football-bundesliga-rule-change.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Did the 3-Point Rule Affect Results in the German Fußball-Bundesliga?\n",
    "\n",
    "**Author**: [Ramiro Gómez](https://ramiro.org/)\n",
    "\n",
    "In this notebook we look at results from the German Fußball-Bundesliga using a dataset of [European football results](https://github.com/jalapic/engsoccerdata) compiled by [James Curley](https://jalapic.github.io/). Specifically we address the question whether the rule change from *2 points for a win* to *3 points for a win* had a visible effect on Bundesliga match results.\n",
    "\n",
    "The question came up [in a Reddit discussion](https://www.reddit.com/r/dataisbeautiful/comments/3elbid/wintieloss_percentages_of_all_2015_english/ctghuuu) on [these nice visualisations](http://graphzoo.tumblr.com/day/2015/07/25) created by [Simon Garnier](http://www.simongarnier.org/) from the same data source for results in the English Premier League (EPL).\n",
    "\n",
    "In the EPL it [does not look like](http://graphzoo.tumblr.com/post/124704781242/average-game-outcome-in-english-soccer-division-1) the rule change had much of an impact on the number of draws. What we can see is an increasing trend of home losses (away wins) for the past 4 decades, which started before the rule was introduced in 1981.\n",
    "\n",
    "Let's now find out whether this is different in the German Bundesliga, where the winning team gets 3 points as of season 1995/96. At the time of writing the Bundesliga season 2014/15 was not included in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%load_ext signature\n",
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "import numpy as np\n",
    "import helpers\n",
    "\n",
    "plt.style.use('ramiro')\n",
    "\n",
    "df = pd.read_csv('csv/bundesliga.csv', parse_dates=['date'], encoding='latin-1')\n",
    "\n",
    "chartinfo = 'Author: Ramiro Gómez - ramiro.org • Data: James Curley - github.com/jalapic/engsoccerdata'\n",
    "infosize = 13"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get an idea of the information encoded in this data, let's look at the first few rows."
   ]
  },
  {
   "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>date</th>\n",
       "      <th>Season</th>\n",
       "      <th>home</th>\n",
       "      <th>visitor</th>\n",
       "      <th>FT</th>\n",
       "      <th>hgoal</th>\n",
       "      <th>vgoal</th>\n",
       "      <th>tier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1963-08-24</td>\n",
       "      <td>1963</td>\n",
       "      <td>Werder Bremen</td>\n",
       "      <td>Borussia Dortmund</td>\n",
       "      <td>3-2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1963-08-24</td>\n",
       "      <td>1963</td>\n",
       "      <td>1. FC Saarbrücken</td>\n",
       "      <td>1. FC Köln</td>\n",
       "      <td>0-2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1963-08-24</td>\n",
       "      <td>1963</td>\n",
       "      <td>TSV 1860 München</td>\n",
       "      <td>Eintracht Braunschweig</td>\n",
       "      <td>1-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1963-08-24</td>\n",
       "      <td>1963</td>\n",
       "      <td>Frankfurter SG Eintracht</td>\n",
       "      <td>1. FC Kaiserslautern</td>\n",
       "      <td>1-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1963-08-24</td>\n",
       "      <td>1963</td>\n",
       "      <td>FC Schalke 04</td>\n",
       "      <td>VfB Stuttgart</td>\n",
       "      <td>2-0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  Season                      home                 visitor   FT  \\\n",
       "0 1963-08-24    1963             Werder Bremen       Borussia Dortmund  3-2   \n",
       "1 1963-08-24    1963         1. FC Saarbrücken              1. FC Köln  0-2   \n",
       "2 1963-08-24    1963          TSV 1860 München  Eintracht Braunschweig  1-1   \n",
       "3 1963-08-24    1963  Frankfurter SG Eintracht    1. FC Kaiserslautern  1-1   \n",
       "4 1963-08-24    1963             FC Schalke 04           VfB Stuttgart  2-0   \n",
       "\n",
       "   hgoal  vgoal  tier  \n",
       "0      3      2     1  \n",
       "1      0      2     1  \n",
       "2      1      1     1  \n",
       "3      1      1     1  \n",
       "4      2      0     1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A data record corresponds to a single match and contains information on the match date, the year the football season started, the names of the home and visitor teams, the full-time result, the number of home and visitor goals and the tier, which in case of the Bundesliga is always 1.\n",
    "\n",
    "To see some summary statistics we can use the `describe` method of [Pandas DataFrames](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Season</th>\n",
       "      <th>hgoal</th>\n",
       "      <th>vgoal</th>\n",
