx4xevRozMzMCAoKUhqCuXPnToYPH8748eOlYzExMdIPAR9SHmvXrqVTp040bdoU+F9P\np7q6+ge3JYVCQUhICIMGDZICwOzsbLGUtSAIgvCvlp7+ipSUzH86G2WSy2UYGGiTlvZSbLNRARTX\n50dP96On+JFdu3atzEVZ3uZdQcub52NiYvDz82PcuHHo6OhQWFjI8OHDOXnyJIWFhRw5coTTp08r\nLXFfll69evHFF18wefJkHj58CMDdu3eZNGmSNGSxNBkZGezZs0daOv9d7/HNN99w5MgRYmJiyMnJ\nITg4mC5dupQY6lcsISEBDw8PzMzM6NChAwD29vasXr2aZ8+eScMee/fu/c53hKIFT/r06YOhoSFG\nRkYYGRlRq1YtunfvzpYtW0q9Jy8vjy1btmBgYED9+vXf+YyDBw9y8eJFLly4wIULFzAxMSEkJIRx\n48YBRUH4zp07USgUXLhwgfDwcGkRlfXr1xMQEEBeXh6PHj0iODhYWmFz165d9O7dW8p38cfOzk6a\nMxcUFMSqVaukRVjWrl3LkCFDaNOmDVeuXJHyFBYWhoGBARcuXMDY2Bi5XM6CBQu4fPkyhYWFRERE\ncP36dXr37k1OTg5jx46lU6dOLF68WCkgSk1N5ejRo/Tv318pTzY2Nmhra/Pzzz9/UHnEx8ezYMEC\n0tLSePHiBfPmzcPGxgZdXd0PbksymYwrV64QHBxMVlYWz549IyQkBHt7+/I0JUEQBEH4LBUWKigo\n+Hw/xUHe555P8Xm/+vzYPvsevsePH78z4Ht9r763HXtd8Tw9KFoAxsTEhBEjRkjbB2hoaBAaGkpQ\nUBBubm7Ur1+fsLAwqlWr9s5nyeVyNm7cyLJlyxg5ciRpaWlUrlyZXr16Ke23JpPJGDBgADKZDJlM\nRqVKlbC0tGTBggXleo/GjRszd+5cvL29SU5Opk2bNsybN6/U9OVyOQYGBtja2iotfjN16lSWL1/O\nwIEDefnyJdbW1krbFpTl1q1bxMXFSfPiXvftt98yfvx4afjrggULWLJkiZSPxo0bExYWJg0XfFdd\nve7Na0NCQpg9ezaLFi3iiy++IDAwUOrRmjZtGt7e3nTo0AEtLS2GDh3KiBEjSElJ4cSJE6WuKNqv\nXz/Wrl3LtWvXCAgIYMaMGbRp00ba087a2vqdeWzXrh0zZ85kxowZJCUlUa9ePcLCwqhatSqRkZE8\nePCAp0+fSiu+ymQybG1tadKkCTVr1iyxuqdMJqNv375s27ZNGhr7d8rDx8eHwMBAevXqRX5+Pt26\ndWPWrFnAx2lLS5YsYc6cOXTr1g2ZTMY333zD1KlT31legiAIgiAIwqcjU7zvuEdBEIRPoFVvd7Hx\nuiAIgvDZKNp4/dvPeuN1FRUZhoY6pKRkUlAgvtL/2xXX58cmAj5BED4LMTExpKe/EnMQKgC5XIa+\nvpaozwpC1GfFIurz/TRp0uyzno8uAr6K5VMFfJ/9kE5BEP5vMDc3F/9gVRDiC0jFIuqzYhH1KQj/\n93z2i7YIgiAIgiAIgiAIf48I+ARBEARBEARBECooEfAJgiAIgiAIgiBUUCLgEwRBEARBEARBqKBE\nwCcIgiAIgiAIglBBiYBPEARBEARBEAShghIBnyAIgiAIgiAIQgUl9uETBOGzIDZerzjExs4Vi6jP\nikXU54f53DdiF4TS/CMBX/fu3UlOTkZFRQUAhUKBTCZj/vz5TJ48GU1NTWQymdI5W1tbFixYoJTO\n7t27Wbx4MefOnVM6vn//frZv387du3dRU1OjTZs2uLu7U6dOHQBu3LjBwIED0dDQkNIfP348Tk5O\n5OXlsXDhQg4ePEheXh7m5ubMmjULExMTpWds3ryZmJgYQkND3/leCxYswNbWlgMHDrBixQqePHlC\nzZo1cXV1xcbGRrr/3r17rFy5knPnzpGbm0utWrUYO3YsvXv3LlGGAQEBqKmpMW3atBLnhg4dSnx8\nPL/99htqampK5zIzM1m5ciVHjhwhLS0NIyMj7OzscHZ2lvJ9+/Zt/P39uXnzJlWrVmXKlClSHoYP\nH87Vq1el/9kVv+PkyZMZOXIkt2/fJiAggNjYWHR0dHBwcMDFxQWA1NRULC0t0dLSku6zs7PDz8+P\ngoICQkND2bt3L7m5udja2uLt7Y2WlhYAUVFRBAcHk5iYSO3atfH09KRDhw4lyl2hUKCpqYmlpSWe\nnp4YGxsDsGLFClavXo26urpUFsV52L17N/Xr13/nuz1+/JiZM2dy/fp1qlWrxvTp0+nWrRsAubm5\n+Pn5ERUVRaVKlXB0dGT8+PEA7N27l5kzZ6KhoSGlW7t2bRwdHRkwYICUn8aNG0ttX6FQoKOjg5WV\nFR4eHujp6QHg7e1NRESEVK8qKio0adIEV1dXWrduXaItREdHM3r0aGJiYtDU1AR4azvMyMhgxowZ\nnDt3Dj09PSZMmICDgwPAO/9u/PDDD/z3v//lxYsXtG/fntmzZ2NkZFQiT2UZ47MVXcOa5b5eEARB\nEP5fepHyiMVTwcys5T+dFUF4L/9YD19oaChdu3Ytcbz4C7ipqelb709ISGDBggWoqiq/QkhICIcO\nHSIoKIhWrVrx6tUrVq5cybBhw9i/fz+GhobcunWLLl26EBYWViLdsLAwbt68yf79+9HR0SEwMJCp\nU6eyfft2ALKysli+fDmbNm2iR48e5X6v+/fvM3PmTDZv3kyLFi2Ijo7GycmJU6dOYWBgQFxcHMOH\nD2fixInMnTsXLS0tTp8+zdSpU8nNzaVfv34ApKWlMX/+fPbt28eoUaNKPOfu3bskJibStGlTIiIi\n6N+/v3Tu5cuXDBo0iBYtWvDjjz9SrVo17t69i4eHB3/99Rfz5s0jOzsbJycnxo4dy9atW7l06RLj\nxo3D3NxcCp68vb0ZOnRoiWcrFAqcnZ0ZPHgwmzZt4vHjx83i0TsAACAASURBVIwePZrq1avj4ODA\nrVu3aNCgARERESXu3bhxI7/88gs//PADNWrUwM/PjxkzZrB06VJSUlLw8PDghx9+wMzMjMjISFxc\nXDh//rwU+Lxe7mlpaSxatIjhw4cTEREhBVo2NjYsW7asxLNfV9a7Abi6utKxY0c2bNjAmTNncHNz\n45dffsHY2JiQkBASExM5fvw4z58/Z/To0dStW5eePXsC0LRpU3bv3i2lFR0djbu7OwUFBQwePBgo\n2fafPn3K7NmzcXJyYseOHdK9I0aMkAL93Nxcdu/ezdixY9m+fTtNmjSRrsvIyGDmzJlK7/Cudujj\n44O2tjbR0dHcunWL/4+9+46K6lofPv6dAUbAAmJNNAoSKxEjImJBsWAUC8YSsWBiARuKYEGigiiI\nRgFFUNDYgCQKEkuMFU0swRKxYESNUSTYRRBEBAZm3j9YnNcRBPK7ydVw92ctVzKn7HL2OYt5Zpfj\n7OxMixYtMDc3L/fZ2L9/P+vWrWPjxo2YmZkRFhbG1KlTiYmJKfd6v6qmUWMMGpT/3AuCIAiCIAh/\nzTs3h0+tVqNWlz/EQKVS4enpKX1RLnH//n02btxIWFgYFhYWyGQyqlevzrx587C1teX27dsAJCcn\na3wxflVeXh7Tpk3DyMgIhULBmDFjSEpKkva7urqSlpZWKu+KGBsbk5CQQLt27SgsLOTJkyfUqFFD\n6k1avnw5n332GZ9//rnUq9WtWzcWLlzI3bt3pXRGjx6Njo5OmcEmQExMDHZ2dgwdOpRvvvlGY9/W\nrVvR09Nj2bJl1K9fHwBTU1NWrlxJfn4+BQUFHDt2jHr16jFmzBgALC0tiY2NlXqYgDe2z5MnTzA1\nNWXSpEnI5XI++OAD+vTpw8WLF4Hyr/uRI0dwcXHBxMQEhULBnDlzOHLkCDk5OTx48ICCggIKCwsB\nkMvlUhBXFkNDQ5YuXYpMJiMuLu6Nx5XlTXW7desWN2/eZPr06WhpadG9e3c6duzIjz/+CBT3Kk+Z\nMoXq1avTtGlTxo4dy65du96YT+fOnfH09NToIX793m/QoAFBQUHcvHmTn3/+ucx0FAoFo0ePpl+/\nfqxfv15j3+LFixkwYIDGtvLuw9zcXI4ePcrMmTPR0dHB3NycQYMGsXv3bqD8Z+PIkSOMHDkSc3Nz\ntLS0mDFjBn/88Qc3b9584zUQBEEQBEEQ/nnvXMBXGRERETRv3hwbGxuN7b/88gtNmjThww8/LHWO\nn58flpaWAFy7do3ExER69+5Nr169WLFiBUqlEoC5c+fSrVs36byjR4/SokUL6fPy5ctZu3btXxqq\nVkJPT4+7d+/Srl075s+fj7u7O9WrV6egoICzZ89iZ2dX6pxBgwbh6uoqfd62bZvUA/i6goIC9uzZ\nw/Dhw7Gzs+Phw4dSsAVw6tSpMgPFDz/8kMDAQBQKBVevXqVp06Z4eXlhbW2Ng4MD9+/fLzO/19Wv\nX5+IiAjps1Kp5OTJk1KQd+3aNVJTU+nfvz82NjYsWLCAnJwcAIqKijSGW5ZsS0tLo02bNnTv3p3R\no0djZmaGl5cXq1atKjVc9VVyuZwuXbqQmJhYYbkrIyUlhUaNGmnkaWJiwu3bt8nOzubp06cavdIl\n+8pjY2NDRkZGucfp6+tjYWFRYT1sbGy4cOGC9Hnv3r08f/4cR0fHUkHsm+7D1NRUdHR0aNSoUZn1\nKO/ZKCoqKhWEy2QyUlNTyy23IAiCIAiC8M96a0M63d3d0dbWluZJ9e7dm4CAAAAcHR2Ry4tj0Vfn\nwfXs2ZPffvuNffv2ERcXp9HzBsVzxIyMjCrM28jICCsrKxwdHUlPT2fmzJmsXbsWDw8PjeP279/P\nhg0b2Lhxo7StXr16/+d6Abz//vskJSXx66+/MnXqVJo2bUqzZs1Qq9WVKnt5+R86dAhjY2OaN28O\nwKeffkp0dDTt27cHiq9P7dq1y00/KyuLAwcOEBAQgJ+fHz/99BNubm7s3buXDz74AICVK1dqDI1s\n3bo127Zt00hHqVTi4eGBQqHgs88+A6BmzZpYW1szadIklEol8+bNw8fHh8DAQHr16sXmzZuxsLCg\nbt26rF69Gm1tbannsX79+mzbto0OHTqwe/duPDw82LdvX7nXw8DAgLS0NOnz0aNHsbKy0jjG1NSU\n7777Tvr8prrl5uaWCmj09PR4/PgxL1++BNDYr6urK20vr3xQfM0rOq6iYwwNDXn27BlQ3NO9du1a\nvvvuO/Lz86X5sK8q6z7U1tYuFXTr6uqSl5dX6vzXn41evXqxevVqevXqhampKREREeTn55Ofn19u\nuQVBEATh30Qul6GlVfrv6tsil8s0/iv8u/1T7fjWAr7g4OAy57oB7Nixo8w5fPn5+Xh5eeHn5yct\nuPKqunXr8vTp0zLTzMzMxNDQEJlMxrp166TtjRs3ZsqUKQQHB2sEfCVfZkNDQ6Wewf+0XoAUyFpb\nW/PJJ58QHx/PvHnz0NbWJj09nSZNmpSqc2FhIdWrV68w75iYGG7cuCH1wiiVSnJzc0lPT6du3brU\nq1fvjdcnIyNDGqrXpk0bBg0aBBTPe2vbti0nTpyQhnnOnTtX+v+yZGZm4urqSlFREVu2bJF6xRYv\nXqxxnLu7O2PHjgXAxcWFFy9eMGbMGKpVq8b48ePR19enZs2afPPNN+Tn59OpUycAhg8fTlxcHIcP\nH66wHIaGhtLn3r17VziH701109PTKxW8vHz5En19fSnQy8/Pl9opLy+vwjbLzMwEqDDQz8zM1Oh1\ne9MxtWvXRq1W4+npibu7O3Xr1pWGA7/+rJR1H3766acUFBRoHJeXl1eqd7esZ2PIkCE8efKEadOm\nUVRUxPDhwzE1NaVmzZrlllsQBEEQ/k0MDPQxMqrxtotRiqFhxd8Thf9d7+RrGd40j+rKlSvcvXuX\nyZMnA1BYWMjLly+xsrJi7969dO3aFW9vb27cuEHLli01zp04cSK9evVi3LhxrF+/nhkzZkhfZPPy\n8qSeDbVazaJFi0hISOCbb77RGM75nzh+/Dhbt25ly5Yt0jalUkmtWrXQ0dGhU6dOHD58GAsLC43z\nduzYQWRkJPHx8eWmn5KSQlJSEvv27dP4gu7q6sr27dtxdXXFxsaGQ4cOSatHlrh+/TqffvopR44c\nwcTERGNoIBTPmaysu3fvMmHCBMzNzQkICNBY8TI4OJiRI0dKwUteXp60//Hjx4wfP15ajOTWrVsU\nFhZiYmLCd999VyoQ0dbWllYVLYtarSYhIUG6V/5TzZo14969eyiVSqnMKSkpWFtbY2BgQJ06dbh9\n+7YUvKWkpFS48NCJEyeoX7++tHpsWXJycrh48SITJ04sN62TJ09iZWXFw4cPSUpK4saNGyxevBiV\nSoVarcbW1pbw8HCeP3/+xvuwadOmFBQU8PDhQ2mBnlfrUd6z8eTJE+zt7XF2dgbg+fPnfP3117Rp\n06bccguCIAjCv0lWVi4ZGTlvuxgSuVyGoWF1nj17IV6zUQWUtOffnu7fnuI/yNLSkosXL3Lu3DnO\nnTtHeHg4hoaGnDt3joYNG9KgQQPGjx+Pm5sbiYmJqNVqMjIy8PHx4enTp4waNYqaNWsSHx/P2rVr\nKSwsJDU1lYiICGk1y7Vr13LmzBliY2P/tmAPwMzMTFrhUK1Wc/z4cU6cOMHAgQMBmD17Njt37iQy\nMpLc3FwKCws5fPgwISEhzJgxo8L0Y2Ji6NatGx988AF16tSR/n366ads376doqIixo4dS05ODgsX\nLuTx48dAcRA9Z84chg0bRuPGjfnkk09IS0sjNjYWtVpNfHw8V69epXfv3hWWIT8/n0mTJtGtWzdW\nrVql8Z4amUzGxYsXCQoK4uXLlzx58oTg4GCGDh0KwJ49e5g7dy65ublkZGSwbNkyRowYgVwup0eP\nHhw7doxTp06hVqs5cOAA169fp2fPnmWWIz09nS+//JJq1aoxePDgCstdGaamppiamrJmzRoKCgo4\nfvw4v/76K/379wdg8ODBhIaGkpWVxZ07d4iOjpZWVn2dSqXi+PHjBAcH4+7u/sY809LSmDNnDubm\n5tIrKF6Xl5dHZGQkR48eZerUqbz33ntcvnxZekb27NkDFAeXFhYWb7wPBw0aRPXq1enduzeBgYHk\n5eVJPyCUXMPyno2S4DozM5OcnByWLl2KjY0NdevW/cvXWhAEQRDeVSqVmqKid+dfSZD3rpVL/PvP\n2vPv9lZ6+MqaU/TqvhEjRmgco1aradiwIQcOHKgw7dmzZ9OwYUMWL17MgwcP0NXVxcrKiujoaGmh\nlfDwcPz8/LC2tkZXVxdHR0ecnJwoGYJYWFgoLaBSMhcvISGh3JUhK6pX3bp1Wb9+PcuWLWPJkiUY\nGxuzbt06TExMgOJl+7du3UpISAjr169HqVRiYmLCsmXL3rgiZwmlUsmePXtYtGhRqX39+/dn2bJl\nHDp0CHt7e7777jsCAwMZPnw4OTk51KtXj2HDhjFp0iSgeOGVyMhI/Pz8WLFiBQ0aNGDNmjVSj095\ndTxy5Aipqak8evRIWqHy1XcoBgYGsmTJEmxtbZHJZAwcOJDZs2cDMGnSJNLS0ujZsydaWloMGjSI\nuXPnAsULknh7e+Pn58fTp08xMTEhIiKCBg0aSHm7ubkhl8uRyWTUqlWLrl27EhUVpTEn7ejRoxo9\nqCVt6+3tzZAhQ8qtGxS/y2/hwoV06dKFevXqERQUJJVh1qxZBAQE0L9/f+RyOePGjdNot2vXrkl5\n6+jo0LRpUxYsWCAFjCXXquTel8vlGBoaYmdnh5ubm0Y5oqKipNc06Ovr89FHH7Ft27YyFysqSbek\n1/xN96GxsTEAS5cuxcfHhx49elC9enU8PT1p27Zthc+Gg4MDN27cwN7eHpVKRc+ePVm+fHm511MQ\nBEEQBEH458nUFb0DQRAE4b+gvb2HePG6IAiC8M4qfvH6p+/Ui9e1tGQYGdUgIyOHoiLxlf7frqQ9\n/24i4BME4Z1w4cIFsrJyxRyEKkAul2FgoC/as4oQ7Vm1iPb8z7RubaYxZeVtEwFf1fJPBXzv5KIt\ngiD877GwsBB/sKoI8QWkahHtWbWI9hSE/z3/qkVbBEEQBEEQBEEQhMoTAZ8gCIIgCIIgCEIVJQI+\nQRAEQRAEQRCEKkoEfIIgCIIgCIIgCFWUCPgEQRAEQRAEQRCqKBHwCYIgCIIgCIIgVFEi4BMEQRAE\nQRAEQaii3vn38G3evJmgoCAUCgVqtRqZTMbGjRvp0KEDAGq1mnHjxtG2bVvmzZsnnffdd9+xceNG\nsrOzMTc3x8/Pj/fffx+A8+fPs2zZMlJSUvjggw/48ssvsba2BqBVq1bo6ekhk8lQq9XUqFGDnj17\nMmfOHGrVqgVA+/btkclkUl5KpRKAK1euaJTdz88PhUKhUa4Sf/zxB8OGDSMuLo4PP/wQgJycHHx8\nfDh58iQ6OjoMHz4cd3d3AAoKCli8eDFHjx5FR0eHsWPHMmXKlFLplpVndHQ0mzdv5tmzZ5iamuLp\n6YmlpSXnz5/H2dlZqotaraagoABra2s2bdoknZ+fn8+4ceOYNm0aPXr0kLavW7eO2NhYXrx4QatW\nrVi0aBHNmzcH4McffyQ0NJQnT55gbm6Oj48PTZs2BSA5ORk/Pz9u3LhB48aN8fDwkNItr54FBQUs\nW7aMQ4cOUVhYiJWVFd7e3jRo0KDcepa4fPky4eHhXLp0iaKiIpo3b8706dPp0qWLdExgYCA7d+5E\npVLh4OCAl5eXRlsDbN26lQsXLhASEiJt69WrF0+fPkVLS0u6ljKZjBUrVmBnZ1fqvqpduzYjR45k\n8uTJUhr79u1j9erVPH36lE6dOuHv70+dOnWA4peS+/v7c+fOHerXr8/06dMZOHAgAE5OTly6dAkd\nHR3UajW6urrY2tqycOFCqlevDpS+r8vKf9u2bURGRvL8+XOsra3x8fGR8t+/fz+hoaE8ePCAxo0b\n4+bmRp8+fcrNf9GiRejr65e6R99EvHi96hAvdq5aRHtWLaI9/37v2svYBeF173zAl5yczJw5c/ji\niy/K3L9p0yYuXLhA27ZtpW3Hjh0jPDyczZs3Y2xsTEBAAIsWLWLTpk08evSIadOmsWzZMvr06cOP\nP/7IzJkzOXXqFAqFAplMxs6dOzE1NQXg0aNH+Pj44OLiwvbt2wG4ePGilNfLly8ZPnw448ePl7Y9\ne/aM5cuXs2fPHo3tJZRKJfPmzaOgoEBju5eXF3p6epw6dYrMzEycnJxo0aIFAwYMIDg4mIcPH3Ls\n2DHS09OZMGECxsbG9OvXr9w8T58+TXh4ONHR0RgbGxMbG4urqytnzpzB0tJSoy5//PEH48aNw9PT\nU9r2+++/4+3tTVJSkkZZv//+e/bu3Ut0dDTvvfceERERTJ48mWPHjnHp0iW8vLxYu3YtNjY2xMXF\nMX78eA4ePEhBQQEuLi44OjoSGRlJcnIyzs7OREVF0aJFi3LruW7dOm7fvs3hw4fR09PD29sbf39/\nQkJCyq0nwIkTJ5g9ezaLFi1izZo1aGtrs2/fPqZPn8769euxtrYmOjqaEydOsG/fPgBcXFzYvHkz\nEydOlNp67dq1bNmyhb59+5Zq15CQEI2A+FWv31epqamMGjUKU1NT+vTpw/Xr11m8eDFbtmyhZcuW\nLFmyBC8vLzZs2IBKpcLV1RVfX1/s7Ow4f/48X3zxBRYWFtKPGF5eXowePRoo/uFg2rRprFmzhi+/\n/LJS+e/fv59169axceNGzMzMCAsLY+rUqcTExHDnzh0WLFjA1q1badeuHadPn8bFxYWTJ09iaGj4\nxvxXr14t5V8ZExdGU9OocaWPFwRBEIS37XnGXVbNBnPzj992UQThjd75gO/atWsMGzaszH3Xr19n\n165dUk9DiW+//ZYpU6ZIX249PDy4d+8eAHv27KFr167SOQMGDKBZs2YavVxq9f//xatBgwYEBQVh\nY2PDzz//jK2trUZeQUFBmJiYMHz4cGnb6NGj6dChQ5lBARQHBl27duXatWvStsePH3PixAl++eUX\nFAoFDRo0YOvWrSgUCgD27t1LcHAw1atXp3r16owdO5Zdu3ZJAd+b8uzcuTNHjhxBT0+P/Px8MjMz\nqV27dqkyqdVq5s2bx9SpU2nRogUA9+/f5/PPP2fy5Mk8fvxY4/isrCymTJlCo0aNABg3bhwhISE8\nfPiQ+Ph47OzspOBnxIgRbN26lYSEBORyOTKZDFdXVwDMzc2xt7dn165deHp6lltPNzc3lEolCoWC\nzMxMcnJypLpUVE8/Pz/c3d0ZPHiwtG3w4MFkZGSQkpKCtbU1e/fu5fPPP5d6tSZPnsyaNWukgM/V\n1RV9fX0cHR3JyMgos23f5PX7qmnTplhaWnLt2jX69OnDvn376NOnj/TDxZw5c+jcuTMZGRnI5XIy\nMzOlnmSZTIaOjo7Um1iSfokaNWrwySefcPDgwUrnf+TIEUaOHIm5uTkAM2bMYOvWrdy8eZPmzZuT\nkJCAnp4ehYWFPHnyhBo1amj8mllR/pVR06gxBg1M/9I5giAIgiAIQvne6Tl8eXl5pKSkEBkZSbdu\n3RgwYABxcXFA8fC++fPn4+fnV2rYWHJyMkqlkhEjRtClSxfmz5+PkZGRtK9+/fq4urrSqVMnHB0d\nUSqV5XbF6+vrY2FhQWJiosb2lJQUdu7cyaJFizS2b9u2jaVLl5Y5nO38+fP88ssvuLm5aXxJvnbt\nGo0aNeLbb7+lZ8+e9O7dmx9//JG6deuSnZ3N06dPpQAWwMTEhNu3b1cqTz09Pc6ePUv79u0JDQ1l\n/vz5pY6Ji4tDqVQyduxYaZuRkRFHjhwps3d1/PjxDBkyRPp89OhRDA0NadiwIUVFRejq6mocL5fL\nuXPnDiqVqsx9qampFdZTJpOhUCgIDQ2lS5cuJCUl4ezsXGE9U1NTSUtLw87OrlQ9vvjiC0aNGgXA\n7du3peG1JXnfuXNH+rx8+XLWrl0rBYT/iWvXrpGUlCQFxbdv39aot6GhIQYGBty+fRtDQ0NGjRqF\nh4cHZmZmODk5aQxlfV16ejoHDx6kZ8+elc6/rDaTyWSkpqYCxdf27t27tGvXjvnz5+Pu7i4NF/2/\n5C8IgiAIgiD8d7zTAV96ejodOnRg9OjR/Pzzz/j6+rJixQpOnDhBUFAQ3bt3p3379qXOy8rKIiYm\nhsDAQI4dO4auri5z586V9sXGxjJmzBgSEhIYPHgwkydP5vnz5+WWxcDAgKysLI1tmzdvxsHBodQX\n73r16pWZRk5ODgsXLmT58uVoa2t2rmZlZZGamsqjR484ePAgGzZsIDo6mh9++IGXL18ik8k0vpDr\n6ury8uXLCvMs0aFDB65cuUJAQABubm6kpKRo7P/666+ZNm2axnw1XV1datSoUW66AOfOnWPx4sVS\n4Nu7d28OHTrE+fPnKSwsJC4ujpSUFAoKCrCwsCAnJ4eoqCiUSiVJSUns37+f/Px8qT7l1ROKh1pe\nvnwZOzs7Jk6cSFFRUbn1zMzMBJCC/jd5+fJlqbxVKpU09Laia+zu7o6VlRUdO3bEysoKLy8vjf2O\njo5YWVnx8ccfM3ToUFq0aCH1pr58+RI9PT2N4/X09MjLy5Pmxa1du5bLly+zfv16/P39uXHjhnTs\nypUrsbKyokOHDnTr1o379++X6u0tL/9evXoRExPD9evXUSqVhIWFkZ+fT35+vnT++++/T1JSEps3\nbyYgIICzZ8/+pfwFQRAEQRCE/753OuBr3LgxUVFR2NjYoK2tjaWlJYMHD8bf35+zZ88yc+bMMs9T\nKBSMHTuWJk2aoKury6xZszhz5gy5ubkoFAp69OhB586d0dLSYvTo0ejr63PhwoVyy/L6EMGCggJ+\n/PFHqXeoMpYuXSp90S6rzGq1mrlz51KtWjVMTU0ZMWIE8fHx6OrqolarNb585+XlvbGHpSza2tpo\naWkxYMAA2rZty/Hjx6V958+fJzs7m08++aTS6ZXYvXs3U6ZMwdvbG3t7ewAsLS1ZsGABCxcupEeP\nHiQnJ9O5c2dq1qxJrVq1iIiI4Mcff8TGxoawsDAcHByoVauWFGxVVE+FQiEtTHPv3j1+//33cutZ\nt25d1Go16enppcr/4sULKaDT1dUlLy9PI28tLS1pWG1FgoODOXfuHL/++ivnzp0jICBAY/+OHTs4\nd+4cly5d4tSpU0DxcOOy8obiIFBfX5/Dhw9z5coV7Ozs0NbWpkePHtja2rJ7927p2Llz53Lu3DkS\nExO5fPkygwYNYtSoURpplpV/yaJAQ4YMYcyYMUybNo0+ffqgVqsxNTWlZs2a0vlyuRwtLS2sra35\n5JNPiI+P/0v5C4IgCEJVJJfL0NJ6O//kctlbL4P49/e359/tnZ7Dl5yczKlTp3BxcZG25efn06lT\nJ3788UdpdcXc3Fy0tLS4ffs24eHhmJiYaAQNJT1AarUaExMT0tLSNPJRqVTlliMnJ4eLFy9Kc7kA\nzpw5Q/369WnZsmWl63Pw4EGqVavG119/LW1zdHTE19eXDz/8UFolsyTwUalUqNVqDAwMqFOnDrdv\n35Z6qVJSUjSGAL5JbGwsiYmJLF++XNqmVCqlFUcBfv75Z/r06YNc/tfi/7CwMKKioggPD8fKykra\n/uzZM9q3by/N4VKpVPTs2RNXV1cKCgrQ1taWFsCB4qCndevWFdbzyy+/pG3btlKQXVhYCEDNmjXL\nrWfjxo0xMTHhyJEjGkNWoXg+ZXJyMlFRUZiampKSkiLNY3t9mOV/6tUhvHXq1GH06NFSwFWSd4mM\njAyys7MxNTUlKSmp1AI/2trapXqJS1SrVg0XFxfCw8O5efOmNC+wvPyfPHmCvb29NET2+fPnfP31\n17Rp04bjx4+zdetWtmzZIp3/+j1UmfwFQRAEoSoyMNDHyKjiEVH/JEPDyncCCP973umAT19fn7Cw\nMIyNjbGzs+PMmTPs37+fb775hiVLlkjHeXl5Ubt2belVBEOHDmXLli306NGDhg0bsnr1amxsbKhe\nvToODg44Ojpy/PhxunfvTnR0NAUFBXTq1KnMMqSlpeHv74+5ubnG8v2XL18uczhpeS5fvqzxuVWr\nVuzYsUMKKtq0acNXX33FokWLuHv3LrGxsSxYsAAoXmAkNDSUNWvWkJmZSXR0tMZqmm/Srl07li1b\nxpAhQ7CysiIuLo60tDSN+VWXL19m6NChf6kucXFxREZGsn37dkxMTDT2/fHHH8yaNYsdO3ZQp04d\n1q1bR506dWjXrh15eXk4OTkRHBxMt27diI+P59SpU9Lwx/LqaW5uzubNm+nevTtGRkb4+/tjaWlJ\n48aNK6ynp6cn8+bNo1atWtJCN99//z0xMTGEh4dLeW/atAlra2u0tLTYsGGDxjzFv1N2djZxcXFY\nWFgAMHDgQJycnBg2bBhmZmbSkGUDAwO6dOlCUFAQu3bt4tNPP+XcuXPEx8cTGRlZZtpKpZKoqCgM\nDQ1p1qxZpfJPSEhg48aNREVFoaOjw9KlS7GxsaFu3bqYmZlx9epV9u7dy6BBgzhx4gQnTpxgxowZ\n/+f8BUEQBKGqyMrKJSMj563kLZfLMDSszrNnL8RrNqqAkvb8u73TAZ+xsTFr1qwhKCgIT09PGjZs\nyPLly2nVqlW5540dO5bCwkKcnZ3JzMykU6dO0vC61q1bs379elauXImHhwfGxsaEh4dLvWoymYwR\nI0Ygk8mQy+UYGhpiZ2eHm5ubRh737t2jfv36/1H9St6JVmLjxo0sWbIEW1tbtLW1GTdunBSczJo1\ni4CAAPr3749cLmfcuHGVmiPVokULVq5cydKlS3ny5AktW7Zky5YtGsNT7927V+H8tNffRbdhwwZe\nvHghraBa8t65nTt3YmlpycSJE6UhfZaWllJQpaurS0hICAEBAbi7u9OsWTPCw8Ol/MurZ8nqmKNG\njaKwsJCuXbuyevXqStXT1taW4OBgwsPD8ff3R61W7fYxqgAAIABJREFU07JlSyIiIqTeydGjR/P0\n6VOGDx+OUqnEwcHhja8Dqej6lLW/5L4qWWWzc+fOrFixAigO/pcuXYqXlxdPnz7F0tKSZcuWSXUL\nCQlh9erV+Pv7895777FixQratGkjpb9ixQoCAwOl+7ZVq1aEh4dLw2Eryt/BwYEbN25gb28v9ciW\n9JbWrVuX9evXs2zZMpYsWYKxsTHr1q3D2Ni43PwjIiL+0rBjQRAEQfg3UqnUFBW93WDrXSiD8O6S\nqV+NOARBEN6S7mODxGsZBEEQhH+VrEe38Bnf8a29h09LS4aRUQ0yMnJEwFcFlLTn3+2d7uETBOF/\nx/OMu2+7CIIgCILwlxT/7er4toshCOUSPXyCILwTLly4QFZWrpiDUAXI5TIMDPRFe1YRoj2rFtGe\nf7/Wrc3KfZ/zP0n08FUtoodPEIQqzcLCQvzBqiLEF5CqRbRn1SLaUxD+97zT7+ETBEEQBEEQBEEQ\n/u9EwCcIgiAIgiAIglBFiYBPEARBEARBEAShihIBnyAIgiAIgiAIQhUlAj5BEARBEARBEIQqSgR8\ngiAIgiAIgiAIVZQI+ARBEARBEARBEKqot