Survival_Analysis_Kaplan_Meier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What is Survival Analysis?\n",
    "\n",
    "[Survival analysis](https://en.wikipedia.org/wiki/Survival_analysis) is used to study the **time** until some **event** of interest (often referred to as **death**) occurs.  Time could be measured in years, months, weeks, days, etc.  The event could be anything of interest.  It could be an actual death, a birth, a Pokemon Go server crash, etc. In this post we are interested in how long drafted NFL players are in the league, so the event of interest will be the retirement of drafted NFL players. The duration of time leading up to the event of interest can be called the **survival time**. In our case, the survival time is the number of years that a player was active in the league (according to [Pro Football Reference](http://www.pro-football-reference.com/)).  \n",
    "\n",
    "Some of the players in this analysis are still active players (e.g. Aaron Rodgers, Eli Manning, etc.), so we haven't observed their retirement (the event of interest).  Those players are considered **censored**. While we have some information about their career length (or survival time), we don't know the full length of their career.  This specific type of censorship, one in which we do not observe end of the survival time, is called **right-censorship**.  The methods developed in the field of survival analysis were created in order to deal with the issue of censored data.  In this post we will use one such method, called the [Kaplan-Meier estimator](https://en.wikipedia.org/wiki/Kaplan%E2%80%93Meier_estimator), to estimate the survival function and construct the survival curve for an NFL career.\n",
    "\n",
    "\n",
    "## A brief comment on the data used\n",
    "\n",
    "I used the draft data scraped from my [previous post](http://savvastjortjoglou.com/nfl-draft.html). The duration of a player's career is just the difference between \"To\" value from the [PFR draft table](http://www.pro-football-reference.com/years/2015/draft.htm) and the year the player was drafted.  Players were considered active, if there name was in bold.  However there are may be some players who are retired that PFR still considers active (e.g. Mike Kafka). You can check out how I prepared the data in [this Jupyter notebook](https://github.com/savvastj/nfl_survival_analysis/blob/master/Data_Prep.ipynb). Let me know if you see any issues/mistakes I've made.\n",
    "\n",
    "# What is the Survival Function?\n",
    "\n",
    "The [survival function](https://en.wikipedia.org/wiki/Survival_function), $S(t)$, of a population is defined as follows:\n",
    "\n",
    "$$S(t) = Pr(T > t)$$\n",
    "\n",
    "Capital $T$ is a [random variable](https://www.khanacademy.org/math/probability/random-variables-topic/random-variables-prob-dist/v/random-variables) that represents a subject's survival time. In our case $T$ represents an NFL player's career length. Lower case $t$ represents a specific time of interest for $T$. In our analysis the $t$ represents a specific number of years played. In other words the survival function just gives us the probability that someone survives longer than (or at least as long as) a specified value of time, $t$.  So in the context of our analysis, $S(3)$ will provide us the probability that an NFL career lasts longer than 3 years.\n",
    "\n",
    "# What is the Kaplan-Meier estimator?\n",
    "\n",
    "To estimate the survival function of NFL players we will use the Kaplan-Meier estimator.  The Kaplan-Meier estimator is defined by the following product (from the [`lifelines` documentation](https://lifelines.readthedocs.io/en/latest/Intro%20to%20lifelines.html#estimating-the-survival-function-using-kaplan-meier)):\n",
    "\n",
    "$$\\hat{S}(t) = \\prod_{t_i \\lt t} \\frac{n_i - d_i}{n_i}$$\n",
    "\n",
    "where $d_i$ are the number of death events at time $t$ and $n_i$ is the number of subjects at risk of death just prior to time $t$. \n",
    "\n",
    "We will walk through a simple example in a bit in order to get a better understanding of the above definition.\n",
    "\n",
    "# Estimating the Survival Function of NFL Players\n",
    "\n",
    "To estimate the survival function of NFL players we will be using the [`lifelines` library](https://lifelines.readthedocs.io/en/latest/index.html).  It provides a user friendly interface for survival analyis using Python. Lets get started by importing what we need and reading in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from lifelines import KaplanMeierFitter\n",
    "\n",
    "draft_df = pd.read_csv(\"data/nfl_survival_analysis_data.csv\")\n",
    "\n",
    "# set some plotting aesthetics, similar to ggplot\n",
    "sns.set(palette = \"colorblind\", font_scale = 1.35, \n",
    "        rc = {\"figure.figsize\": (12,9), \"axes.facecolor\": \".92\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Draft_Yr</th>\n",
       "      <th>Rnd</th>\n",
       "      <th>Pick</th>\n",
       "      <th>Tm</th>\n",
       "      <th>Player</th>\n",
       "      <th>Pos</th>\n",
       "      <th>Age</th>\n",
       "      <th>To</th>\n",
       "      <th>AP1</th>\n",
       "      <th>PB</th>\n",
       "      <th>...</th>\n",
       "      <th>Rec</th>\n",
       "      <th>Rec_Yds</th>\n",
       "      <th>Rec_TD</th>\n",
       "      <th>Tkl</th>\n",
       "      <th>Def_Int</th>\n",
       "      <th>Sk</th>\n",
       "      <th>College</th>\n",
       "      <th>Player_ID</th>\n",
       "      <th>Retired</th>\n",
       "      <th>Duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1967</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>BAL</td>\n",
       "      <td>Bubba Smith</td>\n",
       "      <td>DE</td>\n",
       "      <td>22</td>\n",
       "      <td>1976</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Michigan St.</td>\n",
       "      <td>SmitBu00</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1967</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>MIN</td>\n",
       "      <td>Clint Jones</td>\n",
       "      <td>RB</td>\n",
       "      <td>22</td>\n",
       "      <td>1973</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>38</td>\n",
       "      <td>431</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Michigan St.</td>\n",
       "      <td>JoneCl00</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1967</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>SFO</td>\n",
       "      <td>Steve Spurrier</td>\n",
       "      <td>QB</td>\n",
       "      <td>22</td>\n",
       "      <td>1976</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Florida</td>\n",
       "      <td>SpurSt00</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1967</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>MIA</td>\n",
       "      <td>Bob Griese</td>\n",
       "      <td>QB</td>\n",
       "      <td>22</td>\n",
       "      <td>1980</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Purdue</td>\n",
       "      <td>GrieBo00</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1967</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>HOU</td>\n",
       "      <td>George Webster</td>\n",
       "      <td>LB</td>\n",
       "      <td>21</td>\n",
       "      <td>1976</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Michigan St.</td>\n",
       "      <td>WebsGe00</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Draft_Yr  Rnd  Pick   Tm          Player Pos  Age    To  AP1  PB    ...     \\\n",
       "0      1967    1     1  BAL     Bubba Smith  DE   22  1976    1   2    ...      \n",
       "1      1967    1     2  MIN     Clint Jones  RB   22  1973    0   0    ...      \n",
       "2      1967    1     3  SFO  Steve Spurrier  QB   22  1976    0   0    ...      \n",
       "3      1967    1     4  MIA      Bob Griese  QB   22  1980    2   8    ...      \n",
       "4      1967    1     5  HOU  George Webster  LB   21  1976    3   3    ...      \n",
       "\n",
       "   Rec  Rec_Yds  Rec_TD  Tkl  Def_Int   Sk       College  Player_ID  Retired  \\\n",
       "0    0        0       0  0.0        0  0.0  Michigan St.   SmitBu00        1   \n",
       "1   38      431       0  0.0        0  0.0  Michigan St.   JoneCl00        1   \n",
       "2    0        0       0  0.0        0  0.0       Florida   SpurSt00        1   \n",
       "3    0        0       0  0.0        0  0.0        Purdue   GrieBo00        1   \n",
       "4    0        0       0  0.0        5  0.0  Michigan St.   WebsGe00        1   \n",
       "\n",
       "   Duration  \n",
       "0        10  \n",
       "1         7  \n",
       "2        10  \n",
       "3        14  \n",
       "4        10  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "draft_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The columns of interest for our analysis are the *Duration* and *Retired* columns.  The *Duration* column represents the number of years a player played in the NFL.  The *Retired* column represents whether the player retired from the NFL or not.  1 indicates that he is retired, while 0 indicates that he is still an active player.\n",
    "\n",
    "To calculate the Kaplan-Meier estimate we will need to create a `KaplanMeierFitter` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "kmf = KaplanMeierFitter() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then fit the data by calling the `KaplanMeierFitter`s `fit` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<lifelines.KaplanMeierFitter: fitted with 15592 observations, 1629 censored>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The 1st arg accepts an array or pd.Series of individual survival times\n",
    "# The 2nd arg accepts an array or pd.Series that indicates if the event \n",
    "# interest (or death) occured.\n",
    "kmf.fit(durations = draft_df.Duration, \n",
    "        event_observed = draft_df.Retired)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After fitting our data we can access the event table that contains a bunch of information regarding the subjects (the NFL players) at each time period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": 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>removed</th>\n",
       "      <th>observed</th>\n",
       "      <th>censored</th>\n",
       "      <th>entrance</th>\n",
       "      <th>at_risk</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event_at</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>0</th>\n",
       "      <td>4597.0</td>\n",
       "      <td>4504.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>15592.0</td>\n",
       "      <td>15592.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1310.0</td>\n",
       "      <td>1076.0</td>\n",
       "      <td>234.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10995.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1415.0</td>\n",
       "      <td>1176.0</td>\n",
       "      <td>239.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9685.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1183.0</td>\n",
       "      <td>952.0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8270.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1126.0</td>\n",
       "      <td>942.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7087.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>967.0</td>\n",
       "      <td>822.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5961.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>915.0</td>\n",
       "      <td>782.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4994.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>823.0</td>\n",
       "      <td>715.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4079.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>743.0</td>\n",
       "      <td>667.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3256.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>662.0</td>\n",
       "      <td>609.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2513.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>580.0</td>\n",
       "      <td>523.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1851.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>406.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1271.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>313.0</td>\n",
       "      <td>291.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>865.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>198.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>552.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>149.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>354.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>88.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>205.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>52.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>33.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          removed  observed  censored  entrance  at_risk\n",
       "event_at                                                \n",
       "0          4597.0    4504.0      93.0   15592.0  15592.0\n",
       "1          1310.0    1076.0     234.0       0.0  10995.0\n",
       "2          1415.0    1176.0     239.0       0.0   9685.0\n",
       "3          1183.0     952.0     231.0       0.0   8270.0\n",
       "4          1126.0     942.0     184.0       0.0   7087.0\n",
       "5           967.0     822.0     145.0       0.0   5961.0\n",
       "6           915.0     782.0     133.0       0.0   4994.0\n",
       "7           823.0     715.0     108.0       0.0   4079.0\n",
       "8           743.0     667.0      76.0       0.0   3256.0\n",
       "9           662.0     609.0      53.0       0.0   2513.0\n",
       "10          580.0     523.0      57.0       0.0   1851.0\n",
       "11          406.0     375.0      31.0       0.0   1271.0\n",
       "12          313.0     291.0      22.0       0.0    865.0\n",
       "13          198.0     184.0      14.0       0.0    552.0\n",
       "14          149.0     146.0       3.0       0.0    354.0\n",
       "15           88.0      85.0       3.0       0.0    205.0\n",
       "16           52.0      49.0       3.0       0.0    117.0\n",
       "17           33.0      33.0       0.0       0.0     65.0\n",
       "18           11.0      11.0       0.0       0.0     32.0\n",
       "19            5.0       5.0       0.0       0.0     21.0\n",
       "20            8.0       8.0       0.0       0.0     16.0\n",
       "21            5.0       5.0       0.0       0.0      8.0\n",
       "22            1.0       1.0       0.0       0.0      3.0\n",
       "23            1.0       1.0       0.0       0.0      2.0\n",
       "26            1.0       1.0       0.0       0.0      1.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmf.event_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The *removed* column contains the number of observations removed during that time period, whether due to death (the value in the *observed* column) or censorship. So the *removed* column is just the sum of the *observed* and *censorship* columns.  The *entrance* column tells us whether any new subjects entered the population at that time period.  Since all the players we are studying start at $time = 0$ (the moment they were drafted), the *entrance* value is 15,592 at that time and 0 for all other times.\n",
    "\n",
    "The *at_risk* column contains the number of subjects that are still alive during a given time. The value for *at_risk* at $time = 0$, is just equal to the *entrance* value.  For the remaining time periods, the *at_risk* value is equal to the difference between the time previous period's *at_risk* value and *removed* value, plus the current period's *entrance* value. For example for $time = 1$, the number of subject's *at risk*  is 10,995 which is equal to 15,592 (the previous *at_risk* value) - 4,597 (the previous *removed* value) + 0 (the current period's *entrance* value).\n",
    "\n",
    "Since we have access to the survival table we can calculate the survival probability at different times \"by hand.\"\n",
    "\n",
    "Let us take a look at the definition of the Kaplan-Meier Estimate again:\n",
    "\n",
    "$$\\hat{S}(t) = \\prod_{t_i \\lt t} \\frac{n_i - d_i}{n_i}$$\n",
    "\n",
    "where  $d_i$  are the number of death events at time  $t$  and  $n_i$  is the number of subjects at risk of death just prior to time  $t$. \n",
    "\n",
    "What the above essentially tells us is that the value of the survival function for time $t$, is the product of the survival probabilities for all individual time periods leading up to time $t$. \n",
    "\n",
    "We can define the survival probability for an individual time period as follows:\n",
    "\n",
    "$$S_t = \\frac{\\substack{\\text{Number of subjects} \\\\ \\text{at risk at the start}} - \\substack{\\text{Number of subjects} \\\\ \\text{that died}}}{\\substack{\\text{Number of subjects} \\\\ \\text{at risk at the start}}}$$\n",
    "\n",
    "**NOTE** the number of deaths in the above formula does not include the number of censored observations.  \n",
    "\n",
    "Lets walk through a simple example and calculate the the probability that an NFL career lasts longer than 2 years. First we calculate the individual survival probabilities for $t = 0$, $t = 1$, and $t = 2$. \n",
    "\n",
    "Here's the calculation for the survival probability time for $t = 0$:\n",
    "\n",
    "$$S_0 = \\frac{\\substack{\\text{Number of players at risk at the start} \\\\ \\text{(i.e. Number of players drafted)}} - \\substack{\\text{Number of players} \\\\ \\text{that immediately failed}}}{\\substack{\\text{Number of players at risk at the start} \\\\ \\text{(i.e. Number of players drafted)}}} = \\frac{15,592 - 4,504}{15,592} = \\frac{11,088}{15,592} \\approx 0.711$$\n",
    "\n",
    "And the code for the calculation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.71113391482811694"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the values for time = 0 from the survival table\n",
    "event_at_0 = kmf.event_table.iloc[0, :]\n",
    "# now calculate the survival probability for t = 0\n",
    "surv_for_0 =  (event_at_0.at_risk - event_at_0.observed) / event_at_0.at_risk\n",
    "surv_for_0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What the above means is that about 71.1% of players drafted make it on to the field. \n",
    "\n",
    "Now the individual survival probability for $t = 1$:\n",
    "\n",
    "$$S_1 = \\frac{\\substack{\\text{Number of players} \\\\ \\text{that survive the draft}} - \\substack{\\text{Number of players} \\\\ \\text{that failed in the 1st year}}}{\\substack{\\text{Number of players} \\\\ \\text{that survive the draft}}} = \\frac{10,995 - 1,076}{10,995} = \\frac{9,919}{10,995} \\approx 0.902$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.90213733515234196"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the survival probability for t = 1\n",
    "event_at_1 = kmf.event_table.iloc[1, :]\n",
    "surv_for_1 =  (event_at_1.at_risk - event_at_1.observed) / event_at_1.at_risk\n",
    "surv_for_1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The value for $S_1$ represents the conditional probability that if a player does not immediately fail once drafted, then he has a 90.2% chance of playing 1 year of football. \n",
    "\n",
    "Below is the calculation for $S_2$:\n",
    "\n",
    "$$S_2 = \\frac{\\substack{\\text{Number of players that survive the} \\\\ \\text{1st year and are entering the 2nd year}} - \\substack{\\text{Number of players} \\\\ \\text{that failed in the 2nd year}}}{\\substack{\\text{Number of players that survive the} \\\\ \\text{1st year and are entering the 2nd year}}} = \\frac{9,685 - 1,176}{9,685} = \\frac{8,509}{9,685} \\approx 0.879$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.87857511615900874"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the survival probability for t = 2\n",