/oevvPnz/PVV19x+/ZtjIyMmDhxIiNHjpT2Z2ZmMmLE\nCMLDw/nwww8BCA0NZf369VSrVg21Wo2Wlhbm5uYsXLiQZs2aAZCWlsaSJUu4dOkSRkZGTJ06lSFD\nhgBQVFREQEAABw8epLCwEGtra3x8fKhduzYAarWa6Oho4uLiSEtLQ19fHxsbGzw8PKhbt65G+Vev\nXk14eDixsbG0bdtWY19l0jl06BAeHh5SXWQyGUuWLGHgwIHMnz+fffv2oVAoUKvV6Ojo0KlTJ7y9\nvalXrx4AZ86c4auvvuLOnTvUqVMHZ2dnPvvsM41y5OfnM27cOKZNm0aPHj1KtcHOnTtZtWoVZ86c\nKbXvyJEjREREsHPnzlL78vPz+fTTTxkzZgxjxowBwMvLix9++KHcMicnJ+Pn58eNGzdo3LgxHh4e\nZZZr3rx55Ofns2bNmkqV+ebNm/j6+nL16lXq1avHrFmzsLe3B6BXr148ffoULS0t1Go1enp6dO7c\nmblz59KwYUMAsrOz8fHxISEhAQBbW1sWLVpEjRrFL7+MjY0lIiKCrKwsmjdvzoIFCzAzM9MoV1JS\nEtOnT+fkyZPSNicnJy5duoSOjg5qtRqFQkH79u2ZM2eOdE8XFBSwbNkyDh06RGFhIVZWVnh7e9Og\nQQMAjh49SlBQEA8fPqRJkybMnTuXLl26lKrbq8zMzIiKiuLcuXOMGzcOfX19aV/Jvebl5cWIESMA\nePz4MaGhoRw/fpwXL17QsGFDRo0aJbWtj48Pe/fuRSaTSWm8fPmSwMBABgwYUKocJXmsWLECOzu7\nUm1YFvHi9apDvNi5ahHtWbWI9vz7vc0XrwtCZby1gC87O5vp06fj4+ODvb09ycnJjB8/niZNmtC5\nc2fOnz+Pt7c39+7dK3Vunz59pECgsLCQ1atX4+7uzp49e1CpVEyfPh1zc3N++eUX7t+/z4QJEzAy\nMqJ79+58++23XLt2jYMHD6Ktrc2cOXNYtWoV/v7+AMydO5e7d++yfPlyWrVqRWZmJsuWLePzzz9n\n9+7d0gOtUqnYtWsXI0aMIDo6mhUrVmiUsTLpJCcnM2rUKBYuXFiqjjKZjHHjxjFv3jygOMBasGAB\nPj4+rFu3jpycHKZOncqqVavo3bs3v//+O5999hkff/wxLVq0AOD333/H29ubpKSkMtsgLS2NFStW\noK2teRsUFhayZcsW1q5dK6X1uuXLl5Oamlpqe0VldnFxwdHRkcjISJKTk3F2diYqKkojnwMHDrBv\n374yA4WyypyXl4eLiwuTJk0iOjqa8+fP4+zsjIWFhRTQhYSESIHls2fPWLlyJU5OTvzwww/o6uqy\ndOlS5HI5J06cQKVSMWPGDMLCwvD09OT69esEBgYSExNDkyZN2LBhA25ubsTHx0tl2LlzZ5nXEooD\n4dGjRwOQm5vLxo0bGTt2LHv27KFBgwasW7eO27dvc/jwYfT09PD29sbf35+QkBAyMjKYM2cO27Zt\nw9zcnH379jF9+nTOnj2LQqEoVbey1K5dm9OnT79x/6NHjxg2bBjDhg1jz549GBoakpSUxKxZs3j2\n7BnTp0/H19cXX19f6ZyQkBASExPp16+fxrbyylGRiQujqWnU+P98viAIgiD8tz3PuMuq2WBu/vHb\nLoogvNFbC/ju37+Pra2t1AvTpk0bOnXqxMWLF1EoFMyaNYt58+bh6elZbjra2to4ODiwefNmVCoV\nKSkp3Lp1i9jYWBQKBcbGxowePZqdO3fSvXt3UlNTKSoqorCwELlcjlwuR09PDyjucTx69ChHjx7F\nyMgIKP6y7O/vj6enJ3/++SempqYAHDt2DCMjI1xdXenXrx/z58+Xegkrm861a9fo27dvpa5XtWrV\nGDhwIEuWLAGgRo0a/PLLL+jr66NWq6XelZKenPv37/P5558zefJkHj9+XCo9lUqFp6cnjo6OpXrw\nfH19uXPnDhMmTODUqVOlzj1+/Dg3btygffv2f6nMiYmJyGQyXF1dATA3N8fe3p5du3ZJ7fzo0SOC\ng4MZPnw4WVlZlSrzsWPHqFevntQbZWlpSWxsLLVq1SqzXIaGhixdupR+/foRFxfHmDFjWL58OSqV\nCh0dHR49ekRubq7Unn/++SdqtRqlUklRUZHGPQMQHh7OwYMHmTp1Khs3biyVn1r9/39B1dfXx83N\njcTERLZu3Yqnpydubm4olUoUCgWZmZnk5ORIeT948ICCggIKCwsBkMvl6Orqlnvd/6qQkBA6dOiA\nu7u7tM3c3Bx/f38OHTpU6vjffvuNqKgo9u3bV6pn8T9R06gxBg1M/7b0BEEQBEEQhLc4h69Vq1Ya\nvWJZWVmcP3+eVq1a0aJFC44dO8bgwYM1viyXJT8/n9jYWLp3745cLkelUqGlpaXRtS6TyaTeqM8+\n+4y7d+/SuXNnLC0t+fPPP6UvuidPnsTCwkIK0kooFAqCg4OlYA+Kh/gNHz6cBg0aYG1tTUxMjLSv\nsukkJydz6NAhunfvTt++fdmwYcMb65mTk8OePXvo2bOntE1fX5+ioiLMzc2ZMGECY8eOpXHj4h4S\nIyMjjhw5whdffFFmehERETRv3hwbG5tS+2bOnElUVBRNmzYttS8jIwN/f39WrFghDe+rbJnVanWp\nYEUul2v0FH755ZfMmjWL+vXrV7rMV69epWnTpnh5eWFtbY2DgwP379/XGMb4OrlcTpcuXUhMTASQ\n7hkvLy9sbW3JycnB0dERgG7dutG0aVMGDBiAubk5GzduZOXKlVJaw4cPZ/fu3Xz00UflXo9X2djY\ncOHCBaD4/lQoFISGhtKlSxeSkpJwdnYGin8I6d69O6NHj8bMzAwvLy9WrVol9e79HU6ePFnmDw+d\nO3dm8eLFpbYvX76cKVOmSENOBUEQBEEQhHfXO7Foy/Pnz5kyZQpt27alV69e1KxZs9wvtEePHsXK\nygorKyvat2/P9u3bpSFzzZo1o1GjRgQGBpKfn09KSgoxMTHk5+cDxQFi7969OXXqFKdPn6Zhw4Z4\ne3sDxXMGXw/SyvLgwQPOnTvH4MGDAXB0dOS7775DpVJVOp2XL19iYmLCoEGDiI+PZ+3atWzfvp0d\nO3ZIx0RHR2NlZUXHjh3p2LEjCQkJDB06VCMdLS0tLly4wK5du4iLi2P37t0A6OrqSvPPXvfbb7+x\nb98+vLy8ytxfMt+uLD4+PkycOJEPPvigzP3lldnCwoKcnByioqJQKpUkJSWxf/9+qW0iIyMxNDSU\nen0rW+asrCwOHDhAly5d+OWXX5gxYwZubm6kpaW9sR4ABgYGpXoRfX19+fXXXzExMWH69OlA8T3T\nvHlzvv/+ey5evIiTkxOurq4UFBQAlJrbWRmGhoY8e/ZMY5uLiwuXL1/Gzs6OiRMnUlRUREFBAfXr\n12fbtm1cvnyZRYsW4eHhwZMnT6Tz3N3dpeehY8eOWFlZ8c0330j7nz17Ju1/9Zjs7Gyg8vc9FPfS\n3rp1S3reXlVSjpL033R/CYIgCIIgCP89b3XRFiiekzV16lSaNm1KcHBwpc7p3bu3NIdPpVJx9OhR\n3NzciIqK4qOPPmLdunUsXbqUHj168OGHH+Lg4MDPP/8MFPcgLVy4kDp16gDF86v69+/PkiVLqFev\nHhcvXiwzz4yMDOlL8c6dO1EqlfTv3x8o7rnKyMggPj6evn37ViodPT09oqKipO0tW7bEycmJI0eO\nSAvXjB07VpoPp1QqiYuLY+zYsRw8eFCjd0VHR4dWrVoxcuRIDh8+LC1QU5b8/Hy8vLzw8/NDV1e3\nwh7UV8XFxZGXl6exsM7rKipzREQEy5YtIywsjHbt2uHg4MCjR4+4desWUVFRxMXF/eUyKxQK2rRp\nw6BBg4DiOZ5t27blxIkT0jDPsmRmZmJoaFgqLYVCwdy5c7GzsyM7O5vQ0FAaNmxImzZtAHB1dSU2\nNpaEhARsbW0rde3Kyrtk2OareUPxgjXfffcdv//+O6dPnyY/P59OnToBxb2JcXFxHD58WKpbcHBw\nuXPnDA0Ny53DV69ePdLT00ttV6lUPH/+HAMDA2nbrl27GDx4sMaQ1hIVlUMQBEEQqiK5XIaWVvmj\nnv7JvF/9r/Dv9k+141sN+K5evYqzszMODg4VztV7E7lcjp2dHc2aNePs2bN89NFHvHjxgk2bNklD\nDgMDA2ndujVQPLetpGem5HyZTIaWlhY2NjZs3rxZI7iD4lUUBw8ezOzZs3FwcOD7779n5cqVWFlZ\nScd8/fXXREdH07dv30ql07FjR3bs2MHs2bOl/fn5+VSrVq3Meuro6ODo6Mjq1au5ePEixsbGzJ07\nlx9++EE6RqlUvnHeWokrV65w9+5dJk+eDBQv0PLy5UusrKzYu3evtMhJWfbv38/ly5eler948YLf\nfvuNW7duSb2k5ZW5V69eaGtrs337dukYDw8PWrduzZEjR3j69Cl9+vQBihdiUavVODg4sGjRonLL\nbGJiIg2PLFHS2/omarWahIQEKc2JEycybtw4KWApKChAS0sLPT29MoeHamlp/Ufz106ePCkFcV9+\n+SVt27Zl1KhRUv0AatasWep+heJ5q3/n3Llu3bpx+PBhKWAu8dNPPzFnzhxprmjJtrCwsL8tb0EQ\nBEH4tzMw0MfIqOxRVf8thobV32r+wrvtrQV86enpODs7M2HCBCZNmvQfpZWQkMCtW7ekRUQ8PDyY\nMGECI0eO5NdffyU2NpYtW7YAxcvth4SEYGZmhkKhICgoiJ49e6Krq8vHH39Mz549mTZtGr6+vrRs\n2ZIHDx7g7++PkZER9vb2HD9+nLy8PPr27avxpXvkyJH069ePmzdvViqdgoICduzYQb169XByciI5\nOZno6GhpgZPXqdVq9u7dy8uXL/noo4+oX78+ubm5bNiwgUmTJnHlyhViY2MJCQkp91pZWlpq9D6e\nO3cONze3cnuASmzatEnjs5OTE/369XtjL9rrZVapVDg5OREcHEy3bt2Ij4/n1KlTzJ8/n/r16zNl\nyhTp3NDQUG7evCn15JZX5k8++YTVq1dL8yqPHj3K1atXWbVqVZnlSk9PJzAwkGrVqknDctu0acP6\n9etp27YtWlpafPXVVzg4OKCjo4OtrS3BwcH079+fli1bEhkZiUqlokOHDhVes9fl5OSwYcMGUlJS\npB5tc3NzNm/eTPfu3TEyMsLf3x9LS0saN25Mjx49mDFjBqdOnaJr164cPHiQ69evv7FuZamoF3f6\n9OkMGTKE4OBgJkyYQM2aNTl79iyLFy/GxcVFCvbu3r1LVlbWX5qrKAiCIAhVXVZWLhkZOW8lb7lc\nhqFhdZ49eyFes1EFlLTn3+2tBXxxcXFkZmaybt06qceg5FUEs2bNko4ra2GQo0ePYmFhIe1/7733\nWLx4sbQtODgYHx8fVq5cyfvvv4+/v7/Uw+fr68vy5cul3ozu3btrLDe/cuVKwsPDmTlzJunp6dSo\nUYMePXqwZMkSqlWrRmxsLP369SvVw2JsbMzHH39MdHQ0vr6+FaZTrVo1NmzYQEBAAKtXr8bQ0BBX\nV1d69eolpRkVFcX27duRyWTIZDKMjY0JCQmRFmaJiIjA19eXDRs28N577+Hr60vHjh1LXa+KFlf5\nvyor3YrKHBISQkBAAO7u7jRr1ozw8PAyF2j5K+rXr09kZCR+fn6sWLGCBg0asGbNGo3eSjc3N6k3\nt1atWnTt2pWoqCipR3XGjBmsXLmSQYMGoaWlRd++fZkzZw5QHMxnZ2czY8YMnj9/TuvWrfn666/L\nXRTmVStWrCAwMBCZTEb16tWxtLTk22+/leb+OTo6kpGRwahRoygsLKRr166sXr0aKF7cxdvbGz8/\nP54+fYqJiQkREREaQ3pL6lai5B14JQvSZGVlSc/Gq/sHDBjA0qVLadCgATt27CAoKAh7e3vy8vJ4\n//33cXV11Ri+e+/ePQwNDct89cQ/dY8JgiAIwrtOpVJTVPR2g613oQzCu0um/iuTuARBEP4h3ccG\nidcyCIIgCP8qWY9u4TO+41t7D5+WlgwjoxpkZOSIgK8KKGnPv9tbX7RFEAQBil9eKwiCIAj/JsV/\nu0qPrhKEd4no4RME4Z1w4cIFsrJyxRyEKkAul2FgoC/as4oQ7Vm1iPb8+7Vubabx/uf/JtHDV7WI\nHj5BEKo0CwsL8QerihBfQKoW0Z5Vi2hPQfjf8068eF0QBEEQBEEQBEH4+4mATxAEQRAEQRAEoYoS\nAZ8gCIIgCIIgCEIVJQI+QRAEQRAEQRCEKkoEfIIgCIIgCIIgCFWUCPgEQRAEQRAEQRCqKBHwCYIg\nCIIgCIIgVFHvzHv4fvjhB7y9vZHJZACo1Wry8vIYMWIES5YskbaNGzeOtm3bMm/ePI3z1Wo1M2fO\nxNramjFjxgDg4+PD3r17NdJ8+fIlgYGBDBgwgFatWqGnp4dMJkOtVlO7dm1GjhzJ5MmTpXRjY2OJ\niIggKyuL5s2bs2DBAszMzAA4f/48X331Fbdv38bIyIiJEycycuTIUuV6vcyhoaGsX7+eatWqaRwn\nk8nYuXMnzZo1Izk5GT8/P27cuEHjxo3x8PCgR48eAHh5efHDDz+gUChKnZ+QkICurq60PT09ncGD\nBxMQECCdXyIzM5MRI0YQHh7Ohx9+KG23sLDQSNPS0pINGzYAEBMTw6ZNm3j69CkmJiZ4enpiaWkJ\nwP79+wkNDeXBgwc0btwYNzc3+vTpA4CTkxOXLl1CR0cHtVqNQqGgffv2zJkzRyPvEjt37mTVqlWc\nOXNG2rZkyRJiY2OlNGQyGfv376dhw4YcOnQIDw8PqlWrJu1bsmQJAwcOZP78+ezbtw+FQoFarUZH\nR4dOnTrh7e1NvXr1AOjVqxdPnz5FS0tLoxxmZmZERUVpXOfevXtTvXp1fvjhh1Llfvz4MaGhoRw/\nfpwXL17QsGFDRo0aJd2TANu2bSMyMpLnz59jbW2Nj48PderUAYpfPu7v78+dO3eoX78+06dPZ+DA\ngTx48AB7e3vpXgYoKCigcePGHDx4kOzsbHx8fEhISADA1taWRYsWUaNG8cs7y7uPK3oO/upzVKNG\nDXr27MmcOXOoVatWqWv0JuLF61WHeLFz1SLas2oR7fn3e5svXheEynhnAr5BgwYxaNAg6fPp06fx\n9PTE1dVV2rZp0yYuXLhA27ZtNc69d+8evr6+nDx5Emtra2m7r68vvr6+0ueQkBASExPp168fgBRg\nmZqaApCamsqoUaMwNTWlT58+XL9+ncDAQGJiYmjSpAkbNmzAzc2N+Ph4srOzmT59Oj4+Ptjb25Oc\nnMz48eNp0qQJnTt3rrDMffr0Yc2aNWVei5ycHFxcXHB0dCQyMpLk5GScnZ2JioqiRYsWAIwbN65U\n0FuWBQsWkJWVVWr7+fPn8fb25t69exrbU1NTkcvlnD9/vtQ5Z8+eJTg4mK1bt9KyZUt2797N1KlT\niY+PJzMzkwULFrB161batWvH6dOncXFx4eTJkxgaGgLFgero0aMByM3NZePGjYwdO5Y9e/bQoEED\nKZ+0tDRWrFiBtrbm7Xnt2jWCgoKws7MrVbbk5GRGjRrFwoULS+2TyWQa1ys/P58FCxbg4+PDunXr\npONCQkJKBcWvO3nyJI0aNeLx48ecPXuWTp06SfsePXrEsGHDGDZsGHv27MHQ0JCkpCRmzZrFs2fP\nmD59Ovv372fdunVs3LgRMzMzwsLCmDp1KjExMahUKlxdXfH19cXOzo7z58/zxRdfYGFhwfvvv8/F\nixelvNLT0xk6dCiLFi0CYOnSpcjlck6cOIFKpWLGjBmEhYXh6elZ7n1ccn3Kew7+6nP06NEjfHx8\ncHFxYfv27eVez1dNXBhNTaPGlT5eEARBEN625xl3WTUbzM0/fttFEYQ3emcCvle9ePGC+fPns3jx\nYurXrw/A9evX2bVrl9RjVEKpVDJ06FBGjhxJTk7OG9P87bffiIqKYt++fVIvjlqtRq3+/79uNW3a\nFEtLS65du0afPn34888/UavVKJVKioqKkMvl6OnpAXD//n1sbW2xt7cHoE2bNnTq1ImLFy9KAd+b\nylyRCxcuIJPJpGDX3Nwce3t7du3ahaenZ6XT2b59O9WrV6dhw4Ya2xMTE5k1axbz5s0rlV5ycjIt\nW7YsM72HDx8yadIkaf+QIUMICAjg5s2bWFpakpCQgJ6eHoWFhTx58oQaNWpo/OL16rXW19fHzc2N\nxMREtm7dKpVDpVLh6emJo6MjO3fu1Dj3+vXrtGrVqsyyXbt2jb59+1bqulSrVo2BAwdKPcd/RUxM\nDHZ2duTl5REdHa0R8IWEhNChQwfc3d2lbebm5vj7+3Po0CEAjhw5wsiRIzE3NwdgxowZbN26lZs3\nb1KvXj0yMzNRKpVAcSClo6NTqtcRwNvbG3t7e7p27QrA8uXLUalU6Ojo8OjRI3Jzc6lduzZAufcx\nVPwcvKoyz1GDBg0ICgrCxsaGn3/+GVtb20pd25pGjTFoYFqpYwVBEARBEITKeSfn8H399de0bNmS\nXr16AcVD1+bPn4+fnx/6+voax2pra7N//348PDzK/GJcYvny5UyZMkWjJ+l1165dIykpSerl6dat\nG02bNmXAgAGYm5uzceNGVq5cCRQPg1uxYoV0blZWFufPn5cCkvLKXBGVSqUxLBNALpeTmppa6TRS\nUlLYsmULixcv1vgyDtCiRQuOHTvG4MGDS+27du0a2dnZDBkyhC5duuDm5sajR48AcHBwYOLEidKx\niYmJ5ObmSkMy9fT0uHv3Lu3atWP+/Pm4u7tTvXr1cstpY2PDhQsXpM8RERE0b94cGxsbjePu3LlD\nfn4+K1asoHPnzgwdOpSff/5Z2p+cnMyhQ4fo3r07ffv2lYagliUnJ4c9e/bQs2fPcsv2uidPnpCQ\nkICDgwNDhw7l5MmTPHz4UNp/8uTJMoPOzp07s3jxYgCKiopKta1MJiM1NRVDQ0NGjRqFh4cHZmZm\nODk54e3tXeqePX36NJcuXcLNzU3apqWlhY6ODl5eXtja2pKTk4OjoyNQ/n1cltefg1dV5jmC4oDe\nwsKCxMTEco8TBEEQBEEQ/lnvXMCXm5vLN998ozGUMygoiO7du9O+fftSx8tkMmn+05skJiZy69Yt\naTjhqxwdHbGysuLjjz9m6NChtGjRQho2mZ+fT/Pmzfn++++5ePEiTk5OuLq6UlBQoJHG8+fPmTJl\nCm3btpWC1PLKDHD06FGsrKykfx07dmTUqFFA8Ry6nJwcoqKiUCqVJCUlsX//fvLz86Xzo6OjNc63\nsrJi9uzZQHFQ4enpyaJFi8qcQ1WzZk2N+X+vKplbt3nzZg4fPoy+vj4zZ84sddwff/yBm5sbbm5u\n0pBNgPfff5+kpCQ2b95MQEAAZ8+eLTOfEoaGhjx79gwo7j3at28fXl5epY7Lzs6mU6dOODs7c+rU\nKaZNm8asWbO4efMmL1++xMTEhEGDBhEfH8/atWvZvn07O3bsKHW9OnbsSMeOHUlISGDo0KEaebi7\nu2u0h5WVFd988420//vvv6dnz54YGBhQt25dbG1t+fbbb6X9mZmZGBkZlVvfXr16ERMTw/Xr11Eq\nlYSFhZGfn09+fj5qtRpdXV3Wrl3L5cuXWb9+Pf7+/ty4cUMjjY0bNzJhwgSNXroSvr6+/Prrr5iY\nmDB9+nSgcvdxec9BifKeo7IYGBiUOZxYEARBEARB+O9554Z0xsfH06hRI2nI2+nTpzlz5ozG8L6/\nateuXQwePLjML8g7duyQ5h49ffoULy8vPDw8WLduHaGhoTRs2JA2bdoA4OrqSmxsLAkJCdIwtbS0\nNKZOnUrTpk0JDg6udJl79+79xjl8tWrVIiIigmXLlhEWFka7du1wcHCQetoAxo4d+8Y5fGFhYbRu\n3Zpu3bpVfHFe82qgDeDp6Ym1tTXp6enUrVsXgFOnTuHh4cHEiROZNGmSxvFyefFvCNbW1nzyySfE\nx8drDHt8XWZmJrVr1yY/Px8vLy/8/PzQ1dUt1fPYrl07tmzZIn3u06cP1tbW/PTTT7i4uGgsrNKy\nZUucnJyk4ZOgeb2USiVxcXGMHTuWgwcPSr1VwcHB5c7hi42N5dmzZ9J1zcvLQ6FQ4OrqikKhoF69\neqSnp5c6T6VS8fz5cwwMDBgyZAhPnjxh2rRpFBUVMXz4cExNTalZsyaHDx/mypUrUjl79OiBra0t\nu3fvloa8Pnz4kF9//ZWgoKAyy6hQKFAoFMydOxc7Ozuys7MrdR+X9Ry4u7uzfv16Ke3ynqOyZGZm\n0qhRo0odKwiCIAj/VnK5DC0tWcUH/kN5v/pf4d/tn2rHdy7g++mnn+jfv7/0+cCBA6SlpdGlSxeg\nuAdQS0uL27dvEx4eXuk0w8LCytz3amBRp04w47eLAAAgAElEQVQdRo8eLc3Bun//fqnhmFpaWtLQ\n0atXr+Ls7IyDg4PGXLj/tMwFBQVoa2trLHjh4eFB69atK1XfAwcOkJ6ezoEDB4DiHkh3d3emTp2K\ns7Nzuedu2LCBbt26ScFBfn4+MplMWlE0Li6OgIAAlixZIs1fBDh+/Dhbt27VCMqUSmWFqzSePHmS\nTp06ceXKFe7evSutDFlYWMjLly+xsrJi7969pKSk8Oeff2qsglpQUEC1atW4e/cuO3bskHo4S8r9\n6iqor9LR0cHR0ZHVq1dz8eJFafGR8pw6dYq8vDxpLl6J4cOHs3//foYMGUK3bt04fPiwxuJDUHz/\nzZkzh19++YUXL15gb28vtcPz58/ZtGkTbdq0Yd++faV6j7W1tTUWr/npp5+wsrLS6FUFmDhxIuPG\njZMC1oKCArS0tNDT03vjffxquuU9B6/m/abn6HU5OTlcvHhRYwiwIAiCIFRFBgb6GBnVeKtlMDQs\nfwqN8L/tnQv4Ll++LA1thOKl+F9dXMPLy4vatWtXaoVKgLt375KVlcVHH31U4bHZ2dnExcVJryWw\ntbUlODiY/v3707JlSyIjI1GpVHTo0IH09HScnZ2ZMGFCqV6u/7TMKpXq/7F373E53v8Dx1/3fSsp\n0Qrjx0ZOU8hhOebQkC82csxYmENRIkxyyCmFkIglOSukcj6MVbY5xMwMmzKb2mZ8ZeSUzt33748e\nXd9uHdh3B9b3/Xw89tju6/i5Pp/rXtf7fn8+n4sRI0YQFBREp06diIuL4/Tp0yV2dSxJYaBXqFu3\nbsyfP/+5M1BCwdi/M2fOsHr1ajQaDYsXL6ZHjx6Ymppy9uxZfH192bx5M2+//bbefk2bNuXq1asc\nPHiQvn37cvLkSU6ePMmkSZNKPE96ejphYWGkpKQQFBREtWrV9GahPH/+PJ6enpw9exYomDkyICCA\nhg0b0qpVK44cOcKVK1cICAjAyMiI3bt3U716dUaMGEFiYiIRERGlTsqi0+k4ePAgmZmZL3RfQMFk\nLX369CnWfbhfv36Eh4fTv39/Jk6cSP/+/QkKCmLMmDGYmpry5ZdfsmDBAlxdXTE2NiY2NpYNGzYQ\nHh6OgYEBixYtolOnTlSrVo2OHTuycuVK9u3bx4ABAzh//jxxcXFs27ZNOd/ly5dL7CZsbW3NunXr\naN68ORqNhmXLluHo6IiBgUGp93Hhff6sZ78H8Pu+Rzdv3sTf3x8bGxvlRw8hhBCivHr0KIO0tNIn\nDvwrqdUqzMxMePjwqbxmoxwobM8/2ysV8Gm1Wu7cuaO8G+33KvqOskK3bt3CzMys2BT/hdsPGTIE\nlUqlzIjYoUMHZTKWoUOH8vjxYyZNmsSTJ0+wsrJi06ZNGBsbEx4ezoMHDwgJCVGyHoXT/0+ZMuW5\nZY2Pj9d7oC58d9y8efPo378/wcHBLFmyhKlTp1K/fn1CQ0P16iU8PFwvA1i4//r162nTps1z66W0\ndT4+Pvj7+9O7d2/y8vKwt7dn3rx5QMFkOnl5eUp2qvCcwcHBdOrUiXXr1rF48WJ8fX2pV68eISEh\n1KtXTzl2QEAAgYGBqFQqTExMsLW1ZefOnUpX0bK0a9eOOXPmMHv2bO7evYulpaVenYSFhbFkyRJW\nrVqFmZkZHh4eynjKovVV2Nb16tUjODiYOnX+8xoAT09PpUtq0euLjY3ls88+0xvPV6h///6EhYVx\n+fJlWrRowe7du1m5ciV9+vQhKyuL//u//8PDw0PJTDo6OvL999/Tp08ftFot77zzDkuXLgUKJtMJ\nDg5m1apV+Pv7U6tWLQICApT35UHB/VxSwDdp0iSWL19O37590Wg09OzZk+nTpwMl38cbN25Usn7P\n+x4UnvdFvkdqtRozMzMcHBz0JpURQgghyiutVkd+/ssNtl6FMohXl0r37GApIYR4Cbo4r5TXMggh\nhPhHeZR6g/mj27y09/BpNCrMzSuTlpYuAV85UNief7ZXKsMnhPjf9STt15ddBCGEEOJ3Kfjb1ea5\n2wnxMkmGTwjxSrh48SKPHmXIGIRyQK1WUbWqsbRnOSHtWb5Ie/75rKyaYmBg8FLOLRm+8kUyfEKI\ncq1169byB6uckAeQ8kXas3yR9hTif88r9+J1IYQQQgghhBB/Dgn4hBBCCCGEEKKckoBPCCGEEEII\nIcopCfiEEEIIIYQQopySgE8IIYQQQgghyikJ+IQQQgghhBCinJKATwghhBBCCCHKqVf2PXxXrlxh\n4sSJnDp1CoCbN2/i6+vLpUuXMDc3x83Njf79+wOwdu1a1q1bR8WKFZX9dTodKpWKmJgY6tevz61b\nt5gzZw5XrlyhRo0azJw5E3t7e2X7tWvXEhkZSU5ODu3bt8ff3x9TU1NycnJYvHgxx48fJy8vj7Zt\n2zJ//nxq1KjB+vXrCQ0NRaVSKefMzMxk2rRpuLq6kpiYiJ+fH99//z116tRh2rRpdO3aVe86s7Oz\nGTlyJO7u7sq6ffv2MWfOHIyMjJTtGjduzMyZM2nZsiUAOTk5LFiwgPj4eAwMDHB2dmbChAkl7q/T\n6XjzzTdxdnZmyJAhyjG7devG/fv30Wg0enUWEBCAg4MDmzZtIigoCENDQ2Xdhg0bePvtt5VjREdH\nM3fuXFatWkWvXr30rs3X15fo6GgMDAyU/Y8ePUrNmjU5fvw406ZNo2LFiso6X19f3nvvPZ4+fcqC\nBQs4deoUGo2G7t274+Pjg6Gh4QvVTVn7FxUTE8OKFSs4d+6csiw+Pp6VK1dy584d3nzzTby8vOjY\nsSMAN27cYMGCBVy7dg1TU1NGjRrFqFGjntueACEhIURGRpKVlYWNjQ3z58/njTfeACAhIYElS5bw\n66+/0rRpU/z8/KhXr16xNtLpdFSqVIkOHTrg5eVFzZo1lXu36P2vUqlo2LAhrq6udO/enWcNHz6c\nlJQUvvjiC706KXocnU6HRqPBxsYGHx8f6tevD0Bubi5Lly7lyJEjAPTo0YMFCxZQoUIF3nvvPW7f\nvq0cLy8vj9zcXE6ePEn16tWLleNZ8uL18kNe7Fy+SHuWL9Kef52X+QJ2IcrySgZ8MTExBAQEUKFC\nQfG0Wi0TJ07ExsaGM2fOcPv2bcaMGYO5uTldunQBCh48V69eXeoxPT09sbOzY9OmTZw5c4apU6dy\n5MgRatasSXh4OMePH2fv3r1UrVqV6dOns3z5cnx9fQkJCSE5OZlPP/2USpUqMW/ePPz8/AgODmb8\n+PGMHz9eOceePXvYsmULzs7OpKen4+rqyvvvv8/27dtJTEzExcWF8PBwGjduDMD169eZN28eV65c\nKVZea2trYmJigIJAbOfOnbi7u/PFF19gYGBAUFAQd+7c4cSJE9y7d48xY8ZQr149Jegquj/A2bNn\nmTZtGvn5+bz//vvK8uDg4GJBaKGkpCSmT5/Ohx9+WGq9RkdHM2TIECIiIooFfElJSaxcuRIHB4di\n+yUmJjJs2DB8fHyKrVu7dq0SKGRlZeHi4sLGjRtxd3d/obp53v5Q8ANC0XsMIC0tjenTp7Nt2zZs\nbGw4fPgwEydO5Msvv8TQ0BAvLy/69etHeHg4N27c4P3336dp06bY2toCpbfniRMnOHDgAPv27cPM\nzAx/f398fHzYtm0b9+7dY9KkSaxcuRI7OztCQ0Px8PDg8OHDJbbRw4cPWb58OSNGjODQoUNK4Fv0\n/s/Pzyc2Npbp06ezatUqvfa9ceMGd+7cwdramkOHDjFo0CC9shY9Tl5eHqtWrWLq1KkcOHAAgMDA\nQG7cuEFsbCw6nQ5XV1c2b96Mq6urXpkBRo0aRevWrV8o2AMY6xOBqXmdF9pWCCGEeJU8SfuVFR+B\njU3Ll10UIYp55QK+0NBQjh07hpubGxs2bAAgJSWFGzduEB0djaGhIfXq1WP48OHExMQoAV9Zbty4\nwQ8//MDOnTvRaDR06dKFNm3acOTIEcaOHcvOnTuZPXs2NWrUAGDRokU8evQIKAgUc3NzMTQ05MGD\nB6Snp/Paa68VO8edO3dYsmQJ27dvx9jYmJMnT6JSqfDw8ADAxsaGPn36sG/fPry9vbl9+zajRo1i\n/Pjx3L17t8zyq1QqBgwYwKJFi0hNTaVOnTocPHiQoKAgTExMMDExwdnZmX379hULugp16NABb29v\nli1bphfwlSUpKalYQFDUtWvXuHnzJlu2bMHe3p7r168rwaxOp+PatWs0adKk1GP37NmzxHUpKSkY\nGhqSn5+PVqtFpVJRqVKlErctqW6et79Wq8Xb25v3339fLyj+97//TU5ODnl5eQCo1Wq9TOJPP/1E\nXl4eWq1WyYAVZsjKas+ff/4ZnU5HXl4e+fn5qNVqpTyxsbFYW1srQZm7uzvbt2/n22+/pXnz5sWu\n18zMjEWLFtGrVy/27NnDBx98UGwbjUZDr169+PHHH1m9erVewBcVFYWDgwM2NjZs2rSpzPatUKEC\njo6ObN68Ga1Wi1arJSoqipiYGExNTQFYs2aNUl9Fbd26lfT0dCZPnlzq8Z9lal6Hqq83eOHthRBC\nCCHE871yY/gGDx7M/v37adasmbJMq9Wi0Wj00uQqlYqff/75hY6ZkpJC7dq19bqvWVpakpycTGZm\nJikpKaSmptK3b186derEsmXLlKyESqXC0NCQtWvX0rFjR65cuYKLi0uxc6xcuZJ+/fphbW2tlLlo\nsAAFAURhmV977TViY2PLzJ4Vys/PJzIyksaNG1OnTh0eP37M/fv3adDgPw/HhddTls6dO5OWlvbc\n7QCysrJISUlh+/btdOrUiXfffZc9e/bobRMVFUX//v0xMTHB0dGR8PBwZd1PP/1EdnY2AQEBdOjQ\ngYEDB/L5558r6xMTEzl+/DhdunShZ8+ehIWFKetGjRpFQkICtra2dOjQARMTk2JdJ0urmxfZf/36\n9TRq1IjOnTvrHcva2pouXbowfPhwmjZtyqxZs1ixYoVy30yYMIGgoCBsbGzo27cvzs7O2NjYAGBu\nbl5qe/bp0weVSoW9vT2tWrXixIkTLFy4EIDk5GS9dlSr1bzxxhtltpFaraZjx458/fXXpW4D0KVL\nF65du0ZWVhZQ0A34wIEDDB48GAcHB+7cucM333xT6v7Z2dlER0fTpUsX5d7VarVcunSJf/3rX3Tt\n2pUtW7YoP5QUevz4MR9//DELFixQujsLIYQQQoiX45UL+KpVq1ZsWf369alduzaBgYFkZ2eTkpJC\nVFQU2dnZyjbx8fG0bdtW+adNmzYMGzYMgIyMjGLBV6VKlcjKyuLx48cAHDhwgK1bt3LkyBElW1eU\nq6srly9fxsHBgbFjx5Kfn6+su3XrFrGxsXrdO1u3bk16ejrh4eHk5uZy5coVjh49qpS5UqVKVK5c\nudR6SEpKUq6lRYsWrFixgpEjRwKQmZmJSqXSuyYjIyMyMzPLrNuqVasCKNlLgKlTpyr11bZtW2bN\nmgXAvXv3ePvttxk+fDiff/45CxcuZOnSpcqYyqysLA4fPoyTkxMAQ4cO5fDhwzx58gQoeOhv164d\nLi4unD59Gnd3d6ZMmcIPP/xAZmYmlpaW9O3bl7i4ONasWUNkZCS7d+8GCroSDhkyhC+//JLPP/+c\nx48f63XXLatuStt/1apVAHz33XccPnxYuc6icnJyqFGjBtu2bePy5cvMnTuXadOm8dtvvwEFgdbc\nuXP55ptv2LVrFzt27FDqw8jIqNT2zMnJwdbWlk8//ZQLFy5gZ2eHp6en0pbPZi8L783ntWXRdixt\nG51Op9zjx48fp169ejRq1AhDQ0MGDBhARESE3j5Fv0etWrUiMjKS4cOHAwXdSXNycvj888/Zs2cP\nUVFRnDlzRsnEF9qxYwctW7YsMUMphBBCCCH+Xq9cl86SaDQaQkJCWLRoEV27dqVhw4Y4OjrqZYy6\nd+9e6hi+SpUq6QWHUPCgbWxsrGQNXV1dsbCwAMDNzY3JkyezaNEiZfvCLM+MGTPYtWsX169fx8rK\nCoCDBw9iZ2fH66+/rmxfpUoV1q9fz+LFi/n4449p0aIFjo6OpKamvtA1W1lZ6XU3/Oqrr5g0aRJm\nZma0adMGnU5HdnY2JiYmQEEAVvjfpXnw4AFQkI0qFBQUVOIYvjp16uhl7GxtbXF0dCQuLo7OnTtz\n9OhR0tPTGTFihLJNdnY2MTExjB49mhYtWrBlyxZlXY8ePWjfvj2fffYZrq6uesd+6623GDFiBLGx\nsQwaNIiPPvqIvXv3UrlyZSpXrszUqVOZNm0aU6dOfW7d2Nvbl7r/xIkTmTVrFn5+fhgZGaHT6Q9W\n37FjB9nZ2bRr1w4oyDbv2bOHTz/9lBYtWrBjxw4+++wzAFq2bImTkxPR0dHFMoXP8vf3p2fPnsok\nLT4+PrRu3ZoffvgBIyOjYsFd4b1ZlgcPHmBmZvbcbdRqtRLoR0VF8f3339OpUyegYAKWjIwM7t27\np/zQUvR7pNVqiY+Px9PTk/DwcAwNDdFqtUyZMkWp29GjRxMREaFMGAQFkwbNnDmzzLIJIYQQ5Y1a\nrUKj+Xt7tqjVKr1/i3+2v6od/xEBn06n4+nTp2zatEnpIhYYGKgEXM9TOEtnbm6uEuClpKTQvn17\nzM3NqVq1ql5AmJeXpwQDs2fPpnnz5kq2sHC8UuEYJoDPPvusWJfDnJwcKlSoQGRkpLJs2rRpL1zm\nZxVm4BISEujRowcWFhYkJycrwVtKSope18CSnDx5kho1alC3bt3nni8xMZHTp0/j6uqqLMvOzlay\nUVFRUcokJoWOHDlCeHg4o0eP5uzZs/zyyy8MHTpUWZ+Tk0PFihX59ddf2b17Nx999JHesStWrMjT\np095/PgxOTk5yjq1Wq03uUpZddOmTZtS9//uu+/49ddflUxsXl4emZmZtG3bloMHD3L79m29/aBg\nHJtGo+Hf//43ubm5xdaVVa5Czx5XpVKhVqvRaDQ0aNCAY8eOKeu0Wi2//PILjRo1KvV4Op2OhIQE\nvYxySU6ePEnz5s2pWLEiKSkpXLlyhcOHD+sFkx4eHkRGRipjTYtSq9U4ODhQv359vvzyS5ycnFCr\n1XrXUvS7AgXjZe/fv/9CY2uFEEKI8qRqVWPMzUvvvfVXMjMr+0d/8b/tHxHwqVQqpk2bxpgxYxg6\ndChfffUV0dHRehmksjRo0IAGDRqwevVqJk+ezNmzZ/nqq6+UcVQDBw5k3bp1tGjRAkNDQ0JDQ+nT\npw9QMNnK5s2b6dKlC+bm5vj7+2Nra6uMF8vJySExMVF5JUAhrVbLiBEjCAoKolOnTsTFxXH69On/\nOvNx9epVzp8/r8xq2a9fP9auXcvq1at58OABEREReHt7l7ivVqvl1KlTBAUF6QVZZTE2Nubjjz+m\nXr16ODg4cO7cOY4ePcqOHTu4fv063333HevWrdObwGbgwIEEBgby+eefU6lSJZYuXUrDhg1p1aoV\nR44c4cqVKwQEBGBkZMTu3bupXr06I0aMIDExkYiICHx9falatSotW7Zk+fLlBAYGkpWVxccff8y7\n7777QnVT1v5vv/223pi18+fP4+npydmzZwHo2rUrkyZN4vTp09jZ2XHs2DGuXbvGihUrqFChAjk5\nOaxbt47x48dz/fp1du/erdxDZbG3t2fTpk106tSJGjVqEBgYSKNGjahfvz5VqlQhMDCQuLg4unbt\nyvr166lZs2apk93cu3ePwMBAKlasqBdsF5Wbm8uxY8cIDw8nODgYKAjQO3XqpGQZCw0YMIDg4GDc\n3NxKPFZCQgI3btygVatWmJqa0qNHD1auXElgYCAZGRls27ZNeT0KwOXLl7G2tn6hQFgIIYQoTx49\nyiAtLf1vPadarcLMzISHD5/KazbKgcL2/LP9Y57KgoKCmD9/PsuXL+f//u//8Pf318uWxcfH07p1\na+Vz4bvd5s2bR//+/Vm7di0+Pj507NiR6tWrs3LlSqUL5kcffcSaNWtwcnLi6dOndO/eHS8vLwDe\nf/990tLSGDZsGHl5edjZ2SnjwQDu3r1Lfn5+sannjYyMCA4OZsmSJUydOpX69esTGhpabIILoMSJ\nLZKSkpTrUalUmJub4+LiwnvvvQfAlClTWLJkCb1790atVjNy5Ei9WS+L7m9gYEDdunWZM2cOvXv3\nLvO8herVq8fq1atZuXIl3t7e1KxZk6VLl9KkSRP8/f3p2LFjsdlKK1euTI8ePYiIiGDjxo34+Pgw\ne/Zs7t69i6WlJaGhoUo9hYWFsWTJElatWoWZmRkeHh5069YNgNWrV7N48WK6deuGoaEhvXv31gtU\nn1c3z9u/NJ07d1Zeu3H//n0sLS1Zv369cp+EhYWxdOlSNm3ahIWFBZMnTy7xPXfP1quHhwf5+fkM\nHz6cnJwc3n77bUJCQoCCMashISH4+/vj7e2NlZUVa9eu1dvf09MTtVqNSqWiSpUq2NnZER4ervfe\nyaL3f8WKFWnUqBHBwcF06NCB3NxcDhw4wNy5c4uVtXfv3sp7Jp89jkqlolatWixYsEBZtnTpUpYu\nXUqfPn3Izc1lwIABjB49WjnerVu3SrzHhRBCiPJOq9WRn/9ygq6XeW7x6lPpnh3IJIQQL0EX55Xy\nWgYhhBD/SI9SbzB/dJu//T18Go0Kc/PKpKWlS8BXDhS255/tH5PhE0KUb0/Sfn3ZRRBCCCH+KwV/\nw9q87GIIUSLJ8AkhXgkXL17k0aMMGYNQDqjVKqpWNZb2LCekPcsXac+/jpVVU713Rv8dJMNXvkiG\nTwhRrrVu3Vr+YJUT8gBSvkh7li/SnkL873nlXrwuhBBCCCGEEOLPIQGfEEIIIYQQQpRTEvAJIYQQ\nQgghRDklAZ8QQgghhBBClFMS8AkhhBBCCCFEOSUBnxBCCCGEEEKUUxLwCSGEEEIIIUQ59cq9h+/C\nhQssW7aM5ORkzM3NGTt2LEOHDuXmzZv4+vpy6dIlzM3NcXNzo3///sp+gYGBxMTEoNVqcXR0ZNas\nWahUKgDGjx/PuXPn0Gg06HQ6VCoVFy9e1Duvl5cXn3zyCZ999hnVq1dXlu/bt485c+ZgZGSkLGvc\nuDEzZ86kZcuWAMyaNYtDhw5haGgIgEajwcrKCk9PT95++21lv6ioKDZt2sT9+/extLTE29sbW1tb\nvXJcuXKFiRMncurUKWXZiBEjuHTpEgYGBuh0OgwNDWnVqhXTp0+nYcOGAJw/f56RI0dibGys7KfT\n6TAyMuLs2bPk5+cTHBzMvn37yMnJwcHBgVmzZinbHzlyhLVr1/Lbb79hY2PD/PnzqVu3LgDdunXj\n/v37aDQavbI2bdqU8PBwvfN1794dExMTDh06pLftgwcP6NChA8bGxkob9OvXjwULFrxQHaelpbFo\n0SISEhIwNjbG2dmZsWPHAuDi4sKFCxeU9tZqtWRlZREZGansD/Djjz8yaNAg9uzZo9RboZs3bzJo\n0CC++OILKlWqVGK7Fl6jSqUiISFBr7yl3T8AW7duZfPmzWRkZNCtWzd8fX2VfX/44QcWLlzI1atX\nqV69OlOmTKFPnz4AnDt3jmXLlvHTTz9hYWGBi4sLTk5OADx58gQ/Pz9Onz6NTqejc+fOzJkzhypV\nqgAQGxvLypUruXv3Lo0aNWLBggU0adLkuedMT09n/vz5nDp1CgMDAwYPHszUqVMBuHXrFgsXLuSb\nb77B0NCQd999lxkzZlChQsH/RkJCQoiOjubp06c0adKEuXPn0qhRI16UvHi9/JAXO5cv0p7li7Tn\nq+1lvLxdlH+vVMD3+PFjJk6cyPz58+nTpw+JiYmMHj2a2rVrs2zZMmxsbDhz5gy3b99mzJgxmJub\n06VLFyIiIjh58iSHDx8GwNXVlc2bNysBQVJSErt27cLa2rrU8548eZLevXuza9cuJk+erLfe2tqa\nmJgYoOCBf+fOnbi7u/PFF18oX8qRI0cyY8YMAHJycoiJiWHcuHHs3LkTKysrzp07R1BQEFu3buWt\nt95i//79uLm5ERcXR9WqVQGIiYkhICBAeYAuatasWQwfPhyAjIwMNmzYgLOzMwcOHOD1118H4LXX\nXuPs2bMlXuPmzZs5cuQI27Zto3bt2ixYsIDZs2ezatUqLl26xKxZs1izZg2dO3dmz549jB49mmPH\njinBTnBwMF27di2z/U6dOkXt2rW5e/cuX375Je3atVPWJSUl0ahRo2KB4IvWsbe3N2q1mri4OLKz\ns3F1dcXY2Jhhw4axYcMGvWPNnDkTrVarF+zl5uYyY8YMcnJyip07Li4OX19fnjx5Umxd0XYtTVn3\nz2effcaWLVuIiIjA3NycadOmERAQwPz588nKysLV1ZVx48YRERHBhQsXcHFxoXXr1lSuXBk3NzdW\nrFhB9+7duX79Ok5OTrRs2ZLGjRuzePFiMjMziY2NRavV4uXlhZ+fH8uWLSMxMZE5c+YQGhpK69at\n2bhxI1OmTOHYsWNlnrNmzZrMmjWLSpUqcfr0aR48eMCIESNo3Lgx7777Ll5eXrRq1YrQ0FCePHnC\nyJEjiYyMxNnZmb1793Lw4EEiIiKoVasW69evZ/z48Zw4caLMuitqrE8EpuZ1Xnh7IYQQojx5kvYr\nKz4CG5uWz99YiN/hlQr4bt++jb29vZJtsLa2pl27dly6dIkbN24QHR2NoaEh9erVY/jw4cTExNCl\nSxcOHjzIqFGjsLCwAAoyeqtXr2bs2LHcv3+ftLS0Yhmdovbv30+bNm344IMP8PDwwN3dvcSgC0Cl\nUjFgwAAWLVpEamoqdeoUf0A1NDRk+PDhfPvtt6xbt47g4GBSU1MZN24cb731FgD9+/dnyZIl/PDD\nD9ja2hIaGsqxY8dwc3MrFsBAQRBUyNjYGE9PT77++mu2bt2Kt7f3c+s2NjYWV1dXLC0tAZg+fTpd\nunQhPT2duLg4HBwclIBuyJAhbN26lYSEBOzt7Z977EJRUVE4ODiQlZVFRESEXsCXmJiIlZXVCx3n\n2Tq2sLDg9OnTfPLJJ5iammJqaoqLi9WEmDsAACAASURBVAsbN25k2LBhevvGxcXx5ZdfcuTIEb3l\nwcHB2NnZkZSUpLf80KFDBAcH4+Hhwfz581/4Wosq6/45ePAggwcP5s033wTA09OTkSNHMm/ePOLj\n46levToffPABALa2tkRHR1OlShWMjY05c+aMkhEtzLAWZmS1Wi3u7u7KZycnJxYvXgzA7t27cXJy\nonXr1gB8+OGHdOzYEYATJ06Ues67d+9y8uRJzpw5g6GhIa+//jpbt25Vgv6tW7dSoUIF1Go1Dx48\nIDs7G3NzcwAePnzIhAkTqF27NlAQKK9evZo7d+5Qs2bNF6pHU/M6VH29wX/VBkIIIYQQomSv1Bi+\nJk2aEBAQoHx+9OgRFy5coGrVqmg0Gr0Ut0ql4ueffwYgOTlZL6CztLTkp59+AgoySyYmJowfP54O\nHTowfPhwLl26pHfe6OhoBg8eTMuWLTE3N+fYsWOlljE/P5/IyEgaN25cYrBXVOfOnZWuo46OjkrG\nEeDrr78mIyNDKffgwYPZv38/zZo1K/OYpR3/efLz86lYsWKxZTdv3iQ/P1+veyKAWq1W6vBF/Pbb\nbyQkJODo6MjAgQM5deoUd+7cUdYnJSXx888/07t3b6X7YXp6eqllLVrHWq0WQK+MRdu/6H5Lly7F\n29tbr2vrhQsXOHPmDJ6ennqBM4CdnR3Hjx/Hzs7uha/1WWXdP8nJyTRo8J8gxtLSkoyMDFJTU0lM\nTKRu3brMmjWL9u3b4+joyO3bt5WyGxsbk5+fj42NDWPGjMHZ2Vm55wICApQumgDx8fFKQJ2YmEil\nSpUYNWoU7du3Z/z48ZiYmABw9erVUs+ZlJRE7dq12blzJ++88w7du3fnyJEjVKtWDSj4IUOtVjNq\n1Ch69+5NzZo16dGjBwBjxozR62IdHx/Pa6+99sLBnhBCCCGE+Gu8UgFfUU+ePGHChAk0b96cYcOG\nUbt2bQIDA8nOziYlJYWoqCiys7MByMzM1AsGjIyM0Gq15OTkkJ2dTatWrfDx8eHkyZP07dsXFxcX\n7t+/DxSMG3ry5ImS3Xr//feJiIjQK0tSUhJt27albdu2tGjRghUrVjBy5MjnXoOZmRkPHz4stvzH\nH3/E09MTT09PzMzMAJSH6t/j2eM/fPhQKWebNm1o27YtZ86cAQrG4W3evJmbN2+SmZnJqlWr0Gg0\nZGdn0717d44fP86FCxfIy8tjz549JCcn63V/nDp1arFj79ixQ1m/d+9e3nnnHapWrUq1atWwt7dn\n165dynpTU1Pat29PVFQUBw4cIDU1VS+jVlYdm5iY0K5dO1asWEF6ejqpqals3bpVaf9CR44cwcjI\niF69einL0tPT8fHxYenSpSVmbc3NzVGrS/8aREREKOUq/Oejjz5S1j/v/snMzFTGBALKf2dmZvLo\n0SM++eQTOnbsyJkzZ5g0aRKenp7cvHlT2V6j0XDx4kX27dvHnj172L9/f7Eybt68mU8//ZRp06YB\nBT+UREZG4u3tzalTp7C2tmbChAlotdoyz/no0SN+/vlnUlNTOXbsGGFhYURERBTrhrthwwbOnDlD\nXl5eiVnR8+fPs2DBAubOnVtqvQohhBBCiL/HK9Wls9DNmzdxc3Ojbt26BAUFUaFCBUJCQli0aBFd\nu3alYcOGODo68vnnnwMFAV5WVpayf1ZWFhqNBkNDQ7p370737t2VdcOGDWPnzp18+eWX9OnTh6io\nKB48eEDnzp0ByMvL49GjRyQmJipj/qysrJTxZQBfffUVkyZNwszMTMlwlOTBgwe89tprestOnz7N\ntGnTGDt2LOPGjftD9fTs8c3MzEodw+fq6srTp0/54IMPqFixIqNHj8bY2BhTU1MaNGjAnDlz8PHx\n4cmTJ/Tq1YuOHTtiamqq7B8UFFTmGL7o6GgePnxIp06dgII2OH/+PBMnTsTQ0JAFCxbobT916lSc\nnZ2Vz8+r42XLluHn54eDgwO1atXC0dFRLzCCggl2Cic1KbRo0SIGDhxI48aNSy17WZydncscw/e8\n++fZezMzMxMoyN4ZGhpibW1N3759AejRowfNmzfn5MmTSpdLAAMDA5o0acLQoUP59NNPlUyaVqvF\n39+f48ePs23bNurVqwcUZOJ69uyp3L+enp5s2bKF5OTkMs9pYWGBTqfDy8uLihUr0qBBA4YMGUJc\nXJyyfeHxzc3NmTRpEhMnTmTJkiXKuv379+Pr68u8efOUrtlCCCGEeDFqtQqNRvW7ti/6b/HP9le1\n4ysX8F29ehUXFxccHR2VsWk6nY6nT5+yadMmZSbGwMBApQtbgwYNSElJwcbGBtDvRnf8+HG0Wi29\ne/dWzpGTk4OhoSHp6ekcO3aMbdu28cYbbyjr/fz8CA8P13uQLaoww5WQkFBmwHfq1Cnatm2rfN6z\nZw9LlizB19f3T3kYPnXqlN44ubLcvXuX0aNHK8HLjRs3yMvLw9LSkocPH9KqVSulK6JWq+Wdd95h\n0qRJL3Ts06dPk5WVxfHjx/WWDx48mKNHj+Lo6EhQUBBDhw5VxnhlZWWVOQvVs3X84MEDli1bpmRy\nIyMj9cYEPn36lK+++oply5bpHefYsWNUrFiRjRs3Ksvef/99Fi5cyLvvvvtC11eaF7l/Cu/NQsnJ\nyVSpUoXXX38dS0vLYl1yC7uvXrt2DS8vL73sWm5urjILZ05ODh4eHvz222/ExMTodZ20tLTUy84W\nHlOn05V5TktLS3Q6HTk5OUo9a7VadDqdMvttYGCgEjzn5OQo5QH4+OOPCQ8PJzQ0VO++F0IIIcSL\nqVrVGHPzyr97PzMzk7+gNKK8eKUCvnv37uHi4sKYMWP0sl8qlYpp06YxZswYhg4dyldffUV0dDRb\ntmwBoF+/fmzatIn27duj0WgICwtTsiAZGRnKQ2rdunWVroCdOnUiJiaGevXq6c3mCAWBipubm17A\nWdTVq1c5f/48Pj4+JV5HVlYWUVFRxMfHK90az549i6+vL5s3b9Z7VcN/Iz09nbCwMFJSUggKCnqh\nfQ4cOMD58+cJCQkhKyuLxYsXM2TIENRqNT/++CNTpkxh9+7dWFhYEBISgoWFhRJAP09UVBR9+vRR\nJs0p1K9fPyIiIujfvz/ffPMNt27dws/Pj/T0dIKCghg4cGCpx3y2jpcsWYKNjQ1Tp07l+++/Jyws\njDlz5ijbf/fdd9SoUaPYKxEuX76s97lJkybs3r1bb1xdoWfb+Xn279//3Pun8NUTPXv2pGbNmqxZ\ns0bJlv3rX/9i1apVyhjA+Ph4rl69yooVKzA3NycjI4OwsDDGjRvHt99+S3R0NMHBwQDMnTuXhw8f\nsmPHDr3xigADBgxg5syZ9O3blyZNmrBq1SosLS1p1KgRVatWLfWcNWvWxNrammXLljF37lx+/fVX\noqOjmTNnDmq1mkaNGrF69WqWL1/OkydPCA4OZvDgwUDBjxnbt28nMjJSmRhICCGEEL/Po0cZpKWV\nPMdBSdRqFWZmJjx8+FRes1EOFLbnn+2VCvj27NnDgwcPCAkJ4eOPPwYKgr2RI0cSFBTE/PnzWb58\nOf/3f/+Hv7+/kuEZPnw49+/fZ/DgweTm5uLo6MiHH34IFDz8/vbbb4wbN46HDx/StGlTNm7ciJGR\nEdHR0Xpd1Qp17NgRc3NzoqKiqF69OteuXVNmPFSpVJibm+Pi4sJ7772n7BMeHk5kZCRQ0F2vWbNm\nbNu2TZmUZePGjeTl5eHi4gL8531uwcHBSjfIsgQEBBAYGIhKpcLExARbW1t27tz5wmP/xo0bx82b\nN3nnnXfQaDT07dsXLy8voGCmxrFjxzJs2DCysrKUWUOL8vT01BvrVlj+2NhYPvvsM73xfIX69+9P\nWFgYly9fJjAwEF9fX+zt7VGpVLz33nt6Y+GSkpLKrGM/Pz9mz56Nra2t8h7Gol11b926RY0aNZ5b\nDyqVqtTArjB7XFTRdi163evXrycmJkbvHihU9P5xdXXl1q1buLq6kp6ejr29vVLvNWrUYPv27fj5\n+REQEMDrr7/O6tWrlWzd+vXrWbhwIWFhYdSqVYuFCxfSpk0bUlNTOXDgABUrVsTOzk65JnNzc+Lj\n4+nWrRtz587F29ub1NRUrK2tle/T8865YcMGpZ0qVKjAyJEjlTGRCxcuxNfXl27dumFsbMygQYOY\nMGECAGFhYTx9+pRBgwbp1VNMTAz169d/brsIIYQQArRaHfn5vz9w+2/3E/8bVLrfm9YQQoi/QBfn\nlfJaBiGEEP+zHqXeYP7oNr/rPXwajQpz88qkpaVLwFcOFLbnn+2VyvAJIf53PUn79WUXQQghhHhp\nCv4OtnnZxRDlkGT4hBCvhIsXL/LoUYaMQSgH1GoVVasaS3uWE9Ke5Yu056vNyqppmZPaPUsyfOWL\nZPiEEOVa69at5Q9WOSEPIOWLtGf5Iu0pxP+eV/bF60IIIYQQQggh/hgJ+IQQQgghhBCinJKATwgh\nhBBCCCHKKQn4hBBCCCGEEKKckoBPCCGEEEIIIcopCfiEEEIIIYQQopySgE8IIYQQQgghyqlX4j18\nFy5cYNmyZSQnJ2Nubs7YsWMZOnQojx8/Zvbs2Zw7d44qVarg7u7O4MGDAVi7di3r1q2jYsWK6HQ6\nNBoNNjY2+Pj4UL9+fb3jR0dHM3fuXFatWkWvXr301h05coS1a9fy22+/YWNjw/z586lbt67eNtnZ\n2QwYMIAPPviADz74oNi6kSNH4u7uTteuXQE4f/48I0eOxNjYWNlOp9NhZGTE2bNnAYiNjWXlypXc\nvXuXRo0asWDBApo0aQJAXFwcq1ev5t///je1atXC09OTHj16KMe6fPkyoaGhXLp0ifz8fBo1asTE\niRPp2LGjsk16ejoff/wxn376KQ8fPsTCwoJ+/frh5uaGRqMBICEhgSVLlvDrr7/StGlT/Pz8qFev\nHhcuXMDFxQWVSqWUPScnh/bt27Np0ybWrFlDaGgoFStW1KsLOzs71qxZ84frprD8Xbp0oU2bNqxf\nv17vPNeuXcPPz4+kpCRMTU1xcnLC3d0d0L8vih5fpVIRExND/fr1GTFiBJcuXVJebFq4fvLkyXz4\n4YecO3eOZcuW8dNPP2FhYYGLiwtOTk4AzJo1i0OHDmFoaFjs+AkJCRgZGSnLr1y5wsSJEzl16pSy\nLDU1FV9fXy5cuICBgQG9evXC29sbAwMDcnNzWbZsGZ988gm5ubm0bt2aefPmUatWrWL1ptPpeOON\nN5g2bRr29vZ69aPT6Zg8eTLt27dX7tfS6l2lUjFr1iyGDBlCWloaixYtIiEhAWNjY5ydnRk7diwA\n+/btY86cOXrX17hxY2bOnEnLli0BePLkCX5+fpw+fRqdTkfnzp2ZM2cOVapU4UXIi9fLD3mxc/ki\n7Vm+SHuWL4XtWbu2JWr1K/FYL15BL/3OePz4MRMnTmT+/Pn06dOHxMRERo8ezZtvvsmuXbswMTHh\n7NmzJCUl4eLiQuPGjbGxsQGgR48erF69GoC8vDxWrVrF1KlTOXDggN45oqOjGTJkCBEREXoB36VL\nl5g1axZr1qyhc+fO7Nmzh9GjR3Ps2DG9B/qlS5fy888/Fyv79evXmTdvHleuXCm27rXXXtMLYIpK\nTExkzpw5hIaG0rp1azZs2MCUKVM4duwYKSkpeHt7s27dOtq2bcuZM2fw8PBg7969WFpacvLkST76\n6CPmzp3L6tWrqVChAocPH2bixImsW7eO9u3b8/TpU4YOHUqLFi3YtWsXNWrU4MaNG0yfPp3bt2+z\nePFi7t27x6RJk1i5ciV2dnaEhobi4eHB4cOHsbW15ZtvvlHK++OPPzJy5Ei8vb0BUKlUenVfkv+2\nbgodOnSIrl27cubMGW7evMkbb7wBFAQp7u7ujBkzhoiICP7973/j5OSElZUV77zzDsBzywYFgdvw\n4cOLLU9PT8fNzY0VK1bQvXt3rl+/jpOTEy1btqRx48YAjBw5khkzZpR5/JiYGAICAqhQQf8rNn36\ndN566y1Onz7N48ePcXd3JyQkBE9PT0JDQ7l69SoHDx6kcuXK+Pv789FHH7Fz584S6+3EiRNMnjyZ\nEydOUK1aNQBu3brFwoULOXXqFO3bt9c79/Pq3dvbG7VaTVxcHNnZ2bi6umJsbMywYcMAsLa2JiYm\nBihoh507d+Lu7s4XX3yBgYEBixcvJjMzk9jYWLRaLV5eXvj5+bFs2bIy66rQWJ8ITM3rvNC2Qggh\nhCjwJO1XVnoNpFmzFi+7KOIV9dIDvtu3b2Nvb0+fPn2AgofKdu3acfHiRU6cOMHx48cxMDDAxsaG\nvn37sn//fiXgK6pChQo4OjqyefNmtFotanVBb9Vr165x8+ZNtmzZgr29PdevX1ce3OPi4nBwcFCy\nT0OGDGHr1q0kJCQoWZMvvviC77//nlatWhUr96hRoxg/fjx37979Xde8e/dunJycaN26NQCjR4/G\nzs5OOa6TkxNt27YFCrJmlpaWXLlyBUtLS/z8/Jg6dSr9+vVTjtevXz/S0tJISUmhffv2bN26lUqV\nKrF48WJlmwYNGrB8+XLWrVtHTk4OsbGxWFtbK9fu7u7O9u3b+fbbb2nevLmyn06nY8aMGbi5uSn1\n9jx/pG4KRUdHM3HiRExNTdmxYwczZ84ECoLNo0ePKpmmtLQ0dDodZmZmv+v4Ol3Jv2pWrlyZM2fO\nYGxsjE6n4/79+2g0Gr3M2POEhoZy7Ngx3Nzc2LBhg7I8NzcXExMT3NzcMDAwwMLCgr59+xIXFwdA\nVlYW7u7umJubA/DBBx8wcODAUs/TrVs3jI2NuXHjBtWqVSM3N5eBAwcydOhQ0tPTX7i8AJmZmZw+\nfZpPPvkEU1NTTE1NcXFxYePGjUrAV5RKpWLAgAEsWrSI1NRU6tSpg1arxd3dXakrJycnvXvweUzN\n61D19Qa/q9xCCCGEEKJsL30MX5MmTQgICFA+P3r0iAsXLgAFQVzt2rWVdZaWliQnJ5d4nOzsbKKj\no+nSpYsS7AFERUXRv39/TExMcHR0JDw8XFmXn5+v10UNQK1W89NPPwEFwYS/vz8BAQFK98ZC5ubm\nxMbG8uGHH/7ua05MTKRSpUqMGjWK9u3bM378eOUh2c7OTsmkAdy8eZMff/wRKysrfv75Z27evImD\ng0OxY3744YfKg/np06fp2bNnsW0aNmxIYGAghoaGJCcn06DBfx6u1Wo1b7zxRrH63bNnD7m5uTg7\nO7/w9f2RuoGCrpB3797F3t6eoUOHsm/fPrKyspT1hW3Wo0cPBg8eTMeOHYsF5H+EsbEx+fn52NjY\nMGbMGJydnalT58UzT4MHD2b//v00a9ZMb7mBgQGhoaFYWFgoyz777DOlK6+XlxedOnVS1sXHx5ca\nZOt0Oo4ePYqBgYFyngoVKnD06FGmTZumdNt9UVqtFkDv+6BSqUrMbEPBdycyMpLGjRsrdRMQEKBc\nS2H5i34WQgghhBB/v5ee4SvqyZMnuLm50bx5c9q1a8f27dv11hsZGek9+MfHxyuZsPT0dCpUqMDa\ntWuV9VlZWRw+fJjdu3cDMHToUJycnJgxYwampqZ0796dCRMmMGDAAFq2bMmBAwdISUkhJycHgPnz\n5zN27FilO+GzZSnLw4cPlbIVjpUKCgrCzs6OR48eERkZyfr162nUqBHBwcG4ublx5MgRvWA1NTUV\nV1dXBg0aROPGjbl06RKAkgEqzYMHD3jttdfK3CYzMxNTU1O9ZZUqVdKrX4CNGzfi6elZLOAtWveF\n13fy5EmMjIz+UN1AQXfIgQMHotFoaNq0KW+++SYHDx5UxtEVOnr0KKmpqYwfP561a9fi4eFRrGyF\nGjRowK5du5TPy5cv1+v2aWVlxbZt25TPGo2GixcvcuPGDcaNG4elpSX9+/cHICIiQunaWKhz584E\nBgYCKN0rn8fPz4+UlBSWL19ebN3Ro0cJCwvTyxAWrbfMzEzy8vJwd3fHxMQEKAjQigaTzyq6P/yn\n7uPi4qhSpQrt2rVjxYoVLFiwgKdPn7J161ays7OV7ZOSkpT9MzIy0Gq1+Pr6lniuzZs38+mnnxIV\nFfVCdSGEEEIIIf4ar0zAd/PmTdzc3Khbty5BQUH8+OOPSuBVKCsrS69rXffu3ZWHdq1WS3x8PJ6e\nnoSHh9OsWTOOHj1Keno6I0aMUPbJzs4mJiaG0aNHY2try5w5c/Dx8eHJkyf06tWLDh06YGpqyt69\ne8nMzGTo0KH/1fWYmZmVOl7K0NCQnj17Ym1tDYCnpydbtmwhOTmZhg0bAgVZQDc3N7p168b8+fOB\n/wQS9+7d4/XXX9c75tOnTzEwMMDQ0JDq1atz//79Es+dlpaGubl5seAZCoKIovV74cIFHj9+zL/+\n9a9ixyla979XWXWTkZHB4cOHMTAwYO/evcq1RUREFAv4DA0NeeONNxg3bhzbtm1TAr4XKZuXl1ex\nCXieZWBgQJMmTRg6dCiffvqpEvA5Ozs/dwxfWbKzs/Hy8uKHH34gIiKiWABfGOitXbsWW1tbZfmz\n9Xbt2jUmTZqEqanpC2VTy6p3KAiCFy1ahIODA7Vq1cLR0ZGbN28q662srPQC3a+++opJkyZhZmam\nTCqk1Wrx9/fn+PHjbNu2jXr16j23XEIIIYT4Y9RqFRqN6vkbileaWv3XtOErEfBdvXoVFxcXHB0d\nle6MdevWJTc3lzt37lCzZk0AUlJS9LohFqVWq3FwcKB+/fp8+eWXNGvWjKioKLy8vPTGux05coTw\n8HBGjx7Nw4cPadWqFceOHQMKHlbfeecdPDw8CA4O5vLly0pG4+nTp3z33XfcuHGDefPm/aHrtbS0\n1AtmC7vTFY4rO3nyJNOmTcPDw0PvQb5OnTrUq1eP2NjYYl0sg4ODSUxMJDw8nM6dO3P8+HEmTJig\nt821a9cYMGAAsbGxNGjQQLnuwjL88ssvSsAJ8Pnnn9OjRw+9rONf7dChQ9SvX5+wsDClPjIyMujX\nrx9fffUVDRo0wMnJib179yqzP+bk5LzwTJDPc+3aNby8vDh06JCyLDc39087/qNHjxg3bhyVK1cm\nKipKL8uq0+mYO3cuCQkJ7Nix47ljJps0aUKPHj04e/bsf919tqi0tDSWLVumZGgjIyOxsrIqdfs2\nbdrQtm1bEhIS6NGjBzk5OXh4ePDbb78RExOjfG+FEEII8dcyNa2EuXnll10M8Yp66QHfvXv3cHFx\nYcyYMYwbN05ZbmJiQrdu3QgMDGTRokVcv36dw4cP63Vxe1ZCQgI3btygVatWXL9+ne+++45169bp\ndW8cOHAggYGBfP7551SuXJkpU6awe/duLCwsCAkJwcLCghYtWrBp0ya9Y48YMYJevXo9Nyv0IgYM\nGMDMmTPp27cvTZo0YdWqVVhaWtKoUSN++OEHPD098ff3VyayKcrb25sZM2ZQpUoVZcbRvXv3EhUV\nRWhoKFCQgdqzZw8+Pj5MnjyZGjVq8O233zJr1iwGDRpEnTp1cHBwIDAwkLi4OLp27cr69eupWbOm\n3gP+5cuXy5w05K8QFRVFv3799LJeFhYWdOvWjfDwcIKDg6lWrRpBQUHMnj2bX375hU2bNinZvT+q\nfv36ZGRkEBYWxrhx4/j222+Jjo4mODj4Tzm+h4cH1atXZ82aNcXG2a1Zs4Zz584RHR1dYtfMZyea\n+eWXXzhx4sQLt1FpE9UUWrJkCTY2NkydOpXvv/+esLAw5syZU+r2V69e5fz58/j4+AAwd+5cHj58\nyI4dO37XJDdCCCGE+GOePMkkLe33TdgmXj1qtQozM5M//bgvPeDbs2cPDx48ICQkhI8//hgoGIs0\ncuRI/Pz8mDdvHl27dsXExARvb2+9GSTj4+OVmS5VKhW1atViwYIFtG7dGn9/fzp27FhsLFvlypXp\n0aMHERERbNy4kbFjxzJs2DCysrKwtbVVgqZnPTuG7UXXlaRbt27MnTsXb29vUlNTsba2JiQkBIDw\n8HCys7Px8fFRHraLvivN3t6eoKAgQkND8ff3R6fT8dZbb7F+/XolG2lsbMyuXbsIDAxk8ODBpKen\nU716dQYNGqQE1dWqVSMkJAR/f3+8vb2xsrLSG/8IBVP8V69e/Xdd2x+pm6SkJK5du1ZiGwwYMIAJ\nEyaQmprK6tWrmT9/PnZ2dpiZmTF69GgcHR2VbYveF/CfsWrz5s2jf//+ZZbJ0NCQ9evXs3DhQsLC\nwqhVqxYLFy6kTZs2yjbh4eFERkYWO/769ev1tnvWN998w4ULF6hYsSK2traoVCp0Oh3NmjVj69at\nbNmyhby8PGVSnqLv94OC7GDR+71y5cr07dsXV1fXYucq6RqL7l/0+O+++y6LFi3Cz8+P2bNnY2tr\ni7m5OW5ubnTv3l3ZPikpSe/85ubmuLi48N5775GamsqBAweoWLEidnZ2yrWZm5sTHx9fap0IIYQQ\n4o/TanXk58t7FUXJVLrn/ewvhBB/gy7OK+W1DEIIIcTv9Cj1BgvHtpX38JUDGo3qL+ma+9IzfEII\nAQUvjhVCCCHE71Pw97Ptc7cT/7skwyeEeCVcvHiRR48y0Grlf0n/dGq1iqpVjaU9ywlpz/JF2rN8\nKWzP2rUtUaslj/NP91dl+CTgE0K8MtLS0mUMQjlQ+AdL2rN8kPYsX6Q9yxdpz/Llrwr4/r759oUQ\nQgghhBBC/K0k4BNCCCGEEEKIckoCPiGEEEIIIYQop2QMnxBCCCGEEEKUU5LhE0IIIYQQQohySgI+\nIYQQQgghhCinJOATQgghhBBCiHJKAj4hhBBCCCGEKKck4BNCCCGEEEKIckoCPiGEEEIIIYQopyTg\nE0IIIYQQQohySgI+IYQQQgghhCinJOATQrxUiYmJDBkyhFatWjFgwAAuX778sosknmPz5s00a9aM\n1q1b06pVK1q3bs3XX3/N48ePmThxIra2tnTr1o2YmBi9/QIDA+nQoQPt2rVj8eLF6HS6l3QFAuDK\nlSt07txZ+fz48WM8PDz+q/Y7VGP+bQAABjhJREFUfPgwPXr0oFWrVkyYMIH79+//bdchCjzbnt99\n9x3W1tZ639OwsDBlvbTnq+nChQs4OTlha2tLz5492b17NyDfz3+q0trzb/9+6oQQ4iXJzs7WdenS\nRRcZGanLy8vTxcTE6Dp06KDLyMh42UUTZfjoo490W7ZsKbZ80qRJuhkzZuhycnJ0ly9f1rVt21Z3\n+fJlnU6n04WHh+v69eunu3fvnu7evXu6gQMH6jZu3Pg3l1wUio6O1tna2urat2+vLPtv2y8pKUn3\n9ttv665cuaLLzs7WzZkzR+fi4vJSrut/VUntGRUVpRs/fnyJ20t7vpoePXqka9u2re7IkSM6nU6n\nu3r1qq5t27a6hIQE+X7+A5XVnn/391MyfEKIl+bcuXNoNBqGDh2KRqNh0KBBWFhY8MUXX7zsooky\nJCUl8dZbb+kty8jIID4+nsmTJ2NgYICNjQ19+/Zl//79ABw8eJBRo0ZhYWGBhYUF48ePZ+/evS+j\n+P/zQkNDiYiIwM3NTVn237Tfvn37gP/82ty8eXMMDQ2ZPn06p06dIi0t7aVc3/+aktoTCnpPWFlZ\nlbiPtOer6fbt29jb29OnTx8ArK2tadeuHRcvXuTEiRPy/fyHKa09v/nmm7/9+ykBnxDipUlOTqZB\ngwZ6yywtLUlOTn5JJRLPk5WVRUpKCtu3b6dTp068++677Nmzh59//hkDAwNq166tbFu0LZOTk2nY\nsKHeup9++unvLr4ABg8ezP79+2nWrJmy7Keffvrd7ZeSkqKsK/o9NjMzo2rVqvI9/puU1J5Q8MPM\n119/Tffu3enWrRsBAQH8f3t3D5JsF4cB/HpJTI2IpCmEoBYXQTMySIMcoqGsaJBqkSCyqbG5JXLo\nA26MClqKtmjqAyywLQRpCAqCQKKlwCQVuv3K807dvD7V+0DPi7f6Xr/NcxTO4eIv/vFw7nw+D4B5\nViqz2YxAIKC8TiaTiEajAACNRsP6rDLf5Wk2m8ten2z4iEg1sixDr9eXjOn1emQyGZVWRL8Tj8dh\nt9sxOTmJi4sLLC4uYnl5GeFwGPX19SXv1el0SpayLEOn05XMFYtF5HK5sq6fgJaWlk9jsiz/OD/W\nsbq+yhMAjEYj3G43jo+Psbu7i0gkAkmSADDPapBOpzE3NweLxQKHw8H6rHLpdBp+vx8WiwVut7vs\n9cmGj4hU89WXlCzLMBgMKq2IfsdkMmFvbw8ulwsajQZdXV0YGRlBNBr91LxlMhkly3/+OPmYq6ur\ng1arLev66Wt6vf7H+f06B7COK8HGxgZ8Ph90Oh1MJhP8fj/Ozs4AMM9K9/j4iImJCTQ3N0OSJBgM\nBtZnFfvI02g0Kk1dueuTDR8Rqaa9vV05pvAhFouVHGWgynJ7e1tykxgAZLNZtLa2Ip/P4+npSRmP\nxWLK0ZOOjo6SrL86zkvqaWtr+3F+v84lEgmkUinmq6JUKoVAIIC3tzdlLJPJKP8SMc/KdXNzA6/X\nC5fLhWAwCK1Wy/qsYl/lqUZ9suEjItX09PQgl8thf38fhUIBBwcHSCQScDqdai+NvmEwGBAMBhEK\nhSCEwOXlJU5OTjA1NQW3242VlRVkMhlcX1/j6OgIHo8HAODxeLCzs4Pn52fE43Fsb29jdHRU5d3Q\nh4aGhh/nNzQ0hFAohKurK2SzWayurqKvrw9NTU1qbul/rbGxEefn55AkCYVCAQ8PD9ja2sL4+DgA\n5lmp4vE4ZmZmMD09jYWFBWWc9VmdvstTlfr8z+4eJSL6gbu7O+H1ekVnZ6cYGxtTrpmmyhUOh8Xw\n8LCwWq1icHBQhEIhIYQQr6+vYn5+XnR3d4v+/n5xeHiofOb9/V2sr68Lp9MpHA6HWFpaEsViUa0t\nkBAiEomUXOP/J/mdnp6KgYEBYbfbxezsrHh5eSnrXuhznvf398Ln8wm73S56e3uFJEnKHPOsTJub\nm8JsNgubzSasVquwWq3CZrOJtbU1kUwmWZ9V5t/yLHd9/iUEn3xLRERERERUi3ikk4iIiIiIqEax\n4SMiIiIiIqpRbPiIiIiIiIhqFBs+IiIiIiKiGsWGj4iIiIiIqEax4SMiIiIiIqpRbPiIiIiIiIhq\nFBs+IiIiIiKiGsWGj4iIiIiIqEb9DVYX9x0Tf2ALAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109d422d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp.plot(kind='barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ah! Much better. The labels have gotten some breathing room, making them easier to read. Also, the neater gridlines help read the numbers better."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's see which vendor owns the most number of cabs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10fcb7e10>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WeXm5SktL1dLSovnz56u8vFzBYFCS4p4TAAAAg9NvOF599dX6y1/+ouuuu05n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\ngBHCEQAAAEb+P2jyGrBaXb3vAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fcc9890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = df.groupby('vendor_id').agg({'medallion': pd.np.count_nonzero})\n",
    "temp = temp['medallion'].sort_values(ascending=False)\n",
    "temp.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are apparently just two vendors, and between them VTS seems to have the leading number of cabs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's see the scatterplot. This is a good tool to summarise information where each row is characterised by 3-4 values. Below, we will examine the trip patterns of the 20 medallions which are most frequent in the data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10ab0c650>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sLL/fj6ivcle0Dh09iYzJ94kHdR/8dAOuuvkR8e+axkJAAFpbW1BydCu0+mi0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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ab2f390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we will strip down the data set to the top 20 medallions\n",
    "temp = df[df['medallion'].isin(top_medallions)]\n",
    "# and plot, trip time on the X-axis and trip_distance in the Y-axis\n",
    "temp.plot.scatter(x='trip_time_in_secs', y='trip_distance')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As seen above, most of the trips are less than 20 mile long. But how do we characterise \"most\" here ? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use a histogram to see the various buckets of  _ \"trip_distance\" _ s in the data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11132e590>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YZRIAAADGKJMAAAAwRpkEAACAMcokAAAAjFEmAQAAYIwyCQAAAGOUSQAAABijTAIAAMAY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\n9+5aAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1113496d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.trip_distance.head(10000).plot.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, as a person looking for a cabbie in the city that never sleeps, what time would be ideal to hail a cab ?\n",
    "\n",
    "To answer that we would need get classify a trip as a day/night trip and also get the hour of the trip. Let's do that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# let's first remove all trips which had zero trip time.\n",
    "df.loc[:,('Hour')] = df.pickup_datetime.dt.hour\n",
    "df.loc[:,('avg_speed')] = df.trip_distance * 60 * 60/df.trip_time_in_secs\n",
    "temp = df[df.trip_time_in_secs > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "total_trips = temp.groupby('Hour').sum()['Count']\n",
    "temp = temp.groupby('Hour').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can add multiple plots into one figure with the 'add_subplot' function. Each axes needs to be told the position of each axes in the figure. \n",
    "fig.add_subplot(312) : positions the figure as the second row in a three-row, one-column figure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ajatICzuvYAvTExLgU+lCxnod7HfLli289dZbZGRk0L59ex5++GGGDx9eqd3m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hoaYLVaUVRUFD7x+p577sHOnTsTsUkiug5RMCPDZkaGbejdZ65L8Jw6EbIlMZ9NjdAJ6GooIEYF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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1106a6a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We create plot holders for the three figures\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(411)\n",
    "ax2 = fig.add_subplot(412)\n",
    "ax3 = fig.add_subplot(413)\n",
    "# ax4 = fig.add_subplot(414)\n",
    "\n",
    "# we plot a line chart depicting trip_distance as the y-axis and Hour as x-axis (it automatically becomes the x-axis \n",
    "# as Hour is the index) \n",
    "temp.trip_distance.plot(kind='line', ax=ax1, legend='Trip distance vs Hour')\n",
    "temp.trip_time_in_secs.plot(kind='line', ax=ax2, legend='Trip time')\n",
    "temp.avg_speed.plot(kind='line', ax=ax3, legend='Average speed')\n",
    "# total_trips.plot(kind='line', ax=ax4, legend='number of trips')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best time to take a cab seems to be around 6AM when the average trip time is the least. \n",
    "P.S: Please do not take this information too seriously.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can have more fun with the data. Let's see what we can learn with the trip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Let's make a Day column which will hold the weekday name obtained from the pickup_datetime column\n",
    "df.loc[:,('DoW')] = df.pickup_datetime.dt.weekday_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Let's groupby the Date column\n",
    "temp = df.groupby('DoW')\n",
    "\n",
    "# and for every date, let's use the apply function to group by the Hour column and sum up the total trips\n",
    "temp = temp.apply(lambda v: v.groupby('Hour').sum())['Count']\n",
    "\n",
    "# We have hourly trip count per day of the month now. Let's reset_index to make Hour and Day, columns.\n",
    "temp = temp.reset_index()\n",
    "\n",
    "# Finally, let's pivot it. We want the DoW as columns and Hour as rows, trip counts as values.\n",
    "temp = temp.pivot('Hour', 'DoW', 'Count')\n",
    "\n",
    "# Let's make sure we get columns in the same order as the weekdays occur\n",
    "# we will just reassign the columns in the order that we want them in.\n",
    "temp = temp['Monday,Tuesday,Wednesday,Thursday,Friday,Saturday,Sunday'.split(',')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x110f9e150>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tNptNBw8evGgfCQkJ+vLLLyVJ5zuvt6r9AAAA/8MEm+eqXLAdOnTosgfLycnR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\nQgghhBBmTgI2IYQQQggz93+Twg6tSdBVCwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11069ab90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(temp, fmt=\"d\", linewidths=.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "New York loves to party till the weekend ends! \n",
    "\n",
    "The trip count starts climbing at Friday evening, slows down at Saturday morning. \n",
    "This repeats itself on Saturday evening.\n",
    "Fridays and Saturdays seem to show upward of 1,25,000 trips an hour.\n",
    "Sunday seems to show a slowdown in trip count."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to employ what we have learnt so far to make visualisations in the upcoming sections.\n",
    "\n",
    "It's recommended to go through the documentations for both the visualisation packages.\n",
    "\n",
    "Seaborn  - https://seaborn.github.io/index.html\n",
    "\n",
    "Pandas visualisation = http://pandas.pydata.org/pandas-docs/version/0.18.1/visualization.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 4. Missing data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Missing data may appear in your dataset natively or may even get introduced as a result of a Pandas operation.\n",
    "\n",
    "In the current data set for example, if you were to reindex the dataframe with index-values not present in the data, you'd get a null value in place. \n",
    "\n",
    "Let's take the first few rows of the data. And, group by Hour. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "temp = df.head()\n",
    "temp = temp.groupby('Hour').sum()['Count']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that there are only 4 unique hours of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([7, 16, 17, 22], dtype='int64', name=u'Hour')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "If we were to reindex by Hour with the 24 hours, in a day, Pandas will automatically fill the empty cells with NaN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hour\n",
       "0     NaN\n",
       "1     NaN\n",
       "2     NaN\n",
       "3     NaN\n",
       "4     NaN\n",
       "5     NaN\n",
       "6     NaN\n",
       "7     1.0\n",
       "8     NaN\n",
       "9     NaN\n",
       "10    NaN\n",
       "11    NaN\n",
       "12    NaN\n",
       "13    NaN\n",
       "14    NaN\n",
       "15    NaN\n",
       "16    2.0\n",
       "17    1.0\n",
       "18    NaN\n",
       "19    NaN\n",
       "20    NaN\n",
       "21    NaN\n",
       "22    1.0\n",
       "23    NaN\n",
       "Name: Count, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = temp.reindex(index=range(0,24))\n",
    "temp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing missing data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Pandas provides us with two methods to detect null values - `isnull` and `notnull`. \n",