    "event_at_2 = kmf.event_table.iloc[2, :]\n",
    "surv_for_2 =  (event_at_2.at_risk - event_at_2.observed) / event_at_2.at_risk\n",
    "surv_for_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$S_2$ also represents a conditional probability.  It is the probability that a player plays in their 2nd year given that he did not retire after his 1st year.  This ends up being about 87.9%.\n",
    "\n",
    "Finally to calculate the probability that an NFL career will last more than 2 years, we just multiply the three individual survival probabilities:\n",
    "\n",
    "$$S(2) = S_0 \\times S_1 \\times S_2 = \\frac{11,088}{15,592} \\times \\frac{9,919}{10,995} \\times \\frac{8,509}{9,685}  \\approx 0.564$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.56364147964887967"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The probability that an NFL player has a career longer than 2 years\n",
    "surv_after_2 = surv_for_0 * surv_for_1 * surv_for_2\n",
    "surv_after_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we see that drafted players have about a 56.4% chance of making it past their 2nd year, or having a career as long as 2 years. Hopefully going through that short example gives you a better idea of how the Kaplan-Meier estimator works.  \n",
    "\n",
    "Our `KaplanMeierFitter` object  has already done all of the above calculations for us. We can get the survival probability after a given time by simply using the `predict` method. So to get the value for $S(2)$ we just pass in 2 into the `predict` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.56364147964887934"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmf.predict(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's pretty close to the value we calculated by hand.  (I'm not sure why they aren't exactly the same. Possibly a  rounding issue? If you do know why please let me know).\n",
    "\n",
    "The `predict` method can also handle an array of numbers, returning an array of probabilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.64154045,  0.49875796,  0.37282823,  0.11208943])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The survival probabilities of NFL players after 1, 3, 5, and 10 yrs played\n",
    "kmf.predict([1,3,5,10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the full list of estimated probabilities from our `KaplanMeierFitter`, access the `survival_function_` attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": 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>KM_estimate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timeline</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>0.711134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.641540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.563641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>0.498758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>0.432463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>0.372828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>0.314448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>0.259329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>0.206205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>0.156233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>0.112089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>0.079018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>0.052435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.0</th>\n",
       "      <td>0.034957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14.0</th>\n",
       "      <td>0.020540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.0</th>\n",
       "      <td>0.012023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16.0</th>\n",
       "      <td>0.006988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17.0</th>\n",
       "      <td>0.003440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18.0</th>\n",
       "      <td>0.002258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19.0</th>\n",
       "      <td>0.001720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20.0</th>\n",
       "      <td>0.000860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21.0</th>\n",
       "      <td>0.000323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22.0</th>\n",
       "      <td>0.000215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23.0</th>\n",
       "      <td>0.000108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26.0</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          KM_estimate\n",
       "timeline             \n",
       "0.0          0.711134\n",
       "1.0          0.641540\n",
       "2.0          0.563641\n",
       "3.0          0.498758\n",
       "4.0          0.432463\n",
       "5.0          0.372828\n",
       "6.0          0.314448\n",
       "7.0          0.259329\n",
       "8.0          0.206205\n",
       "9.0          0.156233\n",
       "10.0         0.112089\n",
       "11.0         0.079018\n",
       "12.0         0.052435\n",
       "13.0         0.034957\n",
       "14.0         0.020540\n",
       "15.0         0.012023\n",
       "16.0         0.006988\n",
       "17.0         0.003440\n",
       "18.0         0.002258\n",
       "19.0         0.001720\n",
       "20.0         0.000860\n",
       "21.0         0.000323\n",
       "22.0         0.000215\n",
       "23.0         0.000108\n",
       "26.0         0.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmf.survival_function_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `median_` attribute also provides us the number of years where on average 50% of players are out of the league."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmf.median_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the Kaplan-Meier Estimate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the Kaplan-Meier estimate (along with its confidence intervals) is pretty straightfoward. All we need to do is call the `plot` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtgAAAJPCAYAAABGsKP+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngQjYAAAAgIEI2AAAAICB/hfevJzPCYXvywAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa5a5318ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the KM estimate\n",
    "kmf.plot()\n",
    "# Add title and y-axis label\n",
    "plt.title(\"The Kaplan-Meier Estimate for Drafted NFL Players\\n(1967-2015)\")\n",
    "plt.ylabel(\"Probability a Player is Still Active\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first thing thing that you should notice is that the Kaplan-Meier estimate is a step function.  Each horizontal line represents the probability that a player is still active after a given time $t$.  For example, when $t = 0$, the probability that a player is still active after that point is about 71%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the Kaplan-Meier Estimate by Position\n",
    "\n",
    "Before we plot the career lengths by position, lets clean up some of the data.  We will merge and drop some of the player positions in order to make the plotting a bit more manageable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['DE', 'RB', 'QB', 'LB', 'WR', 'C', 'T', 'DT', 'G', 'HB', 'DB', 'TE',\n",
       "       'K', 'FL', 'E', 'P', 'FB', 'WB', 'NT', 'KR', 'OL', 'DL', 'OLB',\n",
       "       'CB', 'FS', 'ILB', 'SS', 'S', 'LS'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "draft_df.Pos.unique() # check out all the different positions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DB     2668\n",
       "LB     2039\n",
       "RB     1798\n",
       "WR     1793\n",
       "DE     1242\n",
       "T      1174\n",
       "G      1028\n",
       "DT      987\n",
       "TE      873\n",
       "QB      722\n",
       "C       450\n",
       "K       197\n",
       "P       156\n",
       "NT      134\n",
       "FB       91\n",
       "FL       63\n",
       "OL       39\n",
       "CB       29\n",
       "E        29\n",
       "OLB      23\n",
       "HB       23\n",
       "ILB      11\n",
       "SS        7\n",
       "FS        5\n",
       "DL        4\n",
       "KR        3\n",
       "WB        2\n",
       "LS        1\n",
       "S         1\n",
       "Name: Pos, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "draft_df.Pos.value_counts() # get a count for each position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Relabel/Merge some of the positions\n",
    "# Set all HBs to RB\n",
    "draft_df.loc[draft_df.Pos == \"HB\", \"Pos\"] = \"RB\"\n",
    "\n",
    "# Set all Safeties and Cornernbacks to DBs\n",
    "draft_df.loc[draft_df.Pos.isin([\"SS\", \"FS\", \"S\", \"CB\"]), \"Pos\"] = \"DB\"\n",
    "\n",
    "# Set all types of Linebackers to LB\n",
    "draft_df.loc[draft_df.Pos.isin([\"OLB\", \"ILB\"]), \"Pos\"] = \"LB\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# drop players from the following positions [FL, E, WB, KR, LS, OL]\n",
    "# get the row indices for players with undesired postions\n",
    "idx = draft_df.Pos.isin([\"FL\", \"E\", \"WB\", \"KR\", \"LS\", \"DL\", \"OL\"])\n",
    "# keep the players that don't have the above positions\n",
    "draft_df_2 = draft_df.loc[~idx, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the number of positions in order to decide\n",
    "# on the plotting grid dimiensions\n",
    "len(draft_df_2.Pos.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the data organized, lets plot the Kaplan-Meier estimate for each position.  I've commented the code below to walk you through the process of plotting each position in a 5x3 plotting grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2UAAAQ+CAYAAABfmJfpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XlcTun/+PHXXVoQyZZ9N92IJBQio2RpJNmH7CNLmLHO\n+NgZy4xlZqwx9uwGg5GQyL6EsmWGQbJOomSLnN8f/e7zdbvvUraYeT8fjx4PXec651zXue8u532u\n5WgURVEQQgghhBBCCJElTLK6AEIIIYQQQgjxXyZBmRBCCCGEEEJkIQnKhBBCCCGEECILSVAmhBBC\nCCGEEFlIgjIhhBBCCCGEyEISlAkhhBBCCCFEFpKgTAiRJUJDQ/H396d27dpUrlwZV1dX+vTpw+7d\nu7O6aHz33XdotVqio6Pf2zmuX7+OVqslICAgw/s8f/6cLVu20L17d+rXr4+9vT1169bl66+/5uTJ\nk++trB9SfHw8ffv2pXr16jg6OjJ27NisLtJ7p9VqadGixRvvr/suOTg4cPny5TTz9enTB61Wy40b\nN9Q03Xc9vZ8KFSqQlJQEwMyZM9FqtWzatOmNy9ugQQOj56lSpQoNGjRg6NChXLp06Y2Pn1GzZs1C\nq9USGhqqpr148YIVK1bw5MmTdPMJIcS7li2rCyCE+O8ZP348K1asoFixYnh4eGBjY8Pt27fZs2cP\nu3fvpk2bNowbNy7Lyufh4UHRokUpUKBAlpXhVbdv36Z///5ERkZSrFgxateuTb58+bh69SqhoaGE\nhIQwcuRIvvzyy6wu6luZMGECoaGharDu4OCQ1UX6ZCQnJzNq1CiWL19udLtGo0Gj0RhN9/HxoWjR\nomnuZ25unu4xMkuj0RAQEMDLr0pNTk7mwoULbN68mdDQUFasWIFWq33rc6WlZs2aBAQEUKZMGTVt\n4MCBhISE0Lx583TzCSHEuyZBmRDigzp69CgrVqygcePGTJ8+HROT/+uwT0pKolOnTqxbt4769evT\noEGDLCmju7s77u7uWXJuY5KTk+nevTuXLl1iwIAB9OzZE1NTU3X71atX8fPzY8KECRQrVox69epl\nYWnfzrlz58iWLRvz588nWzb5Lyqzjh8/zrp162jdunWm9vP19aVGjRrvqVTG9e3b12h6YGAgM2bM\n4IcffmDRokXv7fw1a9akZs2aeml3797NUD4hhHjXZPiiEOKDCgsLQ6PR0KFDB72ADMDKyopBgwYB\nsHPnzqwo3kdp3rx5XLx4kTZt2tC7d2+9gAygZMmSTJo0iRcvXjBnzpwsKuW7kZycTI4cOSQgewPl\nypXDzMyMqVOnEhcXl9XFeWOdO3cmW7ZsHDlyhOTk5Cwpw8s9eEII8SFIUCaE+KCeP38OwJ9//ml0\ne/Xq1fnpp5/o3Lmzmqabx2JsTkeDBg30nmJv3LgRrVbL9u3b6d69uzpPpXfv3mi1Wg4cOGBwjMjI\nSLRarTpk8ttvv1XnlMXHx1OpUiXatWtntLzdu3enUqVK6k3w8+fPWbp0KW3btqV69erY29vToEED\nRo8eTXx8fAavkr6NGzei0Wjo1atXmnnq1KnD4MGD9eaovVyPV706j0l3jQ8dOkTr1q2pXLkyjRs3\npnPnzmi1Wq5cuWJwjD/++AOtVsvChQvVtLi4OMaMGYObmxuVK1fG3d2dqVOn8vDhw9fWUavVcvPm\nTR48eKDOZdJJSkrihx9+oGHDhtjb26v1fbVcxurRpEkTHj9+nO75g4ODadeuHY6Ojjg5OdGlSxeO\nHDlikC+zn++xY8fw9/fHxcWF6tWr065duzTnJkVGRtKlSxccHR1xdnamf//+XL9+Pd1yv6xkyZL4\n+/uTkJDA+PHjM7zfx8bS0hJra2tevHhBYmKimv7PP/8watQodT5l/fr1GT16NP/884/BMZYvX07L\nli2pVq0aTk5OdOjQge3bt+vlebVd0Wq1HD9+HEVRqFGjBp06dTKaT+fAgQN07doVJycnHBwc8PX1\nZeXKlQYBXYMGDejUqROXLl2iV69eVK9enWrVqtGzZ8/3Om9VCPFpkaBMCPFB1alTB0VRmDx5MhMm\nTODUqVO8ePFC3W5hYUGjRo305pK8yTyWCRMmcO/ePTp16kTlypXp06cPkHrz/aqtW7ei0WjUeSQv\nny9v3rzUqVOHqKgobt26pbffvXv3OHLkCLVq1SJ//vxA6pyUSZMmYWZmRtu2bWnXrh0WFhasWbMG\nf3//TNUBUoPXmzdvUqZMGQoXLpxu3h49euDq6qr+npnrpss3ZMgQsmfPjp+fH87OzrRv3x4wft3+\n+OMPTExM+OKLLwC4efMmLVu2ZO3atdjb29O1a1fKlCnDr7/+ip+fn97iCa+qUKECAQEBWFlZYW5u\nTkBAgDq87f79+7Rq1YrFixeTL18+/Pz8cHR0JDg4mFatWhEVFZVuPWrWrEn27NnTPPfPP//MN998\nQ1xcHC1btqRFixZcvHiRrl27smXLFr28mfl8f//9d7p06UJERARubm60atWK27dv07dvXzZs2KCX\nNzY2Fj8/P0xMTOjYsSMVKlRgx44dr71ur/L396ds2bLs2LHjo1g0500kJSURHx+Pubk5efLkAeDa\ntWv4+Piwbt06ypYti5+fH2XLlmXNmjX4+voSGxur7j9//ny+//57ANq1a4evry8xMTF8/fXX/P77\n72q+V/8+AgICKFy4MBqNBn9/f3x9fY3mg9Sgr3v37pw9exZPT09atWpFUlIS48aNY/DgwQZ1unnz\nJu3bt+fevXu0bdsWZ2dnwsPD6dy5M/fu3Xt3F08I8cmS8SFCiA+qfv36fPnll6xatYqgoCCCgoKw\nsrLCycmJ2rVr07hxY2xtbd/6PGZmZqxevVpdoACgVKlS7Ny5k7Fjx6pDABVFISQkhGLFiqW5qIS3\ntzfh4eEEBwfTtWtXNX379u2kpKTQrFkzILWnY8eOHTRv3pwpU6ao+V68eIGPjw9nzpzh6tWrlCxZ\nMsP10AWCpUqVyvA+b6NIkSIsW7ZM/T05ORkrKyu2bdtG79691fSkpCT2799PzZo11c9L12sxb948\nvXltQUFBTJgwgVmzZhm9YQXUFfg2bNhAUlKSXo/fDz/8wNWrV+nTpw/9+vVT08PDw/H392fo0KEE\nBwfr3Ti/Wo+0REVFMW/ePFxcXJg/f776fenXrx9t2rRh1KhRuLq6YmNjk6nPNzExkQkTJmBjY8PK\nlSspUaIEAL169cLb25sff/yR5s2bq9/DpKQkvvnmG3r27Kket2/fvuzevZv9+/fj4eHx2roAZMuW\njfHjx9OhQwfGjRuHs7MzOXPmfO1+GzZsMNozCGRqhdB34eeff0ZRFD7//HN1GOuIESOIj49nwoQJ\ntGzZUs27evVqxowZw8iRI1m8eDEAixYtomTJkqxfv179TvTo0QNPT0+CgoL0FvF4WUBAAEeOHOHm\nzZt89dVXWFlZGc137do1pkyZQtGiRVm2bJm6QMqTJ0/o1asX27Ztw83NDW9vb3Wf2NhYOnTowIgR\nI9S0UaNGsW7dOnbs2EHbtm3f4ooJIf4NpKdMCPHBjRo1Sr1xNzMz4+HDh4SHhzNp0iTc3d2ZPn36\nW8/pqFu3rl5ABtCsWTMSExPZv3+/mnbs2DHu3LmjBlbGeHh4kCNHDrZt26aXvm3bNiwtLWnYsCEA\nhQoVYvLkyXqBA4CJiQlOTk6A8YUE0vPgwQOADN1Yvy2NRmNw829ubo6npycXL17UW6Z8586dJCcn\nq9ftn3/+Yd++fdSrV89goZEOHTpQuHBhNm7cmOkyPXv2jG3btlGkSBGD61qvXj08PT25evUqx48f\nT7ceaVm/fj2Q2rP28vfF2tqaHj168OTJE7WXMDOf7549e3jw4AGdOnVSAzKAPHnyMHz4cHr06MGj\nR4/UdEtLS7p166Z33M8//xxFUfR6gTKiWrVqtGvXjlu3bjF9+vTX5lcUhU2bNjF79myDn/c5R3HW\nrFl6P5MnT6Z169YsX76c/PnzM3ToUCD1wcSRI0eoXr26XkAGqT1hlStX5vDhw+pS/4qiEB8fz9Wr\nV9V8tra2BAcHs2LFircu9+bNm0lJSSEgIEBvxUpLS0tGjBiBoijq9+plX331ld7v9erVQ1GUTA1R\nFUL8e0lPmRAiS7i5ueHm5sbjx485duwYhw4dYvfu3cTExDB//nwURVEX/XgTxYoVM0jz9vZm5syZ\nBAcH4+bmBqQOwdNoNOkGZbrAa/PmzcTGxlKsWDH++ecfIiIiaNKkCTly5ABSb/x8fHxISUnh3Llz\nXL58mZiYGM6fP8/BgwcB9IZqZoRu+NbLc2vep7Su24YNG9i2bZsakPzxxx/qUFNIXTVRURTu37/P\nrFmz9PZXFAUzMzNu3brFnTt3KFiwYIbLc/nyZZ48eaIGPa9ycnIiJCSE6OhovdUDjdXDmHPnzgEQ\nEhJCWFiY3rZbt26hKIo67yczn++FCxfQaDRUrVrV4JyNGzc2SCtcuLDB4ia6z/518/GMGTRoEKGh\noaxatYpmzZoZLYeORqNh+fLlVK9ePdPneRuzZ8/W+z179uwUKVKEzp07061bN7UH9vz58wBplq9a\ntWqcOXOG6OhoihQpQtu2bVmwYAFNmzalcuXK1K1bV52H9i5cuHAhzfKUK1eO3Llzq3l0LCwsDEYA\n5MqVCyDLFjMRQnxcJCgTQmSp7Nmzq70rw4YNY/369YwcOZKgoCACAgKwsLB4o+NaWloapBUvXpyq\nVasSGhrKs2fPMDExYefOnVSsWJHSpUunezxvb29+//13tm3bRs+ePdm2bRuKougNUYLU4VRz5szh\nzp07aDQacufOjYODA2XLliUqKirTPYDFixcH0Hvqn5bY2FgKFixo0EOYGcaut7OzM4UKFVKDsvv3\n73P48GHc3d3VIV66oDEyMpLIyEijx9ZoNCQkJGQqKNO9tFh3A/sq3bFeXcgjo98bXU/kggULjG7X\nlVkno5+v7nqkNQTuVemV9016ja2srBg1ahQBAQGMHDnSYA7buzjH29IFW6+T0e+Abu7dwIEDKVWq\nFKtXr+b06dNERUUxa9YsSpcuzejRo3FxcXmrcuvKk9ZnW7BgQWJiYvTSjP1N6oZWykqPQgiQoEwI\n8QElJSXh6+tLmTJlmDdvntE8rVq1Ijg4mIMHD3Lr1i29+VfGbl4yswgCpAZX48ePZ9++fVhYWBAf\nH5+hBThq1apFgQIF2L59Oz179iQ4OJg8efLoLawRHBzMmDFjqFChAmPHjqVixYrq0/ExY8boLUiR\nUSVKlKBEiRJcuXKFmzdvprvYh7+/P7GxsWzYsIGyZcuq6a9et8xeM41Gg5eXF4sWLeLChQucOnVK\nby4doPYWvjrv623phm3evn3b6HZd8GNjY/NGx8+RIwempqZERUUZvKLhVZn5fHXXw1gvV3JyMqam\npgavNnjXPDw88PT0ZOfOncyfP/+9nut9yuh3QNezCKnvXfP19SU+Pp5Dhw6xc+dOQkJC6N27N2Fh\nYXp536Y8xr53CQkJb3V8IcR/k8wpE0J8MFZWViQlJXHo0KF0l4fXaDSYmJioKxrqnjK/PAcHUns5\n7t+/n6kyNG3aFFNTU3bv3s327dsxNTXFy8vrtfuZmJjg5eXF+fPnOXnyJJGRkTRp0kTvxlo3FHLq\n1Kl8/vnnesOVdPOx3uSpeIsWLVAUhblz56aZ58CBA1y6dIlChQqpAZmZmRlgeN1efYqfEd7e3iiK\nQmhoKCEhIeTOnVsdAgpgZ2cHwJkzZ4zu/8svvzB//nz1lQgZVbp0aSwsLDh9+jTPnj0z2H706FE0\nGg3lypXL1HF17OzsSElJ4ezZswbbIiMjmTZtGhEREUDmPt/PPvsMRVGMBuILFy7EwcFBbx7c+zJi\nxAisrKwIDAzMUG/rx0j3aoQTJ04Y3a77DpQtW1YdPrtp0yYgdfVULy8vfvrpJ3x9fXny5Ik6ZNWY\njKxWqtVqURRF/V687OrVq/zzzz+UL18+I1UTQgiVBGVCiA+qQ4cOPH36lH79+hl9v1BoaCgHDx6k\nYcOG6hPpMmXKoCgKe/bs0cs7d+7cN5qjVbduXfbs2UNYWBguLi5q8Pc6usBk7NixAAbz0HRD0F59\nce+mTZs4duwYQKaDEoBu3bpRtGhR1q1bx5w5cwzqfPr0aQYPHoxGo2HIkCFqurHr9rrgLi12dnZ8\n9tlnbNu2jaNHj9KoUSM16IPUOVw1atQgPDyckJAQvX03bdrEnDlz2L9/f6ZfCm1ubo6Xlxe3b99m\n5syZetvCw8PZvn07JUuWxNHRMdN1gv8LeCdNmqQOS4PUXt3Ro0fz66+/qtc7M5+vh4cH2bNnZ9my\nZeoCFJC6vP+aNWuwsrJKc7XPd6lgwYIMHjyY5ORkLl68+N7P9z4ULlwYZ2dnzpw5w6pVq/S2rVu3\njpMnT+Li4oKtrS05c+Zk2bJl/PTTT3rDTgF1QY0iRYqkeS7ddzq9eV7e3t5ky5aNwMBArl27pqY/\nfvyYcePGodFo8PHxyXQ9hRD/bTJ8UQjxQfXq1Yu//vqLkJAQPD09cXV1pVSpUjx//pzIyEhOnDhB\nuXLlGD16tLqPm5sbBQsWJDg4mISEBCpUqMDJkye5ePEin332mcH7w17H29ubsLCw1y7w8aqKFStS\ntmxZoqOjKVasmEEg4O3tzR9//EHfvn3x8vLCysqKqKgojh07Rv78+bl7926me/YgNRhYvHgx3bt3\nZ+bMmaxfvx5XV1dy5szJX3/9xcGDB9FoNAwePFhv1cFmzZrx888/s3DhQq5evUrx4sU5cOAADx48\nMHpj+rpePG9vb6ZOnZrmdRs3bhwdO3ZkwIAB1KtXj/Lly3P58mX27NmDjY0NY8aMyXTdIXVlxJMn\nT7JgwQKOHj2Ko6Mj165dY/fu3eTKlYsff/wxU/V4mbOzM35+fgQFBfHFF1/g5uaGubk5u3bt4tat\nW7Rv315dQCQzn6+1tTWjR49m+PDhtGjRAnd3d3LkyEFISAhxcXHMmjVLL6h9n9q2bcvmzZuN9uy8\nCUVRCAwM5LfffjO63c/PD09Pz3dyLp1x48apy/zv3LkTOzs7/vzzTw4cOEChQoXUByVmZmYMGDCA\nCRMm8MUXX9CwYUMsLS05duwYZ86cwcfHR+/1Eq9+V3Tz04YPH06dOnXw8/MzyFe8eHGGDRvGxIkT\n8fX1VVdnDQ8PJzY2Fi8vr0y1K0IIARKUCSE+MFNTU3766Sd27drF5s2bOX36NPv27cPMzIySJUsy\nePBgOnXqpDcx3szMjKCgIKZOncqhQ4eIjIykRo0arFy5kunTpxvMNXndECTdAhUpKSnqcvYZ5e3t\nzU8//WT0psvNzY0ZM2awYMECtmzZQvbs2SlevDhjxoyhatWqtGjRgr1799K0aVO1nBl9uXOJEiXY\ntGkTGzZs4I8//iAsLIz79+9jY2ND06ZN6dy5M5UrV9bbJ1++fAQFBTF9+nT27duHubk5devWZciQ\nIfj7+xuc+3VladasGTNmzMDW1lZvpUOd0qVLs2HDBmbPnk14eDiHDx+mQIEC+Pj40KdPnwyviPhq\nOWxsbFi7di1z585lx44drFixgrx58+Lr64u/v7+6GEpG6/Gq//3vf1SpUoWVK1eyefNmsmXLRunS\npRkwYIBej0dmP18fHx9sbW2ZP38+O3bs4Pnz51SqVImJEydSt25dvfKmVeaMfkdel2/8+PH4+Pi8\nUU+tsXNduXKFK1euGN2ekb+pzH5GJUuW5LfffmP27Nns3buX48ePU7BgQTp37oy/vz958+ZV83bo\n0IG8efOyfPlygoODefz4MaVKleK7776jY8eO6Zajd+/eXL58mYMHD3L16lU1KHs1n5+fH6VLl2bh\nwoXs3LmTFy9eUK5cOXr16mWwbH969c1MGyCE+HfTKLLsjxBCCCGEEEJkGZlTJoQQQgghhBBZSIIy\nIYQQQgghhMhCEpQJIYQQQgghRBaSoEwIIYQQQgghspAEZUIIIYQQQgiRhSQoE0IIIYQQQogsJEGZ\nEOKTNGLECHr37q3+7ufnh1arNfipXLkydevWpV+/fkRGRuod49tvv0Wr1RIdHf2hi//BzZo1y+Da\nVKhQAUdHRxo1asSoUaP4+++/32sZIiMjadOmDQ4ODtSsWZPNmzcDcODAAc6cOfPOz9enTx+0Wi03\nbtx4bd69e/fSrFkzqlSpQq1atTh69Og7L8/7FhYWlu73OTY2lkGDBlGrVi0cHR1p27Yt27ZtS/N4\nmzdvpnXr1lStWhVXV1f69+9v8G6yBg0aGP27e/ln06ZNGSr/1atXGTx4MK6urtjb21O/fn3GjBlD\nfHy8Qd6UlBSWLFmCl5cXDg4OeHh4MGfOnNe+h+3x48c0aNCASZMmGd1+6NChNOvh6uqq5vv777+p\nWrUqf/75Z4bqJoQQryMvjxZCfHIOHz7Mpk2b2Lp1q166RqOhc+fOWFlZqWnPnj3jypUr7Nq1i7Cw\nMObNm6feXP3XXtyq0Whwd3dHq9UCoCgKSUlJREdHs27dOjZv3szPP/+Mm5vbOz+3oigEBARw9+5d\nmjdvToECBahUqRKrVq1i7NixzJ49G3t7+3d6zox+vomJiQwYMABFUWjZsiU5c+akbNmy77Qs79ul\nS5f47rvv0qzvpUuX+PLLL0lMTKR27dp89tlnHDt2jIEDB3Lu3DkGDx6sl3/GjBkEBgZSqlQpvvzy\nS27fvs327ds5cuQIGzdupEiRIgB06dKFxMREg/M9ffqUhQsXYmFhYfBS87TK17ZtWzVoKlGiBGfP\nnmX16tUcOHCAdevWkSdPHjX/2LFjWbt2LTVq1MDd3Z0TJ07wyy+/cOHCBX7++Wej50hJSWHQoEHc\nvHkzzXJcuHABjUZDu3btyJcvn962nDlzqv8uU6YMvr6+DB8+nHXr1v2n2hEhxHuiCCHEJ+T58+eK\np6en8u233+qld+zYUdFqtcr169eN7rd161bFzs5O8fT0VNO+/fZbRavVKufPn3+vZf4YzJw5U9Fq\ntcrGjRuNbt+7d69ib2+vODo6KjExMe/8/Hfu3FHs7OyUDh06GC3Xrl273vk5+/Tpk+53QufkyZOK\nnZ2dMnTo0Hdehg/h0KFDSq1atRStVpvm91n397F48WI17cWLF8rAgQMVrVarHD16VE2PjIxUtFqt\n0qlTJ+Xp06dq+vbt2xU7Oztl+PDhry3T+PHjFa1Wq/z2228ZqkO3bt0UrVar7Ny5Uy99zpw5ip2d\nnTJhwgQ1LSIiQrGzs1O+/vprvbzDhg1TtFqtsmfPHoPj379/X+natatiZ2enaLVaZeLEiUbLoWsT\nkpKSXlvmO3fuKFWqVFGCgoIyUkUhhEiXDF8UQnxStm/fTkxMDJ06dcrUfl5eXhQqVIiYmBiuXr36\nnkr36apXrx4DBgzg0aNHzJkz550f/+nTpwB6vR2Q2oOW1dIq28fu6dOn/O9//6Nbt24oikLFihWN\n5ouLi+PYsWOULVuWLl26qOkajYahQ4eiKApLlixR01esWIFGo2H8+PGYm5ur6Y0aNaJt27aUKFEi\n3XIdP36cFStWUKdOHXx9fV9bj4cPH3L48GHs7e3x8PDQ29azZ08sLCwIDw83KF9AQIBe3oEDBwKw\nbt06vfStW7fSpEkTDh06RJ06ddL9zl24cIEiRYro9YqlpUCBAnh6erJw4UJevHjx2vxCCJEeCcqE\nEJ+UxYsXU6ZMGSpUqJDpfQsWLAjA/fv30823adMm/Pz8qFmzJvb29ri6ujJ48GCuXbum5uncuTNa\nrVYv7eX9tVqt3s1hTEwMgwcPpk6dOlSuXJmmTZsyf/58gzkwDRo0wM/Pjw0bNlCnTh0cHR2ZMmUK\nAKdPn8bf35+6detSpUoVGjVqxLRp00hKSsr0tTCmY8eOWFhYEBISot5kHj16FK1Wy6pVqxg4cCAO\nDg64urpy8uRJAG7cuMHo0aNp2LAhVapUwdHREV9fX1avXq0e97vvvsPDwwONRsOuXbvQarX4+fnh\n5+fH7NmzAejbt6/BZxocHEy7du1wdHTEycmJLl26cOTIEYNyv3jxgl9//ZXGjRvj4OCAt7c3O3fu\nzFCd/fz86Ny5MxqNhqVLl6LVavnuu+8A1H8HBgZSo0YNatSowdKlS4HUYHLlypW0aNECBwcHqlev\nTrdu3Th48KDe8a9fv45Wq2XevHns3LkTX19fHBwcaNCggRoIRURE0KFDBxwdHXF3d2fWrFkZusmP\ni4vjt99+4/PPP+f333/ns88+M5ovNjYWwOgwQltbW/LkyUNERISatm/fPj777DOjwdfYsWPx9/dP\nt1xTpkzB1NSUkSNHvrYOkHothwwZohcw6piammJqasqjR4/UtIiICGxsbAyGmBYsWJBSpUpx7Ngx\nvfQ1a9aQI0cO5s2bl27ZX7x4wcWLF7Gzs8tQuQGaNWvGjRs32L59e4b3EUIIY2ROmRDik3Ht2jXO\nnDlD586dM72voijqzamtrW2a+aZMmcLixYupUKECvr6+aDQajh49ytatWzlx4gTbt2/H3NycFi1a\ncPToUbZs2UKfPn30jrF582YsLS1p0qQJAGfPnqVz584kJyfTsGFDihYtyvHjx5k+fTrHjx8nMDBQ\nb07KxYsXGT9+PN7e3jx79gxHR0euXLlC165dMTU1pXHjxlhbW3Py5EkWLFjA6dOn9Xo63pSlpSUV\nK1bk1KlTnD9/nkqVKqnbZs+eTc6cOfHz8+PixYtUqlSJ2NhYWrVqxdOnT2nYsCGFCxfm9u3bhISE\nMGbMGFJSUujQoQMeHh7kzp2bpUuXUqZMGby8vChatKh67OPHj+Pl5UWZMmXUtJ9//pm5c+dSrFgx\nWrZsCaT2knbt2pUpU6bQrFkzNe+wYcPYsmUL5cuXp127dly5coX+/fsbzAkypmXLlhQrVoyNGzeq\nC1q8HBzu27ePXbt20aJFC+Li4qhatSqKovD1118TEhJCiRIlaNWqFY8ePSI0NJTu3bszatQo2rdv\nr3eekJAQLl26ROPGjalRowabN29mypQpXL9+nTVr1uDm5saXX35JcHAws2fPxtraGj8/v3TLbm1t\nzapVq3ASPJV3AAAgAElEQVR0dEw3n663Kzk52WCboig8evSIZ8+e8fjxYx4/fkx8fDy1a9fm77//\nZvr06Rw5cgRFUahTpw5DhgyhWLFiaZ5rx44dnD59mrZt21KyZMl0y6VjZWVlNCAD2L9/P48ePVLr\nmJyczK1bt6hatarR/EWLFuXKlSvcu3cPGxsbAAICAnB0dMTc3DzdBVwuX75McnIyFhYWDB06lMOH\nD5OYmEjFihXp3bs3devWNdjH2dkZc3Nztm7dStOmTTNUXyGEMEaCMiHEJ+PIkSNoNJo3WhBi6dKl\nxMfHU7lyZQoVKmQ0z+3bt1m6dCk1a9Zk6dKleoGSv78/4eHhHD9+nNq1a+Pp6cnYsWP5448/9IKy\nuLg4jhw5QqNGjdQFR7799lueP3/OmjVr9G74p0yZwpIlS1i9erXeTfz9+/cZMWIEHTp00Mv78OFD\nli1bRo0aNdT0Xr16sXfvXi5duvROFqfQBaz//POPXvrDhw/ZvHkzefPmVdMWLFhAQkICixcvxsXF\nRU3v0KEDrVu3ZuvWrXTo0EFdXEQXlPXt21fNGxsby/Hjx2natCnu7u4AREVFMW/ePFxcXJg/f74a\nVPTr1482bdowatQoXF1dsbGx4fDhw2zZsoV69eoxe/ZszMzMAFi5ciXjxo177QIMPj4+FClShI0b\nN+Lg4GAwJO7u3bvMmzdPb/GTTZs2ERISQr169fjll1+wtLRU69K+fXu+//576tatqxe8REdHM3v2\nbBo0aACkDhft3r07QUFBekHcl19+ibu7O1u3bn1tUGZlZfXagAygXLlyWFpacuTIEZKSkvQWwgkP\nDyc5ORmNRsODBw/UlQ5v375N69atKVmyJK1ateLvv/8mJCSEiIgI1q1bR+HChY2ea/HixZiamtK9\ne/fXlut1njx5wuTJk9FoNLRp0waAhIQEAHLlymV0H116UlKSGpQ5Oztn6HwXLlwAUoP/atWq4e3t\nza1btwgNDaVnz558//33BsMxLSwsKFeunF5PoxBCvAkZviiE+GScO3cOSL3JTMuSJUuYNWuW+vPj\njz/SqVMnJk+eTM6cORkzZkya+1pYWPDjjz8yfPhwg5t5XSB09+5dAHLkyIGnpyd///233hLkW7du\n5cWLFzRv3hxIXQb+r7/+olWrVgbD8/r370+2bNnYsGGDQVk8PT31flcUBUVRiIqK0kufPHkyhw4d\nemerBeoCoFeHRFarVk0vIANo3rw5EydO1AvIIHWYnKWlpdGlzDNi/fr1AAwZMkRvTpO1tTU9evTg\nyZMnBAcHA/DHH3+g0Wj4+uuv1YAMUoObl3ve3pSlpSX16tXTS9u4cSMajYbRo0erARlAsWLF6NWr\nF8+fPzdYBr5o0aJqQAap1xMge/bstGvXTi9f/vz5uX79+luXXcfc3Jwvv/ySuLg4evXqRXR0NI8e\nPWLv3r2MHDmSHDlyAP/XawapvZeenp789ttvDBs2jMDAQEaMGEFcXBwTJ040ep7z589z8uRJPDw8\nXjvv7HWePXtG//79uXjxIh4eHjRq1AhAHe778vfi1brC/80TzIwnT55QsmRJBg0axIoVKxg8eDBT\np05l/fr15MyZk/Hjx6t//y8rV64ciYmJxMTEZPqcQgihIz1lQohPhu6GSPcE/FWKorB8+XK9NEtL\nSwoWLEibNm3o2rUrpUuXTvP4efLkwcvLC0VR+Ouvv7h06RLXrl0jOjqaQ4cOAanLauv4+Pjw+++/\ns2XLFnWZ+c2bN2NjY6Muu697/9bVq1eZNWuWQXlz5sxp8F4pMzMzChQooJfm4+PDqlWr+PHHH1m+\nfDn16tWjXr161KlTh+zZs6dZp8x6+PAhgHqjrmNsyFq1atWoVq0aCQkJnD9/npiYGC5fvsypU6d4\n+vSp3rXKDF3wHRISQlhYmN62W7duoSiKes0uXLiAqampev1f5ujoyOXLl9+oDDqFChUyCNCjo6Ox\ntbXVG4Kp4+TkpJbrZa8O5dN9ZoULFzY4vrm5+TubJ6jzzTffcOvWLYKDg/Hx8QHAxMSE7t2789df\nf7F3716yZ8+OiUnqs1pTU1ODJfY7dOjA0qVL2bNnD0+fPsXCwkLvHLpgtW3btm9V1sePHxMQEMCB\nAwdwcHBQ51QC6jmfPXtmdF/dEM03+Zvw9fU1ujBJ2bJl6dy5M3PmzCE0NFTttdPRtUfx8fFvHYwK\nIf67JCgTQnwydDeqL/dOvEyj0bB79+40h1ZlxI4dO5g2bRpXr15Fo9GQI0cOKlWqRIUKFQwWcXBx\ncaFQoUIEBwczZMgQLl26xLlz5/Dz88PU1BSABw8eAKlzY/bv359muR89eqQGQsbqp9VqWbt2LYGB\ngezZs4d169axdu1asmfPTqdOnfjmm2/euM4v0/XQFC9eXC/dWJkSExOZOHEiW7duJSUlBY1GQ9Gi\nRXFxceHcuXNvvLKi7potWLDA6HaNRqMOY0tISMDCwkINJl5mbW39Rud/mbF6P3z4UF005lW69MeP\nH+ulpxUkpNXj866ZmZkxffp0OnfuTEREBObm5ri6ulKqVClatWqFmZkZuXPnVof/FS1alNy5c+sd\nQ6PRYGdnR2xsLDdu3DB4wLFnzx6sra0Nek4h9TNdsmSJQQDq6+urvvMMUgObnj17cubMGRwdHVmw\nYIHeA4JcuXJhYmKifkeMnUeX712qWLGi3rzUl+k+W913Uggh3oQEZUKIT4buJvvBgwdpLl/+Nkus\nR0VF8fXXX1OoUCFmzJiBvb29GpwsWLDAICjTaDR4e3uzYMECTp06xf79+9FoNOrQRUjtcdJoNEyc\nOJEWLVq8cdkA7OzsmD59Os+fP+fkyZOEh4ezYcMG5s+fT+HChfWGwb2JhIQELl68SO7cudMdIqoz\nePBg9u3bR/v27fH29uazzz5Tb6A3b978xuXIkSMHpqamREVFGQ22XmZtbU1sbCwpKSlqIKzz8op9\n71LOnDm5ffu20W26Fyl/rMvrOzg44ODgoP6enJzMX3/9pQ71LF68OKampmn2ROmGD74aZF6+fJmY\nmBhatWpl9DNLTExk9uzZBkGZs7OzGpRdv36dbt26ERMTQ926dZk5c6ZBUGxmZkaRIkWMBkeQOq8v\nb968BgFlRly6dIk7d+5Qq1Ytg2264ZDGgmhdIJjWwyIhhMgImVMmhPhk6Ib03bt3770c/48//kBR\nFMaMGUOTJk30eosuXboEGAZ9zZs3R1EUdu/ezd69eyldurTeQiR2dnYoisLp06cNzvf8+XMmT55M\nUFDQa8u2adMmJkyYAEC2bNmoUaMGgwYN4ueff0ZRFI4fP/5GdX7ZmjVreP78OU2aNHntAhkPHjwg\nPDwce3t7Ro0aRdWqVdWALDY2lqdPn75xgGxnZ0dKSgpnz5412BYZGcm0adPUhRUqVarEixcvOHXq\nlEFeY9f8XdBqtTx48ICLFy8abNMtx16+fPn3cu431b59e1q1amWQvnfvXp4+faquLGhubo69vT03\nb940eN1DSkoK0dHR5MmTx2AF08jISDQajTpX7lVFixYlOjqa8+fP6/3o5mreu3dPDciaNm3KvHnz\n0gxynJyciIuLM3jf4J07d7hy5UqaKzO+zujRo+natSvnz5832Hb8+HE0Go3R1wro2qO36aEXQggJ\nyoQQn4zy5cujKIrRm+F3QTdfJS4uTi/90KFDbN26FcDgvWJly5bF3t6eLVu2cPbsWXW+jk6NGjUo\nVqwY69evNwgcAgMDWbJkidHg41WRkZEEBQUZvA9J12NgbH5TZhw6dIg5c+ZgZWX12vdQQWqPhYmJ\nCYmJiXq9Kk+fPmX8+PGA4bVK6zigP0eoRYsWKIrCpEmT9OZWJSUlMXr0aH799Vf1PV663sdp06ap\n8+EgNcDOyHV9E7ryff/993rDFK9du6auAPmxLY9eqlQpzp49q7dKYHx8PFOnTsXS0lLvZext2rRB\nURQmTJig9xkuXLiQW7du4ePjYxC06+YBpvUC69cZOXIkMTExeHp6Mm3aNINez5f5+PigKArTp0/X\nC/ynTZumt1JjZjVu3BiAn376SW8+pG7FyRIlShhdFv+vv/4iZ86cBkN+hRAiM2T4ohDik+Hm5oZG\noyEiIsLohPy31bRpUxYtWsSYMWM4evQoBQoU4MKFC+zfv5+8efNy9+5do710LVq0YPz48ZiYmOi9\nPwtSF1OYMmUKX331FR07dqRBgwaUKFGCM2fOcPjwYUqUKMGgQYNeW7YePXqwfft2Bg0axLZt2yhV\nqhTXr18nJCSEggUL0rFjx9ceQ1EUdu7cqQZyL168ICkpiXPnznH8+HGyZ8/O9OnTM/TE39LSkoYN\nG7Jjxw5at25NnTp1ePToEWFhYcTFxWFtbZ3mvJ+X2draoigKc+bM4dy5cwQEBODs7Iyfnx9BQUF8\n8cUXuLm5YW5uzq5du7h16xbt27dXe1iqVKlCt27dWLRoET4+PtSvX5+bN28SGhpKyZIl38uKeD4+\nPuzevZudO3fi7e1NvXr11PeUPXz4kJEjR350N+j9+/cnNDSUHj160KxZMywsLAgODiY+Pp7Jkyfr\nLSzTsmVLwsLCCA0NxcfHh7p163Lp0iXCw8MNXmmgo7vOac21S8+5c+fYtWsXJiYmFC5c2GBBHEjt\nwevZsycAtWrVomnTpgQHB9O2bVucnZ05ceIEJ06coHHjxnqvL8iMdu3aERISQnh4OD4+Pri6unLj\nxg1CQ0OxtLRk2rRpBkMzExMTuXjxIu7u7q/tXRZCiPRIUCaE+GQUKFCAypUrqyshvuptb4q0Wi0L\nFixg5syZhIaGYmJiQtGiRfn6669p2bIl9erVY9++ferNoU6TJk0YP3481atXNxrQODk5sW7dOubO\nncvhw4fZu3cvhQoVonPnzvj7+xssNW+sHkWLFmXVqlXMmTOHiIgI9uzZg42NDS1atKBv374GqzUa\no1sIZffu3WqapaUlxYoVw8/Pj06dOhkNJjQajdEyTZw4kcKFC7Nz505WrFhBgQIFqFKlCj179mTL\nli0sW7aMw4cPqws/GDtO06ZNCQ8PJywsjFWrVuHr60upUqX43//+R5UqVVi5ciWbN28mW7ZslC5d\nmgEDBhj0Rg4ZMoQyZcqwbNky1q1bh62tLd9//z3R0dEGq3Gmd21eLVta9Qb45ZdfWLFiBevXr+e3\n337D0tKSatWq0b17d733yKV3nPSO/65v8AsXLszatWuZOnUqoaGhPH/+HHt7e3r16mVQXkit3/Ll\ny1m/fj0rV64kT548dOzYkX79+um950zn/v37aDSaN1pg49ixY2g0GhRFYenSpUbz5M6dW+/v7scf\nf6R8+fJs3LiRZcuWUbhwYQYMGJCh96Oldd2zZcvGokWLCAwMZOvWrQQFBZErVy4aNWpE//79jb4M\n+8CBAwB88cUXGa2uEEIYpVHeZla8EEJ8YNu2bWPQoEEsWrTI6IT8rLB//3569OjBxIkT30sPnhDi\n4/TVV19x+fJlduzY8dpFaYQQIj3SggghPilNmjShZMmSrF27NquLAqTOhQoMDCRXrlw0adIkq4sj\nhPhAbty4wYEDB+jZs6cEZEKItyatiBDik6LRaBg+fDg7duzgzz//zLJyXLt2jebNm9OwYUOOHz9O\n9+7d3+lLnIUQH7eZM2diZ2dHy5Yts7ooQoh/AQnKhBCfnHr16tGiRQumTZuWZWXIly8fDx48ICkp\niY4dOxrMMxNC/HtdunSJbdu2MWnSpHRXihRCiIySOWVCCCGEEEIIkYWkp0wIIYQQQgghspAEZUII\nIYQQQgiRhSQoE0IIIYQQQogsJEGZEEIIIYQQQmQhCcqEEEIIIYQQIgtJUCaEEEIIIYQQWShbVhdA\nCCGE+ND8/Pw4duyYXpqJiQk5c+bEzs6OXr164erqCoBWq9XLly1bNvLly0f9+vUZOHAg1tbWH6zc\nQoisNWDAAE6dOsXevXv10rds2cKQIUNwdHRk1apVett+/vlnFixYwHfffcf48eMNjpk9e3ZsbW1p\n0qQJAQEB8u67/ygJysQn6dChQwQFBREVFcWDBw8oVKgQHh4edOnShfz586v55GZKCJGWqlWrMnz4\ncPX358+fExsby4IFC+jduzfr16/Hzs4OgI4dO+Lt7Q1AcnIyly9f5pdffuHatWssWrQoS8ovhPjw\nnJ2d2bFjB7dv38bW1lZNP3DgAHny5OH06dMkJSVhZWWlbouIiMDR0ZEcOXKg0WiYP3++3v3HvXv3\n2L59O3PnzuXZs2cMHjz4g9ZJfBwkKBOfnF9++YU5c+bg6enJqFGjsLa25sKFCyxevJjff/+d+fPn\nU6FCBTW/3EwJIYzJlSsXVapU0UurVq0aVapUoXHjxmzZskUNymxtbfXyVq9eHTMzM7777juuXbtG\n8eLFP2jZhRBZw8XFBUVROHHiBE2aNFHTDxw4QKdOnZg1axaHDx/Gw8MDSH3YExUVRc+ePdW8FStW\nJF++fHrHdXNz4/Lly2zcuFGCsv8omVMmPim7d+9mzpw5DBw4kF9++YWGDRtSs2ZN/Pz82LhxI3ny\n5GHAgAE8efJE3Ud3M1WlShWqV69O69atGTRoEIcOHeLatWtZWBshxMdI94Rbo9FkKJ8Q4r+jTJky\nFChQgFOnTqlpFy5cIC4ujiZNmqDVatm/f7+67cyZMzx9+pTatWu/9ti5cuV6L2UWnwYJysQnZd68\neZQvX17viZOOtbU1w4cPJyYmhi1btqR7HLmZEkIoikJKSor68/TpU6Kjoxk2bBhmZmZ4eXkZzZuc\nnEx0dDRz587F1dVVesmE+I9xdnbWC8oOHjxI4cKFKV26NHXq1OHgwYPqtoiICKysrPR62p8/f662\nJ8+fP+fOnTssXLiQAwcO4OPj80HrIj4eMnxRfDLu3btHVFQU/v7+aeZxcXEhT5487N69m9atWwP/\ndzMFkJKSwt9//y03U0II9u/fT6VKlfTSTE1NqVy5MosWLdKbkzpjxgymT5+ul9fGxoZp06Z9kLIK\nIT4eLi4uhISEkJycjLm5Ofv371d7wmrXrs2vv/6qDms+fvw4NWrUwMQktR9EURTc3NwMjlmkSBH6\n9+/PV1999UHrIj4eEpSJT8aNGzeA1IYrLRqNhqJFi6p5QW6mhBDGOTo6MnLkSBRFITY2lqlTp2Jj\nY8PMmTMpUKCAXt6OHTuqT7B1C4IEBgbSvn171q5dKw94hPgPcXFx4dmzZ5w5cwZ7e3siIiKYOHEi\nAE5OTpibm3P48GGKFy/OiRMn6N+/v7qvRqNh4cKFWFtb8+TJE4KCgggPD2f48OHqPDTx3yRBmfjk\nvG6eh6mpKcnJyervcjMlhDDGysqKihUrAlCpUiW0Wi0+Pj707NmTNWvWYG5uruYtWLCgXq+ag4MD\n1apVw8PDg6VLlzJixIgPXn4hRNYoVqwYRYoU4eTJkzx58oTk5GRq1aoFgLm5OdWrV+fYsWNUq1aN\nhIQEXFxc9Pa3s7NTF/pwcnLiq6++4uuvv2bJkiVUr179g9dHfBxkTpn4ZOh6yGJjY9PNd/36db3e\nNN3NVKVKlXBwcMDLy4vAwEASExNZunTpey2zEOLTUbJkSQYOHMj58+eZPXv2a/MXLlwYa2trrl69\n+gFKJ4T4mLi4uBAVFcXhw4fRarXY2Nio22rXrs2JEyc4fvw4tra2lC1bNt1jTZgwAQsLC4YPH673\nUFn8t0hQJj4ZNjY2VK1aldDQUL3027dvEx8fD6ROqL1796760te0yM2UEMKYDh06oNVqWbx48WtX\nZ42NjSU+Pp6SJUt+oNIJIT4WLi4unD59moiICIN7jjp16hAbG8v+/fvVHrT02Nra0rdvX2JiYli4\ncOH7KrL4yH2UQdn9+/epXbs2d+/eNbpdURQmTpyIs7Mzrq6u8gX+D+nduzd///03c+bMUdN+//13\n3NzcmDlzJuPHj6dgwYLqIh9pkZspIYQxJiYm6tNq3RwRgFu3bhEZGan+bN++nb59+5I9e3Y6dOiQ\nhSUWQmQFZ2dnbty4QVRUlMFy91qtlrx587Jnz54MBWUAnTp1olSpUsyfP5/bt2+/jyKLj9xHN6fs\nwYMH9O3bl3v37qWZZ/78+ezdu5eFCxcSFxfHkCFDKFWqFO7u7h+wpCIruLm5MWDAAH755Reio6Np\n1qwZ1apVo0GDBsyePRuNRsOYMWOwtLRU99HdTOncvHmTuXPnys2UEP9xac1PrVmzJo0bNyYkJIT9\n+/ej0WhYuXIlK1euBFIDt1y5clG1alUmTJhA6dKlP2SxhRAfAVtbW0qVKsXt27dxcnIy2F6rVi2C\ng4Mz9H4ygGzZsvHdd9/Rq1cvpkyZYrBAmfj30yiKomR1IXTOnDnDoEGDsLS05M8//2T//v0GbzwH\nqFu3LiNGjKBRo0ZAapB2+PBhFi1a9KGLLLLIsWPHWLJkCZGRkSQlJVGoUCHq169PcnIya9eupXnz\n5owfP56KFSvq3Xi9fDPVt29fKleunIW1EEIIIYQQ4iPrKTt8+DDNmjWjWbNmasD1qtu3bxMXF0e1\natXUNEdHRwIDAz9UMcVHoEaNGtSoUcPoNl9fX/bt24eJiQnR0dEfuGRCCCGEEEJkzkcVlPXo0QNI\nXT0vrWElcXFxAHrvkMmXLx+PHj3i4cOH5MyZ8/0XVHzU7O3tsbe3z+piCCGEEEIIkSEf5UIf6Xn8\n+DFmZmZ6abrfZRlRIYQQQgghxKfmkwvKLCwseP78uV7as2fPAMiRI0e6+35E0+eEEJ8waUuEEO+C\ntCVCCJ2PavhiRtja2qIoCvHx8eTNmxeAu3fvkitXLiwsLNLdV6PRqMMfP1X58+f/5OsA/456SB0+\nDvnz5//g55S25OPwb6gD/Dvq8W+pw4cmbcnHQerw8fg31ONN25JPrqesYMGCFCpUiBMnTqhpERER\nVKlSJQtLJYQQQgghhBBv5qPtKXu5S//Zs2ckJCSQL18+NBoNbdu2ZcqUKeTPn5/79++zcOFCfvzx\nxywsrRBCCCGEEEK8mY82KHt59cWTJ0/SuXNnQkNDKVKkCF999RVxcXH06NGD7Nmz069fP+rXr591\nhRVCCCGEEEKIN/RRBmVFixbl/Pnz6u81a9bU+93U1JQRI0YwYsSIrCieEEIIIYQQQrwzn9ycMiGE\nEEIIIYT4N5GgTAghhBBCCCGykARlQgghhBBCCJGFJCgTQgghhBBCiCwkQZkQQgghhBBCZCEJysQn\nLSAgAFdXVwYNGpRmHl9fX1xdXQkNDX3r87m6urJixQoAjh49iqurK+fOnXvr434IiqLg7+9Pv379\nsrooQnx0pC1J34sXL1i6dCmtW7fG3d2dnj17cvTo0awulhAfFWlH0iftSPokKBOfPBMTE06cOMHD\nhw8Ntp09e5Y7d+7ovffuXalUqRLz58+nTJky7/zY78Pq1as5e/ZsVhdDiI+WtCVpCwwMZNmyZbRp\n04YffviBUqVKMWTIEGlThHiFtCNpk3YkfRKUiU+eVqsFYP/+/QbbwsLCKFeu3Hs5b86cOalYsSKW\nlpbv5fjvUkxMDAsXLqRAgQJZXRQhPlrSlhj39OlTfvvtNzp06EDr1q1xcnJi+PDhFCxYkN9//z2r\niyfER0XaEeOkHXk9CcrEJy9Xrlw4OTmxZ88eg21hYWG4u7ujKIpeekJCAt9//z1eXl64u7szYMAA\nLl26pJcnOjqagIAAPDw8aNeunUEXu7GhAqGhofj7++Ph4UGDBg3o0qULe/fuVbdv27YNDw8PTp06\nRffu3fn8889p27Yt27ZtS7eOrq6uaf5MnDgx3X0VRWHSpEl88cUXr32ClpiYyOeff05QUJBe+s2b\nN6lbty4HDx5U67B+/Xq8vLxo1qwZ9+/f59y5cwQEBODp6UmjRo0YOnQoV65cSfd8QnxMpC0x3paY\nmZkRGBiIr6+vXnq2bNlITk42uo+0JeK/StoRaUfeVLasLoAQ78Lnn3/O9OnTefLkifqU6MyZMyQk\nJODq6kpgYKCaNzk5mYCAAB49ekT//v2xsrJi9erV9O3blyVLllCoUCHu3LlD//79KV++POPGjSMu\nLo5x48YZDDl4+fe9e/cyevRo/Pz86NWrF0lJSaxcuZKxY8eyfv168ubNC8CzZ88YP348fn5+FC9e\nnDVr1jBp0iTs7e0pUaKE0frNnz8/zbrnyZMn3WuzZs0a4uLimDFjBv/73//SzZs7d25q167Njh07\n6Nixo5q+fft2bGxscHFxYfv27SQnJ7NlyxZGjRrFgwcPMDMzY8iQIdSoUYNu3brx6NEjFixYwJAh\nQ1i7du17GaohxPsgbYkhExMTypYtC6Q+5ImLi2P16tXcuHGDoUOHGt1H2hLxXybtiCFpR15PgjLx\nr1C3bl1++OEHDh48SIMGDQDYs2cPLi4uZM+eXS9vcHAwV69eZcmSJWrPkbOzM23btmXp0qUMGzaM\ntWvXYmJiwo8//kiOHDmA1KdfI0eOTLMM165do3nz5vj7+6tptra2dOvWjbNnz1K3bl0gdaJr7969\n8fDwAKBMmTI0a9aMgwcPptkAVqxY8Y2uS2xsLL/++iuTJ0/O8JCGpk2b8u233/LXX39Rvnx5AEJC\nQvD09MTEJLVzXVEUunbtirOzM5A6Tj4hIYFWrVphb2+v1j0sLIxHjx6RM2fONyq/EB+atCXp27Rp\nE9OmTUOj0dC8eXOqVq2aZl5pS8R/lbQj6ZN2xDgJysS/Qu7cuXFycmLv3r1qAxgWFkZAQIBB3hMn\nTmBra0vJkiVJSUlR02vWrMnx48cBiIqKwsHBQW38ANzc3NQGwBjdU5yHDx8SExPDtWvXiIiIQKPR\n8OzZM728lSpVUv9tY2NDtmzZePLkSZrHfrmcrzIxMUnzqc/kyZNxd3enevXqae7/KhcXF2xsbAgJ\nCaF8+fKcOXOG69ev07hxY718LzfWZcqUwdramqFDh+Lu7o6zszM1atSgZ8+eGT6vEB8DaUvSf4Ls\n5AAfATYAACAASURBVOTE7NmzOX/+PL/++iuPHz9O88ZQ2hLxXyXtiLQjb0KCMvGvUb9+fWbNmsWz\nZ8+4cOECCQkJ1K5dm3v37unlS0hI4ObNm7i5uemlazQasmVL/ZNITEykdOnSettNTEzSHSp47949\nJk2axKFDh8iWLRslSpRQJ/S+On781V4rjUbDixcv0jy2m5sbGo3G4DgajYYmTZowfPhwg33WrVtH\nTEwM33//PSkpKSiKov6kpKRgampq9FympqZ4enqya9cuAgICCAkJoUyZMgaTk21sbNR/Z8+enblz\n57JkyRJ27tzJpk2bsLCwwNfXlz59+qRZLyE+RtKWpK1EiRKUKFECBwcHXrx4wdy5c/H39yd//vwG\neaUtEf9l0o6kLa12pGDBggZ5/0vtiARl4l/Dzc2NadOmceTIEU6dOkXt2rWxsLAwyGdlZUXZsmX5\n3//+Z9Cg6FhbWxs0nAAPHjxI8/xjxozh5s2bzJ07lwoVKmBqasqVK1cICQl580r9fwsXLkxzm7W1\ntdH0vXv3cu/ePby8vAy21a9fn5kzZ6rDFV7VtGlT1qxZw6lTp9i7dy9ffvnla8tYokQJRo0axYsX\nLzh9+jSbN29m1apVaLVa9UmhEJ8CaUv03b9/n0OHDlGnTh1y586tpn/22WcAxMXFpXlMaUvEf5W0\nI/oy0o4YC8rgv9OOSFAm/jWsra2pWrUqYWFhREZGpvmSZAcHB44ePUqBAgX0nqxMmzYNc3Nz7Ozs\nqFatGuvXrycxMVFtPI4cOWLQ5f+yM2fO0KZNG3X8MsDhw4eNPk3KLDs7u0zvM2zYMB49eqSXNmPG\nDJ49e8awYcMoXrx4mvuWKVOG8uXLs2DBAhISEvD09Ez3XAcPHmTixIksX74cGxsbHBwcqFChAjt2\n7ODOnTuZLrsQWUnaEn1Pnjzh+++/p1+/frRt21ZNP3LkiPoEPi3Sloj/KmlH9Ek78noSlIl/lQYN\nGjBjxgzMzc2pVauW0TxeXl6sX7+eAQMG0KlTJ2xsbNi1axdbt27l22+/BaBNmzZs3bqVb775hi5d\nupCYmMivv/6KmZmZ3rFebtgqVKjA9u3bKVu2LDY2Nhw7dow1a9YA8Pjx4/dU47QZC7py5szJ/2Pv\n3uOjLO+8j39nyIFwCgmTBCiJQEShWmNQSJVQECgWRbfwEnVrK5442AKuKzElSkWfGmBVXIXFA4q4\nnlAhHupadMujeQwGsJwCNCpqQRTXMIEkQk4DM88fLKljkpk7yWTue+75vF8vXm3uXMn8rrnh5/yu\n+zo0NjY2jUwFMmnSJD3yyCO66KKLmnZpas3p+egFBQW67rrrFB8frzfffFOxsbEaNWpU+zoAmIhc\n8g99+/bVL37xCz311FNyOBzKzMzU5s2b9corr+jGG29Ujx49Av48uQTRijzyD+SR4DinDBHv+wtK\nx4wZI5/Pp1GjRikuLq7FNt26ddPKlSt11lln6eGHH1Z+fr4++eQT3XPPPU1T/ZKSkrRy5Ur17t1b\n9957r5555hndeuut6tmzZ6uvfdddd+nMM8/UAw88oIULF6qsrExLlixRenq6du/eHbQP4dqi1ejr\nXHzxxZLUbDFtSxITE7Vs2TIlJCSosLBQCxYskNvt1rJlywI+kQOshFzSuvz8fP3zP/+z1q9fr/nz\n56u0tFTz58/XDTfcEPRnySWIJuSR1pFHAnP4OvoMM8IEmvseCVwuV8T3QbJHP+zeh/Xr12v16tV6\n/fXXm43GWUlLGwyEg53vfaSwQx8ke/SDXNJ+dr73kYI+WEdr/YiUPCK1P5dYavrikSNHVFBQoC1b\ntqhfv34qKChQbm5us3b19fUqLCzUu+++q7i4OE2ZMkW33367CRED9vP++++rvLxcr732mq699lrL\nJz8A1kQuAdBR0ZRHLDV9MS8vT06nU+vWrdNVV12lefPmtbgg79FHH9XevXv17LPPatmyZSoqKtKL\nL75oQsSA/Rw6dEjr169XTk6OfvOb35gdDoAIRS4B0FHRlEcs86Rs//79Ki0tVUlJiZKTk5WZmalN\nmzapqKhIs2fP9mtbUlKi66+/vmn3l8mTJ2vLli2GtsgEENivfvUr/i0B6DByCYCOiqY8YpknZWVl\nZcrIyPDbUSU7O1s7d+5s1jYpKUkbNmzQ8ePHdfjwYZWUlPidRg4AAAAAkcIyRVllZWWzQ+NcLleL\n0xfz8vL02WefacSIEfrZz36m1NRUzZgxI1yhAgAAAEDIWKYoq6ur89suVJJiY2NbPBjvwIEDGjRo\nkJ5//nk9/vjj+vzzz/XEE0+EK1QAAAAACBnLrCmLj49vVoB5PB4lJCT4XTt27Jjuvvtuvfrqqzrz\nzDMlnTocrqCgQDNnzpTTGbjONGvL21CyQx8ke/SDPkQvO7xv9ME67NAPO/TBDHZ43+iDNdihD5J9\n+tFWlinK0tLSmp1L4Ha7lZKS4nftiy++UENDgwYPHtx0bdiwYTp+/LiqqqqCnvId6Wc42P0cikhC\nH6yBs4Xaxy73PtL7INmjH3bpgxns8L7RB/PZoQ+SPfrR3lximemLWVlZOnDggKqqqpqu7dixQ1lZ\nWX7tUlNT5fP59NlnnzVd++KLL5SQkBC0IAMAAAAAq7FMUZaenq6cnBzl5+dr3759Wr16tXbt2qUp\nU6bI4/HI7XbL5/Opb9++GjVqlBYtWqSPP/5Y27Zt0+LFi3XdddeZ3QUAAAAAaDPLFGWStHTpUnm9\nXk2bNk1FRUVasWKFUlJStGPHDo0ePVrffPONJGnZsmXKyMjQTTfdpDvuuEM///nPddttt5kcPQAA\nAAC0nWXWlEmn5mCuWrWq2fWRI0eqvLy86etevXppyZIl4QwNAAAAADqFpZ6UAQAAAEC0oSgDAAAA\nABNZavoiAABAtPhzeYUOVlQGbZfYNUbjM5PCEBEAs1CUAQAAmKC63qO0HnFB2317rDEM0QAwE0UZ\nAACACea+tkcO+YK2O79vd009JyUMEQEwC2vKAAAALKqy1qPth46ZHQaATsaTMgAAABMsn3KuErz1\nAdvMeGOfvN7gT9MARDaelAEAAACAiXhSBgAAYILErrE6WFETsI3X65PDEaaAAJgmqooytp4FEArk\nEgChMGlYqtwpgSct/bH4oOpPeMMUEQCzRFVRxtazAEKBXAIAAEIpqooyAAgFtrEGAAChxEYfANAJ\n2MYaAAAYFVVPyhjdBhAKbGMNAABCiSdlP8DoNgAAAIBwiqonZYxuAwgFtrEGEE4+n09Few8HbMNu\nr0Bki6qiDABCgW2sAYSTz6egO76y2ysQ2SjKWuCTgo5ISYxKAQCAzne0/oRmvLEvYBvWwwORzVJr\nyo4cOaLZs2crOztbl112mUpKSlptu2bNGo0ZM0YjR47UggUL1NDQELI4ujgdSusRF/RPdf2JkL0m\nAADAD105NFlJXQOPobMeHoh8lnpSlpeXp/j4eK1bt07FxcWaN2+eNmzYoNTUVL92b775plasWKGH\nH35YKSkpmj9/vh555BHdeeedAX+/0XUgrCgDAABWsGjcQJ2X1j3g9EXWwwORzzJF2f79+1VaWqqS\nkhIlJycrMzNTmzZtUlFRkWbPnu3X9plnntHcuXM1evRoSdK8efO0du3aoK9hdB1IbePJ9ncEAP6X\nkcX5ElOhAQCIdpYpysrKypSRkaHk5OSma9nZ2dq5c6dfu2PHjqm8vFyPPvpo07WJEydq4sSJIYvF\nyNxtifnbAAIzsjhfYoE+AADRzjJFWWVlZbNpii6XSxUVFX7XvvrqKzkcDh06dEi33367Dh8+rAkT\nJig/P19xccE//ARz5dBkrS0LPrLN/G0AwTDAAyAUErvGBBy84QgOIPJZpiirq6trVlTFxsbK4/H4\nXTt+/Lgk6f77728qxO655x4tXbpUCxcu7HAcRuZuS8zfBhAYAzwAQiXY9GaO4AAin2WKsvj4+GYF\nmMfjUUJCgt+1mJgY+Xw+zZs3T6NGjZIkFRQU6He/+52hoszlcgVt06tXgxJ7xgds43Q65ZPP0O8L\nNTNeszPYoR/0IXoFe99WXO1S7pCv1TdILrn2+e066SWXtJcd+iDZox926IMZQvG+OZ1OOZ3m3QM7\n3Hv6YB126UdbWaYoS0tLk9vt9rvmdruVkuI/pef014MGDWq6NmjQIDU2NurIkSN+a9Ja8sPXaElN\nTY0SvIGflHm9Xvm8PkO/L5RcLlfYX7Mz2KEf9MEazEre5BLz2aEPkj36YZc+mCEU75vX65XX6zXl\nHtjl3tMHa7BDP9qbSyxzTllWVpYOHDigqqqqpms7duxQVlaWX7v+/fsrJSVFH3/8cdO1zz//XN26\ndVPv3r3DFi8AAAAAhIJlnpSlp6crJydH+fn5mj9/vj744APt2rVLS5YskcfjUXV1tfr06SOHw6Hr\nr79eDz74oFJSUpSQkKAHHnhAU6dOldMZ3hrTJ7HdNYBWBVucL7FAHwAAWKgok6SlS5dqwYIFmjZt\nmgYMGKAVK1YoJSVFW7du1fTp07Vx40b1799fM2bMUENDg/7lX/5FHo9HkydPVl5eXsjiMPpBSmK7\nawCtMzIYwwJ9AABgqaLM5XJp1apVza6PHDlS5eXlftfmzJmjOXPmdEocRj9Iccg0AACwAg6rByKb\npYoyAAAAtB2H1QORjaKsAzgYFgAAWAGfSYDIRlHWThwMCyBUjEw7YsoRgNbwmQSIfBRl7bRo3ECd\nl9Y96FSBGW/s00kvH7gAtM7ItCOmHAFoTVs+k5zeqAyAtVCUhUEXp4MPXAAAAABaRFEGACYzshaE\ndSAAANgXRRkAmMjIWhDWgQAIhsPqgchGUdYBRhMgo+AAWmNkLQjrQAAEw2H1QGSjKOsAIwmw7Nvj\njIIDAAAAaBVFWSdjFBxAMMGeujPlCAAAe6MoswifFHTbfElKT/VqRIqz8wMCEDbBnroz5QgAAHuj\nKLMII9vmS1J1vUdSfOcHBAAAbMfIYfUS56cC4UZRBgAAECWMHFYvcX4qEG4UZQAAAFHCyI7QErtC\nA+FGURYGRhbxG02SORlJmpCeGcrwAEQAphwB6Cgj5yJK7AoNmIGiLAyCfUAysm2+dCpJbv6yKlRh\nAYggTDkC0FFGdoSW2BUaMANFmQW0JUmSIgEAAAB7oSiLMExhAqIT60AAALAvSxVlR44cUUFBgbZs\n2aJ+/fqpoKBAubm5AX+msLBQ5eXleu6558IUpbliujiZwgREGdaBAAiVYOvcJQ6sB8xgqaIsLy9P\n8fHxWrdunYqLizVv3jxt2LBBqampLbb/29/+phdeeEEXXHBBmCMFgPBhHQiAUDEyi4YD64Hws0xR\ntn//fpWWlqqkpETJycnKzMzUpk2bVFRUpNmzZzdr7/P5dM899ygrK8uEaAEAAAAgNCxTlJWVlSkj\nI0PJyclN17Kzs7Vz584W27/wwgtKSUlRdna2Pvjgg3CF2WmMTieorG1gXQkQhZhyBACAfVmmKKus\nrGw2TdHlcqmioqJZ24qKCj3++ON65ZVX9NZbb4UrxE5lZDpB2bfH9fKeSinIHoysKwHshylHAMLJ\n6MZi6alejUhxhiEiwN4sU5TV1dUpLs5/vURsbKw8Hk+ztoWFhZo+fbr69+8frvAsYdG4gcod0k8J\n3vqA7VhXAgAAOsLo2YjV9R5J8Z0fEGBzlinK4uPjmxVgHo9HCQkJfteKi4v16aef6qGHHmrX67hc\nrnbHaAkHv1ZiYmLAJk6nUz75LN9Xq8dnBH2IXlZ835xOp5xO47FZsQ9tZYc+SPbohx36YAYrvm9O\np1NH6xs060+fB207YkCirs3OCUNUncuK96Gt7NAHyT79aCvLFGVpaWlyu91+19xut1JS/NdFbdiw\nQQcPHtSFF14oSTpx4oROnjyp4cOHa/v27UFf54evEYmqq6sDft/r9crn9Vm6ry6Xy9LxGUEfrMGs\n5G3F983r9crr9RqKzS73PtL7INmjH3bpgxms+L5NPqu31pZ55PUGng5dWevR5i+rLNmHtrDL399I\n74Nkj360N5dYpijLysrSgQMHVFVVpd69e0uSduzYoZwc/9GXvLw8/fa3v236+qWXXtK2bdv04IMP\nhjVeAAAAO2rLMRwslgBCwzJFWXp6unJycpSfn6/58+frgw8+0K5du7RkyRJ5PB5VV1crOTm56c9p\nvXv3Vnx8vNLT002M3np8kqEFuoldYwxtIAAgMrA4HwCAyGOZokySli5dqgULFmjatGkaMGCAVqxY\noZSUFG3dulXTp0/Xxo0bo25zjx9K7BqrgxU1Aduc3uTDyALdYFtsA4gsLM4HACDyWKooc7lcWrVq\nVbPrI0eOVHl5eYs/M3PmTM2cObOzQ7OMScNS5Q4yuv3H4oOqbTwZpogAWMnR+hOGzjLMyUjShPTM\nMEQEIBIZPRuxSxcORwRCwVJFGQCg/a4cmqy1ZcGnLp5enA8ArTF6NmIjRyMCIUFRBgA2weJ8AAAi\nE0WZTRmdwvST1G6Gfh8bggAAAACdg6LMhtoyhWl3Ra3msSEIYBusAwEQTkZ3fGVwFwiMosyG2jKF\nCYC9sA4EQDh5De74yuAuEBhFGQAAANrlSG2joUHe8/t219RzUsIQERCZKMqiXGWth2QKAADa7Mqh\nyXp5T6UUZOugylqPth86Fp6ggAhFURbFRmX00qYvAx9ELZFMATtiHQiAjlo0bqByh/RTgrc+YLsZ\nb+yT18uer0AgFGU2ZWSx/6QhSbpkUG+d0Ts+YLsZb+zTSS8f4AA7YR0IAADWQVFmU0YLIyOFliR1\ncTr4AAfYCOtAAIRCYtdYHawIPOvG6/XJwYavQEAUZQAQZVgHAiBUJg1LlTvFGbDNH4sPqv4EW74C\ngVCUAUCUYR0IgHBjHSsQGEUZDDG6S+NPUrsZ+n3pqV6NCDKyBgAA7MHHOlYgIIqyKGdkQ5Dz+3bX\nNgNTmCprPdpdUat5BpJudb1HUuANRgAAgD0crT/BOlYgAIqyKGdkisDUc1JUtPdw0BEuI8kWgDWw\nOB9AuFw5NFlry4JPXWQdK6IZRRkARCEW5wMIl0XjBuq8tO6GBndZx4poRVGGkDK69iwnI0kT0jPD\nEBEAAABgbRRlCJlRGb206cvA06GkU4Xb5i+rwhARAACwAiNr2JkyjWhmqaLsyJEjKigo0JYtW9Sv\nXz8VFBQoNze3WbvvvvtOhYWFev/99xUTE6PLLrtM8+fPV2xsrAlRRwcjyXTSkCRdMqi3zugdeAOP\nGW/sC3I6EgCrYBtrAKFgJD8wZRrRzFJFWV5enuLj47Vu3ToVFxdr3rx52rBhg1JTU/3a3Xvvvfrq\nq6+0Zs0a1dXV6fe//726du2q22+/3aTI7c/ohy0jH94kYx/0+JAHmI9trAGEEwNBiFaWKcr279+v\n0tJSlZSUKDk5WZmZmdq0aZOKioo0e/bspnaNjY1699139eKLL+rss8+WJM2dO1ePPPIIRVkEieni\nDPpBjw95gPnYxhpAODEQhGhlmaKsrKxMGRkZSk5ObrqWnZ2tnTt3Nmu7cuXKpoJMkhwOhxoaGsIS\nJwBEC7axBhBuDAQhWlmmKKusrGw2TdHlcqmiosLvWlxcXLN1Zi+99JKGDx/e6TEiOKMLeb0+5owD\nVsc21gDCiYEgRDPLFGV1dXWKi/P/D39sbKw8Hk/An1u+fLl2796t9evXd2Z4MMjoQt6vaxqCjoQx\nCgYAQPRgIAjRzDJFWXx8fLMCzOPxKCEhodWfefLJJ/X4449r2bJlysw0duaVy+XqUJxWEOl9uCZ7\ngNZ8dFBOZ+v73h4+1qDt3xzXXw4Gn5aa2DVWk4alBm3XGSL9Xkj26IMZ7PC+GelDeqpX1fWBB8d8\ncqhLF4cp74kd7oNkj37YoQ9msMP7Fso+9OrVoMSegXdxdjqd8skX0tflPliHXfrRVpYpytLS0uR2\nu/2uud1upaS0/KTkscce0/Lly1VYWKhLL73U8Ov88DUijcvlivg+5F+UqgsGJCrBW99qm9NP0QK1\nOe1gRY3cKc6QxWeUHe6FXfpgBju8b0b6MCLFKSnwB6SEGIfqT3jD/p7Y4e+vZI9+2KUPZrDD+xbK\nPjgaa/Xp14HPPD1x4qQcjtC9d3b5+xvpfZDs0Y/25hLLFGVZWVk6cOCAqqqq1Lt3b0nSjh07lJOT\n06zt66+/ruXLl+u+++7TL3/5y3CHCgD4AbaxBhAKRpdB1HlOcrQObMUyRVl6erpycnKUn5+v+fPn\n64MPPtCuXbu0ZMkSeTweVVdXq0+fPqqtrVVhYaGmTJmisWPHNlXTDodDffr0MbkXCKXKWg87MAER\n4kjdCd298UDQduf37c6HJAAdZmTrfLbNRySxTFEmSUuXLtWCBQs0bdo0DRgwQCtWrFBKSoq2bt2q\n6dOna+PGjfr000/13XffqaioSEVFRZJOjdDGx8dr165dJvcARiV2jdXBitanJ5zft7u2GdhZqbLW\no22HjjFCD5iIHdMAhJuRrfMZtEUksVRR5nK5tGrVqmbXR44cqfLycklS//79m/4/ItekYakB14FN\nPSdFRXsPG9qBSeKgScBM7JgGIJyMDAQxCIRIY6miDOhM9SdY8wIAQKQzMhDEIBAiDUUZIp7RtWej\nMnrphuy0oO14oga0ndGD4x2tn4QBAIYFyznkG0QaijJEtFEZvbTpy8Bb50qnCrdNX9YYKsoAtJ3R\nHdPqT3jDEA0AuwuWc8g3iDQUZbAsIyPvk4Yk6ZJBvXVG78DnKM14Yx+7OQIAAMCSKMpgWUbXdRlZ\nJ9aWJ2osDAY6D+eZAQgXo/kmPdWrEQE2HwPCgaIMUeGG7DRDUxdZGAx0Ls4zAxAuRvNNTkaNnvll\nZhgiAlpHUYaIZ2Sao3Rq98Vg0xwlySdjT98YWQPahvPMAIRLW/LN5i+rwhAREBhFGSJeKKc5SlIX\np8PQuWfV9R5JwYs8AKdwnhmAcGlLviHbwAooyhA1jG7ZTXIGOg9b5wMA0BxFGaKG0S27v65pMLRL\n4wUDequmJjFoOzYsAP6BrfMBhIvhQSCngw2IYDqKMuB72jIHfcehGs2+IPjW+RxGDQBA+LVlMJYN\niGA2ijLge9oyBx1A5zGylTWb7QDoqCuHJuvlPZVSkMULbECEzkZRBrTT4WPGpjlyGDXQdka2smYb\nawAdtWjcQOUO6acEb33AdmxAhM5GUQb8gJE56Of37a7t3xwP+rsYWQPazsg0YraxBhAqiV1jdbCi\nJmCb0wUZa8/QWSjKgB8wkkinnpOivxxsMDSydtIbfBoWCRz4ByPTiNnGGkCoTBqWKneQqdCsPUNn\noygDOpmRc8/YDATwF+yJNTumAQintmwExgwZtAdFGdBORqc7HK0/EXTtGevOAH/BiihGrQGEU1s2\nAjMyQ0ZiwAj+KMqAdjIy3aHs2+OG1sYwqga0DTumAQg3o+eeVdUH36hIYsAI/ixVlB05ckQFBQXa\nsmWL+vXrp4KCAuXm5jZr5/P5tHjxYr3xxhuKjY3VjTfeqJtvvtmEiIHAjK6NYVQNaJu27JjGvy8A\noWAkPxgZjJWkw8c9+u/PqzT03z8K2vaijF56ZurZhmJE5LJUUZaXl6f4+HitW7dOxcXFmjdvnjZs\n2KDU1FS/dk8++aSKi4v19NNPy+12Ky8vTwMHDtT48eNNihxonZG1MYyqAW1ndAox/74AhMuicQM1\n+oxEVdefCNhu3V63th06JqfTEbBdZa1HpV8GznOwB8sUZfv371dpaalKSkqUnJyszMxMbdq0SUVF\nRZo9e7Zf2+eff1533323zj33XEnSjBkz9MILL1CUwZKCfcgzOqpWWevRtkPHGPEH/leophBL/PsC\nEDpGd3Eu2nvY0Bq1ylpP0CdqY89M0eOTB7YlTFiMZYqysrIyZWRkKDk5ueladna2du7c6dfu22+/\nldvt1vDhw/3aPfHEE2GLFQiltiwerqz1GBrx/0lqt6CjdJLUq1eDamqCj8DxIRSRyuiodcF/79dR\nnqgBCCOj56IGe6J2+LhH63d/o/c/Cz6oxFRI67JMUVZZWdlsmqLL5VJFRYXfNbfbLUlKSfnHTnV9\n+vRRbW2tjh8/ru7du3d+sECItSUxB3P4uEf/9+/V2l1RG7St0+mU1+sN2s5okUfxBisyug7k5d2H\nmw6IbU1l3QnWgQAICaNP1DZ+fjTgf4PX7XVr+zfHFWQmpA4f9+itT46QvyzKMkVZXV2d4uL8nxTE\nxsbK4/E0axcbG9usnSQ1NjZSlCEihSoxS/+Ypx4qbSnyeIKASLVo3EAtGjcweLv/u18v7z4sX5CT\nqyvrThj+8ONwOuULMjjCByQgegX77+rUc1L00WGvDlZUBmxndB1bW4q3UIvmaZiWKcri4+ObFWAe\nj0cJCQnN2p04caJZO0nq1q1b0NdxuVwdjNR8duiDZI9+hLsP1xh4vZljpD+XV6i63hO0baj1iOui\nPVXB2409k3tvBfSh7VZc7dKKq421DeW/Q6P/tiTz/n3Z4e+TGezwvtEH801ySRqWGrCNmZ8PjIqE\nXNdZLFOUpaWlNU1NPM3tdvtNUzzdzufz6ciRI03rzyorK9WzZ0/Fx8eHLV7AyiYFScwAOh//DgFY\nDXnJugJvWxVGWVlZOnDggKqq/lEe79ixQ1lZWX7tUlNT1bdvX23fvr3p2rZt23TeeeeFLVYAAAAA\nCBXLFGXp6enKyclRfn6+9u3bp9WrV2vXrl2aMmWKPB6P3G63fP87if+aa67R0qVLtXPnTr3//vt6\n+umn9etf/9rkHgAAAABA2zl8vmDLlcPH7XZrwYIF+uijjzRgwADdfffd+ulPf6qtW7dq+vTp2rhx\no/r376+TJ09q8eLFev3115WQkKAZM2bo+uuvNzt8AAAAAGgzSxVlAAAAABBtLDN9EQAAAACiEUUZ\nAAAAAJgoKoqyI0eOaPbs2crOztZll12mkpISs0Nqs3feeUdDhw7VsGHDmv532bJlZodlWFVVjQXq\nPQAAIABJREFUlS6++GJVVv7jYMPXXntN48aN0/Dhw7VgwQI1NDSYGGFwLfVh8uTJze5LY2OjiVG2\n7PPPP9cNN9yg4cOH69JLL9Wrr77a9L1IuQ+B+hCu+0AuMZcd8ohELjEbuSQ0yCXmIo+YL+S5xBcF\nbrrpJt+tt97q++yzz3xPP/20Lzs72/ftt9+aHVab/Md//Ifvtttu81VWVvrcbrfP7Xb7amtrzQ7L\nkJqaGt+vfvUr39ChQ31ut9vn8/l8f/3rX33nn3++75133vHt3bvXd+WVV/oKCwtNjrR1LfXh5MmT\nvnPPPddXVlbWdE9Of89KGhoafD//+c99f/jDH3z79+/3/fnPf/adf/75vk2bNvm2bdsWEfchUB/C\neR/IJeaxQx7x+cglZiOXhA65xDzkEfN1Ri6xzOHRnWX//v0qLS1VSUmJkpOTlZmZqU2bNqmoqEiz\nZ882OzzDvvjiC5111llNB2ZHij179uiOO+5Q165d/a6/+OKLmjJliiZOnChJuvfee3XjjTfqjjvu\nUFxcnBmhtqq1Phw8eFCSdO6558rhcJgRmiE7duxQVVWVFi5cqJiYGJ1xxhn68MMP9V//9V+qr6+P\niPvQWh/efvtt/ehHP5LU+feBXGIeO+QRiVxiBeSS0CGXmIM8Yg2dkUtsP32xrKxMGRkZfkkjOztb\nO3fuNDGqtvviiy80cOBAs8Nos82bN+uKK67Qo48+2nTOnCTt3r1bF1xwQdPX5513njwej/bt22dG\nmAG11ocvvvhC6enplk5+kjRkyBCtXLlSMTH/GINxOBxqaGjQnj17IuI+BOpDuO4DucQ8dsgjErnE\nCsgloUMuMQd5xBo6I5fYviirrKxUamqq3zWXy6WKigqTImqfv//979q4caMmTJigX/ziF3rqqafM\nDsmQW265RXPmzFFMTIzfX0632+13X5xOp3r37m3J+9JaH7744gs5HA7dfPPNys3N1YwZM/T3v//d\nxEhblpycrAsvvLDp66qqKm3YsEHDhw/X4cOHI+I+tNaH7OzssN0Hcol57JBHJHKJFZBLQodcYg7y\niDV0Ri6xfVFWV1fX7JFnbGysPB6PSRG13bfffqv6+nr17NlTK1eu1Jw5c7Rq1So9//zzZofWbvX1\n9S3eFysuSG3N3//+d9XU1Gj69Ol64okn1KNHD910002qr683O7RWeTwe/eu//qv69Omjq666KiLv\nww/7EK77QC6xnkj8+9sScok5yCXtRy6xHvKIeUKVS2y/piw+Pr5ZovN4PEpISDAporZLS0vTli1b\n1KtXL0nSWWedJbfbrZdfflm//vWvTY6ufexwX+677z7V1dWpe/fukqSlS5dq7Nix+n//7/81zYe2\nEo/Ho3nz5qm8vFxr165VXFxcxN2HlvoQrvsQae9VS+yWS+xwTyRyiRnIJR1DLrEe8og5QplLbF+U\npaWlye12+11zu91KSUkxKaL2OZ34Ths8eLAOHz5sUjQdl5qa6hf/yZMnVVVV1WxKh5U5nc6mf3CS\nFBcXpwEDBljyvng8Hs2ZM0c7d+7Uf/7nf+qMM86QFFn3obU+hOs+kEusJ5L+/gZCLgkvcklokEus\nhTwSfqHOJbafvpiVlaUDBw6oqqqq6dqOHTuUlZVlYlRts2XLFl100UV+owcff/xxxC2w/b6srCzt\n2LGj6etdu3YpPj5eQ4YMMTGqtrn55pv1+OOPN31dV1enAwcOWPK+LFq0SNu3b9eaNWt09tlnN12P\npPvQWh/CdR/IJdYTSX9/AyGXhBe5pOPIJdZDHgm/UOcS2z8pS09PV05OjvLz8zV//nx98MEH2rVr\nl5YsWWJ2aIb95Cc/UXx8vP7whz9o9uzZKi8v16pVq3T//febHVqbfH+XoGuuuUa33HKLsrKyNHDg\nQN133326+uqr1aVLFxMjDO77ffjZz36mxx57TD/+8Y/Vv39/LV++XP369dOoUaNMjLC5v/71r1q/\nfr3++Mc/KiUlpWmENi4uLmLuQ6A+hOs+kEuswQ55RCKXmIVcEhrkEmsgj5inU3JJJ52pZimHDx/2\n3XLLLb6srCzf5Zdf7istLTU7pDb75JNPfNdff70vOzvbd8kll/hefPFFs0Nqk6+++srvkEOfz+d7\n5ZVXfKNHj/ZdeOGFvrvuusvX0NBgYoTB/bAPXq/Xt2LFCt+YMWN8559/vm/WrFm+b775xuQom1uy\nZIlv6NChzf7ccsstPp8vMu5DsD6E6z6QS8xlhzzi85FLzEQuCR1yibnII+bqjFzi8Pm+V2YDAAAA\nAMLK9mvKAAAAAMDKKMoAAAAAwEQUZQAAAABgIooyAAAAADARRRkAAAAAmIiiDAAAAABMRFEGAAAA\nACaiKAMAAAAAE1GUAQAAAICJKMoAAAAAwEQUZQAAAABgIooyAAAAADARRRkAAAAAmIiiDAAAAABM\nRFEGAAAAACaiKAMAAAAAE8WYHQDQFgsWLNBrr73W6vcdDod++ctf6quvvtJHH33UaptrrrlGixYt\n6qQoAUSiYPlFkqZMmaLFixeHKSIAkeyTTz7Rc889p82bN+vw4cPq2rWrzj33XF133XUaN26c2eHB\nYhw+n89ndhCAUQcPHtTRo0ebvl60aJHi4uJUUFDQdC0pKUkFBQXyeDx+17+vT58++tGPftTp8QKI\nHEbzS3p6uhnhAYggr7/+uhYuXKhzzjlHV199tTIyMlRdXa23335b//Vf/6Xbb79ds2bNMjtMWAhP\nyhBR0tPT/T4Qde/eXV27dtV5553XrG3Pnj1bvA4ALWlLfgGA1uzbt08LFy7UxIkT9dBDD/l9b/z4\n8UpPT9fy5ct1xRVXqH///iZFCathTRkAAAAQIk899ZRiY2N19913t/j9mTNn6mc/+5lqamrCHBms\njCdlsC2fz6eTJ0+2+L0uXbqEORoAABAN3nvvPV100UVKSkpq8fvdunXTypUrwxwVrI6iDLZVUlKi\nc845p9l1h8Oht99+W4MGDTIhKgAAYFc1NTWqqanRwIEDm33vhwPFTqdTDocjTJHB6ijKYFvZ2dla\nuHChWtrLhk0+AABAqLU2Q6e0tFQ33nij3zV2c8X3UZTBtnr06KEf//jHZocBAACiRFJSkhISEnTo\n0CG/61lZWVq/fn3T13Pnzg13aLA4ijIAAAAgRMaOHauSkhLV19era9eukk6tI/v+kor4+HizwoNF\nsfsiAAAAECIzZ85UQ0ODFi5c2OJ0xsOHD7PzIpqx5JOyqqoqXXbZZfrTn/6kPn36NPu+z+fT4sWL\n9cYbbyg2NlY33nijbr75ZhMihZV999132rVrV4vfi4+P19ChQ8McEQAAsLthw4apsLBQCxcu1FVX\nXaVp06YpMzNTdXV1+vDDD7V+/Xp16dJFl1xyidmhwkIsV5R99913+t3vfqejR4+22ubJJ59UcXGx\nnn76abndbuXl5WngwIEaP358GCOFVbS2c9GuXbt07bXXtvi9jIwMvfPOO50ZFgAbYGc0AO0xefJk\n/eQnP9ELL7yg5557Tv/zP/+jmJgYZWZmavbs2Zo2bZp69+5tdpiwEIevpa3pTLJnzx7dcccd6tq1\nqz799FOVlJS0+KRs9OjRuvvuu3XppZdKOlWkbd68WatXrw53yAAAAADQIZZaU7Z582ZdccUVevTR\nR1vcxlySvv32W7ndbg0fPrzpWnZ2dqvT1AAAAADAyiw1ffGWW26RJH399detThlxu92SpJSUlKZr\nffr0UW1trY4fP67u3bt3fqAAAAAAECKWelJmRF1dnWJjY/2unf66sbHRjJAAAAAAoN0iriiLj4/X\niRMn/K55PB5Jp86ACMRCy+cARDByCYBQIJcAOM1S0xeNSEtLk8/n05EjR5ScnCxJqqysVM+ePYMe\nxOdwOJqmP0Yql8sV8X2Q7NEP+mANLpcr7K9JLrEGO/RBskc/7NKHcCOXWAN9sA479KO9uSTinpSl\npqaqb9++2r59e9O1bdu26bzzzjMxKgAAAABoH8s+Kfv+I32Px6Pq6mr16dNHDodD11xzjZYuXSqX\ny6Wqqio9/fTTeuCBB0yMFgAAAADax7JF2fd3X9yxY4emT5+ujRs3qn///poxY4bcbrduueUWJSQk\naO7cuRo7dqx5wQIAAABAO1myKPvRj36k8vLypq9Hjhzp93WXLl1099136+677zYjPAAAAAAImYhb\nUwYAAAAAdkJRBgAAAAAmoigDAAAAABNRlAEAAACAiSjKAAAAAMBEFGWIaHPmzFFubq7uuOOOVttM\nnTpVubm52rhxY4dfLzc3Vy+88IIkaevWrcrNzdXf/va3Dv/ecPD5fJo1a5bmzp1rdiiA5ZBLAvN6\nvXr22Wc1bdo0jR8/XjNnztTWrVvNDguwFPKIcXwmaY6iDBHP6XRq+/btOn78eLPv7d27VxUVFX7n\n3oXKOeecoyeffFKDBw8O+e/uDGvXrtXevXvNDgOwLHJJ65544gn953/+p66++mr927/9mwYOHKi8\nvDxyCvAD5BFj+EzSHEUZIt7QoUMlSSUlJc2+99577+nMM8/slNft3r27fvzjH6tr166d8vtD6csv\nv9TTTz+tlJQUs0MBLItc0rKGhgatX79e1113naZNm6YLLrhABQUFSk1N1RtvvGF2eIClkEeC4zNJ\nyyjKEPF69uypCy64QO+//36z77333nsaP368fD6f3/Xq6mrdf//9uvzyyzV+/Hjddttt+vzzz/3a\nfPzxx5ozZ44mTJiga6+9ttlUnZamCmzcuFGzZs3ShAkTNG7cON1www0qLi5u+v7bb7+tCRMmaOfO\nnbr55pt1ySWX6JprrtHbb78dsI+5ubmt/iksLAz4sz6fT4sXL9bkyZODjqDV1NTokksu0fPPP+93\n/ZtvvtHo0aP14YcfNvVh3bp1uvzyy3XFFVeoqqpKf/vb3zRnzhxNnDhRl156qe68807t378/4OsB\nVkIuaTmXxMbG6oknntDUqVP9rsfExKixsbHFnyGXIFqRR/hM0l4xZgcAhMIll1yiZcuWqb6+vmmU\naM+ePaqurlZubq6eeOKJpraNjY2aM2eOamtrNW/ePPXo0UNr167V7373O61Zs0Z9+/ZVRUWF5s2b\npyFDhui+++6T2+3Wfffd12zKwfe/Li4u1j333KPf/OY3mj17to4dO6YXX3xR9957r9atW6fk5GRJ\nksfj0f/5P/9Hv/nNb5Senq6XX35Zixcv1rnnnquMjIwW+/fkk0+22vfevXsHfG9efvllud1uPfzw\nw7rrrrsCtu3Vq5cuvvhivfvuu/r1r3/ddH3Dhg1KSkrST3/6U23YsEGNjY3605/+pD/84Q/67rvv\nFBsbq7y8PI0YMUI33XSTamtrtWrVKuXl5emVV17plKkaQGcglzTndDqVmZkp6dQHKrfbrbVr1+rQ\noUO68847W/wZcgmiGXmkdXwmaR1FGWxh9OjR+rd/+zd9+OGHGjdunCTp/fff109/+lMlJCT4tf3z\nn/+sAwcOaM2aNU2jNDk5Obrmmmv07LPPKj8/X6+88oqcTqceeOABdevWTdKp0a+FCxe2GsPBgwf1\nT//0T5o1a1bTtbS0NN10003au3evRo8eLenUgvlbb71VEyZMkCQNHjxYV1xxhT788MNWE+CPf/zj\ndr0vX331lZ566iktWbLE8JSGyy67TL///e+1b98+DRkyRJL0zjvvaOLEiXI6Tz1c9/l8uvHGG5WT\nkyPp1Dz56upqXXXVVTr33HOb+v7ee++ptrZW3bt3b1f8QLiRSwJ7/fXX9dBDD8nhcOif/umfdP75\n57fallyCaEUeaRmfSQKjKIMt9OrVSxdccIGKi4ubEuB7772nOXPmNGu7fft2paWl6YwzztDJkyeb\nro8cOVJ//etfJUllZWXKyspqSn6SNGbMmKYE0JLTozjHjx/Xl19+qYMHD2rbtm1yOBzyeDx+bc85\n55ym/5+UlKSYmBjV19e3+ru/H+cPOZ3OVkd9lixZovHjx+vCCy9s9ed/6Kc//amSkpL0zjvvaMiQ\nIdqzZ4++/vpr/eIXv/Br9/1kPXjwYCUmJurOO+/U+PHjlZOToxEjRmjmzJmGXxewAnJJ4BHkCy64\nQP/xH/+h8vJyPfXUU6qrq2v1gyG5BNGKPMJnkvagKINtjB07VitWrJDH49Enn3yi6upqXXzxxTp6\n9Khfu+rqan3zzTcaM2aM33WHw6GYmFP/JGpqajRo0CC/7zudzoCP5Y8eParFixertLRUMTExysjI\naFrQ+8P54z8cIXI4HPJ6va3+7jFjxsjhcDT7PQ6HQ5MmTVJBQUGzn3n11Vf15Zdf6v7779fJkyfl\n8/ma/pw8eVJdunRp8bW6dOmiiRMn6i9/+YvmzJmjd955R4MHD262ODkpKanp/yckJOixxx7TmjVr\n9N///d96/fXXFR8fr6lTp+q3v/1tq/0CrIhc0rqMjAxlZGQoKytLXq9Xjz32mGbNmiWXy9WsLbkE\n0Yw84o/PJMFRlME2xowZo4ceekhbtmzRzp07dfHFFys+Pr5Zux49eigzM1N33XVXs4RyWmJiYrPE\nKUnfffddq6+/aNEiffPNN3rsscc0bNgwdenSRfv379c777zT/k79r6effrrV7yUmJrZ4vbi4WEeP\nHtXll1/e7Htjx47V8uXLm6Yr/NBll12ml19+WTt37lRxcbF+9atfBY0xIyNDf/jDH+T1erV79269\n+eabeumllzR06NCmkUIgEpBL/FVVVam0tFSjRo1Sr169mq6fddZZkiS3293q7ySXIFqRR/wZ+UzS\n2nToaMkjFGWwjcTERJ1//vl67733tGvXrlYPJMzKytLWrVuVkpLiN7Ly0EMPKS4uTmeffbaGDx+u\ndevWqaampulDyJYtW5o98v++PXv26Oqrr26avyxJmzdvbnE0qa3OPvvsNv9Mfn6+amtr/a49/PDD\n8ng8ys/PV3p6eqs/O3jwYA0ZMkSrVq1SdXW1Jk6cGPC1PvzwQxUWFuq5555TUlKSsrKyNGzYML37\n7ruqqKhoc+yAmcgl/urr63X//fdr7ty5uuaaa5qub9mypWkEvjXkEkQr8og/PpMER1EGWxk3bpwe\nfvhhxcXF6aKLLmqxzeWXX65169bptttu0/XXX6+kpCT95S9/0VtvvaXf//73kqSrr75ab731lm6/\n/XbdcMMNqqmp0VNPPaXY2Fi/3/X9xDZs2DBt2LBBmZmZSkpK0kcffaSXX35ZklRXV9dJPW5dSwmu\ne/fuamxsbBrhDmTSpEl65JFHdNFFFzXt0tSa0/PRCwoKdN111yk+Pl5vvvmmYmNjNWrUqPZ1ADAR\nueQf+vbtq1/84hd66qmn5HA4lJmZqc2bN+uVV17RjTfeqB49egT8eXIJohV55B/4TBIc55Qh4n1/\nQemYMWPk8/k0atQoxcXFtdimW7duWrlypc466yw9/PDDys/P1yeffKJ77rmn6bF6UlKSVq5cqd69\ne+vee+/VM888o1tvvVU9e/Zs9bXvuusunXnmmXrggQe0cOFClZWVacmSJUpPT9fu3buD9iFcW7Qa\nfZ2LL75Ykpotpm1JYmKili1bpoSEBBUWFmrBggVyu91atmxZwNEvwErIJa3Lz8/XP//zP2v9+vWa\nP3++SktLNX/+fN1www1Bf5ZcgmhCHmkbPpP8g8PX0WeYESbQ3PdI4HK5Ir4Pkj36Yfc+rF+/XqtX\nr9brr7/ebDTOSlraYCAc7HzvI4Ud+iDZox/kkvaz872PFPTBOlrrR6TkEan9ucRS0xePHDmigoIC\nbdmyRf369VNBQYFyc3Obtauvr1dhYaHeffddxcXFacqUKbr99ttNiBiwn/fff1/l5eV67bXXdO21\n11o++QGwJnIJgI6KpjxiqemLeXl5cjqdWrduna666irNmzevxQV5jz76qPbu3atnn31Wy5YtU1FR\nkV588UUTIgbs59ChQ1q/fr1ycnL0m9/8xuxwAEQocgmAjoqmPGKZJ2X79+9XaWmpSkpKlJycrMzM\nTG3atElFRUWaPXu2X9uSkhJdf/31Tbu/TJ48WVu2bDG0RSaAwH71q1/xbwlAh5FLAHRUNOURyzwp\nKysrU0ZGht+OKtnZ2dq5c2eztklJSdqwYYOOHz+uw4cPq6SkxO80cgAAAACIFJYpyiorK5Wamup3\nzeVytTh9MS8vT5999plGjBihn/3sZ0pNTdWMGTPCFSoAAAAAhIxlirK6ujq/7UIlKTY2tsWD8Q4c\nOKBBgwbp+eef1+OPP67PP/9cTzzxRLhCBQAAAICQscyasvj4+GYFmMfjUUJCgt+1Y8eO6e6779ar\nr76qM888U9Kpw+EKCgo0c+ZMOZ2B60yztrwNJTv0QbJHP+hD9LLD+0YfrMMO/bBDH8xgh/eNPliD\nHfog2acfbWWZoiwtLa3ZuQRut1spKSl+17744gs1NDRo8ODBTdeGDRum48ePq6qqKugp35F+hoPd\nz6GIJPTBGjhbqH3scu8jvQ+SPfphlz6YwQ7vG30wnx36INmjH+3NJZaZvpiVlaUDBw6oqqqq6dqO\nHTuUlZXl1y41NVU+n0+fffZZ07UvvvhCCQkJQQsyAAAAALAayxRl6enpysnJUX5+vvbt26fVq1dr\n165dmjJlijwej9xut3w+n/r27atRo0Zp0aJF+vjjj7Vt2zYtXrxY1113ndldAAAAAIA2s0xRJklL\nly6V1+vVtGnTVFRUpBUrViglJUU7duzQ6NGj9c0330iSli1bpoyMDN10002644479POf/1y33Xab\nydEDAAAAQNtZZk2ZdGoO5qpVq5pdHzlypMrLy5u+7tWrl5YsWRLO0AAAAACgU1iqKOtsfy6v0MGK\nyqDtErvGaHxmUhgiAgAAABDtoqooq673KK1HXNB23x5rDEM0AAAAAGCxNWUAAAAAEG2i6knZ3Nf2\nyCFf0Hbn9+2uqeekBG0HAAAAAB3Fk7IfqKz1aPuhY2aHAQAAACBKRNWTsuVTzlWCtz5gmxlv7JPX\nG/xpGgAAAACEAk/KAAAAAMBEFGUAAAAAYCKKMgAAAAAwUVStKUvsGquDFTUB25xeT1a097CB38ch\n0wAAAAA6JqqKsknDUuVOCfxw8I/FB1XbeJJDpgEAAACERVQVZQAAAFbx5/IKHayoDNqOmTmA/VGU\nteBo/QnNeGNf0HYcMg0AANqrut7DzBwAkijKmrlyaLLWlgVfT8Yh00D0YnQbAACEEkXZDywaN1Dn\npXUPOnLFIdNA9GJ0G0AozH1tjxwK/lmCmTmA/bElPgAAgEUxMweIDjwpA4A2YnQbQCgsn3KuErz1\nAdvMeGOfTnp9QY/qYbo0ENkoygCgEzC6DSBUujgdQadMM10aiGyWKsqOHDmigoICbdmyRf369VNB\nQYFyc3NbbLtmzRo988wzqqur0/jx47Vo0SLFx8eHJI7ErjFBk5vX65PDEZKXAxBhjI5us+4UAAAY\nYamiLC8vT/Hx8Vq3bp2Ki4s1b948bdiwQampqX7t3nzzTa1YsUIPP/ywUlJSNH/+fD3yyCO68847\nQxKHkcf/fyw+qPoT3pC8HgAAQGsqaz1Bj+phujQQ2SxTlO3fv1+lpaUqKSlRcnKyMjMztWnTJhUV\nFWn27Nl+bZ955hnNnTtXo0ePliTNmzdPa9euDXvMPl/wOd4S87yBaOWTyBEAWpXYNVYHK2oCtjm/\nb3dtCzIVmunSQOSzTFFWVlamjIwMJScnN13Lzs7Wzp07/dodO3ZM5eXlevTRR5uuTZw4URMnTgxb\nrKf5fGJbbCAKGfkgdXrqIjkCQGsmDUuVOyXwRthTz0lR0d7DAXMJ06WByGeZoqyysrLZNEWXy6WK\nigq/a1999ZUcDocOHTqk22+/XYcPH9aECROUn5+vuLjgH34AoKOMfJD6Y/FB1TaeDFNEAAAgklmm\nKKurq2tWVMXGxsrj8fhdO378uCTp/vvvbyrE7rnnHi1dulQLFy4MW7wAEMzR+hNB14FIrAUBACDa\nWaYoi4+Pb1aAeTweJSQk+F2LiYmRz+fTvHnzNGrUKElSQUGBfve73xkqylwuV0jidTqdOlrfoFl/\n+jxo2xEDEjVzzLCQvK4Uuj6YzQ79oA/RK9j7dk32AK356KCczsDbtB4+1qAd/1Nryn2ww723Qx8k\ne/TDDn0wg5H3rVevBiX2bH2HaafTKZ98pt0DO9x7+mAddulHW1mmKEtLS5Pb7fa75na7lZLiP3p8\n+utBgwY1XRs0aJAaGxt15MgRvzVpLfnha7TX5LN6a22ZR15v4B0YK2s9Kt1/JGSv63K5Qva7zGSH\nftAHazAreQd73/IvStWQXsHPFjq1FsQb9vtgl3sf6X2Q7NEPu/TBDEbeN0djrT79uvV1rCdOnJoq\n/WRxedDfFeqNhexy7+mDNdihH+3NJZYpyrKysnTgwAFVVVWpd+/ekqQdO3YoJyfHr13//v2VkpKi\njz/+WJmZmZKkzz//XN26dWv6uXBYNG6gzkvrbvADF4tvAQBA+wQrok6vYWVjISByBV6pHkbp6enK\nyclRfn6+9u3bp9WrV2vXrl2aMmWKPB6P3G63fL5Txc3111+vBx98UFu3btXu3bv1wAMPaOrUqXI6\nLdMdAAAAADDEMk/KJGnp0qVasGCBpk2bpgEDBmjFihVKSUnR1q1bNX36dG3cuFH9+/fXjBkz1NDQ\noH/5l3+Rx+PR5MmTlZeXZ3b4AAAApmBjISCyWaooc7lcWrVqVbPrI0eOVHm5/zzpOXPmaM6cOeEK\nrUOMHCDL4bGA/SR2jQk6Vcjr9ckReC8QAAjoyqHJWlsW/KB6DpkGrMtSRVmkMfqBSwp+gCxzvAH7\nMTLQ8sfig6o/EXjDIAAIhHXuQOSjKOsAox+4OEAWQCA+n4+n6QAARDGKMgAwmc/H03QAAKIZRVkY\nGFl8y8JbAAAAIDpRlHUyI4tvWXgLRDcGbgB0VFvWuQebLn369zFlGggfirJOZmTxLQsctM7oAAAg\nAElEQVRvgejFwA2AUGjLOncOmQash6IMAEzEwA0AAKAoAwAAiBIcMg1Yk9PsAAAAAND5rhyarKSu\nwcfjmTINhB9PysIg2OJbr9cnhyOMAQGIOD6xOB9Ax3DINGBdFGVhEOwD0h+LD6rOc9LQB670VK9G\npPCAE7ATIwM3UvCzzCQW5wMAEIkoyizCyOGxklRd75EU3/kBAQgbIwM3tY0nwxQNAAAIN4oyAIgA\nLM4HEE5Gp0wzgwcIDYoyALA4I2eZSSzOBxBcWw6ZZgYPED4UZRZhdBQ8JyNJE9IzwxARAKtgcT6A\nUGnLIdMAwofnzRbQli1qN39ZFYaIAAAAAIQLT8osoC2j4IyBAwiEdSAAQoEZPEB4UZQBgI10cTpY\nBwKgQ9qyjpUZPEBoWKooO3LkiAoKCrRlyxb169dPBQUFys3NDfgzhYWFKi8v13PPPRemKAEg/Iwu\nzudpOoCOYgYPEH6WKsry8vIUHx+vdevWqbi4WPPmzdOGDRuUmpraYvu//e1veuGFF3TBBReEOVIA\nCC+ji/O/rmlgyhEAABHGMkXZ/v37VVpaqpKSEiUnJyszM1ObNm1SUVGRZs+e3ay9z+fTPffco6ys\nLBOiDT2jo+AOp8PQepHErjGGPsQBsA+mHAEIN5/Px+cSIAQsU5SVlZUpIyNDycnJTdeys7O1c+fO\nFtu/8MILSklJUXZ2tj744INwhdlpjI6C153wGVovEqzAA2A/TDkCECqGzzNzGFvHyucSIDDLFGWV\nlZXNpim6XC5VVFQ0a1tRUaHHH39cr7zyit56661whQgAtsHoNoBA2jJYDKDjLFOU1dXVKS7Of6Ql\nNjZWHo+nWdvCwkJNnz5d/fv3D1d4AGArMV2cjG4DAGARlinK4uPjmxVgHo9HCQkJfteKi4v16aef\n6qGHHmrX67hcrnbHaDan06kjtXWa9afPg7YdMSBRM8cMC0NU7RfJ9+I0+hC9rPq+9erVoMSegbe6\ndzpPnU+WmJgY9PfVORss21fJuvehrezQDzv0wQyR/L7Z6XNJJN+H0+zQB8k+/WgryxRlaWlpcrvd\nftfcbrdSUlL8rm3YsEEHDx7UhRdeKEk6ceKETp48qeHDh2v79u1BX+eHrxFJJp/VWy/vOSmv1xuw\nXWWtR6X7j1i6ry6Xy9LxGUEfrMGs5G3V983RWKtPv64J2ObEiZOSw6Hq6uqgv6/mWKPcbmueZ2aH\nv7+SPfphlz6YIZLfN7t8LrHL399I74Nkj360N5dYpijLysrSgQMHVFVVpd69e0uSduzYoZycHL92\neXl5+u1vf9v09UsvvaRt27bpwQcfDGu8Zlg0bqByh/RTgrc+YLsZb+w7tfgWQNQJ9db55/ftrqnn\npARtByD68LkECB2n2QGclp6erpycHOXn52vfvn1avXq1du3apSlTpsjj8cjtdsvr9So5OVnp6elN\nf3r37q34+Hilp6eb3QUAiAhXDk1Wcrfg68kqaz3afuhYGCICACC6WeZJmSQtXbpUCxYs0LRp0zRg\nwACtWLFCKSkp2rp1q6ZPn66NGzeyuQcAdBCj2wAAWIulijKXy6VVq1Y1uz5y5EiVl5e3+DMzZ87U\nzJkzOzu0iOOT2O4aQIeRSwAEktg1VgcrAq9jPT24Qy4BWmepogzBtSX5sd01gI7q4uRgWACtmzQs\nVe6UwKth/lh8ULWNJ8klQAAUZRGmLckPAFpjdICHyYsAAHQ+ijIAiEJGB3jYpRFAKBytP0EuAQKg\nKLMpkh+AjrpyaLLWlgVfA8IujQACIZcAwVGU2RDJD0AoLBo3UOeldQ+6DoRdGgEEQi4BgqMos6G2\nJL+TXh+7IQEAAAAmoiiLcuysBiAU2DofAID2oygDALQqsWtM0EEZjuEAAKBjKMoAAK0y8lSLYzgA\nBGN0gMfhCFNAgMVQlNmU0eTHLo0AQoFcAiAQowM89Se8YYgGsB6KMpsykvzKvj3OLo0AOowdXwEA\n6BiKsijGFrUAQoFcAiBUfD52hUZ0oigDAACAJfh8bBqE6ERRBgAIG7bOBwCgOYoyAECHsXU+gFBg\n0yBEK4qyKNeWD1KMbgNoDVvnA+goNg1CNKMoi3Jt+SDF6DaAjmIUHEBr2DQI0cxpdgAAgOhw5dBk\nJXUNPhbIKDgAINpY6knZkSNHVFBQoC1btqhfv34qKChQbm5us3bfffedCgsL9f777ysmJkaXXXaZ\n5s+fr9jYWBOiBgAYwSg4AAAts1RRlpeXp/j4eK1bt07FxcWaN2+eNmzYoNTUVL929957r7766iut\nWbNGdXV1+v3vf6+uXbvq9ttvNyly+2PKEQAA6GyhXOvOOndEkjYXZSdOnNDRo0eVlJSkmJjQ1XT7\n9+9XaWmpSkpKlJycrMzMTG3atElFRUWaPXt2U7vGxka9++67evHFF3X22WdLkubOnatHHnmEoqyT\nsPAWAACEQyjXurPOHZHEcFVVVlamf//3f9dHH32kkydP6tVXX9UzzzyjM844Q3Pnzu1wIGVlZcrI\nyFBycnLTtezsbO3cubNZ25UrVzYVZJLkcDjU0NDQ4RjQMqYcAQiVUI6Cp6d6NSKFpdEAgMhnqCj7\n6KOPdNNNN+mcc87RrbfequXLl0uSBg8erOXLlyspKUm//vWvOxRIZWVls2mKLpdLFRUVftfi4uKa\nrTN76aWXNHz48A69PgCg84VyFLy63iMpPkSRAYgkRpZVsKQCkcRQUfbggw/qkksu0aOPPqoTJ07o\n0UcflST99re/VX19vV566aUOF2V1dXWKi/P/D3BsbKw8Hk/An1u+fLl2796t9evXd+j1ERo+GTvP\njBFuAADQHkaWVbCkApHGUFFWXl6uOXPmSDo1VfD7Ro0apWeffbbDgcTHxzcrwDwejxISElr9mSef\nfFKPP/64li1bpszMTEOv43K5OhSnFZjRh/RU7/+OSrfOJ4ccDumsHwUflfqf7xrkcv0oVOGZhr9P\n0csO75tV++B0OnW0vkGz/vR5wHYjBiTq2uycMEXVuax6L9rCDn0wgx3et3D3YcXVLuUO+Vp9e7b+\npPza57frpNdnODbug3XYpR9tZagoS0xM1Ndff93i9w4ePKhevXp1OJC0tDS53W6/a263WykpLX/A\nf+yxx7R8+XIVFhbq0ksvNfw6P3yNSONyuUzpw6mnWoGnCSXEOFTb6FV1dXXwX+jsyr2wALv0wQx2\neN+s2ofJZ/XW2jKPvF5vq20qaz3a/GWVZfvQFla+F0bZpQ9msMP7ZkYfampqlOBtfYqz1+uVz+sz\nFJtd/v5Geh8ke/SjvbnEUFE2adIkPfzww+rXr58uuugiSaeemH322WdauXJlm4qi1mRlZenAgQOq\nqqpS7969JUk7duxQTk7zUdDXX39dy5cv13333adf/vKXHX5thI7RrfNzMpI0Id3Y000A0cXI5kIz\n3tgnthUCANiFoaLsX//1X/XZZ59p1qxZTQc033TTTaqurlZWVlZItqJPT09XTk6O8vPzNX/+fH3w\nwQfatWuXlixZIo/Ho+rqavXp00e1tbUqLCzUlClTNHbs2KZq2uFwqE+fPh2OA+3Xlq3zN39ZFYaI\nAACAHQXbydXr9ekHK24ASzNUlHXt2lWrV69WSUmJtmzZoqqqKvXo0UMjRozQ2LFj5XSGZsOGpUuX\nasGCBZo2bZoGDBigFSv+P3t3HhZVvf8B/H3YBkRElgFcwAUXSBNRERfcTXNNfXK5ae5bBZp7orll\nLnnVclcyzaVccMm8iZYLqSmigqSX0kxNs8AZBVS2kTm/P7zOzwlm5gDDbLxfz9Nz4cyXmc+Xg597\nPud8lzWQy+W4cOEChg0bhuPHj+P69et4/Pgx9u/fj/379wMARFGETCbDlStXjBIHlUxxls7nHW4i\n0kfKBZe9Pa+4iMorQyu5Loy/ixxVARcfI6shqSjbv38/unbtioiIiELL0RuTt7c3YmJiCh1v3rw5\nUlNTAQBVq1bVfE1ERLZJygVXXoEo6YLL3dlB0lL8RGRbRBEGbxQD3F6DLIOkouzDDz/E/Pnz0a5d\nO/Tu3Rvt2rXTDGMkIiIyB7XECy5Dm1UTkW3iPHeyJpKKsh9//BFHjhzBkSNHEBUVBTc3N3Tp0gW9\nevUqciEOKr8MDTkCOOyIiIzjYXa+pAsubiBLVP5wnjtZG0lFmZeXF4YMGYIhQ4bg77//1hRow4cP\nh4+PD3r06IHp06eXdaxkBaQMEZI67IhDjohIl95Bnth9VQkYmKHKDWSJyifOcydrI6koe5mfnx9G\njBiBli1bYteuXdi7dy+2bNnCooyKRcqwIw45IiJd5nWsiYi6VeCiztXbbsw3N6BW85KLiHQTRc5P\nJfMrVlH266+/ap6S/fHHHwgICMD48ePRq1evsoqPiIiIiKjMONjbcX4qmZ2komzVqlWIi4vDrVu3\n4OnpiW7duqF3795o1KhRWcdHNkrKXBDOAyEifdydHXE3PUtvG+5VRFR+SZ3nrszO4/xUMjtJRdnW\nrVvRuXNnzJw5E61atYK9vX1Zx0U2TMpcEM4DISJDugX7QGFgb6Hi7FXEoUlEtkXKv+eUtKecn0oW\nQVJR9tNPP8HZ2bmsY6FyQspcEM4DISJjkbpXEYcmEZU/nJ9KlkJnURYTE4M+ffpALpdj+/btet9E\nEASMHj3a6MERERGVltS9ijg0iYj0EQE+dacyo7MoW758