    "\n",
    "You can get indexes of non-null items from a Series like :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 18, 19, 20, 21,\n",
       "            23],\n",
       "           dtype='int64', name=u'Hour')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.index[temp.isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's apply these functions to a DataFrame with null values. Let's make a dataframe that contains null values.\n",
    "\n",
    "We will use random.choice function to choose from a list of values and use a list comprehension to generate it.\n",
    "Please see that we are using `xrange` function to loop through ten thousand times. Unlike `range` function, it _does not_ create a list of numbers in memory but generates them on demand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nan_df = pd.DataFrame({'one': [random.choice([0, pd.np.NaN, 2]) for i in xrange(10**6)],\n",
    "              'two': [random.choice([0, pd.np.NaN, 2]) for i in xrange(10**6)],\n",
    "              'three': [random.choice([0, pd.np.NaN, 2]) for i in xrange(10**6)],\n",
    "                    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>three</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one  three  two\n",
       "0  0.0    0.0  2.0\n",
       "1  2.0    2.0  0.0\n",
       "2  2.0    2.0  NaN\n",
       "3  NaN    0.0  0.0\n",
       "4  0.0    NaN  NaN"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nan_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "The `isnull` method applied directly to the dataframe applies itself to every value and returns a dataframe containing True/False values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>three</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     one  three    two\n",
       "0  False  False  False\n",
       "1  False  False  False\n",
       "2  False  False   True\n",
       "3   True  False  False\n",
       "4  False   True   True"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nan_df.head().isnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "We could use `isnull` and `notnull` to get only those rows which have null/not-null values as shown below. To continue the culture of this notebook to time every function, let's time these two as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nan_df[nan_df['one'].notnull()] takes:  0.02 seconds\n",
      "nan_df[~nan_df['one'].isnull()] takes:  0.02 seconds\n"
     ]
    }
   ],
   "source": [
    "print \"nan_df[nan_df['one'].notnull()] takes: \", timer(\"nan_df[nan_df['one'].notnull()]\")\n",
    "print \"nan_df[~nan_df['one'].isnull()] takes: \", timer(\"nan_df[~nan_df['one'].isnull()]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "They seem to take almost the exact same time.\n",
    "\n",
    "One thing to note is, by adding a tilde(`~`) in front of a condition, we can negate the condition. \n",
    "What we mean is, `~nan_df['one'].isnull()` becomes where column 'one' is _not null_ thanks to the tilde."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Types of missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Null values are represented in a few different ways depending on the dtype of the column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "temp = nan_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's change our favourite dataframe `temp` and make one numerical column, one datetime column and a object column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fibinse/anaconda/lib/python2.7/site-packages/pandas/core/indexing.py:549: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item_labels[indexer[info_axis]]] = value\n",
      "/Users/fibinse/anaconda/lib/python2.7/site-packages/pandas/core/indexing.py:465: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "temp.loc[:,'one'] = 1\n",
    "temp.loc[:,'two'] = pd.datetime.now()\n",
    "temp.loc[:,'three'] = 'a'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's change the rows 1,2 and 5 to contain null values, across all the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "temp.loc[(1,2,5),:] = pd.np.NaN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'd notice that rows 1, 2, and 5 of column 'one' store NaNs as NaN itself.\n",
    "Where as column 'two' which is a datetime column stores it as `NaT`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>three</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>a</td>\n",
       "      <td>2016-10-22 17:41:51.351307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>a</td>\n",
       "      <td>2016-10-22 17:41:51.351307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>a</td>\n",
       "      <td>2016-10-22 17:41:51.351307</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one three                        two\n",
       "0  1.0     a 2016-10-22 17:41:51.351307\n",
       "1  NaN   NaN                        NaT\n",
       "2  NaN   NaN                        NaT\n",
       "3  1.0     a 2016-10-22 17:41:51.351307\n",
       "4  1.0     a 2016-10-22 17:41:51.351307"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Now, let's assign a None value to the third row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "temp.loc[3,:] = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how Pandas stores `None` as `NaN` in the numeric column 'one', as-is in the object column and as `NaT` in the datetime column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>three</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>a</td>\n",
       "      <td>2016-10-22 17:41:51.351307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>a</td>\n",
       "      <td>2016-10-22 17:41:51.351307</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one three                        two\n",
       "0  1.0     a 2016-10-22 17:41:51.351307\n",
       "1  NaN   NaN                        NaT\n",
       "2  NaN   NaN                        NaT\n",
       "3  NaN  None                        NaT\n",
       "4  1.0     a 2016-10-22 17:41:51.351307"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "We have learnt how null values are stored. \n",
    "\n",
    "But before we move on to understand how we can deal with them, it would be a good idea to write a function which gives us a visual summary of the null values in a dataframe, for the purposes of our future analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def visualise_nulls(df):\n",
    "    '''\n",
    "        Takes a DataFrame as input and prints a horizontal bar graph\n",
    "        of null values.\n",
    "    '''\n",
    "    fig = plt.figure()\n",
    "    ax = plt.subplot(111)\n",
    "    maxrows, maxcolumns = df.shape\n",
    "    values = []\n",
    "    for column in list(df):\n",
    "        null_values = df[df[column].isnull()].shape[0]\n",
    "        values.append([null_values, maxrows])\n",
    "\n",
    "    ax.legend(bbox_to_anchor=(.5, .5), loc='top right')\n",
    "    pd.DataFrame(values, index=list(df), columns=['Nulls', 'Max Row size']).plot(kind='barh', figsize=(10,8), ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fibinse/anaconda/lib/python2.7/site-packages/matplotlib/axes/_axes.py:519: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
      "  warnings.warn(\"No labelled objects found. \"\n"
     ]
    },
    {
     "data": {
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InOADAEic4AMASJzgAwBInOADAEic4AMASJzgAwBInOADAEic4AMASJzgAwBInOADAEic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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fc12ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualise_nulls(nan_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "As you will notice quite a sizeable rows of each column in the `temp` dataframe is filled with null values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Let's pass some real data through the visualisation function and see what we find."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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16rn29/b25syZMxiNRo4cOULjxld+arNarZjNZjp27MjJkyexWq20b98eq9WKwWDAYrFQ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110ced390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualise_nulls(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we see, a tiny percentage of 'avg_speed' column and a sizeable chunk of the 'store_and_fwd_flag' is filled with null values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Working with missing data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you have null data in your data, you can either remove that data or you can fill the null values with an appropriate value. Let's discuss this."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Filling missing data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `fillna` method replaces the missing data with the value that is passed to it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "      <th>8</th>\n",
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       "    </tr>\n",
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       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "      <td>2016-10-22 17:41:51.351307</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             one          three                         two\n",
       "0              1              a  2016-10-22 17:41:51.351307\n",
       "1  missing value  missing value               missing value\n",
       "2  missing value  missing value               missing value\n",
       "3  missing value  missing value               missing value\n",
       "4              1              a  2016-10-22 17:41:51.351307\n",
       "5  missing value  missing value               missing value\n",
       "6              1              a  2016-10-22 17:41:51.351307\n",
       "7              1              a  2016-10-22 17:41:51.351307\n",
       "8              1              a  2016-10-22 17:41:51.351307\n",
       "9              1              a  2016-10-22 17:41:51.351307"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.fillna('missing value')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`fillna` also has an argument `method` which let's use forward-fill/backward-fill values.\n",
    "\n",
    "You can either fill the missing data with a value or use the `method` argument to forward fill/backward-fill with an existing value. \n",
    "\n",
    "While using `method` argument, you could pass on a integer value to the `limit` argument to fill as many missing values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one three                        two\n",
       "0  1.0     a 2016-10-22 17:41:51.351307\n",
       "1  1.0     a 2016-10-22 17:41:51.351307\n",
       "2  1.0     a 2016-10-22 17:41:51.351307\n",
       "3  NaN  None                        NaT\n",
       "4  1.0     a 2016-10-22 17:41:51.351307\n",
       "5  1.0     a 2016-10-22 17:41:51.351307\n",
       "6  1.0     a 2016-10-22 17:41:51.351307\n",
       "7  1.0     a 2016-10-22 17:41:51.351307\n",
       "8  1.0     a 2016-10-22 17:41:51.351307\n",
       "9  1.0     a 2016-10-22 17:41:51.351307"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.fillna(method='ffill', limit=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`limit=1` fills only the first missing value with the last value before/after it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "   one three                        two\n",
       "0  1.0     a 2016-10-22 17:41:51.351307\n",
       "1  1.0     a 2016-10-22 17:41:51.351307\n",
       "2  NaN   NaN                        NaT\n",
       "3  NaN  None                        NaT\n",
       "4  1.0     a 2016-10-22 17:41:51.351307\n",
       "5  1.0     a 2016-10-22 17:41:51.351307\n",
       "6  1.0     a 2016-10-22 17:41:51.351307\n",
       "7  1.0     a 2016-10-22 17:41:51.351307\n",
       "8  1.0     a 2016-10-22 17:41:51.351307\n",
       "9  1.0     a 2016-10-22 17:41:51.351307"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.fillna(method='ffill', limit=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 5. Grouping by "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There will be times when you want to group your dataframe by an axis and aggregate it across multiple columns differently. Here is where `.groupby` comes to your rescue. Let's see how this goes - "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Let's group by the column `medallion`\n",
    "temp = df.head(10).groupby('medallion')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `.groups` method returns all the group names and their labels in a dictionary form."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'0CF8B9F42FED24FA1CA8AACA36D1A25B': [0],\n",
       " '4D3E527682E42F1FACDFBF2D56757AC6': [1],\n",
       " '6B3D1A9F93C3769EF8F8446DE7CCB9F4': [9],\n",
       " '8F832FF1330148CD9C4FBA7EC1D3D99A': [8],\n",
       " '9BA35AC8B9018EBCF66913620278FF0E': [6],\n",
       " 'CFF1FDD049E5433E6FBCC96EDA9E66A5': [3],\n",
       " 'D28DD94BE7682B7434EC5CA4D523A788': [2],\n",
       " 'DB0D656FD074AC9E5B039E9A5A17F408': [7],\n",
       " 'F2967DBF707C06314CEB745A83332D62': [5],\n",
       " 'F9B4C49F95496C0EFA6674364F4B54AE': [4]}"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.groups"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you wanted to know only the group names, you could do that by:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['9BA35AC8B9018EBCF66913620278FF0E',\n",
       " '6B3D1A9F93C3769EF8F8446DE7CCB9F4',\n",
       " 'CFF1FDD049E5433E6FBCC96EDA9E66A5',\n",
       " 'F2967DBF707C06314CEB745A83332D62',\n",
       " 'F9B4C49F95496C0EFA6674364F4B54AE',\n",
       " 'D28DD94BE7682B7434EC5CA4D523A788',\n",
       " '0CF8B9F42FED24FA1CA8AACA36D1A25B',\n",
       " '4D3E527682E42F1FACDFBF2D56757AC6',\n",
       " 'DB0D656FD074AC9E5B039E9A5A17F408',\n",
       " '8F832FF1330148CD9C4FBA7EC1D3D99A']"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.groups.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's time the directly available aggregation functions and measure the time actually taken."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "temp.mean() takes:  1.15 seconds (13438, 11)\n",
      "temp.median() takes:  4.84 seconds (13438, 11)\n",
      "temp.min() takes:  55.98 seconds (13438, 17)\n",
      "temp.max() takes: "
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-57-cecbe7f68f05>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0;34m\"temp.median() takes: \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"temp.median()\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmedian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0;34m\"temp.min() takes: \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"temp.min()\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mprint\u001b[0m \u001b[0;34m\"temp.max() takes: \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"temp.max()\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-3-523bce363aa6>\u001b[0m in \u001b[0;36mtimer\u001b[0;34m(fn)\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;34m'''Takes a function, executes it and returns the time taken to exceute it in seconds'''\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0mtime_taken\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mu'timeit -oq $fn'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0;34m'%.2f seconds'\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0mtime_taken\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/fibinse/anaconda/lib/python2.7/site-packages/IPython/core/interactiveshell.pyc\u001b[0m in \u001b[0;36mmagic\u001b[0;34m(self, arg_s)\u001b[0m\n\u001b[1;32m   2144\u001b[0m         \u001b[0mmagic_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marg_s\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpartition\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m' '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2145\u001b[0m         \u001b[0mmagic_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmagic_name\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprefilter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mESC_MAGIC\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2146\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmagic_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2148\u001b[0m     \u001b[0;31m#-------------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/fibinse/anaconda/lib/python2.7/site-packages/IPython/core/interactiveshell.pyc\u001b[0m in \u001b[0;36mrun_line_magic\u001b[0;34m(self, magic_name, line)\u001b[0m\n\u001b[1;32m   2065\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'local_ns'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf_locals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2066\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2067\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2068\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2069\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<decorator-gen-58>\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, line, cell)\u001b[0m\n",
      "\u001b[0;32m/Users/fibinse/anaconda/lib/python2.7/site-packages/IPython/core/magic.pyc\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m    186\u001b[0m     \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 188\u001b[0;31m         \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    189\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    190\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/fibinse/anaconda/lib/python2.7/site-packages/IPython/core/magics/execution.pyc\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, line, cell)\u001b[0m\n\u001b[1;32m   1043\u001b[0m                     \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1044\u001b[0m                 \u001b[0mnumber\u001b[0m \u001b[0;34m*=\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1045\u001b[0;31m         \u001b[0mall_runs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtimer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1046\u001b[0m         \u001b[0mbest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_runs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mnumber\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1047\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/fibinse/anaconda/lib/python2.7/timeit.pyc\u001b[0m in \u001b[0;36mrepeat\u001b[0;34m(self, repeat, number)\u001b[0m\n\u001b[1;32m    228\u001b[0m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 230\u001b[0;31m             \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    231\u001b[0m             \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    232\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/fibinse/anaconda/lib/python2.7/site-packages/IPython/core/magics/execution.pyc\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, number)\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0mgc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    138\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 139\u001b[0;31m             \u001b[0mtiming\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    140\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mgcold\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<magic-timeit>\u001b[0m in \u001b[0;36minner\u001b[0;34m(_it, _timer)\u001b[0m\n",
      "\u001b[0;32m/Users/fibinse/anaconda/lib/python2.7/site-packages/pandas/core/groupby.pyc\u001b[0m in \u001b[0;36mf\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    101\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mSpecificationError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maggregate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnpfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    104\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0m_convert\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/fibinse/anaconda/lib/python2.7/site-packages/pandas/core/groupby.pyc\u001b[0m in \u001b[0;36maggregate\u001b[0;34m(self, arg, *args, **kwargs)\u001b[0m\n\u001b[1;32m   3595\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mAppender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSelectionMixin\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_agg_doc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3596\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0maggregate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3597\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDataFrameGroupBy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maggregate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3598\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3599\u001b[0m     \u001b[0magg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maggregate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/fibinse/anaconda/lib/python2.7/site-packages/pandas/core/groupby.pyc\u001b[0m in \u001b[0;36maggregate\u001b[0;34m(self, arg, *args, **kwargs)\u001b[0m\n\u001b[1;32m   3132\u001b[0m                         name=self._selected_obj.columns.name)\n\u001b[1;32m   3133\u001b[0m                 \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3134\u001b[0;31m                     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_aggregate_generic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3135\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3136\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_index\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/fibinse/anaconda/lib/python2.7/site-packages/pandas/core/groupby.pyc\u001b[0m in \u001b[0;36m_aggregate_generic\u001b[0;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m   3153\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3154\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3155\u001b[0;31m                     result[name] = self._try_cast(func(data, *args, **kwargs),\n\u001b[0m\u001b[1;32m   3156\u001b[0m                                                   data)\n\u001b[1;32m   3157\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/fibinse/anaconda/lib/python2.7/site-packages/pandas/core/groupby.pyc\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m    101\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mSpecificationError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maggregate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnpfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    104\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0m_convert\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/fibinse/anaconda/lib/python2.7/site-packages/numpy/core/fromnumeric.pyc\u001b[0m in \u001b[0;36mamax\u001b[0;34m(a, axis, out, keepdims)\u001b[0m\n\u001b[1;32m   2254\u001b[0m                                   out=out, keepdims=keepdims)\n\u001b[1;32m   2255\u001b[0m         \u001b[0;31m# NOTE: Dropping the keepdims parameter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2256\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mamax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2257\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2258\u001b[0m         return _methods._amax(a, axis=axis,\n",
      "\u001b[0;32m/Users/fibinse/anaconda/lib/python2.7/site-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36mstat_func\u001b[0;34m(self, axis, skipna, level, numeric_only, **kwargs)\u001b[0m\n\u001b[1;32m   5308\u001b[0m                                       skipna=skipna)\n\u001b[1;32m   5309\u001b[0m         return self._reduce(f, name, axis=axis, skipna=skipna,\n\u001b[0;32m-> 5310\u001b[0;31m                             numeric_only=numeric_only)\n\u001b[0m\u001b[1;32m   5311\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5312\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mset_function_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstat_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/fibinse/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_reduce\u001b[0;34m(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)\u001b[0m\n\u001b[1;32m   4771\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnumeric_only\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4772\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4773\u001b[0;31m                 \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4774\u001b[0m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4775\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/fibinse/anaconda/lib/python2.7/site-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36mvalues\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   2850\u001b[0m         \u001b[0mint32\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2851\u001b[0m         \"\"\"\n\u001b[0;32m-> 2852\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2853\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2854\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/fibinse/anaconda/lib/python2.7/site-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36mas_matrix\u001b[0;34m(self, columns)\u001b[0m\n\u001b[1;32m   2832\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_consolidate_inplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2833\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_AXIS_REVERSED\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2834\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2835\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2836\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/fibinse/anaconda/lib/python2.7/site-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36mas_matrix\u001b[0;34m(self, items)\u001b[0m\n\u001b[1;32m   3148\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mmgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblocks\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3149\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3150\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mmgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_interleave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3152\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_interleave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/fibinse/anaconda/lib/python2.7/site-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36m_interleave\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   3175\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mblk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblocks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3176\u001b[0m             \u001b[0mrl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mblk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmgr_locs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3177\u001b[0;31m             \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mblk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3178\u001b[0m             \u001b[0mitemmask\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3179\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/fibinse/anaconda/lib/python2.7/site-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36mget_values\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m   1516\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1517\u001b[0m             return lib.map_infer(self.values.ravel(),\n\u001b[0;32m-> 1518\u001b[0;31m                                  self._box_func).reshape(self.values.shape)\n\u001b[0m\u001b[1;32m   1519\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1520\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "print \"temp.mean() takes: \", timer(\"temp.mean()\"), temp.mean().shape\n",
    "print \"temp.median() takes: \", timer(\"temp.median()\"), temp.median().shape\n",
    "print \"temp.min() takes: \", timer(\"temp.min()\"), temp.min().shape\n",
    "print \"temp.max() takes: \", timer(\"temp.max()\"), temp.max().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "temp = df.groupby('medallion').get_group('00005007A9F30E289E760362F69E4EAD')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take the data of one medallion and look at the other facets of `.groupby` functionality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1475, 18)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aggregation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`.agg` or `.aggregate` functions let you aggregate a DataFrame across a column/multiple columns.\n",
    "\n",
    "Let's see the example below:\n",
    "\n",
    "For the column, 'hack_license', we are passing a dictionary `{'no_of_times':'count'}`, so Pandas will aggregate by 'count' and provide the result under the label 'no_of_times'.\n",
    "\n",
    "If you were to just mention how you want to aggregate by a column, you could just pass `'rate_code': pd.np.sum,` in the dictionary as argument to aggregate with.\n",
    "\n",
    "You could also aggregate through the same column in multiple ways, as shown for 'pickup_datetime' and get multiple columns as a result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>rate_code</th>\n",
       "      <th colspan=\"2\" halign=\"left\">pickup_datetime</th>\n",
       "      <th>hack_license</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>min_pickup_date</th>\n",
       "      <th>max_pickup_date</th>\n",
       "      <th>no_of_times</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>medallion</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0053334C798EC6C8E637657962030F99</th>\n",