OcLDwyGXy7F8+XK9b8KijMoCkx8RlVZx\n9iri0CSi8knq/FSAT92p7Ogsyn755ZcivyYyBkMJkMmPiIyhOHsVcWgSUfkkdX7qn1lcEITKjqQ5\nZWvWrEH//v3h6+tb6LV79+7hiy++wJw5c4weHNkuQwmQyY+IiIgsBZ+6U1nTWZTdv39f8/XatWsR\nHByMgoKCQu1OnDiB2NhYFmVkVEx+RGQsUpfFBjhkmoiKxqfuVNZ0FmUfffQRTp06pfk+MjKyyHai\nKKJVq1ZGD4zKNyY/IjIWKQXUi6fzs4/fMdi2sZ8rizIiIjIqnUXZggULkJiYCFEUMWXKFEycOBE1\natTQamNnZ4dKlSqhadOmZR4okS5cEISISotP54nIED51p7KksyiTy+Xo3r07ACA/Px/t27eHh8f/\n/+Hk5+dDrVZz/zIqM1KTX0buM97dJqJS4dN5IjKET92pLEla6KNXr15YtmwZrly5gl27dgEALl26\nhHfeeQfDhg3D+++/D0EQyjRQKn+kJKqUtKe8u01EJsWn80SkC5+6U0lJKspWrVqFPXv2ICoqSnOs\nfv36iIqKwrp16+Dq6oqxY8eWOpiHDx8iOjoaCQkJqFKlCqKjoxEREVGonSiKWLx4Mb755hs4Ojpi\nxIgRGDVqVKk/n6xPce5uF6hFXkgRkU7FGZrE7TqIqCh86k4lJako+/bbb/HBBx9g4MCBmmOenp4Y\nNWoUnJ2dsXXrVqMUZdOmTYNMJkNsbCzi4+MxYcIExMXFwcfHR6vdpk2bEB8fj82bN0OhUGDatGmo\nWbMmOnXqVOoYyHbZ2wm8kCIinYozNInbdRCRLpx7RiUhqSh79OgRqlWrVuRrNWvWRHp6eqkDuX37\nNs6dO4czZ87A09MTgYGBOHv2LPbv34/x48drtd2xYwdmz56Nhg0bAgDGjBmDnTt3sigrp6Qmv0e5\nz3ghRUSlwqFJRGQI555RSUgqyurVq4dDhw4VOZTwP//5DwIDA0sdSEpKCgICAuDp6ak5FhoaiuTk\nZK12aWlpUCgUaNKkiVa7jRs3ljoGsk7Gnnt26f4TSXeu/H3UCNOzATYR2Z55HWuiTQ13ZOY+09su\n+vvbeJT7DEGfJhp8z/Z15NjQs6aRIiQia1CcGzxSrkt4TWL9JBVl48aNQ2RkJP766y907NgRnp6e\nePToEU6ePInExESsWrWq1IEolcpCwxS9vb0LPYVTKBQAnq8O+YKXlxeys7Px9OlTuLq6ljoWsj3F\nvZCScudKLd4BRMPjwZtUrYg3G3jrbcOhCUTWozg3guzs9C+CpcxWIf53JYCaxgmOiKyCsa9LpF6T\ntAyohC396hcrVjINSUVZp06dsGbNGqxbtw6ffPIJxP+d9Pr162PVqlXo3LlzqQPJycmBk5P2fB9H\nR0eoVKpC7RwdHQu1A54v08+ijHQx5hM1AHCwt4Nardbb5sFTFb6/mYHkv5/qbfeqTwWDibks8M4a\nUdkozgXXw+x8SU/UpBAEwMXR3mC73kGemNexplE+k4hKxpjXJVKvSQ7/+lBSvpGaS6RizjFMUlEG\nPC/MOnXqhLy8PGRkZKBixYqaAujx48dwc3MrVSAymaxQAaZSqeDi4lKo3bNnzwq1A4AKFSqUKgYi\nqRdSAFCpUiVkZWXpbRN7TYFLBuaVPHiqwolbmfg5PbtYsRpDeEAWtvQp/fBjIipM6gXX3mtKFBTo\nv5gS8XyxIkMK1CKy8wv0tlHmPMPahL8k34CSgkMwicqG1OuS4lyTGHqCD0jLJVIVJ+eU51wiuSh7\nQSaTwdfXFwBw8eJF7N27F0ePHi0096u4fH19NUMTX1AoFFrDFF+0E0URDx8+1Mw/UyqVcHNzg0wm\nM/g53t76h5FZA1voA2C5/RhYrLiKXgDnhbHtgCOp6cjMVeltZy4VnexxNUN6+/Z1LPOcmYOl/v0W\nB/tgfmsGeGONhHaWnEeA4uUS5hFt1v43DLAPZU36dYl1X5MA5TuXFLsoy8jIwMGDB7Fnzx7cunUL\n9vb2aNOmTakDCQkJwZ07d5CRkYHKlSsDAJKSkhAeHq7VzsfHB35+frh8+bJm2OSlS5fQqFGjUsdA\nVBa6BfsYbkREpAfzCBEZA3OJ5ZJclJ0/fx579uzBDz/8AJVKheDgYMyePRvdu3eHh0fpFyjw9/dH\neHg4ZsyYgalTp+L06dO4cuUKlixZApVKhczMTHh5eUEQBAwcOBBLly6Ft7c3MjIysHnzZixbtqzU\nMRAREREREZmaIIq6l2pRKpXYt28fYmNj8ccff6Bq1aro2rUrtm7dim3btiEsLMyowSgUCsycOROJ\niYmoXr06Zs+ejRYtWuDChQsYNmwYjh8/jqpVq6KgoACLFy/GwYMH4eLigjFjxmDo0KFGjYWIiIiI\niMgUdBZlEyZMwIkTJyCTydClSxf06dMH4eHhePz4McLCwrB9+3ajF2VERERERETljc7hi8eOHUO9\nevUwdepUtGrVCg4OxZ5+RkRERERERAbo3KBo0aJFqFy5MsaNG4eWLVtizpw5SEpKMmVsRERERERE\nNk9nUdavXz9s27YNP/zwA4YNG4bz58/jrbfewhtvvAFBEPDggfH2NylrDx8+xPjx4xEaGoru3bvj\nzJkz5g6p2I4ePYqgoCAEBwdr/nfFihXmDkuyjIwMtGrVCkqlUnPswIED6NixI5o0aYKZM2ciLy/P\njBEaVlQfevbsWei85OfnmzHKot28eRPDhw9HkyZN0LVrV+zdu1fzmrWcB319MNV5YC4xL1vIIwBz\nibkxlxgHc4l5MY+Yn9FziVgMiYmJYnR0tNi0aVMxKChI7NGjh7hu3Trx1q1bxXkbkxs5cqT4zjvv\niL/99pu4efNmMTQ0VExLSzN3WMWydu1aceLEiaJSqRQVCoWoUCjE7Oxsc4clSVZWlvjWW2+JQUFB\nokKhEEVRFC9evCg2btxYPHr0qHjt2jWxd+/e4qJFi8wcqW5F9aGgoEBs2LChmJKSojknL16zJHl5\neeJrr70mzpkzR7x9+7Z45MgRsXHjxuLZs2fFS5cuWcV50NcHU54H5hLzsYU8IorMJebGXGI8zCXm\nwzxifmWRS4pVlL2Qm5srHjp0SBw5cqQYHBwsBgUFleRtTOLWrVticHCwqFQqNcdGjhwprl+/3oxR\nFd+UKVPEtWvXmjuMYvv555/FLl26iL1799ZKHpMnTxbnz5+vaZeUlCQ2btxYzMvLM1eoOunqw+3b\nt8WGDRuKarXazBHqd/78eTEsLExUqVSaYx9++KEYHR1tNedBVx9mzZplsvPAXGI+tpBHRJG5xBIw\nlxgPc4l5MI9YhrLIJTqHL+ojk8nQq1cvbN68GSdPnsSkSZNK8jYmkZKSgoCAAHh6emqOhYaGIjk5\n2YxRFd/vv/+OmjVrmjuMYjt//jx69eqFVatWQXxpoc+ff/4ZTZs21XzfqFEjqFQq3Lhxwxxh6qWr\nD7///jv8/f0hCIIZozOsbt26WLdundZiPYIgIC8vD1evXrWK86CvD6Y6D8wl5mMLeQRgLrEEzCXG\nw1xiHswjlqEsckmJirKX+fr6YuzYsaV9mzKjVCrh46O9e7m3tzfS09PNFFHJ3Lp1C8ePH0fnzp3x\n+uuv4/PPPzd3SJKMHj0akZGRcHBw0PrjVCgUWufFzs4OlStXtsjzoqsPv//+OwRBwKhRoxAREYEx\nY8bg1q1bZoy0aJ6enmjWrJnm+4yMDMTFxaFJkyZ48OCBVZwHXX0IDQ012XlgLjEfW8gjAHOJJWAu\nMR7mEvNgHrEMZZFLSl2UWbqcnBw4OTlpHXN0dIRKpTJTRMWXlpaG3NxcuLm5Yd26dYiMjERMTAx2\n7Nhh7tBKLDc3t8jzYokTUnW5desWsrKyMGzYMGzcuBEVK1bEyJEjkZuba+7QdFKpVJg8eTK8vLzw\n5ptvWuV5+GcfTHUemEssjzX+/RaFucQ8mEtKjrnE8jCPmI+xconNbz4mk8kKJTqVSgUXFxczRVR8\nvr6+SEhIQKVKlQAA9erVg0KhwO7duzFkyBAzR1cytnBeFixYgJycHLi6ugIAli5divbt2+PHH39E\nly5dzBxdYSqVChMmTEBqaip27doFJycnqzsPRfXBVOfB2n5XRbG1XGIL5wRgLjEH5pLSYS6xPMwj\n5mHMXGLzRZmvry8UCoXWMYVCAblcbqaISuZF4nuhdu3aVrUtwT/5+PhoxV9QUICMjIxCQzosmZ2d\nneYfHAA4OTmhevXqFnleVCoVIiMjkZycjG3btqFGjRoArOs86OqDqc4Dc4nlsaa/X32YS0yLucQ4\nmEssC/OI6Rk7l9j88MWQkBDcuXMHGRkZmmNJSUkICQkxY1TFk5CQgJYtW2rdPfjll1+sboLty0JC\nQrQ2I79y5QpkMhnq1q1rxqiKZ9SoUdiwYYPm+5ycHNy5c8ciz8u8efNw+fJlbN26FfXr19cct6bz\noKsPpjoPzCWWx5r+fvVhLjEt5pLSYy6xPMwjpmfsXKLzSdmYMWMkByUIAjZt2iS5vSn5+/sjPDwc\nM2bMwNSpU3H69GlcuXIFS5YsMXdokr366quQyWSYM2cOxo8fj9TUVMTExODjjz82d2jF8vIqQQMH\nDsTo0aMREhKCmjVrYsGCBRgwYADs7e3NGKFhL/ehbdu2WL9+PV555RVUrVoVq1evRpUqVdC6dWsz\nRljYxYsXsW/fPixcuBByuVxzh9bJyclqzoO+PpjqPDCXWAZbyCMAc4m5MJcYB3OJZWAeMZ+yyCU6\nizJLnhhYXEuXLsXMmTPRv39/VK9eHWvWrLGqYQIVKlTApk2b8PHHH6Nv376oXLkyJk+ebJFjhPV5\neZWgpk2bIjo6GkuXLkVOTg66du1q0VsrvPByH4YOHYonT55gzpw5yMzMRHh4uNadEUtx/PhxCIKA\nDz/8UOt4REQEYmJirOI8GOqDqc4Dc4n52UIeAZhLzIW5xDiYSywD84j5lEUuEcSXy2wiIiIiIiIy\nKZ1PypRKZbHeyMvLq9TBEBERERERlTc6n5QFBQUVayfq1NRUowVFRERERERUXuh8UrZo0aJiFWVE\nRERERERUfJxTRkREREREZEY6n5TFxMSgT58+kMvliImJ0fsmgiBg9OjRRg+OiIiIiIjI1umdU7Zn\nzx40atQIQUFB+t9EEDinjIiIiIiIqAQ4fJGIiIiIiMiM7KQ0WrNmDdLS0op87d69e1iwYIFRgyIi\nIiIiIiovdM4pu3//vubrtWvXIjg4GAUFBYXanThxArGxsZgzZ07ZREhERERERGTDdA5ffOedd3Dq\n1CmDbyCKIlq1aoUvvvjC2LERERERERHZPJ1F2YMHD5CYmAhRFDFlyhRMnDgRNWrU0GpjZ2eHSpUq\noWnTppDJZCYJmIiIiIiIyJZIWujjwIEDaN++PTw8PEwRExERERERUbkhqSh79uwZHBz+f/rZuXPn\ncPPmTdSrVw/Nmzcv0wCJiIiIiIhsmc6FPgBg586dWL16NcaNG4cRI0YAACZPnowjR45AFEUIgoAO\nHTpg9erVsLe3N0nAREREREREtkTnkvjff/89PvroI4SFhSEsLAwAEBcXh++++w5dunRBYmIi9uzZ\ngytXrmD79u0mC5iIiIiIiMiW6By+OGzYMFSpUgVLlizRHBs9ejTOnTuHH3/8EV5eXgCAzZs349Ch\nQ/jmm29MEzEREREREZEN0Tl8MTU1FcOHD9d8r1KpkJiYiIYNG2oKMgB49dVXsXr16jINkkiqU6dO\n4euvv8a1a9fw9OlTVK9eHb169cLQoUPh7Oxs7vCIyEK8/fbbSExM1HxvZ2cHNzc3BAcHY8iQIejc\nubPmtaCgIJ3vIwgCli9fju7du5dpvERkmaTmko4dO2rtAfxPgiBg8eLF6NOnT5nHTJZJZ1GWm5sL\nV1dXzfc///wz8vLyEB4ertVOpVLBzk7nKEgik1m4cCG++uor9O7dG/PmzUPFihWRkpKCmJgYxMfH\n44svvuDWDUSk0bhxY0RHRwN4vqDVw4cPcfDgQURGRmL69OkYOXKkpu2QIUPQu3fvIt/nn9vFEFH5\nIiWXrFu3Dvn5+ZqfGTBgQKG84u/vb/LYyXLoLMqqVauG33//XbO64o8//ghBENCmTRutdomJiahW\nrVrZRklkwMGDB7Fjxw7Mnz8fAwcO1Bxv0aIFQkNDMXToUGzZsgXjx483Y5REZEnc3NzQqFEjrWOd\nO3fGlClTsGLFCrz22muaiyRfX99CbYmIAGm5pKgn7swr9DKdj7i6du2KTZs2IclaWRAAACAASURB\nVCkpCRcvXsTu3btRtWpVzaIfwPMhjjt27EDHjh1NEiyRLps3b0ZQUJBWQfZCWFgYIiMjeQeKiCSZ\nOHEinj17htjYWHOHQkRWjLmEikPnk7IxY8YgMTER//rXvyAIApydnbFq1SrN6+PHj8eZM2fg7++P\n0aNHmyRYoqI8ePAAN27cwLhx43S2ee+990wYERFZs4CAAFStWhVJSUmaY6IooqCgoFBbQRA4hJ+I\nilRULiHSRWdR5urqip07d+LixYtQKBQICwvTWuDDxcUFI0eOxKhRo+Dm5maSYImK8vfffwMAqlat\nauZIiMhWeHt7Q6lUar5fuXIlVqxYUWS7M2fOmDI0IrIi/8wlRLro3TwaAJo1a1bk8ZUrVxo9GKKS\neLFxuVqtNnMkRGSrhgwZUuSqaA4OBv9vlIiIyCD+vwlZvSpVqgAA/vrrL51tlEol3N3deQFFRJKk\npaWhTp06mu99fHzQoEEDM0ZERNYoLS0NgYGB5g6DrAAHwpPV8/DwwCuvvILTp0/rbDN9+nS8/vrr\nJoyKiKzVrVu3kJaWhqZNm5o7FCKyYi9yia5RZ0QvY1FGNmH48OH45ZdfsHfv3kKvnT17FufOnUOv\nXr3MEBkRWZu1a9fCyckJ/fr1M3coRGTFXuSSvn37mjsUsgIWOZYrIyMD3bt3x7fffqu1uMgLoihi\n8eLF+Oabb+Do6IgRI0Zg1KhRZoiULEXv3r1x5swZzJ07F8nJyejcuTMcHR2RkJCA7du3o0mTJnjn\nnXfMHSYRWZDHjx/jypUrAICCggI8ePAAhw4dwokTJzB79mz4+flp2v7999+atv/k6enJLTeIyjFD\nueTFNAsifUpclKWkpODvv/9GixYtUKlSJaMF9PjxY7z33nt49OiRzjabNm1CfHw8Nm/eDIVCgWnT\npqFmzZro1KmT0eIg6/PJJ5+gZcuWiI2NRXR0NPLy8hAQEIAJEyZgyJAhcHJyMneIRGRBrly5gkGD\nBgF4vmCQXC5HnTp1EBMTg4iICE07QRDw1Vdf4auvviryfd5880189NFHJomZiCyP1FzyMkEQIAiC\nKcMkCyeIoigaapSWloYpU6agRYsWiIyMxJYtW/DJJ59AFEVUrlwZX375JerXr1/qYK5evYopU6bA\n2dkZ169fx5kzZ4p8UtamTRvMnj0bXbt2BfC8SDt//jy++OKLUsdARERERERkSpLmlH3yySf4888/\n0bRpUzx79gwbNmxAmzZtcOTIEQQFBWHZsmVGCeb8+fPo1asXVq1aBV21YlpaGhQKBZo0aaI5Fhoa\nqnNYCRERERERkSWTVJSdPXsW06dPR8uWLZGYmIjMzEwMGzYMtWrVwogRI4y2U/no0aMRGRkJBwcH\nnY90FQoFAEAul2uOeXl5ITs7G0+fPjVKHERERERERKYiqSjLy8vTDCOMj4+Hi4sLwsLCnr+BnR3s\n7Ey3iGNOTg4cHR21jr34Pj8/32RxEBERERERGYOkaqpOnTr4/vvv8eDBAxw5cgStWrWCk5MTnj17\nhp07d6Ju3bplHaeGTCbDs2fPtI6pVCoAQIUKFUwWBxERERERkTFIKsqioqKwZ88etG3bFhkZGRg3\nbhwAoEuXLjh//jyioqLKNMiX+fr6QhRFPHz4UHNMqVTCzc0NMplM789KWNOEiMgg5hIiMgbmEiJ6\nQdKS+G3btsWhQ4dw7do1hISEoFq1agCAcePGISwsDLVr1y7TIF/m4+MDPz8/XL58GZ07dwYAXLp0\nCY0aNTL4s4IgaOakWStvb2+r7wNgG/1gHyyDt7e3yT+TucQy2EIfANvoh630wdSYSywD+2A5bKEf\nJc0lkvcpq1GjBmrUqKF1bODAgSX6UClevnukUqmQmZkJLy8vCIKAgQMHYunSpfD29kZGRgY2b95s\ntBUgiYiIiIiITElnUTZmzBhER0ejVq1aGDNmjN43EQQBmzZtMmpgL6++mJSUhGHDhuH48eOoWrUq\nxowZA4VCgdGjR8PFxQVRUVFo3769UT+fiIiIiIjIFHQWZbm5uVCr1ZqvTalatWpITU3VfN+8eXOt\n7+3t7TF79mzMnj3bpHEREREREREZm86ibPv27UV+TURERERERMZjug3GiIiIiIiIqBAWZURERERE\nRGbEooyIiIiIiMiMWJQRERERERGZkaSibPr06bh8+XJZx0JERERERFTuSCrKjh07hry8vLKOhajY\nIiMjERERgSlTpuhs069fP0REROD48eOl/ryIiAjs3LkTAHDhwgVERETgv//9b6nf1xREUcS4ceMQ\nFRVl7lCILA5ziX55eXlo06YNIiIitP4bMWKEuUMjshjMI/qp1Wp8+eWX6N+/Pzp16oSxY8fiwoUL\n5g7LYuhcEv9lLVq0wPfff48WLVpobepMZAns7Oxw+fJlPH36FK6urlqvXbt2Denp6WXyd9ugQQNs\n2rQJtWvXNvp7l4Vdu3bh2rVrCA0NNXcoRBaJuUS333//HQCwePFieHl5aY47OzubKyQii8Q8otvG\njRsRGxuL8ePHo3bt2jh69CimTZuGdevWoUGDBuYOz+wkFWUeHh7Ys2cPvv32W/j7+2slZAAQBAGb\nNm0qkwCJDAkKCsKNGzdw5swZdO3aVeu1kydPok6dOrh586bRP9fV1RWvvPKK0d+3LPzxxx/YvHkz\n5HK5uUMhsljMJbrduHEDMpkMbdq0MXcoRBaNeaRoeXl52LdvHwYPHoz+/fsDAJo2bYqkpCR88803\nLMogcfjivXv3EBoaiqCgILi6uiI3N1frv5ycnLKOk0gnNzc3NG3aFKdOnSr02smTJ9GpUyeIoqh1\nPDMzEx9//DF69OiBTp06YeLEiYWS5C+//ILIyEh07twZgwYNKvSIvaihAsePH8e4cePQuXNndOzY\nEcOHD0d8fLzm9e+++w6dO3dGcnIyRo0ahQ4dOmDgwIH47rvv9Pbxn0OGXv5v0aJFen9WFEUsXrwY\nPXv2NHgHLSsrCx06dMCOHTu0jv/1119o06YNfvrpJ00fYmNj0aNHD/Tq1QsZGRn473//i8jISHTp\n0gVdu3bF9OnTcfv2bb2fR2RJmEt055KbN2+iVq1aet/7ZcwlVF4xjxSdRxwdHbFx40b069dP67iD\ngwPy8/OL/JnylkckPSnbvn17WcdBVCodOnTAihUrkJubqxlOc/XqVWRmZiIiIgIbN27UtM3Pz0dk\nZCSys7MxYcIEVKxYEbt27cJ7772HrVu3ws/PD+np6ZgwYQLq1q2LBQsWQKFQYMGCBYWGHLz8fXx8\nPObOnYu3334b48ePx5MnT/DVV19h/vz5iI2NhaenJwBApVLho48+wttvvw1/f3/s3r0bixcvRsOG\nDREQEFBk//Q9ia5cubLe383u3buhUCiwcuVKzJo1S2/bSpUqoVWrVjh27BiGDBmiOR4XFwcPDw+0\naNECcXFxyM/Px7fffos5c+bg8ePHcHR0xLRp0xAWFoaRI0ciOzsbMTExmDZtGvbs2cNhz2Q1mEuK\n9ttvv0EURURFReHatWuoUKECevTogTFjxsDBofClBHMJlWfMI4XZ2dkhMDAQwPObxQqFArt27cL9\n+/cxffr0In+mvOURSUXZC5cvX8ZPP/2EBw8eYNy4cbhx4wYaNGgAb2/vsoqPSJI2bdrgk08+wU8/\n/YSOHTsCAE6dOoUWLVrAxcVFq+2RI0dw584dbN26VfPkKDw8HAMHDsSXX36JGTNmYM+ePbCzs8Oy\nZctQoUIFAM/vfn344Yc6Y7h79y7eeOMNjBs3TnPM19cXI0eOxLVr1zTDftRqNd555x107twZAFC7\ndm306tULP/30k84EWNIhCffu3cPnn3+OJUuWSJ770b17d3zwwQe4ceMG6tatCwA4evQounTpAju7\n5w/XRVHEiBEjEB4eDuD5OPnMzEy8+eabaNiwoabvJ0+eRHZ2dqFx9USWirmkaC/u2kdGRmLUqFFI\nTk7Gl19+iYyMDMycObPIn2EuofKKeUS/gwcPYvny5RAEAW+88QYaN26ss215yiOSirK8vDxMmTIF\nP/zwA2QyGfLz8zFgwABs3boVN27cwI4dO1CzZs0yDpVIt0qVKqFp06aIj4/XJMCTJ08iMjKyUNvL\nly/D19cXNWrUQEFBgeZ48+bNcfHiRQBASkoKQkJCNMkPANq1a6dJAEV5cRfn6dOn+OOPP3D37l1c\nunQJgiBApVJptX157LSHhwccHByQm5ur871fjvOf7OzsdN71WbJkCTp16oRmzZrp/Pl/atGiBTw8\nPHD06FHUrVsXV69exZ9//onXX39dq93Lybp27dpwd3fH9OnT0alTJ4SHhyMsLAxjx46V/LlEloC5\npHAuEUURS5YsgYeHB2rUqAEAaNy4Mezt7RETE4Phw4cXeXOWuYTKK+YR/U+imjZtirVr1yI1NRWf\nf/45cnJydBaY5SmPSCrKVqxYgcTERHz++edo3rw5GjVqBOD5Bd+YMWOwYsUKrFq1qkwDJTKkffv2\nWLNmDVQqFX799VdkZmaiVatWePTokVa7zMxM/PXXX2jXrp3WcUEQNMNwsrKyCs2fsLOz0zu859Gj\nR1i8eDHOnTsHBwcHBAQEoE6dOgBQaPz4P59aCYIAtVqt873btWsHQRAKvY8gCOjWrRuio6ML/cze\nvXvxxx9/4OOPP0ZBQQFEUdT8V1BQAHt7+yI/y97eHl26dMEPP/yAyMhIHD16FLVr19b05QUPDw/N\n1y4uLli/fj22bt2K77//HgcPHoRMJkO/fv3w7rvv6uwXkSViLkGh14q6k92iRQts3LgRv/32G159\n9dVCrzOXUHnGPKJbQEAAAgICEBISArVajfXr12PcuHHw8fEp1LY85RFJRdl//vMfTJ48GREREVrV\nsa+vL959913Mnz+/zAIkkqpdu3ZYvnw5EhISkJycjFatWkEmkxVqV7FiRQQGBmLWrFmFEsoL7u7u\nhRInADx+/Fjn58+bNw9//fUX1q9fj+DgYNjb2+P27ds4evRoyTv1P5s3b9b5mru7e5HH4+Pj8ejR\nI/To0aPQa+3bt8fq1as1wxX+qXv37ti9ezeSk5MRHx+Pt956y2CMAQEBmDNnDtRqNX7++WccOnQI\nX3/9NYKCgjR3ComsAXOJNqVSibNnz6JVq1ZaT8ReTM7Xd2HIXELlFfOItoyMDJw7dw6tW7dGpUqV\nNMfr1asHAFAoFEUWZUD5ySOSirLHjx+jatWqRb5WsWJFrr5IFsHd3R2NGzfGyZMnceXKFZ2bJIeE\nhODChQuQy+Vad1aWL18OJycn1K9fH02aNEFsbCyysrI0ySMhIaHQI/+XXb16FQMGDNCMXwaA8+fP\nF3k3qbjq169f7J+ZMWMGsrOztY6tXLkSKpUKM2bMgL+/v86frV27NurWrYuYmBhkZmaiS5cuej/r\np59+wqJFi7B9+3Z4eHggJCQEwcHBOHbsGNLT04sdO5E5MZdoU6vV+OSTTzB69GgMHz5cc/zEiROo\nWLFioTvWL2MuofKKeURbbm4uPv74Y0RFRWHgwIGa4wkJCZonebqUlzwiqSgLDg7G3r17i9yfJC4u\nDkFBQUYPjKgkOnbsiJUrV8LJyQktW7Yssk2PHj0QGxuLiRMnYujQofDw8MAPP/yAw4cP44MPPgAA\nDBgwAIcPH8akSZMwfPhwZGVl4fPPP4ejo6PWe72c2IKDgxEXF4fAwEB4eHggMTERu3fvBgCz3Lgo\nquhydXVFfn6+5s6UPt26dcNnn32Gli1balZp0uXFePTo6GgMHjwYMpkMhw4dgqOjI1q3bl2yDhCZ\nEXPJ/5PL5ejZsye2bdsGBwcHBAUF4fz589i7dy+ioqIKLVzwT8wlVF4xj/w/Pz8/vP766/j8888h\nCAICAwNx/vx57NmzByNGjEDFihX1/nx5yCOS9il7//33ceLECQwYMADr16+HIAg4duwYJk6ciAMH\nDuC9994r6ziJdHp5Qmm7du0giiJat24NJyenIttUqFAB69atQ7169bBy5UrMmDEDv/76K+bOnasZ\n6ufh4YF169ahcuXKmD9/PrZs2YJ33nkHbm5uOj971qxZqFOnDpYtW4YPP/wQKSkpWLJkCfz9/fHz\nzz8b7IOplmiV+jmtWrUCgEKTaYvi7u6OFStWwMXFBYsWLcLMmTOhUCiwYsUKvU/kiCwJc4luU6dO\nxdChQ3H48GFMnz4d586dw9SpUzWbwOrDXELlCfOIbjNmzMC//vUv7Nu3D1OnTtXkkZefwOtSHvKI\nIEp8hpmQkICVK1ciJSUFarUagiAgODgYEydOLDQ5saQePnyI6OhoJCQkoEqVKoiOjkZEREShdrm5\nuVi0aBGOHTsGJycn9O3bF5MmTZL0GQqFwiixmou3t7fV9wGwjX7Yeh/27duHL774AgcPHix0N86S\nmGtLDls+99bCFvoA2EY/mEtKzpbPvbVgHyyHrn5YSx4BSp5LJA1fVKvVCA8Px65du5Cbm4vMzExU\nrFjR6Ov8T5s2DTKZDLGxsYiPj8eECRMQFxdXaOLfqlWrcO3aNXz55Zd4/PgxJk2aBF9fX0kT/4hI\nv1OnTiE1NRUHDhzAoEGDLD75EZFlYi4hotIqT3lE0vDFtm3bYunSpbh+/TqcnZ3h6+tr9ILs9u3b\nOHfuHBYuXIjAwECMHDkSoaGh2L9/f6G2Z86cwb/+9S/Ur18fzZo1Q8+ePZGQkGDUeIjKq/v372Pf\nvn0IDw/H22+/be5wiMhKMZcQUWmVpzwi6UnZoEGDcOjQIWzduhX169dHnz590KtXL3h5eRktkJSU\nFAQEBGhN3gsNDUVycnKhth4eHoiLi0O3bt2QnZ2NM2fOoFevXkaLhag8e+utt/jUmYhKjbmEiEqr\nPOURSU/KIiMjcezYMezYsQONGjXC+vXr0a5dO4wbNw5xcXGavUpKQ6lUFhqm6O3tXeTSldOmTcNv\nv/2GsLAwtG3bFj4+PhgzZkypYyAiIiIiIjI1SUXZC02bNsWCBQtw+vRprF27Fk+fPsWkSZMQERGB\nhQsX4s6dOyUOJCcnR2tlGgBwdHQscg+GO3fuoFatWtixYwc2bNiAmzdvYuPGjSX+bCIiIiIiInMp\nVlEGAL/99htWr16Njz/+GBcvXkRwcDAGDx6M5ORk9OzZs8g5YFLIZLJCBZhKpSq0/8mTJ08we/Zs\nzJo1C02aNEG7du0QHR2Nzz//HGq1ukSfTUREREREZC6S5pSlp6fj8OHDOHToEH799Vd4eXmhV69e\n6NOnj2ZX74kTJ2LChAlYtmwZ+vXrV+xAfH19Cy2BqVAoIJfLtY79/vvvyMvLQ+3atTXHgoOD8fTp\nU2RkZBjcUM5cS94aky30AbCNfrAP5Zct/N7YB8thC/2whT6Ygy383tgHy2ALfQBspx/FJakoa9++\nPezt7dGhQwdMnDgRbdu2hb29faF29erVK/EQxpCQENy5cwcZGRmoXLkyACApKQnh4eFa7Xx8fCCK\nIn777TfUq1cPwPNCzcXFxWBBBnA/EEthC/1gHyyDOZL3kdR03E1XGmzn7uyAToEeJoio+Gzl3Ft7\nHwDb6Iet9MEcbOH3xj6Yny30AbCNfpTpPmWzZs1Cjx49NMWSLpGRkYiMjCxRIP7+/ggPD8eMGTMw\ndepUnD59GleuXMGSJUugUqmQmZkJLy8v+Pn5oXXr1pg3bx7mzJmDp0+fYvHixRg8eHCJPpeIqLgy\nc1XwrehksF3ak9IvgkRERES2T9KcssGDB+ssyAoKCpCYmGiUYJYuXQq1Wo3+/ftj//79WLNmDeRy\nOZKSktCmTRv89ddfAIAVK1YgICAAI0eOxJQpU/Daa69h4sSJRomBiIiIiIjIlCQ9KUtLS8P8+fOR\nmJgIlUoFURQBAGq1Gs+ePQMApKamljoYb29vxMTEFDrevHlzrfevVKkSlixZUurPIyIqiagDVyFA\nNNiusZ8r+jWQG2xHRERE5ZukJ2ULFy7EhQsX0KdPH9SuXRsNGjTAkCFDEBgYCEEQsHr16rKOk4jI\nqiizVbh8/4m5wyAiIiIrIOlJWUJCAiZNmoTBgwdj586d+P777zFt2jRMmTIFo0ePxvHjx9G5c+ey\njpWIyCKs7tsQLupcvW3GfHMDarXhp2lEREREkp6UZWdna5a+r127tmYooZ2dHd566y0kJCSUXYRE\nREREREQ2TNKTMj8/P9y/fx8AULNmTWRlZeHvv/+Gn58fXF1d8fDhwzINkojIkrg7O+JuepbeNmq1\nCEEwUUBERERk1SQVZa+99hqWL18ONzc3dOjQAQEBAVi1ahXGjh2LHTt2oFq1amUdJxGRxegW7AOF\nXP9Ag4Xxd5H7TG2iiIiIiMiaSRq+GBUVhVdeeQU7d+4EAMycOROHDx9Gt27dEB8fX+K9yYiIiIiI\niMo7SU/KKlSogPXr1yM///lGqO3bt8fhw4dx7do1vPLKK6hRo0aZBmksR1LTcTddabCdu7MDOgV6\nmCAiIiIiIiIq7yQVZS84OTlpvg4ICEBAQIDRAypLmbkq+FZ0Mtgu7Um+CaIhIlsniiL2X3tgsB1v\nBBEREZVvOouybt26QZA4S10QBPznP/8xWlBERLZAFMEbQURERGSQzqIsJCREclFGRERERMXDaRVE\n9ILOomzJkiWmjIOIyOY8yn2GMd/cMNiusZ8r+jWQmyAiIrIknFZBRC/onVOmVCpx4MAB/PnnnwgI\nCEDPnj0hl1vvhUPUgasQIBpsxwskIiqt3kGe2JVieD6ZMluFy/efmCAiIiIislQ6i7JffvkFw4YN\nQ2ZmpubYmjVrsHr1arRq1cokwZmDMluFS/efcHI+EZXKvI410cjX1eBd8DHf3IBabfhmERHZHt4s\nJqIXdBZln376Kdzd3bF+/Xo0aNAAt27dwqxZs7BgwQLExcWZMkajWd23IVzUuXrbvBhqxOEERERE\nZG58mk5UPugsypKTkzF37lw0adIEABAUFIQ5c+Zg0KBBUCgU8Pb2NlmQRERERLZG6s1iPk0nsn06\ni7LHjx/Dz89P61jdunUhiiKUSiWLMiIiA9ydHQw+UVerRXChWyIiovJNZ1FWUFAAe3t7rWMymQwA\n8OzZs7KNiojIBkiZc7ow/i5yn6lNEA0RERFZKr2rL5ZXymwVl7EmIiIiIiKT0FuU/fbbb1rfFxQU\nAABu3Lih+fqFRo0alTqYhw8fIjo6GgkJCahSpQqio6MRERFRZNutW7diy5YtyMnJQadOnTBv3jzN\nk7zSaB1QCWf/yDLYjhNvichYRFE0uOIrV3slsj3uzo64m67/moNDnInKB71F2axZs4o8/sEHH0D4\nX4YQRRGCICA1NbXUwUybNg0ymQyxsbGIj4/HhAkTEBcXBx8fH612hw4dwpo1a7By5UrI5XJMnToV\nn332GaZPn673/aUkv251PdChVmXUqKy/wOPEWyIyFlE0vOIrV3slsj3dgn2gkNvpbbMw/i5yVAW8\ncUNk43QWZdu2bTNlHLh9+zbOnTuHM2fOwNPTE4GBgTh79iz279+P8ePHa7XdsmULoqKi0KZNGwDA\nhAkTsGvXLoOfISX5AZC0RxkRERGRKfDGDZHt01mUNW/e3JRxICUlBQEBAfD09NQcCw0NRXJysla7\nJ0+eIDU1FatWrdIc69KlC7p06WKyWImIjOlR7jOD81g5h5WIiMh2GX5sZCJKpbLQMEVvb2+kp6dr\nHbt37x4EQcD9+/fx5ptvol27dvjoo4+Qn887RERkfXoHecLDWf+aS5zDSkREZNssZvXFnJwcODlp\nP5p3dHSESqXSOvb06VMAwMcff4wZM2bAyckJc+fOxdKlS/Hhhx+aLF4AECFtqCPHeRORLvM61kQj\nX1e9Q5M4h5WofOPTdCLbZzFPymQyWaECTKVSwcXFReuYg4MDRFHEhAkT0Lp1a4SFhSE6OhqxsbGm\nDBcAYG8nwLeik8H/MnO5rxsREREVH5+mE5UPFvOkzNfXFwqFQuuYQqGAXK591+fF97Vq1dIcq1Wr\nFvLz8/Hw4UOtOWlF8fb2NhiLv48ambkqvW1ECBBFEe7u7gbfL8cuT9LnSmXM9zInW+gH+1B+GfP3\nVqlSHtzddK/4amdnBxGi0c+VLZx7W+gDYBv9sIU+mIOh39uaAd6IqPsn/PTkiEE7LqNAbfwcIZUt\nnHv2wXLYSj+KyyhF2f3791G1atVSvUdISAju3LmDjIwMVK5cGQCQlJSE8PBwrXZVq1aFXC7HL7/8\ngsDAQADAzZs3UaFCBc3P6fPPwq8oYXI7APqXxHdxEJCdr0ZmZqbB98t6kg+FovR7qAHP/1Cl9MHS\n2UI/2AfLYK7kbczfm5Cfjet/6t6u49mzAgiCcT/TVs69tfcBsI1+2EofzEHK7y0rKwsuat1DnNVq\nNUS1aJZzYCvnnn2wDLbQj5LmEklF2ZMnT/DZZ58hMTERKpUKovh8boNarUZOTg7S09NLvU+Zv78/\nwsPDMWPGDEydOhWnT5/GlStXsGTJEqhUKmRmZsLLywuCIGDo0KH497//DblcDhcXFyxbtgz9+vWD\nnZ3FjMYkIpLM0JxTqfsUAZzDSkREZI0kFWULFy7E4cOH0bZtW9y8eRPOzs6oVasWLl26BKVSifnz\n5xslmKVLl2LmzJno378/qlevjjVr1kAul+PChQsYNmwYjh8/jqpVq2LMmDHIy8vD+++/D5VKhZ49\ne2LatGlGiYGIyBJJ2acI4F5FRLbI3dlB779ttVqEIJgwICIyOklF2Y8//ogJEyZg7Nix2LJlC86f\nP49PP/0U2dnZePvtt3Hjhv4VgaTy9vZGTExMoePNmzcv9CQuMjISkZGRRvnckpKyGhLAFZGIiIio\n5KQ8Tc99pjZRNERUFiQVZVlZWWjcuDEAoE6dOti8eTMAoEKFChg5ciQ+/fRTzJo1q+yitEC9gzyx\nK8XwUCJltgqX7j8xOOyIQ46ISB/eBCIifURR5BBnIismqSjz8vJCRkYG1aBDDQAAIABJREFUAKBm\nzZpQKpVQKpXw8vKCXC7HgweGk4CtkbK3EADNRZShdhxyRES6FOcmEJfFJiqfOMSZyLpJKsratm2L\n1atXo1q1amjQoAG8vb2xbds2vPfee/j222/L7dKVRESmUJybQNxkmoiIyPpIWq7w/fffh729PT75\n5BMAwKRJk7Bp0yY0btwYsbGxGDFiRJkGSUREREREZKskD188cOAA0tLSAAD9+vVDQEAAkpOT8eqr\nrxbaS4yIiIiITIfzTomsm+TNowVBgJ+fn+b7Zs2aoVmzZmUSFBERaTO0JDYAzdBFKZP9/X3UCJNz\nb0ciW8B5p0TWT3JRRiWnzFYZvHvFO1dEpI+U1dIWxt9Fdn6BpMn+mbkqADIjREZE5sZ5p0TWj0VZ\nKUi5c93YzxWXDNyVkrpsPsC720RERFRyIqQ9TefS+USmxaKsFKQkq34N5Nh/7YHeu1dSl80HeHeb\niPSTOq8kPMADnf0DTRAREVkSezuBS+cTWaBSF2WiKCItLU1rvhkREZleceaVnP8jwwQREZGpSJ13\nysGLRJZJUlEWHByM3bt3o1GjRoVeu3jxIsaOHYukpCSjB0dERNIVZ14JL8yIbEtx5p0SkeXRWZQt\nXboUGRnP76SKooh169bBw6PwP/hff/0VLi4uZRchERERERkFl84nskw6i7L69etj48aNAJ4vh3/9\n+nXIZNpzmezt7eHm5obZs2eXbZRERGRUoihysj9ROcOl84ksl86irE+fPujTpw8AoGPHjli3bh2C\ngoJMFlh5I2XZfABoWr0ysrLcDbbjhRQR6eNgb8fJ/kTlDJfOJ7JckuaUnThxQu/rubm5cHZ2NkpA\ntsjQ5Fspy+YDzwu3pPtZGN/U8HACXkgRlU9SJ/urRbWJIiIiIiJDJBVlKpUKO3fuRGJiIlQqFUTx\n+d0TtVqNnJwcpKamcqEPPQw9sZKybD4ASU/SiKh8kzrZP+cZ74ITERFZCklF2bJly7Bt2zbUr18f\nSqUSMpkMnp6euH79OlQqFd59992yjpOIiIzoYXY+J/sTERFZCElFWVxcHEaPHo2pU6di48aNuHbt\nGlatWoW0tDQMHTpU8+SMiIgsX+8gT+y+qgQMLIzPyf5EtkfqEGcAkhYD8vdRI0xuZ5TYiMozSUXZ\nw4cPERERAeD5qoxff/01AMDX1xfjxo1DTEwMoqKiSh3Mw4cPER0djYSEBFSpUgXR0dGaz9Vl0aJF\nSE1Nxfbt20v9+URE5cG8jjURUbcKXNS5ettxsj+R7SnOfmZSFgPKzFUBkBlsR0T6SSrK3N3dkZv7\n/P+8a9SogbS0NDx+/Bhubm7w9/fH33//bZRgpk2bBplMhtjYWMTHx2PChAmIi4uDj49Pke3/+9//\nYufOnWjatKlRPt8aPHiSxyFHREREREQ2RFJR1qJFC8TExOCVV15BQEAAKlWqhEOHDmHw4ME4e/Ys\n3N0NL9FuyO3bt3Hu3DmcOXMGnp6eCAwMxNmzZ7F//36MHz++UHtRFDF37lyEhISU+rOtReuASjh3\n7wnUav2rpimzVbh0/wn3ICIiIqISkbrJdHiABzr7B5ogIiLbJqkomzhxIgYPHozJkydjx44dGDVq\nFD766CNs2LABCoWiyKKpuFJSUhAQEABPT0/NsdDQUCQnJxfZfufOnZDL5QgNDcXp06dL/fnmJmWM\nd7e6HujdqDo87VV6271IotyDiIh0cXd2xN30LL1tijOvhDd4iGxHcTaZPv9HhgkiIrJ9koqygIAA\nHD16FLdu3QIAjB07Fn5+fkhKSsKrr76Kfv36lToQpVJZaJiit7c30tPTC7VNT0/Hhg0bsGfPHhw+\nfLjUn20JpF7M/HA3D1DrL8qIiAzpFuwDhYHJ+cWZV8IbPES2ozibTHPWKZFxSCrKAKBChQpo0KCB\n5vvevXujd+/eRgskJycHTk7a//gdHR2hUhUuQBYtWoRhw4ahatWqRvt8IiIiIiIic5BclMXHx+P8\n+fPIysoqNKdJEAQsWrSoVIHIZLJCBZhKpYKLi0uhOK5fv47ly5eX6vOIiIiIqHREUeQQZyIjkFSU\nxcTEYPny5ahQoQLkcjns7LSHvAiCUOpAfH19oVAotI4pFArI5dorCMbFxeHu3bto1qwZAODZs2co\nKChAkyZNcPnyZYOf4+3tXepYzerunwYXVnlxfqQswJJjl2e234nVnwuwD+WZLfzeDPXBzs4Oj3Lz\nMO7bmwbfK6y6O8a2CzZWaJLZwnkAbKMfttAHc7DU31ulSnlwd9O/1L2dnR3sANSrZni1578fm+96\nQwpLjk0qW+gDYDv9KC5JRdnOnTvRt29fLFiwAI6OjmUSSEhICO7cuYOMjAxUrlwZAJCUlITw8HCt\ndtOmTcO7776r+f7rr7/GpUuX8O9//1vS5/yz8LNGmZmZel9Xq9VQZqswYNtFg+/V2M8Vnf1Nv7+I\nt7e31Z8L9sEymCt528LvzVAfetarjF0pKkkrvp67/dDkvxNb+PsFbKMfttIHc7DU35uQn43rf+pf\nDOjZswJAEAxelwBA1pN8KBSWuZ+Zrfz9WnsfANvoR0lziaSi7NGjR3jjjTfKrCADAH9/f4SHh2PG\njBmYOnUqTp8+jStXrmDJkiVQqVTIzMyEp6en5r8XKleuDJlMBn9//zKLzZJIWTGtsZ8rLt1/YvC9\nlNkqXJbQjojKp+JM9ucm00S2Reom039mcf9UImOQVJQ1btwYqampaNGiRZkGs3TpUsycORP9+/dH\n9erVsWbNGsjlcly4cAHDhg3D8ePHy/3iHlJWTOvXQI791x7wQoqIiIjKTO8gT+y+qgQMrMHIm8BE\nhuksylJSUjRf9+zZE4sWLUJOTg6aNGmCChUqFGrfqFGjUgfj7e2NmJiYQsebN2+O1NTUIn9m7Nix\nGDt2bKk/u7wSwT2IiIiIqPjmdayJiLpV4KLO1duON4GJDNNZlA0YMEBrAQ9RFLFq1apCi3qIoghB\nEHQWTWTZ7O0E7kFERDpJ2dherRZhhPWeiIiIyi2dRdm2bdtMGQcREVkgqfNKclQFfOpORERUQjqL\nMkEQ8Morr8DV1dWU8RARkRUSRfCpOxERUQnpLMqGDh2K3bt3G2WuGBERERHZHimrQr+YT8an6US6\n6SzKRJETMq2Z1HkgPMtERERUUlJWhV4YfxfZ+QV8mk6kh6Ql8cn6cH8RIjKlR7nPmEuIiIhKSG9R\ndu7cOdy7d0/SG3Xv3t0oAZHp9A7yxK4Uw0MJlNkqXLr/hMMOiKhIxckl3KuIqHzijRsi/fQWZZ9+\n+qmkYYyCILAos0LzOtZEI19XSZtMA5zET0RFK04u4V5FROUPb9wQGaa3KFu9ejWCg4NNFQsRERER\n2RjeuCEyTG9R5uPjg2rVqpkqFiIiIiIionKHC30QEVGpSV3xFeCy2ERERP/EooyIiEpN6oqvXBab\niPQRwRs3VD7pLMp++eUXU8ZBZiL17jZXTSIiIqKSKs7TdN64ofKIT8rKOSl3mVLSnhp96Xx/HzXC\nDGw2SUS2hzd4iMqn4jxNJyqPWJSRQWWxdH5mrgqAzBjhEZGV4LLYRGQIb9xQecWijIiITILLYhOR\nPrxxQ+UZizIiIiIiMjveuKHyTFJR9uabb6Jv377o0aMHKleuXNYxERERERHpJGWVRq7QSNZEUlFW\nt25drFixAkuWLEHbtm3Rt29ftG/fHg4Oxn3Q9vDhQ0RHRyMhIQFVqlRBdHQ0IiIiCrV7/PgxFi1a\nhFOnTsHBwQHdu3fH1KlT4ejoaNR4qPiU2SpJY8HDAzzQ2T/QBBERkSWRugKbIJgoICKySvZ2gsEn\nalyhkayJpKpq8eLFmDt3Lo4dO4ZDhw5h4sSJcHNzQ/fu3dGnTx80atTIKMFMmzYNMpkMsbGxiI+P\nx4QJExAXFwcfHx+tdvPnz8e9e/ewdetW5OTk4IMPPoCzszMmTZpklDioMCkXUo39XHFJwhhvZbYK\n5//IMFZoRGRFpK7AlvtMbYJoiIiILIPkR13Ozs7o3bs3evfuDYVCoSnQvv76a9SqVQv9+/fHm2++\nCTc3txIFcvv2bZw7dw5nzpyBp6cnAgMDcfbsWezfvx/jx4/XtMvPz8exY8fw1VdfoX79+gCAqKgo\nfPbZZyzKypCUC6l+DeTYf+2BtLHgoshhB0SkkyghR3BrDSLbY8z9U7lCI1mTYo8/zMrKwg8//IDv\nv/8eV69ehaenJxo2bIgvvvgCGzZswLJly9C2bdtiB5KSkoKAgAB4enpqjoWGhiI5OblQ23Xr1mkK\nMgAQBAF5eXnF/kwyHwd7Ow47ICKdRNHw9hrcWoPI9hhr/1Su0EjWRlJRlp+fj5MnT+LQoUP48ccf\nAQAdOnTAqlWr0K5dO9jb20OlUmHMmDGYM2cOTp06VexAlEploWGK3t7eSE9P1zrm5ORUaJ7Z119/\njSZNmhT7M4mIiIjIukhZpZErNJK1kVSUtWrVCk+fPkVwcDBmzJiBnj17FlqF0dHREc2aNcONG4YX\neShKTk4OnJy0/3E5OjpCpVLp/bnVq1fj559/xr59+0r0uUREZHmkDE3igkFERGQrJBVl/fv3R9++\nfVGvXj297YYOHYqxY8eWKBCZTFaoAFOpVHBxcdH5M5s2bcKGDRuwYsUKBAby/5iJiGyBlA1kuWAQ\nERkiZdl8gPNTyTJIKsoyMjJ0Fkc3b97EsmXLsGHDBlSqVKnEgfj6+kKhUGgdUygUkMuLnqC5fv16\nrF69GosWLULXrl0lf463t3eJY7QUltwHfx/1/+Z56CZCwIMneRj37U297cKqu2Nsu2Bjhmd0lnwu\npLKFPpiDLfzeLLUPawZ4I6Lun/Bz0z1fbNCOyyhQixbbh+KyhX7YQh/MwRZ+b+bog6HrDRECBAGo\nV83wQh9/P86Dt3c1Y4ZnFrbwtwTYTj+KS2dRdvHiRYji87G4Bw8eREhISJFPo06fPo1z586VOpCQ\nkBDcuXMHGRkZmqGRSUlJCA8PL9T24MGDWL16NRYsWIA+ffoU63P+WfhZG29vb4vuw/M7Tfon3g9s\n6IXdV5VQq3Uvea3MVuHc7YcW3VdLPxdS2EofzMEWfm+W3IesrCy4qHXPF3mePwSL7oNUln4upLCV\nPpiDLfzezNEHQ9cbLg4CsvPVyMzMNPxmds48DxbCFvpR0lyisyjbt28fDhw4AOF/O3jOmzdP55v0\n7NmzRB/+Mn9/f4SHh2PGjBmYOnUqTp8+jStXrmDJkiVQqVTIzMyEl5cXsrOzsWjRIs0G1i9OnCAI\n8PLyKnUcVPbmdayJiLpV4KLO1dmGE3SJiIioNKTMTQU4P5Usg86ibNasWRg0aBBEUcSgQYOwcOFC\n1K1bV6uNnZ0dKlWqhBo1ahglmKVLl2LmzJno378/qlevjjVr1kAul+PChQsYNmwYjh8/juvXr+Px\n48fYv38/9u/fD+D5fjYymQxXrlwxShxERGRehvYqUqtF2NsLJoyIiKyJlLmpAOenkuXQWZRVrFgR\nISEhAIBt27ahQYMGcHV1LdNgvL29ERMTU+h48+bNkZqaCgCoWrWq5muybVIn6HKTaSLbY+jf9ML4\nu8jXPQKaiMo5KcvmA89H5nBcDlkCnUXZd999h9atW8Pd3R0KhQLx8fF636h79+5GD47KN3s7wWAy\nBbjJNFF5JYoib9wQEZFN0FmUTZ48GXv27EGjRo0wefJkvW8iCAKLMiIiMim1CN64ISIim6CzKDt+\n/LhmOfrjx4+bLCAqH9ydHXE3PUvn62q1yOEERKTXw+x8SZP4G/u5ol8Dw8tiE5FtMTQ3FeD8VLIc\nOouyatWqFfk1kTF0C/aBQs9GjQvj7yI7v8CEERGRNekd5IndV5WAgds3ymwVLt9/YpqgiMiiSBm2\nzPmpZCl0FmX6lsD/J0EQMHfuXGPEQ6QhdSlb3gUnKn+kbK0BcHsNIjKM81PJEugsyn788UfJb/Ji\nLzMiYynOUraX7j9hMiUinbiSKxHpw/mpZAl0FmUnTpwwZRxE/8fevcdFVad/AP/MCAyIXB1ATfBC\nKl6SixdMMU3N8pK7tpm75V1J20XaUjJJzcwLdtFNzVLUzMzVNNPWTayo2FDUUi7qamoKYrniDALK\ndWDO7w9/Tk4wMwcY5pyZ+bxfL18bZ77MPF8OPHuec74XI/VZyhZgMiVyRpbmpgIwPCVjjiAiUzg/\nleTAZFFG5Ggqqjk8gciRWJqbCtyZL/JLSSUvuIioTpyfSnJhsigLDw/H9u3b8cADD6Bnz54Whyhm\nZ2dbPTgia2rnqxLVjnfLiRxHfYZC84KLyPlwfirJhcmiLDY2FoGBgYb/5rwxsjWxS9mKXRBkQIg3\npkQGWSs8IrID9RkKzQsuIjKH81OpKZksyuLi4gz/PXv2bJsEQ3QvMQkt53qp6Lvgh6+UsCgjIiKi\nBmmmVHB+KjUZ0XPKysvL8fnnn+PEiRO4desWWrZsiejoaIwYMQIuLpyaRtKoz11wbZmO80qInJDY\np+4A74ITOSOxiwbxWTo1JVHVVH5+PiZPnoxr166hbdu2aNmyJc6fP489e/bggw8+wObNm+Hnx/+D\nIvkaEOKNw1fMJ1yA80qIHJHYDWR/KanEgtQ8i20jWnmyKCNyIFw0iORAVFGWlJQEFxcX7N+/H507\ndzYcP3XqFOLj47F8+XK8+eabTRYkUWNNiQwSNXSR80qInBMXBCEic5gjqKmJKsqOHDmCpKQko4IM\nAB544AG8+OKLWLx4cVPERiSKmKFJwJ0l8cWuwEhEzoULghCROfXJETV6bsFD9SeqKPPy8oJer6/z\ntRYtWkCl4oUuSUdsQhOTIAHxqysFB+rRx8JwByJyPFyBjcg51Wd+KhcEofoSVZTFxsbi7bffRufO\nnREaGmo4fv36daxduxZTp05tsgCJrMXaybS4QgeANySIHAUvuIjInPrMT+XcM6ovk0XZiBEjjPYm\nu3btGsaMGYOOHTtCrVajuLgY58+fh4uLCzIyMhAbG2uTgIkaytrJNDrED8OCQy22IyL7wAsuImos\nzj2jhjJZlIWHhxsVZeHh4Uavt2rVCl26dLFqMIWFhUhMTMSxY8fQunVrJCYmIiYmplY7QRCwYsUK\n7N+/H66urpg6dSqmT59u1VjIOdUnmWbk3eQQJiInU58cceLX2xwKTeRkOPeMGspkUZaUlGTLOAAA\nCQkJUKlU2LNnD9LS0hAfH4+UlBQEBgYatdu4cSPS0tKwefNmaDQaJCQkoH379hg6dKjNYybHUp9k\nqlQqOYSJyMksHtIeA9v5oLii2my7xK9ycbOiWtQS+9EhJfjgj3zqTuQoxA6FLhKZI7gNh3MQvetz\nVVUVLl26hKqq337J9Ho9ysvLceLECcTFxTUqkNzcXGRkZCA9PR3+/v4IDQ3F4cOHsXfvXsyaNcuo\n7fbt27FgwQL06NEDwJ05bx9//DGLMrKpG7c5hInIGYm5OMq5Xir6idrRK0XWCIuIZMLaOYLDHJ2D\nqKLsxx9/xN///ndotdo6X/fw8Gh0UZaTk4OQkBD4+/sbjkVGRiIrK8uo3fXr16HRaBAVFWXUbsOG\nDY36fKK7xNzhimjliZPXSi2+l9ghTByaQORY6vPUnQvsEzkfaw9z5DBo+yeqKHvrrbfg7e2NxYsX\nY//+/VAoFPjTn/6E77//Hjt27MCmTZsaHYhWq601TFGtVqOgoMDomEajAQAEBPz25KFly5YoKytD\naWkpPD09Gx0LOTcxxdET3QPwdX4lPPQVZtvF7r8AbZnO4vCEBwKbWxwOBbB4I7InYocwKZQK3rgh\nckLWHObYq20xxnbxEfWZzCXyJKoo++mnn7Bs2TIMGzYMt27dwscff4xBgwZh0KBBqK6uxvr16xtd\nmJWXl8PNzfhugaurK3Q6Xa12rq6utdoBd4ZYsigjW/Fxd0V+QYnZNhGtPHHCwrCDG6U6fHO5GKcK\nyix+JseVE9mP+qzmaOmCq0YvQGG2RdN4MMQbHzxh3UW9iOgOaw1zvFGqQ8pPN3DiquWh0GJzCf/2\nbU9UUabX6w1Psdq3b4+LFy9CEAQoFAoMHz4cCQkJjQ5EpVLVKsB0Oh08PDxqtauurq7VDgCaN2/e\n6DiIxBrRNRAaC0MFnugegNSfb5p9CrbnjAYnf71t2P/IFG15NTKvcVw5kSMZE+aP3We0qKnRm20n\ntiATADRTWqd8u1GqQ8YV8zeeiKhpiVlcaM8ZDTL/Vwa93nweAcTlEm15NY7m82/f1kQVZR06dMCp\nU6fQu3dvdOjQARUVFbhw4QI6d+6M8vJylJeXNzqQoKAgw9DEuzQajdEwxbvtBEFAYWGhYf6ZVquF\nl5cXVCrLG/mq1epGxyo1R+gD4Bj9ENOH8RbaPDtI/Od9d1GD0yLXBBh8v7ifryOcByk4ws+NfZDe\nuqfUWGfF9zt4tuD/N7a3jhZuzcTlnCKN6JxDxuz9dxhgH5qaNa8jxGqK6w2x5HwumpKoomzcuHFY\nsWIFSktLERcXhz59+iAxMRFPPfUUtm3bhm7dujU6kPDwcOTl5aGoqAi+vr4AgMzMTERHRxu1CwwM\nRKtWrXDy5EkMGzYMAHDixAn07Nmz0TEQyR0veojInBFdAy03IiKygNcbttds8eLFiy016tmzJ5o3\nbw6NRoOBAwciMjISe/fuxb59++Du7o6VK1c2uqr18fHBiRMn8M033yAsLAz79+/HgQMHsHTpUri5\nueHmzZvw8PCAQqFARUUFkpOT0bNnT5w7dw4rV67E3Llz0b59+0bFQEREREREZGsKQRAatBqvIAi4\nefOm0RL2jaXRaDB//nz88MMPaNu2LRYsWIB+/frh+PHjmDx5MlJTU9GmTRvU1NRgxYoV2LdvHzw8\nPBAbG4tJkyZZLQ4iIiIiIiJbqVdRlp6ejhMnTuDWrVtQq9Xo27ev0X5hREREREREVD+i5pQVFhZi\n5syZOHXqFFxcXODr64uioiLU1NRg0KBBeOedd0QtskFERERERETGRG39vXLlSvzyyy94//33cerU\nKaSnpyMnJwerV6/GyZMn8cYbbzR1nERERERERA5JVFGWmpqKl156CYMHD4ZCcWeHA6VSicceewxz\n5szBgQMHmjTIxiosLMSsWbMQGRmJkSNHIj09XeqQ6u3QoUMICwtD165dDf+7atUqqcMSraioCP37\n94dWqzUc++yzzzBkyBBERUVh/vz5qKyslDBCy+rqw+jRo2udl6qqKgmjrNvPP/+MKVOmICoqCo8+\n+ih2795teM1ezoO5PtjqPDCXSMsR8gjAXCI15hLrYC6RFvOI9KyeSwQRoqOjhdTU1DpfS09PF3r3\n7i3mbSQzbdo04bnnnhMuXrwobN68WYiMjBSuX78udVj18u677wrPP/+8oNVqBY1GI2g0GqGsrEzq\nsEQpKSkRnn76aSEsLEzQaDSCIAjCjz/+KERERAiHDh0Szpw5I4wZM0ZYvny5xJGaVlcfampqhB49\negg5OTmGc3L3NTmprKwUHnnkEWHRokVCbm6ucPDgQSEiIkI4fPiwcOLECbs4D+b6YMvzwFwiHUfI\nI4LAXCI15hLrYS6RDvOI9Joil4iaUzZ+/HisW7cOUVFRhj3EAKCiogKbNm3Ck08+2bAS0wZyc3OR\nkZGB9PR0+Pv7IzQ0FIcPH8bevXsxa9YsqcMT7dKlS+jcubNVV7u0hdOnT2POnDlwd3c3Or5jxw6M\nHTsWw4cPBwC89tprmDp1KubMmQM3NzcpQjXJVB/y8/MBAD169DA8QZajzMxMFBUVYeHChXBxcUG7\ndu1w5MgR/Pvf/0ZFRYVdnAdTffjiiy9w3333AWj688BcIh1HyCMAc4kcMJdYD3OJNJhH5KEpconJ\n4YuxsbGGf2fOnMFPP/2EIUOGYNasWViwYAFmz56NQYMGISsrC8XFxY3vXRPJyclBSEiIUdKIjIxE\nVlaWhFHV36VLl+xyH7ajR4/i8ccfx5o1ayDcs9DnqVOn0KtXL8PXPXv2hE6nw4ULF6QI0yxTfbh0\n6RKCg4NlnfwAoFOnTli/fj1cXH67B6NQKFBZWYnTp0/bxXkw1wdbnQfmEuk4Qh4BmEvkgLnEephL\npME8Ig9NkUtMFmUVFRWGf5WVlYiKikL37t1RWlqKvLw8FBUVoXPnzujRoweuXr3a8F41Ma1Wi8DA\nQKNjarUaBQUFEkXUMJcvX0ZqaiqGDRuGxx57DJs2bZI6JFFmzJiBuLg4uLi4GP1yajQao/OiVCrh\n6+sry/Niqg+XLl2CQqHA9OnTERMTg9jYWFy+fFnCSOvm7++P3r17G74uKipCSkoKoqKicOPGDbs4\nD6b6EBkZabPzwFwiHUfIIwBziRwwl1gPc4k0mEfkoSlyicmi7KOPPhL9b9u2bdbpYRMoLy+v9cjT\n1dUVOp1Ooojq7/r166ioqICXlxfWr1+PuLg4JCcnY/v27VKH1mAVFRV1nhc5Tkg15fLlyygpKcHk\nyZOxYcMGtGjRAtOmTUNFRYXUoZmk0+nw4osvomXLlnjyySft8jz8vg+2Og/MJfJjj7+/dWEukQZz\nScMxl8gP84h0rJVLRM0puystLQ3Hjx/HrVu34Ofnh6ioKDz00EOyflSqUqlqJTqdTgcPDw+JIqq/\noKAgHDt2DN7e3gCAzp07Q6PRYNeuXZgwYYLE0TWMI5yXJUuWoLy8HJ6engDubB0xePBg/Oc//zGM\nh5YTnU6H+Ph4nD17Fjt37oSbm5vdnYe6+mCr82BvP6u6OFoucYRzAjCXSIG5pHGYS+SHeUQa1swl\nooqy8vJyPPfcczh69Cjc3d3h7+8PrVaLjRs3olevXti0aVOtCYdyERQUBI1GY3RMo9EgICBAooga\n5m7iu6tjx464ceOGRNE0XmBgoFH8NTU1KCoqqjWkQ86USqXhDw4A3Nzc0LZtW1meF51Oh7i4OGRl\nZWHbtm1o164dAPs6D6b6YKvzwFwiP/b0+2sOc4ltMZdYB3OJvDDWNjNfAAAgAElEQVSP2J61c4mo\nfcpWr16NM2fO4N1330VWVha++eYbZGdnY+3atTh//jzeeeedRnSpaYWHhxvmwN2VmZmJ8PBwCaOq\nn2PHjuHBBx80untw7tw5u5tge6/w8HBkZmYavs7OzoZKpUKnTp0kjKp+pk+fjvfff9/wdXl5OfLy\n8mR5XhYvXoyTJ09i69at6NKli+G4PZ0HU32w1XlgLpEfe/r9NYe5xLaYSxqPuUR+mEdsz+q5RMxa\n/DExMcL27dvrfG379u3CoEGDRK7qL41p06YJzz77rHD+/Hlh8+bNQq9evYSCggKpwxKttLRUGDRo\nkPDyyy8b9kLo3bu3cOjQIalDE+3q1atCly5dau0JcuDAAeH06dPCH/7wByEpKUniKM37fR+2bt0q\nREdHC2lpacKFCxeE+Ph44Q9/+IPEUdb2ww8/CF26dBF2794t3Lhxw/CvuLjYbs6DuT7Y8jwwl0jL\nEfKIIDCXSIm5xDqYS6THPCKtpsglooYv3r5922R11759exQWFop5G8msXLkS8+fPx7hx49C2bVus\nW7fOroYJNG/eHBs3bsSyZcswduxY+Pr64sUXX5TlGGFz7p172KtXLyQmJmLlypUoLy/Ho48+ihde\neEHC6MS5tw+TJk3C7du3sWjRIhQXFyM6OtrozohcpKamQqFQYOHChUbHY2JikJycbBfnwVIfbHUe\nmEuk5wh5BGAukQpziXUwl8gD84h0miKXKAThnk0OTBg/fjy6dOmCJUuW1Hpt4cKFyM7Oxueff16P\nrhAREREREREgcqGPmTNn4q9//SuKioowYsQIBAQE4MaNGzh48CC++uorJCUlNXWcREREREREDknU\nkzIA2L17N/7xj39Aq9VCoVBAEAT4+/tj9uzZ+Mtf/tLUcRIRERERETkkUUVZWloaevXqBU9PT1y6\ndAlFRUXw9fVFhw4doFSKWsCRiIiIiIiI6iCqKIuKisLrr7+OUaNG2SImIiIiIiIipyHqMZdarW7q\nOIiIiIiIiJySqIU+nnnmGSxZsgSZmZlo3749/P39a7UZOXKk1YMjIiIiIiJydKKGL4aFhZl/E4UC\nZ8+etVpQREREREREzkJUUfbLL79YfKP77rvPKgERERERERE5E4vDF2tqauDh4VHnkEUiIiIiIiJq\nHJMLfej1eqxatQr9+vXDgAEDEBUVhTfeeAOVlZW2jI+IiIiIiMihmXxStmHDBmzcuBH9+/dHt27d\ncPnyZXzwwQcoKSnB0qVLbRkjERERERGRwzI5p2zUqFEYOHAgXn75ZcOxLVu24B//+AdOnDgBV1dX\nmwVJRERERETkqEwOX7x69Soefvhho2OjRo1CVVUVrl692uSBEREREREROQOTRVllZSU8PDyMjt3d\nRLq8vLxpoyIiIiIiInISJosyc0Ssok9EREREREQiNKgoUygU1o6DiIiIiIjIKZlc6CMsLAwRERHw\n8vIyHBMEAenp6YiMjESLFi1+exOFAhs3bmz6aIkATJw4EZmZmdi3bx/uv/9+o9c0Gg1iYmKwYsUK\nrF27Fr/++qvJ91EoFFixYgX++Mc/NnXIRCRDEydOhLu7O5KTk02+/sMPPxi+VigU8Pb2RmRkJObO\nnVsr/xCR8/l9nlAqlfDy8kLXrl0xYcIEDBs2zPBaWFiY0fe6uLigZcuWGDx4MF588UX4+PjYLG6S\nH5NL4vfp0wcAUFFRIeo4kS3V1NRgwYIF2LlzZ52vKxQKrF+/HlVVVYZjTz31FCZMmIAxY8YYjgUH\nBzd5rERkvyIiIpCYmAgAqK6uhkajwZYtWzB58mQcPHgQ3t7eEkdIRFL7fZ4oLCzEvn37EBcXh5de\negnTpk0ztL33OqSqqgqXL1/GmjVrkJ+fjy1btkgSP8mDyaLso48+smUcRPXi5eWF7OxsbNu2DZMm\nTaqzze/vSAFAUFAQevbs2dThEZGD8PLyqpUzevTogSFDhuCrr77Cn/70J4kiIyK5qCtPDBs2DHPm\nzMGqVavwyCOPGG4C//46pHfv3nB1dcX8+fORn5/Pm8VOrEFzyoikFh4ejkceeQT/+Mc/zA5RJCKy\ntrvD97noFRGZ8/zzz6O6uhp79uwx2+7eKUHkvFiUkd1atGgRXFxcsGjRIqlDISIHJQgCampqUFNT\nA51Oh//9739Yvnw5AgICMHz4cKnDIyIZCwkJQZs2bZCZmWk4dm9Oqaqqwrlz5/Dee+8hJiaGT8mc\nnMnhi0Ryp1arkZCQgIULF2Lfvn1csIOIrC49PR3du3c3OqZUKrFq1SrOJyMii9RqNbRareHr1atX\nY9WqVUZt/Pz88Pbbb9s6NJIZFmVk18aNG4cDBw4gKSkJDz30kNThEJGDiYyMxMKFCyEIAvR6PbRa\nLfbs2YMXX3wRLi4uRiurERFZMmHCBMNN5Orqaly9ehUbNmzAX/7yF3zyySd8WubEOHyR7N7rr7+O\niooKvP7661KHQkQOpkWLFujWrRu6d++OBx54AIMHD8aaNWvQrl07vPPOO1KHR0Qyd/36dbRq1crw\ndWBgILp3747u3bsjPDwco0aNwoYNG1BSUoIPP/xQwkhJaizKyO6FhIRg9uzZSElJQWpqqtThEJGD\nUyqV6NKlC65cuSJ1KEQkY5cvX8b169fRq1cvs+1at24NHx8f5OXl2SgykiMWZeQQpk6dim7duuHN\nN9+EQqGQOhwicmB6vR5nz55FSEiI1KEQkYy9++67cHNzwxNPPGG23dWrV1FYWIh27drZKDKSI1nO\nKSsqKsLIkSPxr3/9Cy1btqz1uiAIWLFiBfbv3w9XV1dMnToV06dPlyBSkgulUolly5bhySeflDoU\nIrIjv/zyS51Dhvr06QMAuHXrFrKzsw3HS0tLsWPHDly5cgUrV660WZxEJF/35omamhrcuHEDn3/+\nOb755hssWLDAaPji//73P6Occu3aNbz33nvw8PDAM888Y/PYST5kV5TdunULf/vb33Dz5k2TbTZu\n3Ii0tDRs3rwZGo0GCQkJaN++PYYOHWrDSElKdT0NCwsLw/Tp07Fx40aT38OnaER0r8uXLyMpKanW\n8ZdeegkKhQLZ2dn485//bDjevHlzdOrUCW+99RZGjRply1CJSKbuzRPNmjVDQEAA7r//fiQnJyMm\nJsbQTqFQYMeOHdixYweAOzeUvby8EBERgaVLl6JDhw6SxE/yoBBktPvl6dOnMWfOHLi7u+P8+fNI\nT0+v80nZwIEDsWDBAjz66KMA7hRpR48exZYtW2wdMhERERERUaPIak7Z0aNH8fjjj2PNmjUwVSte\nv34dGo0GUVFRhmORkZFGj4KJiIiIiIjshayGL86YMQPAnTH+poaZaTQaAEBAQIDhWMuWLVFWVobS\n0lJ4eno2faBERERERERWIqsnZWKUl5fD1dXV6Njdr6uqqqQIiYiIiIiIqMFk9aRMDJVKherqaqNj\nOp0OwJ0J2Oa0X/q1xfe/WlyBtj7uyF0wrOFBEpFDEwSBi8YQUaMxlxDRXXZXlAUFBUEQBBQWFsLf\n3x8AoNVq4eXlBZVKZfZ7cxcMMwx/NCVq/Uno9XqL7aSiVqtlG1t9OEI/2Ad5UKvVNv9MhULhED83\n9kEeHKEfjtIHW2MukQf2QT4coR8NzSV2N3wxMDAQrVq1wsmTJw3HTpw4gZ49e0oYFRERERERUcPI\n9knZvasv6nQ6FBcXo2XLllAoFBg/fjxWrlwJtVqNoqIibN68GW+++aaE0RIRERERETWMbIuye8dY\nZ2ZmYvLkyUhNTUWbNm0QGxsLjUaDGTNmwMPDA7Nnz8bgwYOlC5aIiIiIiKiBZFmU3XfffTh79qzh\n6759+xp93axZMyxYsAALFiyQIjwiIiIiIiKrsbs5ZURERERERI6ERRkREREREZGEWJQRERERERFJ\niEUZERERERGRhFiUERERERERSYhFGRERERERkYRYlJFdi4uLQ0xMDObMmWOyzRNPPIGYmBikpqY2\n+vNiYmLw8ccfAwCOHz+OmJgY/Pe//230+zaVyspKDBw4EDExMUb/pk6dKnVoRLLCXCKeIAiYOXMm\nZs+eLXUoRLLCPCIe80htstynjKg+lEolTp48idLSUnh6ehq9dubMGRQUFBhtRm4t3bt3x8aNG9Gx\nY0erv7e1XLp0CQCwYsUKtGzZ0nDc3d1dqpCIZIu5RJydO3fizJkziIyMlDoUItlhHhGHeaQ2Pikj\nuxcWFgYASE9Pr/Xat99+i/vvv79JPtfT0xPdunWTdYFz4cIFqFQqDBw4EN26dTP8s5ekTWRLzCWW\nXblyBZs3b0ZAQIDUoRDJEvOIZcwjdWNRRnbPy8sLvXr1wnfffVfrtW+//RZDhw6FIAhGx4uLi7Fs\n2TKMGjUKQ4cOxfPPP4+ff/7ZqM25c+cQFxeHYcOG4c9//jOOHz9u9HpdQwVSU1Mxc+ZMDBs2DEOG\nDMGUKVOQlpZmeP2LL77AsGHDkJWVhenTp+Phhx/G+PHj8cUXX5jt4++HH977b/ny5Sa/7+eff0aH\nDh3Mvve9SkpK8PDDD2P79u1Gx69du4aBAwfiyJEjhj7s2bMHo0aNwuOPP46ioiL897//RVxcHIYP\nH45HH30UL730EnJzc0V/NpHUmEtM5xLgznCjFStWYPTo0RZv7DCXkLNiHmEeaSgOXySH8PDDD2PV\nqlWoqKgw3CU6ffo0iouLERMTgw0bNhjaVlVVIS4uDmVlZYiPj0eLFi2wc+dO/O1vf8PWrVvRqlUr\nFBQUID4+Hp06dcKSJUug0WiwZMmSWkMO7v06LS0Nr776KiZOnIhZs2bh9u3b2LFjB1577TXs2bMH\n/v7+AACdTofXX38dEydORHBwMHbt2oUVK1agR48eCAkJqbN/GzduNNl3X19fk69dvHgRgiBg9uzZ\nOHPmDJo3b45Ro0YhNjYWLi61//y9vb3Rv39/fPnll5gwYYLheEpKCvz8/NCvXz+kpKSgqqoK//rX\nv7Bo0SLcunULrq6uSEhIQJ8+fTBt2jSUlZUhOTkZCQkJ+OSTT5pkqAZRU2AuMW3Xrl3QaDRYvXo1\nXnnlFbNtmUvImTGPmMY8YhqLsjqU62qw98wNi+183F0wNNTPBhGRJQMHDsQbb7yBI0eOYMiQIQCA\n7777Dv369YOHh4dR24MHDyIvLw9bt2413KWJjo7G+PHj8eGHH2LevHn45JNPoFQq8eabb6J58+YA\n7tz9WrhwockY8vPz8Yc//AEzZ840HAsKCsK0adNw5swZDBw4EACg1+vx3HPPYdiwYQCAjh074vHH\nH8eRI0dMJsBu3bo16Ody905bXFwcpk+fjqysLHz44YcoKirC/Pnz6/yekSNH4uWXX8aFCxfQqVMn\nAMChQ4cwfPhwKJV3Hq4LgoCpU6ciOjoawJ1x8sXFxXjyySfRo0cPQ9+//fZblJWV1RpXTyRXzCV1\nu3r1KjZt2oSkpCTRw6OYS8hZMY/UjXnEPBZldRAEIKiFm8V2129X2SAaEsPb2xu9evVCWlqaIQF+\n++23iIuLq9X25MmTCAoKQrt27VBTU2M43rdvX/z4448AgJycHISHhxuSHwAMGjTIkADqcvcuTmlp\nKa5cuYL8/HycOHECCoUCOp3OqG337t0N/+3n5wcXFxdUVFSYfO974/w9pVJZ510fQRCQlJQEPz8/\ntGvXDgAQERGBZs2aITk5GVOmTIFara71ff369YOfnx8OHTqETp064fTp0/jll1/w2GOPGbW7N1l3\n7NgRPj4+eOmllzB06FBER0ejT58+ePbZZ03GTSRHzCV130FOSkrC0KFD0bt3b5Pf/3vMJeSsmEeY\nRxqCRRk5jMGDB2PdunXQ6XT46aefUFxcjP79++PmzZtG7YqLi3Ht2jUMGjTI6LhCoTAM6SspKak1\nF0upVJp9LH/z5k2sWLECGRkZcHFxQUhIiGFC7+/Hj//+DpFCoYBerzf53oMGDYJCoaj1PgqFAiNG\njEBiYmKt71EoFIiIiKh1vF+/ftiwYQMuXryIBx54oNbrzZo1w/Dhw/H1118jLi4Ohw4dQseOHWtN\nTvbz++0psYeHB9577z1s3boVX331Ffbt2weVSoUnnngCf/3rX032i0iOmEuM7d69G1euXMGyZctQ\nU1MDQRAM/2pqatCsWbM6P4u5hJwZ84gx5hHLWJSRwxg0aBDefvttHDt2DFlZWejfvz9UKlWtdi1a\ntEBoaCheeeWVWgnlLh8fn1qJEwBu3bpl8vMXL16Ma9eu4b333kPXrl3RrFkz5Obm4tChQw3v1P/b\nvHmzydd8fHzqPK7VanH48GH079/f6IlYVdWdJ7zmkvnIkSOxa9cuZGVlIS0tDU8//bTFGENCQrBo\n0SLo9XqcOnUKn3/+Of75z38iLCzMcKeQyB4wlxhLS0vDzZs3MWrUqFqvDR48GGvXrjUMffo95hJy\nVswjxsTkkbpuJAPOk0dYlJHD8PHxQUREBL799ltkZ2eb3JAwPDwcx48fR0BAgNGdlbfffhtubm7o\n0qULoqKisGfPHpSUlMDb2xsAcOzYsVqP/O91+vRpPPXUU4bxywBw9OjROu8m1VeXLl3q/T16vR5v\nvPEGZsyYgSlTphiOf/PNN2jRooXZZXk7duyITp06ITk5GcXFxRg+fLjZzzpy5AiWL1+Ojz76CH5+\nfggPD0fXrl3x5ZdfoqCgoN6xE0mJucTYvHnzUFZWZnRs9erV0Ol0mDdvHoKDg01+L3MJOSvmEWPM\nI5axKCOHMmTIEKxevRpubm548MEH62wzatQo7NmzB88//zwmTZoEPz8/fP311zhw4ABefvllAMBT\nTz2FAwcO4IUXXsCUKVNQUlKCTZs2wdXV1ei97k1sXbt2RUpKCkJDQ+Hn54cffvgBu3btAgCUl5c3\nUY9NCwgIwOjRo7Ft2za4uLggLCwMR48exe7duzF79uxak41/b8SIEXjnnXfw4IMPGlZpMuXuePTE\nxEQ888wzUKlU+Pzzz+Hq6ooBAwZYrU9EtsJc8pu6LpY8PT1RVVWFzp07W/x+5hJyVswjv2EesYz7\nlJHdu3dC6aBBgyAIAgYMGAA3N7c62zRv3hzr169H586dsXr1asybNw8//fQTXn31VcNjdT8/P6xf\nvx6+vr547bXX8MEHH+C5556Dl5eXyc9+5ZVXcP/99+PNN9/EwoULkZOTg6SkJAQHB+PUqVMW+9AU\nS7TOnTsXkyZNwoEDB/DSSy8hIyMDc+fOxbhx4yx+b//+/QGg1mTauvj4+GDVqlXw8PDA8uXLMX/+\nfGg0Gqxatcrs3S8iOWEuqR+xn8NcQs6EeaR+mEd+oxAa+wzTigoLC5GYmIhjx46hdevWSExMRExM\nTK12FRUVWL58Ob788ku4ublh7NixeOGFF0R9hkajMft61PqT+KWkEi2bu5ptBwARrTyx46muoj7X\nWtRqtcU+2ANH6Iej9+HTTz/Fli1bsG/fvlp34+SkrhUkbcGRz729cIQ+AI7RD+aShnPkc28v2Af5\nMNUPe8kjQMNziayGLyYkJEClUmHPnj1IS0tDfHw8UlJSEBgYaNRuzZo1OHPmDD788EPcunULL7zw\nAoKCgkRN/LNkTJg/duZY3qNMW6bDyV9vN/rziOTmu+++w9mzZ/HZZ5/hz3/+s+yTHxHJE3MJETWW\nM+UR2RRlubm5yMjIQHp6Ovz9/REaGorDhw9j7969mDVrllHb9PR0TJo0yTDRcPTo0Th27JhVirLF\nQ9qjZ5CnxX3KYvdfgF4vm4eMRFbz66+/4tNPP8WDDz6IiRMnSh0OEdkp5hIiaixnyiOyKcpycnIQ\nEhJiNHkvMjISWVlZtdr6+fkhJSUFI0aMQFlZGdLT0/H444/bMlwih/X0009b5QYHETk35hIiaixn\nyiOyWehDq9XWGqaoVqvrXLoyISEBFy9eRJ8+ffDQQw8hMDAQsbGxtgqViIiIiIjIamRTlJWXlxut\nTAMArq6ude7BkJeXhw4dOmD79u14//338fPPP2PDhg22CpWIiIiIiMhqZDN8UaVS1SrAdDpdrb2U\nbt++jQULFmD37t2GzW8TExORmJiIZ599Fkql+TpTzIoo3t6V8PGqvev6vZRKJfSCHl/nV1p8Px93\nV4zoGmixnVhSrRBlbY7QD/bBeTnCz419kA9H6Icj9EEKjvBzYx/kwRH6ADhOP+pLNkVZUFBQrSUw\nNRoNAgICjI5dunQJlZWV6Nixo+FY165dUVpaiqKiIosbyolZLrSkpAQeevMLfej1eigVgIe+wuL7\n5ReUQBNgnYeSjr7kqT1hH+SBy1g3jKOce3vvA+AY/XCUPkjBEX5u7IP0HKEPgGP0o6G5RDbDF8PD\nw5GXl4eioiLDsczMTISHhxu1CwwMhCAIuHjxouHYpUuX4OHhYbEgIyIiIiIikhvZFGXBwcGIjo7G\nvHnzcOHCBWzZsgXZ2dkYO3YsdDodNBoNBEFAq1atMGDAACxevBjnzp3DiRMnsGLFCjzzzDNSd4GI\niIiIiKjeZFOUAcDKlSuh1+sxbtw47N27F+vWrUNAQAAyMzMxcOBAXLt2DQCwatUqhISEYNq0aZgz\nZw4eeeQRPP/88xJHT0REREREVH+ymVMG3BmDmZycXOt43759cfbsWcPX3t7eSEpKsmVoREQGB88W\nIL9Aa7Gdj7sLhob62SAiIiIismeyKsrkwsfdBddvV5lto9cLEGwUDxHJS3GFDkEtzC8GBMBiHiEi\nIiICWJTVScyd7aVp+SirqrFBNEQkN7M/Ow2FiNsyEa088UT3AIvtiIiIyLmxKCMiagLaMh1O/npb\n6jCISMY4FJqI7mJRRkRUT2vH9rC4R2Hs/gvQ6znImYhMm/TPTNFP3VmUETk2FmWNcLOiGrH7L1hs\nxyFMRERE1BB86k7kHFiUNdCYMH/szLlhsR2TKZHj8XF3RX5Bidk2er0AhcJGARGRXeJTdyK6i0VZ\nAy0e0h49gzwtrsAWu/8CavQC9p4xX8BxvDiR/RjRNRCaAPPbPC5Ny0dFtd5GEREREZE9Y1FmA82U\nCovFG5fOJiIici586k5Ed7EoIyIiIpIAn7oT0V3mMwERERERERE1KT4pIyJqIoJgeT4pwDmlRERE\nzo5FGRFRExEEWJxPCnBOKRERkbNjUUZE1ES4lyERWYOYp+584k5k31iUERE1Ae5lSETWUlhejQWp\neWbbRLTyZFFGZMdYlBERNYH67GXIjWGJyBQxN3h4c4fI/rEoIyIiIpIpMTd4eHOHyP6xKCMiaiI+\n7i4WF/HgxrBERETEoqwRxF5wiZnsz4n+RI5HzPwObgxLRJZYut7gzR0i+yeroqywsBCJiYk4duwY\nWrdujcTERMTExNTZduvWrfjggw9QXl6OoUOHYvHixVCpVDaNV8wFV871Uo4FJyIiogazdL3BmztE\n9k9WRVlCQgJUKhX27NmDtLQ0xMfHIyUlBYGBgUbtPv/8c6xbtw6rV69GQEAA5s6di3feeQcvvfSS\nRJGbxrHgRERERERkjmyKstzcXGRkZCA9PR3+/v4IDQ3F4cOHsXfvXsyaNcuo7QcffIDZs2dj4MCB\nAID4+Hjs3LlTirCtRgAs7kECAMGBevQJUDZ9QEREREREZBOyKcpycnIQEhICf39/w7HIyEhkZWUZ\ntbt9+zbOnj2LNWvWGI4NHz4cw4cPt1msTaGZUmFx6WwAKK7QAbDtME0iIiIiImo6snnkotVqaw1T\nVKvVKCgoMDp29epVKBQK/Prrr3jyyScxaNAgvP7666iqMr/gBhERERERkRzJ5klZeXk53NyMnxS5\nurpCp9MZHSstLQUALFu2DPPmzYObmxteffVVrFy5EgsXLrRZvERERERyIQiCqGkQPu4uohYqIyLb\nkk1RplKpahVgOp0OHh4eRsdcXFwgCALi4+MxYMAAAEBiYiL+9re/iSrK1Gq19YIWydu7Ej5epocc\nKpV3Hlj6+PhYfK/yW5WS9KEpOEI/2AfnZa2fm1KphEIh4Ov8SrPtfNxdMaJroNk29eUI594R+gA4\nRj8coQ9SsMbPTalUorC8Eou+zbfYtk9bH4yP7tToz7yXI5x79kE+HKUf9SWboiwoKAgajcbomEaj\nQUCA8d5dd7/u0KGD4ViHDh1QVVWFwsJCozlpdfn9Z9hCSUkJPPSm54vp9XeWsS0uLrb8Zkp3Sfpg\nbWq12u77wT7Ig1TJ21o/N71ej5oaPTz0FWbb5ReUQGPFRX4c5dzbex8Ax+iHo/RBCtb4uY3u7Iud\nOTrD9YQp2jIdMnILrXquHOXcsw/y4Aj9aGgukU1RFh4ejry8PBQVFcHX1xcAkJmZiejoaKN2bdq0\nQUBAAM6dO4fQ0FAAwM8//4zmzZsbvo+IyJ5wg3kiagwx2+8A3IKHSM5ks9BHcHAwoqOjMW/ePFy4\ncAFbtmxBdnY2xo4dC51OB41GA0G4k0gmTZqEt956C8ePH8epU6fw5ptv4oknnjAMAyQishdjwvzh\n527+/hg3mCciInJssnlSBgArV67E/PnzMW7cOLRt2xbr1q1DQEAAjh8/jsmTJyM1NRVt2rRBbGws\nKisr8fe//x06nQ6jR49GQkKC1OETEdUbN5gnImvwcXfB9dvmV6LW6wUoFDYKiIjqRVZFmVqtRnJy\ncq3jffv2xdmzZ42OxcXFIS4uzlahEREREcmWmBUVl6blo6La/LwzIpIGx/sRERERERFJSFZPyhyV\npSEFer