       "      <td>1</td>\n",
       "      <td>2013-09-09 00:11:00</td>\n",
       "      <td>2013-09-09 00:11:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00790C7BAD30B7A9E09689A13ED90042</th>\n",
       "      <td>1</td>\n",
       "      <td>2013-09-13 06:36:00</td>\n",
       "      <td>2013-09-13 06:36:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00EEA9525C3C7B6DF582BFBDBB938A7D</th>\n",
       "      <td>28</td>\n",
       "      <td>2013-09-02 14:27:00</td>\n",
       "      <td>2013-09-08 20:27:00</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00FD1D146C1899CEDB738490659CAD30</th>\n",
       "      <td>1</td>\n",
       "      <td>2013-09-09 07:08:00</td>\n",
       "      <td>2013-09-09 07:08:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>011D4D788982F461B5CEC68008AD5D10</th>\n",
       "      <td>27</td>\n",
       "      <td>2013-09-02 13:00:00</td>\n",
       "      <td>2013-09-06 20:27:00</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 rate_code     pickup_datetime  \\\n",
       "                                       sum     min_pickup_date   \n",
       "medallion                                                        \n",
       "0053334C798EC6C8E637657962030F99         1 2013-09-09 00:11:00   \n",
       "00790C7BAD30B7A9E09689A13ED90042         1 2013-09-13 06:36:00   \n",
       "00EEA9525C3C7B6DF582BFBDBB938A7D        28 2013-09-02 14:27:00   \n",
       "00FD1D146C1899CEDB738490659CAD30         1 2013-09-09 07:08:00   \n",
       "011D4D788982F461B5CEC68008AD5D10        27 2013-09-02 13:00:00   \n",
       "\n",
       "                                                     hack_license  \n",
       "                                     max_pickup_date  no_of_times  \n",
       "medallion                                                          \n",
       "0053334C798EC6C8E637657962030F99 2013-09-09 00:11:00            1  \n",
       "00790C7BAD30B7A9E09689A13ED90042 2013-09-13 06:36:00            1  \n",
       "00EEA9525C3C7B6DF582BFBDBB938A7D 2013-09-08 20:27:00           28  \n",
       "00FD1D146C1899CEDB738490659CAD30 2013-09-09 07:08:00            1  \n",
       "011D4D788982F461B5CEC68008AD5D10 2013-09-06 20:27:00           22  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aggregations = {\n",
    "    'hack_license': {'no_of_times':'count'},\n",
    "    'rate_code': pd.np.sum,\n",
    "    'pickup_datetime': {\n",
    "        'min_pickup_date': lambda v: v.dropna().min(),\n",
    "        'max_pickup_date': 'max',\n",
    "    }\n",
    "}\n",
    "df.head(1200).groupby('medallion').aggregate(aggregations).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TODO: Transform"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `.transform` function is often confused with the `.apply` function.\n",
    "\n",
    "Difference betweent the two lies in the fact that `.apply` when applied on a group-by, it acts on the DataFrame group as a whole. While `.transform` works on the individual columns of the DataFrame **one at a time** and not the DataFrame as a whole."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's say you want to count the number of unique hack licenses which have appeared against each medallion. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# temp = df.copy().head(1000)\n",
    "\n",
    "# You can do that with the transform function below - \n",
    "\n",
    "# print \"Transform function takes: \", timer(\"temp.groupby('medallion')['hack_license'].transform(lambda v: v.size)\")\n",
    "\n",
    "# And, with the `apply` function like shown here - \n",
    "\n",
    "# print \"Column reduction using apply function takes: \", timer(\"temp.groupby('medallion').apply(lambda v: v['hack_license'].nunique())\")\n",
    "\n",
    "# Observe how we are able to access the column by name within `apply` function. This is what primarily differentiates `.apply` and `.transform`.\n",
    "\n",
    "# temp.groupby('medallion')[['rate_code', 'passenger_count']].transform(sum).reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Common operations after group-by"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### UNSTACK"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One scenario when we could unstack is when we would like to compare a metric across groups. For example, say, we want to compare vendors across passenger count, trip time and trip distance. We would groupby the 'vendor_id' and aggregate it with `mean`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "temp_df = temp.groupby(['vendor_id']).mean()[['passenger_count', 'trip_time_in_secs', 'trip_distance']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And, then simply unstack it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                   vendor_id\n",
       "passenger_count    CMT            1.259494\n",
       "                   VTS            2.393112\n",
       "trip_time_in_secs  CMT          668.158228\n",
       "                   VTS          689.073634\n",
       "trip_distance      CMT            3.041772\n",
       "                   VTS            2.993456\n",
       "dtype: float64"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df.unstack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you were to groupby by multiple columns, `unstack` lets use pass a level to make columns from."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">passenger_count</th>\n",
       "      <th colspan=\"2\" halign=\"left\">trip_time_in_secs</th>\n",
       "      <th colspan=\"2\" halign=\"left\">trip_distance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vendor_id</th>\n",
       "      <th>CMT</th>\n",
       "      <th>VTS</th>\n",
       "      <th>CMT</th>\n",
       "      <th>VTS</th>\n",
       "      <th>CMT</th>\n",
       "      <th>VTS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rate_code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.25974</td>\n",
       "      <td>2.397546</td>\n",
       "      <td>630.792208</td>\n",
       "      <td>643.361963</td>\n",
       "      <td>2.646753</td>\n",
       "      <td>2.544086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.25000</td>\n",
       "      <td>2.181818</td>\n",
       "      <td>2106.750000</td>\n",
       "      <td>2378.181818</td>\n",
       "      <td>18.250000</td>\n",
       "      <td>18.830909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>870.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.405000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.656667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          passenger_count           trip_time_in_secs               \\\n",
       "vendor_id             CMT       VTS               CMT          VTS   \n",
       "rate_code                                                            \n",
       "1                 1.25974  2.397546        630.792208   643.361963   \n",
       "2                 1.25000  2.181818       2106.750000  2378.181818   \n",
       "3                     NaN  3.000000               NaN   870.000000   \n",
       "5                     NaN  2.333333               NaN   600.000000   \n",
       "\n",
       "          trip_distance             \n",
       "vendor_id           CMT        VTS  \n",
       "rate_code                           \n",
       "1              2.646753   2.544086  \n",
       "2             18.250000  18.830909  \n",
       "3                   NaN   9.405000  \n",
       "5                   NaN   4.656667  "
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df = temp.groupby(['vendor_id', 'rate_code']).mean()[['passenger_count', 'trip_time_in_secs', 'trip_distance']]\n",
    "temp_df.unstack(level=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Stack"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stack let's you _stack_ columns like rows. Let me explain - "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When you unstack, you get this - "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">passenger_count</th>\n",
       "      <th colspan=\"4\" halign=\"left\">trip_time_in_secs</th>\n",
       "      <th colspan=\"4\" halign=\"left\">trip_distance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rate_code</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>5</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>5</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vendor_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CMT</th>\n",
       "      <td>1.259740</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>630.792208</td>\n",
       "      <td>2106.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.646753</td>\n",
       "      <td>18.250000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VTS</th>\n",
       "      <td>2.397546</td>\n",
       "      <td>2.181818</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>643.361963</td>\n",
       "      <td>2378.181818</td>\n",
       "      <td>870.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>2.544086</td>\n",
       "      <td>18.830909</td>\n",
       "      <td>9.405</td>\n",
       "      <td>4.656667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          passenger_count                          trip_time_in_secs  \\\n",
       "rate_code               1         2    3         5                 1   \n",
       "vendor_id                                                              \n",
       "CMT              1.259740  1.250000  NaN       NaN        630.792208   \n",
       "VTS              2.397546  2.181818  3.0  2.333333        643.361963   \n",
       "\n",
       "                                     trip_distance                              \n",
       "rate_code            2      3      5             1          2      3         5  \n",
       "vendor_id                                                                       \n",
       "CMT        2106.750000    NaN    NaN      2.646753  18.250000    NaN       NaN  \n",
       "VTS        2378.181818  870.0  600.0      2.544086  18.830909  9.405  4.656667  "
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df.unstack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And, when you stack it, you get - "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "vendor_id  rate_code                   \n",
       "CMT        1          passenger_count         1.259740\n",
       "                      trip_time_in_secs     630.792208\n",
       "                      trip_distance           2.646753\n",
       "           2          passenger_count         1.250000\n",
       "                      trip_time_in_secs    2106.750000\n",
       "                      trip_distance          18.250000\n",
       "VTS        1          passenger_count         2.397546\n",
       "                      trip_time_in_secs     643.361963\n",
       "                      trip_distance           2.544086\n",
       "           2          passenger_count         2.181818\n",
       "                      trip_time_in_secs    2378.181818\n",
       "                      trip_distance          18.830909\n",
       "           3          passenger_count         3.000000\n",
       "                      trip_time_in_secs     870.000000\n",
       "                      trip_distance           9.405000\n",
       "           5          passenger_count         2.333333\n",
       "                      trip_time_in_secs     600.000000\n",
       "                      trip_distance           4.656667\n",
       "dtype: float64"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df.stack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Things will become clearer when you observe the columns in the DataFrame we are using - "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_time_in_secs</th>\n",
       "      <th>trip_distance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vendor_id</th>\n",
       "      <th>rate_code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">CMT</th>\n",
       "      <th>1</th>\n",
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       "      <td>630.792208</td>\n",
       "      <td>2.646753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.250000</td>\n",
       "      <td>2106.750000</td>\n",
       "      <td>18.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">VTS</th>\n",
       "      <th>1</th>\n",
       "      <td>2.397546</td>\n",
       "      <td>643.361963</td>\n",
       "      <td>2.544086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.181818</td>\n",
       "      <td>2378.181818</td>\n",
       "      <td>18.830909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>870.000000</td>\n",
       "      <td>9.405000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     passenger_count  trip_time_in_secs  trip_distance\n",
       "vendor_id rate_code                                                   \n",
       "CMT       1                 1.259740         630.792208       2.646753\n",
       "          2                 1.250000        2106.750000      18.250000\n",
       "VTS       1                 2.397546         643.361963       2.544086\n",
       "          2                 2.181818        2378.181818      18.830909\n",
       "          3                 3.000000         870.000000       9.405000"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### DESCRIBE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use the `describe` method to know more about your groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "temp = df.head(10).groupby('medallion')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "      <td>468.0</td>\n",
       "      <td>468.0</td>\n",
       "      <td>468.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 88 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Count                                    \\\n",
       "                                 count mean std  min  25%  50%  75%  max   \n",
       "medallion                                                                  \n",
       "0CF8B9F42FED24FA1CA8AACA36D1A25B   1.0  1.0 NaN  1.0  1.0  1.0  1.0  1.0   \n",
       "4D3E527682E42F1FACDFBF2D56757AC6   1.0  1.0 NaN  1.0  1.0  1.0  1.0  1.0   \n",
       "6B3D1A9F93C3769EF8F8446DE7CCB9F4   1.0  1.0 NaN  1.0  1.0  1.0  1.0  1.0   \n",
       "8F832FF1330148CD9C4FBA7EC1D3D99A   1.0  1.0 NaN  1.0  1.0  1.0  1.0  1.0   \n",
       "9BA35AC8B9018EBCF66913620278FF0E   1.0  1.0 NaN  1.0  1.0  1.0  1.0  1.0   \n",
       "CFF1FDD049E5433E6FBCC96EDA9E66A5   1.0  1.0 NaN  1.0  1.0  1.0  1.0  1.0   \n",
       "D28DD94BE7682B7434EC5CA4D523A788   1.0  1.0 NaN  1.0  1.0  1.0  1.0  1.0   \n",
       "DB0D656FD074AC9E5B039E9A5A17F408   1.0  1.0 NaN  1.0  1.0  1.0  1.0  1.0   \n",
       "F2967DBF707C06314CEB745A83332D62   1.0  1.0 NaN  1.0  1.0  1.0  1.0  1.0   \n",
       "F9B4C49F95496C0EFA6674364F4B54AE   1.0  1.0 NaN  1.0  1.0  1.0  1.0  1.0   \n",
       "\n",
       "                                  Hour         ...   trip_distance       \\\n",
       "                                 count  mean   ...             75%  max   \n",
       "medallion                                      ...                        \n",
       "0CF8B9F42FED24FA1CA8AACA36D1A25B   1.0  16.0   ...             2.6  2.6   \n",
       "4D3E527682E42F1FACDFBF2D56757AC6   1.0  17.0   ...             3.5  3.5   \n",
       "6B3D1A9F93C3769EF8F8446DE7CCB9F4   1.0  17.0   ...             2.8  2.8   \n",
       "8F832FF1330148CD9C4FBA7EC1D3D99A   1.0  20.0   ...             2.2  2.2   \n",
       "9BA35AC8B9018EBCF66913620278FF0E   1.0  20.0   ...             9.9  9.9   \n",
       "CFF1FDD049E5433E6FBCC96EDA9E66A5   1.0   7.0   ...             9.6  9.6   \n",
       "D28DD94BE7682B7434EC5CA4D523A788   1.0  16.0   ...             3.1  3.1   \n",
       "DB0D656FD074AC9E5B039E9A5A17F408   1.0  23.0   ...             3.9  3.9   \n",
       "F2967DBF707C06314CEB745A83332D62   1.0  23.0   ...             8.3  8.3   \n",
       "F9B4C49F95496C0EFA6674364F4B54AE   1.0  22.0   ...             1.5  1.5   \n",
       "\n",
       "                                 trip_time_in_secs                      \\\n",
       "                                             count    mean std     min   \n",
       "medallion                                                                \n",
       "0CF8B9F42FED24FA1CA8AACA36D1A25B               1.0   767.0 NaN   767.0   \n",
       "4D3E527682E42F1FACDFBF2D56757AC6               1.0   871.0 NaN   871.0   \n",
       "6B3D1A9F93C3769EF8F8446DE7CCB9F4               1.0  1024.0 NaN  1024.0   \n",
       "8F832FF1330148CD9C4FBA7EC1D3D99A               1.0   671.0 NaN   671.0   \n",
       "9BA35AC8B9018EBCF66913620278FF0E               1.0  1006.0 NaN  1006.0   \n",
       "CFF1FDD049E5433E6FBCC96EDA9E66A5               1.0   869.0 NaN   869.0   \n",
       "D28DD94BE7682B7434EC5CA4D523A788               1.0   872.0 NaN   872.0   \n",
       "DB0D656FD074AC9E5B039E9A5A17F408               1.0   787.0 NaN   787.0   \n",
       "F2967DBF707C06314CEB745A83332D62               1.0  1113.0 NaN  1113.0   \n",
       "F9B4C49F95496C0EFA6674364F4B54AE               1.0   468.0 NaN   468.0   \n",
       "\n",
       "                                                                  \n",
       "                                     25%     50%     75%     max  \n",
       "medallion                                                         \n",
       "0CF8B9F42FED24FA1CA8AACA36D1A25B   767.0   767.0   767.0   767.0  \n",
       "4D3E527682E42F1FACDFBF2D56757AC6   871.0   871.0   871.0   871.0  \n",
       "6B3D1A9F93C3769EF8F8446DE7CCB9F4  1024.0  1024.0  1024.0  1024.0  \n",
       "8F832FF1330148CD9C4FBA7EC1D3D99A   671.0   671.0   671.0   671.0  \n",
       "9BA35AC8B9018EBCF66913620278FF0E  1006.0  1006.0  1006.0  1006.0  \n",
       "CFF1FDD049E5433E6FBCC96EDA9E66A5   869.0   869.0   869.0   869.0  \n",
       "D28DD94BE7682B7434EC5CA4D523A788   872.0   872.0   872.0   872.0  \n",
       "DB0D656FD074AC9E5B039E9A5A17F408   787.0   787.0   787.0   787.0  \n",
       "F2967DBF707C06314CEB745A83332D62  1113.0  1113.0  1113.0  1113.0  \n",
       "F9B4C49F95496C0EFA6674364F4B54AE   468.0   468.0   468.0   468.0  \n",
       "\n",
       "[10 rows x 88 columns]"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.describe().unstack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparing the max trip distance of the medallion taxis wouldbe like - "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x111c4b4d0>"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TUwkODqZQoUKUK1fO+GKcpXHjxkyaNMkYcRg5ciS9evXiueeeo1OnTjg4OLBnzx4WLVrE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+rp6VGxYkXatWunjLZCzhTkyZMnM2vWLN577z2CgoKUEbzC+jIpKYlDhw5p/T5hrg4dOrBs2TLO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\nnCE2NhaAgwcPsmPHDqKiooiNj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cC+ffvg4eEBQRAwf/58CIIAMzMzqNVq+Pv7i2sQ6+rq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\nJQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111ce6c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp.describe().unstack().loc[:,('trip_distance', 'max')].plot(kind='barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ADD_PREFIX"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of renaming the aggregated dataframe after a groupby operation, you could use the `add_prefix` operation to rename the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_ rate_code</th>\n",
       "      <th>mean_ passenger_count</th>\n",
       "      <th>mean_ trip_time_in_secs</th>\n",
       "      <th>mean_ trip_distance</th>\n",
       "      <th>mean_ pickup_longitude</th>\n",
       "      <th>mean_ pickup_latitude</th>\n",
       "      <th>mean_ dropoff_longitude</th>\n",
       "      <th>mean_ dropoff_latitude</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>medallion</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>022562E7A0FEB7A9D4A31B06BF0B82E8</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>-73.979286</td>\n",
       "      <td>40.739658</td>\n",
       "      <td>-73.978127</td>\n",
       "      <td>40.748402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>040D8BEC832C494FA4EBED53768CFD4C</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1005.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>-73.951576</td>\n",
       "      <td>40.741520</td>\n",
       "      <td>-73.971893</td>\n",
       "      <td>40.765099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0656C4136113432E5D0F1200BD7B0ED7</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1860.0</td>\n",
       "      <td>9.1</td>\n",
       "      <td>-73.863640</td>\n",
       "      <td>40.769920</td>\n",
       "      <td>-73.967384</td>\n",
       "      <td>40.798637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0C5296F3C8B16E702F8F2E06F5106552</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>790.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>-73.974327</td>\n",
       "      <td>40.743904</td>\n",
       "      <td>-74.006973</td>\n",
       "      <td>40.740772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0CF8B9F42FED24FA1CA8AACA36D1A25B</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>767.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>-73.987900</td>\n",
       "      <td>40.724079</td>\n",
       "      <td>-73.994598</td>\n",
       "      <td>40.750580</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  mean_ rate_code  mean_ passenger_count  \\\n",
       "medallion                                                                  \n",
       "022562E7A0FEB7A9D4A31B06BF0B82E8              1.0                    1.0   \n",
       "040D8BEC832C494FA4EBED53768CFD4C              1.0                    1.0   \n",
       "0656C4136113432E5D0F1200BD7B0ED7              1.0                    1.0   \n",
       "0C5296F3C8B16E702F8F2E06F5106552              1.0                    1.0   \n",
       "0CF8B9F42FED24FA1CA8AACA36D1A25B              1.0                    2.0   \n",
       "\n",
       "                                  mean_ trip_time_in_secs  \\\n",
       "medallion                                                   \n",
       "022562E7A0FEB7A9D4A31B06BF0B82E8                    228.0   \n",
       "040D8BEC832C494FA4EBED53768CFD4C                   1005.0   \n",
       "0656C4136113432E5D0F1200BD7B0ED7                   1860.0   \n",
       "0C5296F3C8B16E702F8F2E06F5106552                    790.0   \n",
       "0CF8B9F42FED24FA1CA8AACA36D1A25B                    767.0   \n",
       "\n",
       "                                  mean_ trip_distance  mean_ pickup_longitude  \\\n",
       "medallion                                                                       \n",
       "022562E7A0FEB7A9D4A31B06BF0B82E8                  0.8              -73.979286   \n",
       "040D8BEC832C494FA4EBED53768CFD4C                  2.9              -73.951576   \n",
       "0656C4136113432E5D0F1200BD7B0ED7                  9.1              -73.863640   \n",
       "0C5296F3C8B16E702F8F2E06F5106552                  2.7              -73.974327   \n",
       "0CF8B9F42FED24FA1CA8AACA36D1A25B                  2.6              -73.987900   \n",
       "\n",
       "                                  mean_ pickup_latitude  \\\n",
       "medallion                                                 \n",
       "022562E7A0FEB7A9D4A31B06BF0B82E8              40.739658   \n",
       "040D8BEC832C494FA4EBED53768CFD4C              40.741520   \n",
       "0656C4136113432E5D0F1200BD7B0ED7              40.769920   \n",
       "0C5296F3C8B16E702F8F2E06F5106552              40.743904   \n",
       "0CF8B9F42FED24FA1CA8AACA36D1A25B              40.724079   \n",
       "\n",
       "                                  mean_ dropoff_longitude  \\\n",
       "medallion                                                   \n",
       "022562E7A0FEB7A9D4A31B06BF0B82E8               -73.978127   \n",
       "040D8BEC832C494FA4EBED53768CFD4C               -73.971893   \n",
       "0656C4136113432E5D0F1200BD7B0ED7               -73.967384   \n",
       "0C5296F3C8B16E702F8F2E06F5106552               -74.006973   \n",
       "0CF8B9F42FED24FA1CA8AACA36D1A25B               -73.994598   \n",
       "\n",
       "                                  mean_ dropoff_latitude  \n",
       "medallion                                                 \n",
       "022562E7A0FEB7A9D4A31B06BF0B82E8               40.748402  \n",
       "040D8BEC832C494FA4EBED53768CFD4C               40.765099  \n",
       "0656C4136113432E5D0F1200BD7B0ED7               40.798637  \n",
       "0C5296F3C8B16E702F8F2E06F5106552               40.740772  \n",
       "0CF8B9F42FED24FA1CA8AACA36D1A25B               40.750580  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(100).groupby('medallion').mean().add_prefix('mean_').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 6. Multi-indexing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "A multi-index is just what it is read as, a multiple index. There can be created with various Python methods  explicitly and these also occur as a result of a `.groupby`, `.merge` (we will be discussing this soon) and other such operations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's aggregate our favourite columns, 'medallion' and 'hack_license', observe its index.\n",
    "`.sum` aggregates our grouped DataFrame and prints out the index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "temp = df.head(100).groupby(['medallion', 'hack_license']).sum().index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The type of the index is of MultiIndex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.indexes.multi.MultiIndex"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(temp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Let's see what a Multi-index look like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('022562E7A0FEB7A9D4A31B06BF0B82E8', 'B706829B6520B5C6A15EF434DDE3A8D4'),\n",
       " ('040D8BEC832C494FA4EBED53768CFD4C', '69ABC2D581D53C940263EEF06A1BF6EE')]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.tolist()[:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "As we see, the multi-index is basically an array of tuples, where the combination of each of the combinations is unique. Earlier, we had stated that there are explicit Python functions that generate such array of tuples.\n",
    "\n",
    "These return a MultiIndex object (which is nothing but a hierarchial Index object):\n",
    "\n",
    "1. `pd.MultiIndex.from_tuples`, takes an array of tuples and names of the index levels.\n",
    "\n",
    "2. `pd.MultiIndex.from_product`, takes a list of lists, and creates an index with every combination of the lists passed. Let's see how that works below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "iterables = [['Fiat', 'Citroen', 'Puegeot'], ['Petrol', 'Diesel'], ['Europe', 'Rest of the World']]\n",
    "ind = pd.MultiIndex.from_product(iterables, names=['Manufacturer', 'Engine Type', 'Geography'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Manufacturer  Engine Type  Geography        \n",
       "Fiat          Petrol       Europe               0.897292\n",
       "                           Rest of the World    0.619647\n",
       "              Diesel       Europe              -0.069696\n",
       "                           Rest of the World   -0.796698\n",
       "Citroen       Petrol       Europe              -0.976738\n",
       "                           Rest of the World   -0.390676\n",
       "              Diesel       Europe               0.013190\n",
       "                           Rest of the World   -0.821640\n",
       "Puegeot       Petrol       Europe               0.689430\n",
       "                           Rest of the World    0.764132\n",
       "              Diesel       Europe               0.563199\n",
       "                           Rest of the World   -0.394786\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_of_values = pd.Series(np.random.randn(12), index=ind)\n",
    "list_of_values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "One thing to notice here is the ordering of the indexes. We have every unique combination of the list values that we have passed to the `pd.MultiIndex.from_product` but its in left to right order. That is, to access these you have to specify a 'Manufacturer' and then the subsequent labels.\n",
    "\n",
    "It will become clearer with the example below - "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fibinse/anaconda/lib/python2.7/site-packages/ipykernel/kernelbase.py:370: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  user_expressions, allow_stdin)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Geography\n",
       "Europe               0.897292\n",
       "Rest of the World    0.619647\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_of_values.xs(('Fiat', 'Petrol'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "You may also want to filter by values across multiple indexes, in which case, use `.loc`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Engine Type\n",
       "Petrol    0.897292\n",
       "Diesel   -0.069696\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_of_values.loc['Fiat',:, 'Europe']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 7. The art of combining dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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