0gaqI/AESH+GFYcKg1wyMiiYnJERxyRERE5LhYlNmApSEFOddLsTPH8oaP2jIdjl4pslZY\nRCQTlnIEhxwRERE5NhZlMlCfpWw51Z+IiIgaShAE7D1j+Uawj7uLqHlqRGQdLMqIiOwAL6SIyBoE\nARZvAgOwuJIjEVkXizIiIjvACykisgaxc9i5YT2RbbEoIyKyA7yQIqLGGhPmL3oOOzesJ7ItFmV2\nhkOYiJwPL6SIyBrqM4edG9YT2RaLMjvj0kzJIUxEToYXUvnf5GoAACAASURBVERERI6NRRkRERGR\nk7C0LyLAvRGJpMCijIjIDvBCioisQczUBu6NSGR7LMqIiOwAL6SIiIgcl1LqAIiIiIiIiJwZizIi\nIiIiIiIJcfgiERERERkRuwVPcKAefQJ4j5+osViUyYTYSfx6gfNFiMg0XkgRkTUIAkRtwVNcoQOg\navqAiBwcizKZEDuJv7yaexARkWm8kCIia7hZUY3Y/RcstosO8cOw4FAbRETk2FiUERE5EF5IEVFj\njQnzx84cy0/ctWU6HL1SZIOIiByfrIqywsJCJCYm4tixY2jdujUSExMRExNj9nuWL1+Os2fP4qOP\nPrJRlERE8sQLKSKyhsVD2qNnkKfFp+6x+y+A43eIrENWRVlCQgJUKhX27NmDtLQ0xMfHIyUlBYGB\ngXW2/+9//4uPP/4YvXr1snGk0iksqxJ1FzyilSee6B5gg4iISC54IUVERGSfZFOU5ebmIiMjA+np\n6fD390doaCgOHz6MvXv3YtasWbXaC4KAV199FeHh4RJEK40xYf7YdVoLWLic0pbpcPLX27YJioiI\niByO2AXImjVT2CgiIscmm6IsJycHISEh8Pf3NxyLjIxEVlZWne0//vhjBAQEIDIyEt9//72twpTU\n4iHtEdOpNTz0FWbbxe6/AL2e98GJnBEvpIjIGsQuQFbFRaGJrEI2RZlWq601TFGtVqOgoKBW24KC\nArz//vv45JNPcODAAVuFaFcEQNSy2D7uLqISLxHZB15IERER2R/ZFGXl5eVwczOeB+Hq6gqdTler\n7fLlyzF58mS0adPGVuHZnWZKhahlsS3dUSciIiIioqYlm6JMpVLVKsB0Oh08PDyMjqWlpeH8+fN4\n++23G/Q5arW6wTHKQv4v8PHxMdtEqbyzIayldgBQrqyU7Gdi9+cC7IMzs+efm1KphFBTg6/zKy22\n9XF3xYiudS+2JAf2fB7u5Qj9cIQ+SMGef27MJfLiCH0AHKcf9SWboiwoKAgajcbomEajQUCA8QqC\nKSkpyM/PR+/evQEA1dXVqKmpQVRUFE6ePGnxc37/GfaouLjY7Ot6vV5UOwAouV0Fjcb2G8iq1Wq7\nPxfsgzxIlbzt+eem1+uhF2BxfioA5BeUQBOgtEFU9ecIv7+AY/TDUfogBXv+uTGXyIcj9AFwjH40\nNJfIpigLDw9HXl4eioqK4OvrCwDIzMxEdHS0UbuEhAT89a9/NXz9z3/+EydOnMBbb71l03iJiIiI\nnB236iGyDtkUZcHBwYiOjsa8efMwd+5cfP/998jOzkZSUhJ0Oh2Ki4vh7+9v+HeXr68vVCoVgoOD\nJYyeiMi+8EKKiBqLW/UQWY9sijIAWLlyJebPn49x48ahbdu2WLduHQICAnD8+HFMnjwZqampXNzD\nyiqqBa7SSORkeCFFRNbArXqIrEdWRZlarUZycnKt43379sXZs2fr/J5nn30Wzz77bFOH5rDa+Yqb\nT8ZVGokcBy+kiIiI5EVWRRkRERER2Q8fd1fkF5SYbaPXC1Bwv3ois1iUERE5IV5IEZE1jOgaaHFV\nxaVp+aio5o71ROawKCMickK8kCIiIpIPFmV2Ruzd7ZsV1aJWVhsQ4o0pkUEW23FBECLnJAj82yei\nxmMuITKPRZmdEXN3O+d6KXbmWE582jIdDl8pEVWUcUEQIuckCEBQCzeL7fi3T0TmMJcQmceizAEt\nHtIePYM8LSY/MU/SiMi5iX3qzv3MiMgc5hIi88w/ciEiIqc1Jswffu6W791xPzMiMoe5hMgyPikj\nIqI61eepO/czIyJTmEuILOOTMiIiIiIiIgnxSZmT05bprLpKIxE5Fh93F4sT77mfGRFZwlxCZB6L\nMic2IMQbh6+YX14fqN8qjUTkWMQsTc39zIjIEuYSIvNYlDmxKZFBogotrtJIRERERNR0WJQREVGj\ncWNYIrIG5hJyVizKiIio0bgxLBFZA3MJOSsWZSSK2AVBuOkjkXPixrBEZA3MJeSsuCQ+WTQgxBst\nm7tabMdNH4mcEzeGJSJrYC4hZ8YnZQ5KzNKzAFBRLaCdr8psm/osCMJNH4mcDzeGJSJrYC4hZ8ai\nzEGJnfwqZjJtfQgi3zM4UI8+AXxQS0REREQkq6KssLAQiYmJOHbsGFq3bo3ExETExMTUanfr1i0s\nX74c3333HVxcXDBy5EjMnTsXrq6Wh9hR02qmVIiaoFtcoQNg/gkdEdkPbgxLRNbAXELOSlZFWUJC\nAlQqFfbs2YO0tDTEx8cjJSUFgYGBRu1ee+01XL16FVu3bkV5eTlefvlluLu744UXXpAociIi58aN\nYYnIGphLyFnJZvxYbm4uMjIysHTpUoSGhmLatGmIjIzE3r17jdpVVVXhyy+/xIIFC9ClSxdERERg\n9uzZOHjwoESRExERERERNZxsnpTl5OQgJCQE/v7+hmORkZHIysqq1Xb9+vXo0qWL4WuFQoHKykqb\nxElERERE0hKzyTQ3mCZ7IpuiTKvV1hqmqFarUVBQYHTMzc2t1jyzf/7zn4iKimryGB2RNVdpJCKy\nRMyFFMCLKSIyT8wm09xgmuyJbIqy8vJyuLkZ/3G5urpCp9OZ/b61a9fi1KlT+PTTT5syPIcl1SqN\n5boa3uEickJiLqQAXkwRkXliNpnmBtNkT2RTlKlUqloFmE6ng4eHh8nv2bhxI95//32sWrUKoaGh\noj5HrVY3Kk45kKIP3t6V8PEy/6RMqbwzRdHHx8fi+91p0dxsm//dqpT9+ZJ7fGI4Qh+k4Ag/N1v3\nQalU4mZFJWb+62eLbfu09cGzg7pabOcI5wFwjH44Qh+k4Ag/N1v3YXxkW2z9IR9KpeklGG/crkTm\n/8pEx8bzIB+O0o/6kk1RFhQUBI1GY3RMo9EgIKDuOxzvvfce1q5di+XLl+PRRx8V/Tm//wx7o1ar\nJelDSUkJPPTm727r9XdWQiouLrb4fj4+PhbbldyugkYj3yGTUp0La3KUPkjBEX5utu7D6M6+2Jmj\nM+QKU7RlOmTkFlqMzxF+fwHH6Iej9EEKjvBzs3Uf5j0YiE7e5rfgubPBtF5UbI7y+2vvfQAcox8N\nzSWyKcrCw8ORl5eHoqIi+Pr6AgAyMzMRHR1dq+2+ffuwdu1aLFmyBH/84x9tHapTErtviGCjeIjI\n/iwe0h49gzwtDl+8czHFbEJEplm6LuFeZmRvZFOUBQcHIzo6GvPmzcPcuXPx/fffIzs7G0lJSdDp\ndCguLkbLli1RVlaG5cuXY+zYsRg8eLChmlYoFGjZsqXEvXBcYvcN+aWk0uIYbwAYcr8af+nO+WJE\nRERUf5auS7iXGdkb2RRlALBy5UrMnz8f48aNQ9u2bbFu3ToEBATg+PHjmDx5MlJTU3H+/HncunUL\ne/fuNexhJggCVCoVsrOzJe6BcxsT5o+dOZYXBNGW6fDdpUKLRVlFNVdpI3I0Yp+68w43ERE5E1kV\nZWq1GsnJybWO9+3bF2fPngUAtGnTxvDfJC/1GZokhtgl+LlKG5H9EPvUnXe4iYjImciqKCMiIgLE\n7WcWHKhHnwCljSIiInsjdl9E5hKSAxZlREQkO4Xl1ViQmme2TXRICT74o7jtUIjI+YjJIwBzCckD\nizKSxI3blhcEGRDijSmRQTaKiIjkQsz8VG2ZDkevFNkoIiKyN/WZ585cQnLAooxsbkCINzKu3ja7\nV5G2TIfDV0pYlBE5ITHzU2P3X+AWHERkUn3muTOXkBywKCObmxIZhOcHdza7ebTYxUCIiIiIiOwd\nizIiIpIdMRvDNmvGdfOJyDSxW3Awl5AcsCgjIiLZEbMxbBVXzSciM8RuwcFcQnLAooysSsxdKQAo\nrCmDfzPzbbRlOlHDGB8IbC46Nm4yTeQ4xC53zb99IiKSOxZlZFViL3y+zq8E9DqTrw8I8cbhKyUW\n30dbpsOpgjLEW5jIC3CTaSJHoymtErXcdUQrTxZlRGQSb/CQHLAoI1maEhkkauVFLghC5JzGhPlj\n12ktYGHdNG2ZDid/vW2boIjILvEGD8kBizIiIrI7i4e0R0yn1vDQV5htF7v/AvR6LnhNRHXjDR6S\nCxZlJAkfd1fkF5gfnlhRLaCdr8pGERGRvRGTR/R6AQourEZEJiwe0h6jHghBfoHWbLvEr3KZS6hJ\nsSgjSYzoGghNgNJsGzHju+ujoppjxokciZg8sjQtHxXVXFqNiExjLiE5YFFGTkPsUzcuCELkWDiJ\nn4isgbmEmhKLMrJ7YpfOHxDiLWrxECJyLIXl1ZzET0SNxlxCTcn8s1oiCd3d88zcv4hWnvB1t3xv\nQVumE7XEPhE5ljFh/vATmSM4iZ+ITGEuoabGJ2UkW2LuMj3RPQB7z9xAkIV9yrh0PpFzWjykPQa2\n80FxRbXZdpzET0TmMJdQU2NRRkREDk3MDZ6lafn4paQSYf/4wWLbB0O88cETXawRGhHZEbG5pFxX\nw7lnVG+yKsoKCwuRmJiIY8eOoXXr1khMTERMTEytdoIgYMWKFdi/fz9cXV0xdepUTJ8+XYKIyZ6I\nnXv2QGBzUe/n7V2JkhLrDIlkYiaS1pgwf+zMuQGl0vwtbm2ZDhkcCk1EZnDuGTWErIqyhIQEqFQq\n7NmzB2lpaYiPj0dKSgoCAwON2m3cuBFpaWnYvHkzNBoNEhIS0L59ewwdOlSiyEnuBoR4i5pTpi3T\n4VRBGeItDIcEAB8vFTz0ltuJwRUfiaS1eEh79AzyFDUUmptRE5Epd2/wWMK5Z/R7sinKcnNzkZGR\ngfT0dPj7+yM0NBSHDx/G3r17MWvWLKO227dvx4IFC9CjRw8AQGxsLD7++GMWZU7q7oIg5ozo5IeH\nO/haXBafc8+InJeYXKLXC7hZUc1hjkRUp/rMPWMuoXvJpijLyclBSEgI/P39DcciIyORlZVl1O76\n9evQaDSIiooyardhwwabxUryIvbRv7U3oyYixyIml+RcL+UwRyIyi7mEGkI2RZlWq601TFGtVqOg\noMDomEajAQAEBAQYjrVs2RJlZWUoLS2Fp6dn0wdLdqk+d8HFPDFTKpXQ6/VWiS2ilSee6B5guSER\nSaop7oI3a6aEqpn5C7MxYf5YPKR9fUIlIhmzdi4ZfH8A3h/d3ooRkq3JpigrLy+Hm5vxWH5XV1fo\ndLpa7VxdXWu1A4CqqioWZWRSfe5c2dKNUo4rJ7InYnPJrlM3IIiYfqYXgLKqGpOva8ur8e6xazbP\nTQCgUAAers0sthsf2RbzHgy02I6IfmOtXKItr8anp67hu4vWyxFi//atzZlziWyKMpVKVasA0+l0\n8PDwqNWuurq6VjsAaN7c8qp5arW6kZFKzxH6AMizH+ueUmPUAwUortBZbmxFLdya4XRR/b5n8P3W\n+fnJ8TzYA0f4ubEPTWfdU2qse0pc24NnbZ9zxGrh1gwtVOIuFcTmMGvlLkch19/h+mAfmo7YXGLt\nPFKfv31rc9ZcIpuiLCgoyDA08S6NRmM0TPFuO0EQUFhYaJh/ptVq4eXlBZXK/CIORGKM6Oqcd2iI\nSBrMOUTUWMwj9k8pdQB3hYeHIy8vD0VFv5XHmZmZCA8PN2oXGBiIVq1a4eTJk4ZjJ06cQM+ePW0W\nKxERERERkbXIpigLDg5GdHQ05s2bhwsXLmDLli3Izs7G2LFjodPpoNFoIPz/gNrx48dj5cqVyMrK\nwnfffYfNmzdjwoQJEveAiIiIiIio/hSCIGYasm1oNBrMnz8fP/zwA9q2bYsFCxagX79+OH78OCZP\nnozU1FS0adMGNTU1WLFiBfbt2wcPDw/ExsZi0qRJUodPRERERERUb7IqyoiIiIiIiJyNbIYvEhER\nEREROSMWZURERERERBJyiqKssLAQs2bNQmRkJEaOHIn09HSpQ6q3Q4cOISwsDF27djX876pVq6QO\nS7SioiL0798fWq3WcOyzzz7DkCFDEBUVhfnz56OyslLCCC2rqw+jR4+udV6qqqokjLJuP//8M6ZM\nmYKoqCg8+uij2L17t+E1ezkP5vpgq/PAXCItR8gjAHOJ1JhLrIO5RFrMI9Kzei4RnMC0adOE5557\nTrh48aKwefNmITIyUrh+/brUYdXLu+++Kzz//POCVqsVNBqNoNFohLKyMqnDEqWkpER4+umnhbCw\nMEGj0QiCIAg//vijEBERIRw6dEg4c+aMMGbMGGH58uUSR2paXX2oqakRevToIeTk5BjOyd3X5KSy\nslJ45JFHhEWLFgm5ubnCwYMHhYiICOHw4cPCiRMn7OI8mOuDLc8Dc4l0HCGPCAJzidSYS6yHuUQ6\nzCPSa4pcIpvNo5tKbm4uMjIykJ6eDn9/f4SGhuLw4cPYu3cvZs2aJXV4ol26dAmdO3c2bJhtL06f\nPo05c+bA3d3d6PiOHTswduxYDB8+HADw2muvYerUqZgzZw7c3NykCNUkU33Iz88HAPTo0QMKhUKK\n0ETJzMxEUVERFi5cCBcXF7Rr1w5HjhzBv//9b1RUVNjFeTDVhy+++AL33XcfgKY/D8wl0nGEPAIw\nl8gBc4n1MJdIg3lEHpoilzj88MWcnByEhIQYJY3IyEhkZWVJGFX9Xbp0Ce3bt5c6jHo7evQoHn/8\ncaxZs8awzxwAnDp1Cr169TJ83bNnT+h0Oly4cEGKMM0y1YdLly4hODhY1skPADp16oT169fDxeW3\nezAKhQKVlZU4ffq0XZwHc32w1XlgLpGOI+QRgLlEDphLrIe5RBrMI/LQFLnE4YsyrVaLwMBAo2Nq\ntRoFBQUSRdQwly9fRmpqKoYNG4bHHnsMmzZtkjokUWbMmIG4uDi4uLgY/XJqNBqj86JUKuHr6yvL\n82KqD5cuXYJCocD06dMRExOD2NhYXL58WcJI6+bv74/evXsbvi4qKkJKSgqioqJw48YNuzgPpvoQ\nGRlps/PAXCIdR8gjAHOJHDCXWA9ziTSYR+ShKXKJwxdl5eXltR55urq6QqfTSRRR/V2/fh0VFRXw\n8vLC+vXrERcXh+TkZGzfvl3q0BqsoqKizvMixwmpply+fBklJSWYPHkyNmzYgBYtWmDatGmoqKiQ\nOjSTdDodXnzxRbRs2RJPPvmkXZ6H3/fBVueBuUR+7PH3ty7MJdJgLmk45hL5YR6RjrVyicPPKVOp\nVLUSnU6ng4eHh0QR1V9QUBCOHTsGb29vAEDnzp2h0Wiwa9cuTJgwQeLoGsYRzsuSJUtQXl4OT09P\nAMDKlSsxePBg/Oc//zGMh5YTnU6H+Ph4nD17Fjt37oSbm5vdnYe6+mCr82BvP6u6OFoucYRzAjCX\nSIG5pHGYS+SHeUQa1swlDl+UBQUFQaPRGB3TaDQICAiQKKKGuZv47urYsSNu3LghUTSNFxgYaBR/\nTU0NioqKag3pkDOlUmn4gwMANzc3tG3bVpbnRafTIS4uDllZWdi2bRvatWsHwL7Og6k+2Oo8MJfI\njz39/prDXGJbzCXWwVwiL8wjtmftXOLwwxfDw8ORl5eHoqIiw7HMzEyEh4dLGFX9HDt2DA8++KDR\n3YNz587Z3QTbe4WHhyMzM9PwdXZ2NlQqFTp16iRhVPUzffp0vP/++4avy8vLkZeXJ8vzsnjxYpw8\neRJbt25Fly5dDMft6TyY6oOtzgNzifzY0++vOcwltsVc0njMJfLDPGJ71s4lDv+kLDg4GNHR0Zg3\nbx7mzp2L77//HtnZ2UhKSpI6NNEeeOABqFQqLFq0CLNmzcLZs2eRnJyMZcuWSR1avdy7StD48eMx\nY8YMhIeHo3379liyZAmeeuopNGvWTMIILbu3Dw899BDee+89dOvWDW3atMHatWvRunVrDBgwQMII\na/vxxx/x6aefYunSpQgICDDcoXVzc7Ob82CuD7Y6D8wl8uAIeQRgLpEKc4l1MJfIA/OIdJoklzTR\nnmqycuPGDWHGjBlCeHi4MGrUKCEjI0PqkOrtp59+EiZNmiRERkYKDz/8sLBjxw6pQ6qXq1evGm1y\nKAiC8MknnwgDBw4UevfuLbzyyitCZWWlhBFa9vs+6PV6Yd26dcKgQYOEiIgIYebMmcK1a9ckjrK2\npKQkISwsrNa/GTNmCIJgH+fBUh9sdR6YS6TlCHlEEJhLpMRcYj3MJdJiHpFWU+QShSDcU2YTERER\nERGRTTn8nDIiIiIiIiI5Y1FGREREREQkIRZlREREREREEmJRRkREREREJCEWZURERERERBJiUUZE\nRERERCQhFmVEREREREQSYlFGREREREQkIRZlREREREREEmJRRkREREREJCEWZURERERERBJiUUZE\nRERERCQhFmVEREREREQSYlFGREREREQkIRZlREREREREEmJRRkREREREJCEXqQMgqq/58+fjs88+\nq/M1hUKBL774AtOnT8evv/5qOK5UKuHn54d+/fohISEBrVq1slW4RGRHJk6cCHd3dyQnJxsdv3bt\nGp555hlUVVXhww8/RGhoqEQREpE9mDhxIn744QejYyqVCm3btsXo0aMxc+ZMKJV8NkK/YVFGdum+\n++7D6tWrTb4GAMOHD8eMGTMAAFVVVbh27RrWr1+P6dOn41//+heTIRGJcv36dUyePBk6nQ7btm1D\nx44dpQ6J/q+9O4+Lqt7/B/4alE2QdVg0wQU1ERVwwwQT1zKz0tt2K5dUXAoxS74ErpmhXlNLzQ1N\n85ZLmVq30kpTrpJiCUgapUWuecUBGXYcmfP7wx+T08DMQYc5Z2Zez8fDx43DZ+Z8PnOY9z3vz/ks\nRFYgIiICKSkpup8rKytx6NAhvPvuu6isrMSrr74qYe1IbpiUkVVydnZGt27djJbx8fExKBMYGIjR\no0fjxIkT6NOnT2NWkYhsQGFhIcaNG4fKykps3boVbdu2lbpKRGQlmjdvbnAfEhUVhbNnz2L79u2Y\nPn06mjRpIlHtSG74qIDsiru7OwBAEASJa0JEcnfjxg2MGzcO5eXlTMiIyGw6d+6MsrIyqNVqqatC\nMsInZWS1ampqDI7d2eMkCIKuTE1NDa5evYply5ahY8eO6N27t8XqSUTWp7S0FOPHj0dhYSG2bduG\nNm3aSF0lIrIRf/zxB1xdXeHj4yN1VUhGmJSRVcrPz0dYWJjeMYVCgXXr1qF///4AgJ07d2Lnzp16\nZVxcXPD+++9zuAAR1ausrAwTJkzAr7/+CkdHR1RXV0tdJSKyQnd2DgOASqXCl19+ie+++w4vvvii\nhDUjOWJSRlbpvvvuw6pVqwyGIbZu3Vr330OHDsXkyZMB3H5Sdu3aNXzwwQcYP348tm7danJOGhHZ\np+zsbAQEBGDbtm2Ij4/HzJkz8emnn8LJyUnqqhGRFTl69KhBB7KTkxP+8Y9/YPr06RLViuSKSRlZ\nJWdnZ3Tu3NloGR8fH4NgGB0djdjYWKxZswbr1q1rzCoSkZXy9fXF1q1b0bp1a7z55puYOnUqlixZ\ngjlz5khdNSKyIpGRkZgzZw4EQYBCoYCrqytatWoFR0dHqatGMsSkjOxKs2bN0Lp1a1y8eFHqqhCR\nTHXu3Fn31H3AgAEYOXIktm3bhn79+iE2NlbayhGR1XB3dzfZgUxUi6svkl0pLy/H77//rjfMkYjI\nmFmzZqFFixaYNWsWCgsLpa4OERHZIFkmZcXFxejbt2+9/+cnCAJSU1MRFRWFmJgYbNq0ycI1JGtQ\nVFSEU6dO6f4dPnwYU6ZMQXV1NcaPHy919YjISri7u+Ott95CUVERkpKSpK4OERHZINkNXywtLcXL\nL7+MGzdu1Ftmw4YNSE9Px6ZNm6BSqZCYmIg2bdpg0KBBFqwpSUmhUJgs8+233+Lbb7/VlXd3d0do\naCg2bNiAXr16NXYVichK1RVfHnjgATz//PP46KOPsHXrVowZM0aCmhGRNRFzr0JUSyHIaBfd06dP\n47XXXoOLiwvOnj2Lo0ePwtfX16Bcv379MHv2bDz00EMAbidpx48fx/vvv2/pKhMREREREd0TWQ1f\nPH78OEaMGIGVK1caLHVe69q1a1CpVOjevbvuWGRkJE6dOmWpahIREREREZmNrIYvTpw4EQBw5cqV\neh/5qlQqAICfn5/umK+vLyoqKlBeXg43N7fGrygREREREZGZyOpJmRiVlZUG+zvU/nzz5k0pqkRE\nRERERHTXrC4pc3Z2xq1bt/SOaTQaALf3oDJGRtPniMiKMZYQkTkwlhBRLVkNXxQjICAAgiCgqKgI\nPj4+AIDCwkI0b94czs7ORl+rUCh0wx+tlVKptPo2ALbRDrZBHpRKpcXPyVgiD7bQBsA22mErbbA0\nxhJ5YBvkwxbacbexxOqelPn7+yMwMBBZWVm6YydPnkS3bt0krBUREREREdHdke2Tsjsf6Ws0GqjV\navj6+kKhUOCZZ57BkiVLoFQqUVxcjE2bNmHp0qUS1paIiIiIiOjuyDYpu3P1xezsbIwdOxYHDx5E\ny5YtERcXB5VKhYkTJ8LV1RXTpk1DbGysdJUlIiIiIiK6S7JMyu677z7k5eXpfu7du7fez02aNMHs\n2bMxe/ZsKapHRERERERkNlY3p4yIiIiIiMiWMCkjIiIiIiKSEJMyIiIiIiIiCTEpIyIiIiIikpAs\nF/poLPvyCnCpoNBkOU+XphgU4m2BGhERERERkb2zqydl6ioNAtydTP5TV92SuqokUnx8PGJiYvDa\na6/VW2bUqFGIiYnBwYMH7/l8MTEx+OijjwAAJ06cQExMDH7++ed7ft/GlJOTg6lTp2LQoEF44okn\n8M4776CqqkrqahHJCmOJcRqNBps3b8azzz6LwYMH44UXXsCuXbv09hQlsneMI+IJgoDJkydj2rRp\nUldFNuwqKSPb5ODggKysLJSXlxv87syZMygoKNDb985cwsLCsGHDBrRr187s720up0+fxowZM+Dv\n749//etfGDduHPbt24e3335b6qoRyQ5jSf1WrlyJbdu24YknnsCSJUsQGxuLlStX4sMPP5S6akSy\nwjgizo4dO3DmzBmpqyErTMrI6nXq1AkAcPToUYPfKaqkIgAAIABJREFUHTp0CO3bt2+U87q5uaFz\n585wcXFplPc3h7Vr1yI8PBxvvPEGevTogSeeeAITJ07EmTNncOsWnwgT3YmxpG7l5eX47LPPMHHi\nRDz77LPo0aMHJk6ciMcffxzbt2+XunpEssI4YtrFixexadMm+Pn5SV0VWWFSRlavefPm6NGjBw4f\nPmzwu0OHDmHQoEEGQ2zUajXeeustDB8+HIMGDcL06dPx+++/65X55ZdfEB8fj8GDB+PZZ5/FiRMn\n9H5f11CBgwcPYvLkyRg8eDAGDhyIcePGIT09Xff7r776CoMHD0ZOTg4mTJiAAQMG4JlnnsFXX31l\ntI0xMTH1/ktNTa3zNWq1Grm5uRg5cqTe8aeeegrbt29H06aGU0pLSkowYMAAg97vq1evol+/fvj+\n++91bdi1axeGDx+OESNGoLi4GD///DPi4+MxdOhQPPTQQ/i///s/nD9/3mi7iOSEsaTuWFJeXo4n\nnngCffv21TseHByM0tLSOp8IMJaQvWIcqTuO1BIEAYsWLcKjjz5q8qmevcURu1rog2zXgAEDsHz5\nclRVVel6iU6fPg21Wo2YmBisX79eV/bmzZuIj49HRUUFEhIS4O7ujh07duDll1/Gli1bEBgYiIKC\nAiQkJKBDhw5YsGABVCoVFixYYDDk4M6f09PTMW/ePIwePRpTpkxBWVkZtm3bhjfeeAO7du2Cj48P\ngNtzM958802MHj0aQUFB2LlzJxYtWoQuXbogODi4zvZt2LCh3rZ7eXnVebw2oLu5uWHWrFk4fvw4\nHB0d8fDDD+Pll1+Go6OjwWs8PDzQt29ffPPNN3jhhRd0x/fv3w9vb2/06dMH+/fvx82bN/Gf//wH\nc+fORWlpKRwdHZGYmIhevXph/PjxqKioQFpaGhITE/Hxxx83ylANosbAWGLI398fr776qsHxI0eO\nwNfXF25ubga/Yywhe8Y4Ur+dO3dCpVJhxYoVmDVrltGy9hZHmJSRTejXrx/+9a9/4fvvv8fAgQMB\nAIcPH0afPn3g6uqqV3bfvn24cOECtmzZouuliYqKwjPPPIMPPvgASUlJ+Pjjj+Hg4IClS5eiWbNm\nAG73fs2ZM6feOly6dAmPP/44Jk+erDsWEBCA8ePH48yZM+jXrx8AQKvVYurUqRg8eDAAoF27dhgx\nYgS+//77egNg586dG/yZFBcXQxAELFy4ELGxsVi6dCnOnj2LtLQ03Lp1CzNnzqzzdY888ghef/11\nnDt3Dh06dAAAfP311xg6dCgcHG4/XBcEAS+++CKioqIA3B4nr1ar8eSTT6JLly66th86dAgVFRV1\n3rQRyRFjiTh79uxBVlYWXnnllXrLMJaQvWIcqdvly5exceNGLF68WPQwS3uKI3aVlE3bcxoKmF4p\nKiLQDaPCOM7Vmnh4eKBHjx5IT0/XBcBDhw4hPj7eoGxWVhYCAgLQunVr1NTU6I737t0bP/74IwAg\nNzcX4eHhuuAHAP3799cFgLrU9uKUl5fj4sWLuHTpEk6ePAmFQgGNRqNXNiwsTPff3t7eaNq0qdEV\nEe+s5985ODjU2etTe86IiAjdjVP37t1RU1OD9evXY9y4cVAqlQav69OnD7y9vfH111+jQ4cOOH36\nNK5cuYKHH35Yr9ydwbpdu3bw9PTE//3f/2HQoEGIiopCr169MGnSpHrrTSRHjCWme5C//PJLrFix\nAoMGDcKTTz5ZbznGErJXjCN1x5HFixdj0KBB6NmzZ72v/zt7iiN2lZSJUVihwck/y7D7zHWTZbmf\nmbzExsZi9erV0Gg0+PXXX6FWq9G3b1/cuHFDr5xarcbVq1fRv39/veMKhUI3z6qkpARt27bV+72D\ng4PRx/I3btzAokWLcOzYMTRt2hTBwcG6Cb1/Hz/+9x4ihUIBrVZb73v3798fCoXC4H0UCgWGDRuG\nlJQUg9fUBu8HHnhA73hUVBTWrl2L/Px83YTkOzVp0gRDhw7FgQMHEB8fj6+//hrt2rUzmJzs7f3X\n376rqyvWrl2LLVu24Ntvv8XevXvh7OyMUaNG4aWXXqq3XURyxFhSvy1btmDjxo0YMGAA5s6da7Qs\nYwnZM8YRfZ988gkuXryIt956CzU1NRAEQfevpqYGTZo0qfNc9hRH7CopWzWyC1y1xvdnivvsHAAg\nwN3J5PtdK7tplnqRefTv3x/Lli1DZmYmcnJy0LdvXzg7OxuUc3d3R0hICGbNmlXvHjuenp4GgRMA\nSktL6z3//PnzcfXqVaxduxahoaFo0qQJzp8/j6+//vruG/X/bdq0qd7feXp61nm8VatWAG6PV79T\nbQ+ZsR7xRx55BDt37kROTg7S09Px3HPPmaxjcHAw5s6dC61Wi59++gmff/45tm/fjk6dOul6Coms\nAWNJ3ZYtW4Y9e/bg8ccfx8yZM0U9VWMsIXvFOKIvPT0dN27cwPDhww1+Fxsbi1WrViEiIqLO19pL\nHLGrpIxsm6enJyIiInDo0CGcOnWq3g0Jw8PDceLECfj5+en1rCxbtgxOTk64//770b17d+zatQsl\nJSXw8PAAAGRmZho88r/T6dOn8fTTT+vGLwPA8ePH6+xNaqj777+/wa9p27Yt/P39ceDAAYwYMUJ3\nPCMjA05OTkbHhLdr1w4dOnRAWloa1Go1hg4davRc33//PVJTU/Hvf/8b3t7eCA8PR2hoKL755hsU\nFBQ0uO5EUmIsMbRhwwbs3bsXY8aMadAQIMYSsleMI/qSkpJQUVGhd2zFihXQaDRISkpCUFBQva+1\nlzjCpIxsysCBA7FixQo4OTkZDNurNXz4cOzatQvTp0/HmDFj4O3tjQMHDuCLL77A66+/DgB4+umn\n8cUXX2DGjBkYN24cSkpKsHHjRoMVC+8MbKGhodi/fz9CQkLg7e2NH374ATt37gQAVFZWNlKLjZs6\ndSoWLFiAhQsXYtiwYThz5gw+/PBDPPfccyYnug4bNgzvvvsuHnjgAd0qTfWpHY+ekpKC559/Hs7O\nzvj888/h6OiI6Ohos7WHyFIYS/7yxx9/4MMPP0RoaCiio6MNNnytaxj0nRhLyF4xjvylrqTLzc0N\nN2/eRMeOHU2+3h7iCPcpI6t35xCa/v37QxAEREdHw8nJqc4yzZo1w5o1a9CxY0esWLECSUlJ+PXX\nXzFv3jzdY3Vvb2+sWbMGXl5eeOONN7B582ZMnToVzZs3r/fcs2bNQvv27bF06VLMmTMHubm5WLx4\nMYKCgvDTTz+ZbENjLNE6ZMgQLFy4EPn5+UhMTMTnn3+OuLg4UT3dtXsS/X0ybV08PT2xfPlyuLq6\nIjU1FcnJyVCpVFi+fLnR3i8iOWEsqVtGRgYEQUBeXh6mTJli8K+kpMTo6xlLyJ4wjjSM2PPYQxxR\nCPf6DNOK7Mi+InpOWdrjHUy+37WymxZfpVGpVEKlUln0nI3BFtph62349NNP8f7772Pv3r117mkm\nF3WtIGkJtnztrYUttAGwjXYwltw9W7721oJtkI/62mEtcQS4+1giq+GLRUVFSElJQWZmJlq0aIGU\nlBTExMQYlKuqqkJqaiq++eYbODk5YeTIkZgxY4bF61t1S+AqjWRzDh8+jLy8POzZswfPPvus7IMf\nEckTYwkR3St7iiOySsoSExPh7OyMXbt2IT09HQkJCdi/fz/8/f31yq1cuRJnzpzBBx98gNLSUsyY\nMQMBAQGiVmMxp9Zehqvo1IWrNJI1+fPPP/Hpp5/igQcewOjRo6WuDhFZKcYSIrpX9hRHZJOUnT9/\nHseOHcPRo0fh4+ODkJAQZGRkYPfu3ZgyZYpe2aNHj2LMmDG61V8effRRZGZmWjwpI7JFzz33HL9L\nRHTPGEuI6F7ZUxyRTVKWm5uL4OBgvRVVIiMjkZOTY1DW29sb+/fvx7Bhw1BRUYGjR4/qLfl9rwor\nNLq5ZcZEB3tgXGSA2c5LRERERET2RzarLxYWFhoMU1QqlXXuJ5CYmIjffvsNvXr1woMPPgh/f3/E\nxcWZpR7RwR7wbWZ6vGphhQYZF42vOEVERERERGSKbJ6UVVZW6i0XCgCOjo51box34cIFtG3bFsuX\nL0dpaSnmzZuH9evXGwxzvBvjIgNEPf0S8ySNiIiIiIjIFNkkZc7OzgYJmEajgaurq96xsrIyzJ49\nG5988gnat28P4PbmcCkpKZg0aRIcHIw//PP09DRLfWvPI+b9Kh2qzbrUrlTL9pqbLbSDbbBftvC5\nsQ3yYQvtsIU2SMEWPje2QR5soQ2A7bSjoWSTlAUEBBjsS6BSqeDnp78PWH5+Pqqrq9GuXTvdsdDQ\nUJSXl6O4uNjoLt+eLo44e8X0EvZVtwSTKytqtVoAgFqtNvl+JWU3oVKJW6nRFFvfh8KasA3ywL2F\n7o6tXHtrbwNgG+2wlTZIwRY+N7ZBerbQBsA22mH1+5SFh4fjwoULKC4uhpeXFwAgOzsbUVFReuX8\n/f0hCAJ+++03dOzYEcDtRM3V1dVoQgYAw0L9ofIzPY1OzN5jRERERPdiX14BLhUUmizH/U6JbJ9s\nkrKgoCBERUUhKSkJM2fOxJEjR3Dq1CksXrwYGo0GarUavr6+CAwMRHR0NObPn4+5c+eivLwcixYt\nwvPPPy91E+olZpNpBlwiIiL7oq7SIMDdyWQ57ndKZPtkk5QBwJIlS5CcnIynnnoKrVq1wurVq+Hn\n54cTJ05g7NixOHjwIFq2bInly5cjNTUV48ePh5OTE0aMGIHp06dbvL7mXDqfAZeIiIiIyD7JKilT\nKpVIS0szON67d2/k5eXpfvbw8MDixYstWTUD0cEeopbEr106n/uZERERERFRXWSVlFkTLp1PRERE\nRETmIJvNo4mIiIiIiOwRkzIiIiIiIiIJMSkjIiIiIiKSEOeUEREREUlg2p7TUEAwWS4i0A2jwvws\nUCMikgqflBERERHJVGGFBll/lkldDSJqZHxSRkRERCSBVSO7wFVbZbRM3GfnoNWafppGRNaNSVkd\nPF2aitrMueqWgNZezhaoEREREdkrAcDuM9eNlvF0aYpBId6WqRARmR2TsjqIDWqmAiQRERHRvWri\noECAu5PRMmI6k4lIvpiUWUBhhcbkJtJd/ZuJeq8gfy16+XEqIBERERGRrWBS1siigz2QcbHEaJnC\nCg1+KqhAgoleMABQV2kAcMgkEREREZGtYFLWyMZFBmBcZIDRMqaeohERERERke3iODgiIiIiIiIJ\nMSkjIiIiIiKSEIcvEhE10L68AlwqKDRZjktUExERkRhMyu4B9zMjsk/qKo3J5akBLlFNRERE4jAp\nuwfcz4yIiIgam5itdSIC3TAqzM9CNSIic2NSRkRERCQBTxdHXCowvm1ORKAbTv5ZZrRMYYUGJ/8s\nE9UJzGHVRPIkq6SsqKgIKSkpyMzMRIsWLZCSkoKYmJg6y27ZsgWbN29GZWUlBg0ahPnz58PZmUME\niYiIyDoMC/WHys/4mmujwvyw+8x1o0Oma5+icVg1kfWS1eqLiYmJcHBwwK5du/Dkk08iISEBBQUF\nBuU+//xzrF69GgsXLsTWrVvx008/4d1335WgxkRERERERPdGNk/Kzp8/j2PHjuHo0aPw8fFBSEgI\nMjIysHv3bkyZMkWv7ObNmzFt2jT069cPAJCQkIAdO3ZIUW0iIiIiIqJ7IpsnZbm5uQgODoaPj4/u\nWGRkJHJycvTKlZWVIS8vDwMHDtQdGzp0KN5//32L1ZWIiIiIiMhcZPOkrLCwEP7+/nrHlEqlwfDF\ny5cvQ6FQ4M8//8SMGTNw/fp1DB48GElJSXByMj2WWq7ErKwEAD1aeaGkxNNkOU7kJWo80/achgKC\nyXJcDY2IiIjEkM2TssrKSoOkytHRERqNRu9YeXk5AOCtt97CjBkz8Pbbb+PYsWNYsmSJxepqbtHB\nHvBt5miyXGGFBtl/liDA3cnkP3XVLQvUnIjqU1ihQZaJFdOIiIiIABk9KXN2djZIwDQaDVxdXfWO\nNW3aFIIgICEhAdHR0QCAlJQUvPzyy5gzZ47J8yiVSvNVWiQPj2p4Nq9/ZcjpsZ6YLuJ9nv0wCwDg\n6Wn6SVmlQ7UkbW0IuddPDLbBPq0a2QWBRr7TwO3va41WkPXnK+e6iWULbQBsox220AYpiPncTN1H\nODjc7mOX6v7AFq492yAfttKOhpJNUhYQEACVSqV3TKVSwc9Pf+hP7c9t27bVHWvbti1u3ryJoqIi\nvTlpdfn7OSyhpKQErtp7H1qp1Wrh4OAAtVpt+pxlN6FSyXeLAKVSKcm1MCe2QR6kCt6mvodarRaC\nVpDt52sr197a2wDYRjtspQ1SEPO5mbqP0Gq1AEzHJQAoKK7GhnTje6MB4qdB2Mq1ZxvkwRbacbex\nRDZJWXh4OC5cuIDi4mJ4eXkBALKzsxEVFaVXrmXLlvDz88Mvv/yCkJAQAMDvv/+OZs2a6V4nN54u\nTU3uC1J1S0BrL/kmUUTUcALAzVyJSFbE3mtwPzMiy5JNUhYUFISoqCgkJSVh5syZOHLkCE6dOoXF\nixdDo9FArVbD19cXCoUCY8aMwdtvvw0/Pz+4urpi6dKlGDVqlO7xvdyIudkSc+PWEFW3BN4MEkms\niYOCm7kSkUWIXTAsOtgD4yIDLFAjImoI2SRlALBkyRIkJyfjqaeeQqtWrbB69Wr4+fnhxIkTGDt2\nLA4ePIiWLVsiLi4O1dXVeOWVV6DRaPDoo48iMTFR6urLCnvCiIiI7EN0sAcyLpoeklhYoUHGxRIm\nZUQyJKukTKlUIi0tzeB47969kZeXp3csPj4e8fHxlqoaERERkSRMTYMY1sEbA9p6meyQFfMkjYik\nIaukjIiIiIj0STENgogsi0kZEREREekROzc9yF+LXn7ynNNPZE2YlBERSUzMzQ8X5SEiSxI7N11d\npQHA1aOJ7hWTMiIiiYm5+eGiPERERLaLSRkRUQN5ujjiUoHxlc60WgGChepDRERE1o1JGRFRAw0L\n9YfKxByKhemXcKWkmvsGEZGscD8zInliUmZlrpfxJo/IGjzWyQc7ck1Pkue+QURkKdzPjEi+mJRZ\nkehgDxy7XAatVmu0HIMpkfTmD2yDbgFuCHB3MlqO+wYRkaWMiwwQdW/QkLhUqakRtUojFysiMo5J\nmRUZFxmA6bEdoVarjZbjTR4RERFZQlufZlCrNSbLcbEiIuOYlMmEp0tTUQGrqKYCPk0sUCEiIiIi\nIrIIJmUyIfaR/oFL1YDWdI8UERER2Q+xnbtVtwTRe5ARkeUwKbNRYldXigh0w6gwPwvUiIjqI+b7\n2tW/maj34rwNIvsk9nsvZv4XIP4+YmB7Jf4ZxphDdK+Mr+lMVik62AO+zRxNlius0CDrzzIL1IiI\n6iPm+1pYocFPBRUIcHcy+U9ddctCNSciW9WQ+4jD+UUWqBGR7eOTMhvUkNWVtFpub0skJTHfVy7e\nQ0SW1BirNBKRcUzKiIiIiKhRVd0SuHQ+kRFMyoiIiIioUYldXIRL55O94pwyIiIiIiIiCfFJmZ0T\nIG4lJg4nIGo4LlFNREREYsgqKSsqKkJKSgoyMzPRokULpKSkICYmxuhrUlNTkZeXh3//+98WqqVt\naeKgQIC7k8lyHE5A1HDmXqKaiEhurpdVi1rwIzrYQ9TiIUT2SlZJWWJiIpydnbFr1y6kp6cjISEB\n+/fvh7+/f53lf/75Z3z00Ufo0aOHhWtKRCRPYifTB/lr0cuPI9iJ7I05n+BHB3vg2OUyaLVao+UK\nKzTIuFjCpIzICNkkZefPn8exY8dw9OhR+Pj4ICQkBBkZGdi9ezemTJliUF4QBMybNw/h4eES1JaI\nyLLEbuQqtjdaXaUBwCGTRPbGnE/wx0UGYHpsR6jVaqPluHQ+kWmy6SbNzc1FcHAwfHx8dMciIyOR\nk5NTZ/mPPvoIfn5+GDBggKWqSEQkiYZs5JpxscQCNSIiIiJzks2TssLCQoNhikqlEgUFBQZlCwoK\nsG7dOnz88cf44osvLFVFIqJGYWo40bAO3hjQ1svkUCL2RhORteN+ZmSvZJOUVVZWwslJf8EJR0dH\naDQag7KpqakYO3YsWrZsaanqyYaniyMuFZjuCedqbkTWQ8yNhbkXA6nU1PDGh4hkh/uZkb2STVLm\n7OxskIBpNBq4urrqHUtPT8fZs2exbNmyuzqPUqm86zrKwTAlgNC6Fz65047sK/BsbjywOTjcHr3q\n6elp8v0qHarN/tlZ+7UA2AZ7ZunPzcOj2qzf6dslmpks979S83/3zUnOdWsIW2iHLbRBCnL93MTE\nnFqmYk5DYpNY5r4vket1aAhbaANgO+1oKNkkZQEBAVCpVHrHVCoV/Pz89I7t378fly5dQs+ePQEA\nt27dQk1NDbp3746srCyT5/n7OayNUqkU1YaSkhK4ao0vdV+7WpKpCboAUFJ2EyqV+Z68iW2HnLEN\n8iBV8Lb052bu77Snp6ck331zsoW/X8A22mErbZCCXD83xc0KnL1iemSOo6sbfJoYjmq6k1arRWGF\nBk9v/dHk+4ldrMicsclW/n6tvQ2AbbTjbmOJbJKy8PBwXLhwAcXFxfDy8gIAZGdnIyoqSq9cYmIi\nXnrpJd3P27dvx8mTJ/H2229btL5EREREtkrssOUDl6oBrfGkLDrYQ9QiRFw6n+yZbJKyoKAgREVF\nISkpCTNnzsSRI0dw6tQpLF68GBqNBmq1Gj4+Prp/tby8vODs7IygoCAJa09EREREdRkXGSAq0WrI\nYkViFgThnFiyJrJJygBgyZIlSE5OxlNPPYVWrVph9erV8PPzw4kTJzB27FgcPHjQLhf3ICISS+x+\nZgPbK/HPMN6sEJF1ErMgCBcDIWsiq6RMqVQiLS3N4Hjv3r2Rl5dX52smTZqESZMmNXbVrI6pJbYB\nQKsVIFioPkTU+BoyROhwfpGopIzLUxMRETU+WSVlZD5ibo4Wpl9Cxc0aC9SGiCyhMYYIcXlqIiKi\nxsekzM7dqLol6gYtItANo8L8TJYjosYh5uk3wD0KiciyuH8qkXkwKbNjj3XywY5c08OSCis0yPqz\nzAI1IqL6iB0aKHaT6etl1aI6ZMQuT01E9mlYqD9Ufg4my4mNTWLnxTI2ka1hUmbH5g9sg24Bbghw\nN773Udxn56DVcvYZka2IDvbAsctlun3N6tOQ5ak594yI7pW5l84XG5eC/LXoJSKxJGpMTMpIFAHi\nerl4w0Ukf+MiAzA9tqPJzaM594yILMnc82LFxiV1lQYAh1aStJiUkShNHBQmn6gBvOEiIiIiImoo\nJmVERGQxHOZIRHJTqalhXCLJMSkjIiKL4TBHIpKbtj7NoFZrTJZjXKLGxKSMiIjqxZXQiMgczL2t\nh5jYxLhE1oRJGRGRDRF741NUUwGfJsbLmHslNCKyX+bc1kNMbGJcImvDpIzMisvPEklL7I3PgUvV\ngNb4cB1zr4RGRGQOYmIT4xJZGyZlZFZcfpaIzEFMBw87d4iIyFYwKSMiontm7rlnYjp42LlDRES2\ngkkZEZEd8nRxxKUC0/PFxEy659wzIpIjsZ1FA9sr8c8w00O/uaUHNSYmZUREdmhYqD9UIob+ibkB\n4dwzIjIXc63S2JDOosP5RaKSMm7pQY2JSRkREVkMl9gnImPMtUojO4vI2jApIyIii+AwRyKSo+tl\n1ewsIskxKbNzYoYJaLUCblTdYsAionti7p7rSk0N53cQ0T2JDvbAsctl0Gq1Rsuxs4gam6ySsqKi\nIqSkpCAzMxMtWrRASkoKYmJiDMqVlpYiNTUVhw8fRtOmTfHII49g5syZcHR0lKDW1k3MjUrutXLs\nyDV948OARWR7zDW/ozG09WkGtdr4XmsA53cQUf3GRQZgemxHqNVqo+U4zJEam6ySssTERDg7O2PX\nrl1IT09HQkIC9u/fD39/f71yb7zxBi5fvowtW7agsrISr7/+OlxcXDBjxgyJam7b5g9sg24Bbghw\ndzJajgGLyPaYa34HEZE5iekwkqKzqPa8fIpPDSWbpOz8+fM4duwYjh49Ch8fH4SEhCAjIwO7d+/G\nlClTdOVu3ryJb775Btu2bcP9998PAJg2bRreffddJmVERDZCzIIgYpexJiLbIyaZkaqziKs00t2Q\nTVKWm5uL4OBg+Pj46I5FRkYiJyfHoOyaNWt0CRkAKBQKVFdXW6SeRETUuMQsCNKQZazZa01ERHIn\nm6SssLDQYJiiUqlEQUGB3jEnJyeDeWbbt29H9+7dG72ORERUN3POPROzIEhDhkuz15qIzIFbelBj\nkk1SVllZCScn/TlLjo6O0GiMT+JetWoVfvrpJ3z66aeNWT0SSWzAigr2xuCgEAvUiIgsgXPPiMiW\nNcaWHnyKT3eSTVLm7OxskIBpNBq4urrW+5oNGzZg3bp1WL58OUJCxN3gK5XKe6qnHEjRBg+Pang2\nN97bPLC9Eofzi0y+1/Wyahy/WMxrIRO20AYp2MLnJtdYIoaDgwMAwNPT857fq1alQ7Vk15V/T/bL\nFj43ubYhyF8LdZXpFVqLiirQ1sd4LJke64npIs757IdZAMTFpm4iw9f/SsXFJrleh4aylXY0lGyS\nsoCAAKhUKr1jKpUKfn5+dZZfu3YtVq1ahdTUVDz00EOiz/P3c1gbpVIpSRtKSkrgqjW++uI/w7xF\nze+I++wctIKADel5RsvJvWdIqmthTrbSBinYwucmRRsUNytw9orp3mZTwxy1Wi0KKzR4euuPJt9L\n7FCikrKbUKksv1KbrXwPbaENUrCFz02ubejl5wDA9Hf6wKVqk0vii1W735m53g8ACoqrsSHdeNwM\n8vf9/+21bnL+exLrbmOJbJKy8PBwXLhwAcXFxfDy8gIAZGdnIyoqyqDs3r17sWrVKixYsABPPPGE\npatKZtC0iYPJJfY5v4PI9phrmCM3fCUieyFmXuztJ4KW71Qi85FNUhYUFISoqCgkJSVh5syZOHLk\nCE6dOoXFixdDo9FArVbD19cXFRUVSE1NxcgYy9rPAAAYq0lEQVSRIxEbG6vLphUKBXx9fSVuBZkT\nx1oTUX0aY8NXxhwi++Tp4ohLBff+BJ/oXsgmKQOAJUuWIDk5GU899RRatWqF1atXw8/PDydOnMDY\nsWNx8OBBnD17FqWlpdi9ezd2794NABAEAc7Ozjh16pTELSBz4oppRGRJjDlE9mlYqD9UIob+iV2o\niKs00t2QVVKmVCqRlpZmcLx3797Iy7s9/6hly5a6/yYiItskZon9opoK+DQx/V68QSIiS2mMVRrJ\nPsgqKSMiIgLEzT07cKka0BpfWY03SERkSWL2WQQaNrRajEpNDYdfWzkmZSSKOTeGJSKyFKlukIiI\nLKmtTzOo1aaX/+fwa/liUkaicGNYIqLbuCAIkX0ydwc1h1bTnZiUERERNQAXBCGyT+bsoBY7tPp6\nuQZ78wpNlh3YXilqr1iSLyZlRERklcy9jLW5e63FPlEL8tfaxKavRCSe2KHVW7KvmUzICis0OJxf\nxKTMyjEpI7MS82hfqxWgFYxv+EpEZIo5l7FujAVBxD5R+19pFXYXlIsqawqHTBLZFjHJG+fE2gYm\nZWRWYm4GFqZfwpWSapNBhGOoichSpFwQROwEfTE4ZJKIyDoxKSOLe6yTD3aeLgQg1FumIb3RnHRP\nREREcsEVq+luMCkji5s/sA1iOrSAq7aq3jJxn50z+/wO9iAT2SeumEZEliTFitXXy0yPQAKAiEA3\njArzM9t5yXyYlJEsccNXIjIXKVZMY2wiIkuJDvbAsctl0GqNz9cvrNAg688yC9WKGopJGckSN3wl\nIjmSe2wSO5zb3LiCJJF0xkUGYHpsR6jVaqPl4j47hxotp3zIFZMyIiKiRiB2mKM59xeSan6K2BUk\neaNH9Bcp5p41cVAgwN3JZDlO+bA8JmVkN7ggCBEZY84bpIZsDLvz1FV895tKdD1NnVeKIZNiV5Dk\njR7RX8Teaxz8/YbJ705RTQV8mpijViQVJmVkN8T2MvGmgcg+mXPuWUM2hhUzF0QMzmMjsk1iYtOB\nS9WA1jxbawDsyJYCkzIiIiKJiJ0LIgbn2BKRubAj2/KYlJEkPF0ccanA+NAeqZanFts75OFRjZIS\n08OT2ItEZFvkvAeR2HhobuacF0dEZI+YlJEkhoX6Q2VipS6plqcWexPl2dwZrlpOliWyN1LsQSSG\n2HhoboUVGhzOLxKVlJlzdUh2eBH9RUxnt1YrQLBQfajhmJSRbInpjR7WwRsD2nqZTKQ4rIeILE3s\nEzVzTdAXO4/N3OI+Oyd641pzLkTCDi+iv4jp7F6YfglXSiz/XSVxZJWUFRUVISUlBZmZmWjRogVS\nUlIQExNjUE4QBCxatAifffYZHB0d8eKLL2LChAkS1Jgak1x7o4mIxBAbw364rsUlEcvJiyHFkMmG\nbFxrzoVIzL0nm4dHNZ6NvM9s70ckN4918sGOXNPfGS4aJA1ZJWWJiYlwdnbGrl27kJ6ejoSEBOzf\nvx/+/v565TZs2ID09HRs2rQJKpUKiYmJaNOmDQYNGiRRzckamHvuGRGROYjp4RZLik6qhmxca07m\nTj49m0uzxxuRpcwf2AbdAtxM7lMW99k50fdMEYFuGBXmZ64q2jXZJGXnz5/HsWPHcPToUfj4+CAk\nJAQZGRnYvXs3pkyZolf2ww8/xOzZs9GlSxcAQFxcHD766CMmZVSvxph7RkREDWPOhUjYgUbUOBpy\nz5T1Z5kFamQfZJOU5ebmIjg4GD4+PrpjkZGRyMnJ0St37do1qFQqdO/eXa/c+vXrLVZXkhdzzz0T\ne9Pg4OAgam8h9iIRkaWIncdmbpUO1Sgxcd6IQDecNNMNHDvQiBqP2PmpcZ+dg1bLpUPMRTZJWWFh\nocEwRaVSiYKCAr1jKpUKAODn99dNrq+vLyoqKlBeXg43N7fGryzJijnnnpl79TL2IhGRJUm1GqFS\nqYRKZbzTa1SYH3afuW5y6JQYDelAE+vjMT3N9l5EciXnLT3snWySssrKSjg56QdqR0dHaDQag3KO\njo4G5QDg5s2bTMqoXuZ8ogYAnp6eouZQsBeJiOg2cz3JM+dTNyJ7Irbj5uDvN0x+V7VaAQqFOWpF\ngIySMmdnZ4METKPRwNXV1aDcrVu3DMoBQLNmzUyeR6lU3mNNpWcLbQAs345nRJ5vX14B1FUak+Uq\nS6sBBxejZVaO7Ap3pyY4XSzq1Ihtb/lrayt/T5ZmC58b2yAfttAOMW0QG4dNmdRffKwW63+l1WZ7\nr4awl2svd2yDPjHf1Un9Q3H4N5XZ73Fs4VrcDdkkZQEBAbqhibVUKpXeMMXacoIgoKioSDf/rLCw\nEM2bN4ezMx+z0r0bFupvuhAREUmKsZpIelJ0Jtsq86zBawbh4eG4cOECiov/Srezs7MRHh6uV87f\n3x+BgYHIysrSHTt58iS6detmsboSERERERGZi2ySsqCgIERFRSEpKQnnzp3D+++/j1OnTmHkyJHQ\naDRQqVQQhNtzc5555hksWbIEOTk5OHz4MDZt2oQXXnhB4hYQERERERE1nEKozXRkQKVSITk5GT/8\n8ANatWqF2bNno0+fPjhx4gTGjh2LgwcPomXLlqipqcGiRYuwd+9euLq6Ii4uDmPGjJG6+kRERERE\nRA0mq6SMiIiIiIjI3shm+CIREREREZE9YlJGREREREQkIbtIyoqKijBlyhRERkbikUcewdGjR6Wu\nUoN9/fXX6NSpE0JDQ3X/u3z5cqmrJVpxcTH69u2LwsJC3bE9e/Zg4MCB6N69O5KTk1FdLc0eMWLV\n1YZHH33U4LrcvHnvG6Oa2++//45x48ahe/fueOihh/DJJ5/ofmct18FYGyx1HRhLpGULcQRgLJEa\nY4l5MJZIi3FEemaPJYIdGD9+vDB16lTht99+EzZt2iRERkYK165dk7paDfLee+8J06dPFwoLCwWV\nSiWoVCqhoqJC6mqJUlJSIjz33HNCp06dBJVKJQiCIPz4449CRESE8PXXXwtnzpwRHnvsMSE1NVXi\nmtavrjbU1NQIXbp0EXJzc3XXpPZ3clJdXS0MGTJEmDt3rnD+/Hlh3759QkREhJCRkSGcPHnSKq6D\nsTZY8jowlkjHFuKIIDCWSI2xxHwYS6TDOCK9xoglstk8urGcP38ex44dw9GjR+Hj44OQkBBkZGRg\n9+7dmDJlitTVEy0/Px8dO3bUbZhtLU6fPo3XXnsNLi4uese3bduGkSNHYujQoQCAN954Ay+++CJe\ne+01ODk5SVHVetXXhkuXLgEAunTpAoVCIUXVRMnOzkZxcTHmzJmDpk2bonXr1vj+++/x5Zdfoqqq\nyiquQ31t+Oqrr3DfffcBaPzrwFgiHVuIIwBjiRwwlpgPY4k0GEfkoTFiic0PX8zNzUVwcLBe0IiM\njEROTo6EtWq4/Px8tGnTRupqNNjx48cxYsQIrFy5UrfPHAD89NNP6NGjh+7nbt26QaPR4Ny5c1JU\n06j62pCfn4+goCBZBz8A6NChA9asWYOmTf/qg1EoFKiursbp06et4joYa4OlrgNjiXRsIY4AjCVy\nwFhiPowl0mAckYfGiCU2n5QVFhbC399f75hSqURBQYFENbo7f/zxBw4ePIjBgwfj4YcfxsaNG6Wu\nkigTJ05EfHw8mjZtqvfHqVKp9K6Lg4MDvLy8ZHld6mtDfn4+FAoFJkyYgJiYGMTFxeGPP/6QsKZ1\n8/HxQc+ePXU/FxcXY//+/ejevTuuX79uFdehvjZERkZa7DowlkjHFuIIwFgiB4wl5sNYIg3GEXlo\njFhi80lZZWWlwSNPR0dHaDQaiWrUcNeuXUNVVRWaN2+ONWvWID4+Hmlpafjwww+lrtpdq6qqqvO6\nyHFCan3++OMPlJSUYOzYsVi/fj3c3d0xfvx4VFVVSV21emk0Grz66qvw9fXFk08+aZXX4e9tsNR1\nYCyRH2v8+60LY4k0GEvuHmOJ/DCOSMdcscTm55Q5OzsbBDqNRgNXV1eJatRwAQEByMzMhIeHBwCg\nY8eOUKlU2LlzJ1544QWJa3d3bOG6LFiwAJWVlXBzcwMALFmyBLGxsfjvf/+rGw8tJxqNBgkJCcjL\ny8OOHTvg5ORkddehrjZY6jpY22dVF1uLJbZwTQDGEikwltwbxhL5YRyRhjljic0nZQEBAVCpVHrH\nVCoV/Pz8JKrR3akNfLXatWuH69evS1Sbe+fv769X/5qaGhQXFxsM6ZAzBwcH3RcOAJycnNCqVStZ\nXheNRoP4+Hjk5ORg69ataN26NQDrug71tcFS14GxRH6s6e/XGMYSy2IsMQ/GEnlhHLE8c8cSmx++\nGB4ejgsXLqC4uFh3LDs7G+Hh4RLWqmEyMzPxwAMP6PUe/PLLL1Y3wfZO4eHhyM7O1v186tQpODs7\no0OHDhLWqmEmTJiAdevW6X6urKzEhQsXZHld5s+fj6ysLGzZsgX333+/7rg1XYf62mCp68BYIj/W\n9PdrDGOJZTGW3DvGEvlhHLE8c8cSm39SFhQUhKioKCQlJWHmzJk4cuQITp06hcWLF0tdNdG6du0K\nZ2dnzJ07F1OmTEFeXh7S0tLw1ltvSV21BrlzlaBnnnkGEydORHh4ONq0aYMFCxbg6aefRpMmTSSs\noWl3tuHBBx/E2rVr0blzZ7Rs2RKrVq1CixYtEB0dLWENDf3444/49NNPsXDhQvj5+el6aJ2cnKzm\nOhhrg6WuA2OJPNhCHAEYS6TCWGIejCXywDginUaJJY20p5qsXL9+XZg4caIQHh4uDB8+XDh27JjU\nVWqwX3/9VRgzZowQGRkpDBgwQNi2bZvUVWqQy5cv621yKAiC8PHHHwv9+vUTevbsKcyaNUuorq6W\nsIam/b0NWq1WWL16tdC/f38hIiJCmDx5snD16lWJa2lo8eLFQqdOnQz+TZw4URAE67gOptpgqevA\nWCItW4gjgsBYIiXGEvNhLJEW44i0GiOWKAThjjSbiIiIiIiILMrm55QRERERERHJGZMyIiIiIiIi\nCTEpIyIiIiIikhCTMiIiIiIiIgkxKSMiIiIiIpIQkzIiIiIiIiIJMSkjIiIiIiKSEJMyIiIiIiIi\nCTEpIyIiIiIikhCTMiIiIiIiIgkxKSMiIiIiIpIQkzIiIiIiIiIJMSkjIiIiIiKSEJMyIiIiIiIi\nCTEpIyIiIiIikhCTMiIiIiIiIgkxKSMiIgBAcnIyOnXqZPRfcnIyAGDgwIGYP39+o9fp9ddfx7Bh\nw3Q/W+q8tc6dO4ehQ4eiqqoKgwcPxoMPPojy8vI6y44ePRp9+vRBYWGhxepXnzlz5mDDhg1SV4OI\niERqKnUFiIhIHl566SX885//1P08f/58ODk5ISUlRXfM29sbALBmzRo0b9680eukUCigUCh0P1vq\nvACg1Woxa9YsxMfHw8XFBW+++SZefPFFrFixArNnz9Yr+8knn+DHH3/EsmXL4Ovra5H6GfPKK69g\n+PDhGDJkCNq2bSt1dYiIyAQmZUREBAAICgpCUFCQ7mc3Nze4uLigW7duBmU7depkyapJct4vvvgC\n169fx4gRIwAADzzwAEaNGoXt27fj8ccfR9euXQEARUVFePvttzFkyBA88sgjFqufMb6+vhgxYgSW\nL1+OVatWSV0dIiIygcMXiYiowe4cRnjlyhV06tQJBw4cQFxcHCIiIjBgwADs3r0bV69exeTJkxEZ\nGYkhQ4Zg3759eu+Tl5eH8ePHIzIyElFRUUhOToZarW7weadOnYrIyEj07dsXS5YsgVar1b1Gq9Vi\n9erVGDhwILp27YrHHnsM3377rck2bt68GcOGDdN7UpecnAxvb2/MmzcPgiAAAN566y04ODgYDKu8\ndOkS4uPj0bNnT/To0QMJCQn43//+p1cmIyMDY8eORY8ePdC1a1c88sgj+OSTT3S//+STT9CrVy98\n8MEH6NOnD2JiYlBcXIycnByMHj0a3bt3R69evTB16lTk5+frvffw4cNx8OBBXLhwwWRbiYhIWkzK\niIjILGbNmoVevXph3bp1aN++PebOnYvx48ejR48eWLt2LQIDA5GcnKybc/X777/j+eefh0ajwfLl\nyzF79mwcP34cEydORE1Njejzzp49Gx07dsS6devw1FNPYfPmzdi1a5fu93PmzMHGjRvx3HPPYd26\ndejevTsSEhJw4MCBet/zt99+Q15eHh566CG9482bN8e8efOQl5eHnTt34sSJE/jyyy8xb948+Pj4\n6MqpVCo8++yzuHz5MhYtWoTU1FT88ccfeOGFF3Rz0k6dOoW4uDi0bt0a7733HlavXo2goCDMnTsX\nZ8+e1b1XeXk59uzZg2XLliElJQUKhQKTJ09GYGAg1q5di8WLF+Py5cuYMmWKXl0jIiLg6+uLr776\nSvRnSURE0uDwRSIiMouHHnoIkyZNAgA0a9YMR44cQZ8+fXTHmjdvjn/84x/Iy8tDTEwM1qxZA09P\nT2zatAlOTk4AgM6dO+PRRx/FV199pRs2aMrQoUMxY8YMAEBUVBQOHDiAQ4cO4emnn0Z+fj4+/fRT\nzJ07F8899xwAIDo6GiUlJVi6dCkGDx5c53tmZmaiadOmCA0NNfjdkCFDMGTIELzzzjtQKpUYNmwY\nHn74Yb0y77//Pqqrq7F582bdPLxevXph0KBB2LZtG+Li4nDu3DkMHjwYCxYs0L2ua9eu6Nu3L374\n4Qd07NgRACAIAqZNm4bo6GgAwMmTJ6FWqzFmzBjdEMrAwEB8++23qKioQLNmzXTvFxYWhszMTEyd\nOlXUZ0lERNLgkzIiIjKL2gQBgG6xizuPeXl5AQBKSkoA3E58+vTpgyZNmqCmpgY1NTVo3bo1goOD\n8f3334s+b0REhN7PgYGBqKys1J1DoVDgwQcf1J2jpqYG/fv3x8WLF/Hnn3/W+Z5XrlyBj4+PLln8\nu9rhi0VFRZg7d67B7zMzMxEeHg4PDw/dOT08PNC1a1ccO3YMAPDkk09i5cqVqKqqQl5eHvbt24e0\ntDQoFApoNBq992vTpo3uv++//354enoiLi4OCxYswOHDh9G+fXu88soregkZALRq1QpXrlwx/gES\nEZHk+KSMiIjMws3NzeCYi4tLveWLi4uxd+9e7NmzR++4QqHQW3DElL+fQ6FQ6OaUqdVqCIJQ5xMx\nBwcHFBQUoGXLlga/Kysrg6ura73n9PX1RceOHeHi4qJ7Enan4uJi/PzzzwgLCzOoW/v27QEAlZWV\nmDt3rm6eXXBwMHr06AFBEHTz1e48Xy13d3ds27YNa9asweeff47t27fD1dUVL7zwAl599VW917m6\nuqK0tLTedhARkTwwKSMiIkm4u7tjwIABGD16tEESUleCdzeaN28OhUKB7du3w9HR0eD39S0X7+Xl\npXuid7fn7devH1555RWDtjk7OwMAFixYgIyMDGzYsAE9e/aEk5MTysrK9Bb6qE9ISAiWLVsGrVaL\nrKws7NixA2lpaejatSuGDBmiK6dWq+tMGomISF44fJGIiCTRq1cv5Ofno3PnzggLC0NYWBjatWuH\nd999F1lZWWY5R48ePQDcTk5qzxEWFoaff/4Z7733Xr2va9GiBUpKSlBdXX1X5+3Zsyfy8/PRvn17\n3Tk7d+6M9evX47///S8AICsrC/369UPfvn11wyRrf3fn6pF/d/DgQfTt2xfFxcVwcHBAz549sWDB\nAgiCgKtXr+qVre9JIBERyQuTMiIiksRLL72EX375BS+//DLS09Nx6NAhxMXF4YcffkCXLl3Mco5O\nnTrh4YcfRlJSErZu3YrMzExs3LgRb775Jjw8PAzmYNWKjo5GTU0NcnJy7uq848ePR2lpKSZMmIAD\nBw7gyJEjeOmll/Ddd9/phjR27doV3333HXbv3o0TJ05g/fr1mD17NhwcHHRz4uoSGRkJrVaLl19+\nGYcOHUJGRgZef/11uLi4IDY2Vq9sdnY2+vXrd1dtICIiy+HwRSIiqtede3T9/fidv6urnKljoaGh\n+Pe//4133nkHr7zyChwdHdG1a1ds3bpVt/Lg3Zz378eXLl2K9957D1u2bIFKpUJAQAAmTZpkdEXC\n4OBg3H///Thy5AiioqLqLVff+Vu2bInt27dj2bJlSE5OhlarRadOnZCWlqZ7v5SUFNTU1Oj2VWvT\npg3efPNN7Nq1y+iTQh8fH2zcuBErVqzA66+/jps3byIsLAybNm1CcHCwrlxubi5KS0sxaNCget+L\niIjkQSH8fbA7ERER4T//+Q+WLFmC9PR0NGnSROrqNNjs2bNRVlaGd955R+qqEBGRCRy+SEREVIdH\nH30U/v7+BqtDWoNr167h22+/xbRp06SuChERicCkjIiIqA4KhQKLFi3CunXr7nrBD6msXLkSEyZM\nQEhIiNRVISIiETh8kYiIiIiISEJ8UkZERERERCQhJmVEREREREQSYlJGREREREQkISZlRERERERE\nEmJSRkREREREJCEmZURERERERBL6f/qNOIEzwgBgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa5a4e1b048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a new KMF object\n",
    "kmf_by_pos = KaplanMeierFitter()\n",
    "\n",
    "duration = draft_df_2.Duration\n",
    "observed = draft_df_2.Retired\n",
    "\n",
    "# Set the order that the positions will be plotted\n",
    "positions = [\"QB\", \"RB\", \"WR\",\n",
    "             \"TE\", \"T\", \"G\", \n",
    "             \"C\", \"DE\", \"DT\", \n",
    "             \"NT\", \"LB\", \"DB\",\n",
    "             \"FB\", \"K\", \"P\"]\n",
    "\n",
    "# Set up the the 5x3 plotting grid by creating figure and axes objects\n",
    "# Set sharey to True so that each row of plots share the left most y-axis labels\n",
    "fig, axes = plt.subplots(nrows = 5, ncols = 3, sharey = True,\n",
    "                         figsize=(12,15))\n",
    "\n",
    "# flatten() creates a 1-D array of the individual axes (or subplots)\n",
    "# that we will plot on in our grid\n",
    "# We zip together the two 1-D arrays containing the positions and axes\n",
    "# so we can iterate over each postion and plot its KM estimate onto \n",
    "# its respective axes\n",
    "for pos, ax in zip(positions, axes.flatten()):\n",
    "    # get indices for players with the matching position label\n",
    "    idx = draft_df_2.Pos == pos\n",
    "    # fit the kmf for the those players\n",
    "    kmf_by_pos.fit(duration[idx], observed[idx])\n",
    "    # plot the KM estimate for that position on its respective axes\n",
    "    kmf_by_pos.plot(ax=ax, legend=False)\n",
    "    # place text indicating the median for the position\n",
    "    # the xy-coord passed in represents the fractional value for each axis\n",
    "    # for example (.5, .5) places text at the center of the plot\n",
    "    ax.annotate(\"Median = {:.0f} yrs\".format(kmf_by_pos.median_), xy = (.47, .85), \n",
    "                xycoords = \"axes fraction\")\n",
    "    # get rid the default \"timeline\" x-axis label set by kmf.plot() \n",
    "    ax.set_xlabel(\"\")\n",
    "    # label each plot by its position\n",
    "    ax.set_title(pos)\n",
    "    # set a common x and y axis across all plots\n",
    "    ax.set_xlim(0,25)\n",
    "    ax.set_ylim(0,1)\n",
    "\n",
    "# tighten up the padding for the subplots\n",
    "fig.tight_layout()\n",
    "\n",
    "# https://stackoverflow.com/questions/16150819/common-xlabel-ylabel-for-matplotlib-subplots\n",
    "# set a common x-axis label\n",
    "fig.text(0.5, -0.01, \"Timeline (Years)\", ha=\"center\")\n",
    "# set a common y-axis label\n",
    "fig.text(-0.01, 0.5, \"Probability That a Player is Still Active\", \n",
    "         va=\"center\", rotation=\"vertical\")\n",
    "# add the title for the whole plot\n",
    "fig.suptitle(\"Survival Curve for each NFL Position\\n(Players Drafted from 1967-2015)\",\n",
    "             fontsize=20)\n",
    "# add some padding between the title and the rest of the plot to avoid overlap\n",
    "fig.subplots_adjust(top=0.92)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking the Conditional Survival Time\n",
    "\n",
    "Another interesitng attribute in our `KaplanMeierFitter` is the `conditional_time_to_event_`.  It is a `DataFrame` that contains the estimated median remaining lifetime, conditioned on surviving up to time $t$.  So from the table below we see that if a player is in the league for 1 year, their expected remaining career length is 5 years.  Please note that some of the conditional survival times for later time values are a bit funky due to the smaller sample sizes of those time periods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>KM_estimate - Conditional time remaining to event</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timeline</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.0</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14.0</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.0</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16.0</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17.0</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18.0</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19.0</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20.0</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21.0</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22.0</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23.0</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26.0</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          KM_estimate - Conditional time remaining to event\n",
       "timeline                                                   \n",
       "0.0                                                     6.0\n",
       "1.0                                                     5.0\n",
       "2.0                                                     5.0\n",
       "3.0                                                     5.0\n",
       "4.0                                                     4.0\n",
       "5.0                                                     4.0\n",
       "6.0                                                     3.0\n",
       "7.0                                                     3.0\n",
       "8.0                                                     3.0\n",
       "9.0                                                     3.0\n",
       "10.0                                                    2.0\n",
       "11.0                                                    2.0\n",
       "12.0                                                    2.0\n",
       "13.0                                                    2.0\n",
       "14.0                                                    2.0\n",
       "15.0                                                    2.0\n",
       "16.0                                                    1.0\n",
       "17.0                                                    2.0\n",
       "18.0                                                    2.0\n",
       "19.0                                                    1.0\n",
       "20.0                                                    1.0\n",
       "21.0                                                    2.0\n",
       "22.0                                                    1.0\n",
       "23.0                                                    3.0\n",
       "26.0                                                    0.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmf._conditional_time_to_event_()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Resources\n",
    "\n",
    "Here are the resources I used to help write up this post and learn about survival analysis:\n",
    "\n",
    "## Papers, Articles, and Documentation\n",
    "- [The `lifelines` documentation](https://lifelines.readthedocs.io/en/latest/index.html)\n",
    "- [The PDF to the original paper by Kapalan and Meier](http://www.csee.wvu.edu/~xinl/library/papers/math/statistics/kaplan.pdf)\n",
    "- [Survival Analysis: A Self Learning Text](https://www.amazon.com/Survival-Analysis-Self-Learning-Statistics-Biology/dp/1441966455)\n",
    "- [A Practical Guide to Understanding Kaplan-Meier Curves](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3932959/)\n",
    "- [Understanding survival analysis: Kaplan-Meier estimate](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3059453/)\n",
    "- [What is Survival Analysis (PDF)](https://www.cscu.cornell.edu/news/statnews/stnews78.pdf)\n",
    "- [A short article by Kaplan](http://www.garfield.library.upenn.edu/classics1983/A1983QS51100001.pdf)\n",
    "\n",
    "## Videos\n",
    "- [Lifelines: Survival Analysis in Python](https://www.youtube.com/watch?v=XQfxndJH4UA), by Cameron Davidson-Pilon (the creator of the `lifelines` library)\n",
    "- [Survival Analysis in Python and R](https://www.youtube.com/watch?v=fli-yE5grtY), by Linda Uruchurtu\n",
    "\n",
    "As always you can find my code and data on [github](https://github.com/savvastj/nfl_survival_analysis).  Please let me know if you see any mistakes/issues or have any suggestions on improving this post."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  },
  "nav_menu": {},
  "toc": {
   "navigate_menu": true,
   "number_sections": false,
   "sideBar": false,
   "threshold": 6,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}