       "      <th>tier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>15508.000000</td>\n",
       "      <td>15508.000000</td>\n",
       "      <td>15508.000000</td>\n",
       "      <td>15508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1988.215115</td>\n",
       "      <td>1.872517</td>\n",
       "      <td>1.202863</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.591496</td>\n",
       "      <td>1.459254</td>\n",
       "      <td>1.162654</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1963.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1976.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1988.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2001.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2013.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Season         hgoal         vgoal   tier\n",
       "count  15508.000000  15508.000000  15508.000000  15508\n",
       "mean    1988.215115      1.872517      1.202863      1\n",
       "std       14.591496      1.459254      1.162654      0\n",
       "min     1963.000000      0.000000      0.000000      1\n",
       "25%     1976.000000      1.000000      0.000000      1\n",
       "50%     1988.000000      2.000000      1.000000      1\n",
       "75%     2001.000000      3.000000      2.000000      1\n",
       "max     2013.000000     12.000000      9.000000      1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset contains 15508 records, the home team scores ~1.87 and the visitor team ~1.2 goals per game on average. The highest number of goals for a home team was 12 and for a visitor 9.\n",
    "\n",
    "## Wins, losses and draws over time\n",
    "\n",
    "To be able to compare wins, losses and draws over time, we first add a `resulttype` column so the data frame can be grouped by `Season` and `resulttype`. The grouped dataframe is then unstacked to yield a data structure suitable for the stacked bar plot we want to create."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>resulttype</th>\n",
       "      <th>Draw</th>\n",
       "      <th>Home loss</th>\n",
       "      <th>Home win</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Season</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1963</th>\n",
       "      <td>61</td>\n",
       "      <td>53</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <td>62</td>\n",
       "      <td>56</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1965</th>\n",
       "      <td>68</td>\n",
       "      <td>70</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1966</th>\n",
       "      <td>84</td>\n",
       "      <td>64</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967</th>\n",
       "      <td>73</td>\n",
       "      <td>66</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count                   \n",
       "resulttype  Draw Home loss Home win\n",
       "Season                             \n",
       "1963          61        53      126\n",
       "1964          62        56      122\n",
       "1965          68        70      168\n",
       "1966          84        64      158\n",
       "1967          73        66      167"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def result(row):\n",
    "    if row.hgoal > row.vgoal:\n",
    "        return 'Home win'\n",
    "    elif row.hgoal < row.vgoal:\n",
    "        return 'Home loss'\n",
    "    return 'Draw'\n",
    "\n",
    "df['resulttype'] = df.apply(result, axis=1)\n",
    "resulttypes_by_season = df.groupby(['Season', 'resulttype']).agg(['count'])['date']\n",
    "df_rs = resulttypes_by_season.unstack()\n",
    "df_rs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the first 2 Bundesliga seasons had 16 teams and [season 1991/92](https://en.wikipedia.org/wiki/1991%E2%80%9392_Bundesliga) 20 teams as opposed to 18 in the remaining seasons, the number of matches is not the same in all seasons. So instead of total counts, we want to compare percentages of result types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>resulttype</th>\n",
       "      <th>Draw</th>\n",
       "      <th>Home loss</th>\n",
       "      <th>Home win</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Season</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1963</th>\n",
       "      <td>25.416667</td>\n",
       "      <td>22.083333</td>\n",
       "      <td>52.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <td>25.833333</td>\n",
       "      <td>23.333333</td>\n",
       "      <td>50.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1965</th>\n",
       "      <td>22.222222</td>\n",
       "      <td>22.875817</td>\n",
       "      <td>54.901961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1966</th>\n",
       "      <td>27.450980</td>\n",
       "      <td>20.915033</td>\n",
       "      <td>51.633987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967</th>\n",
       "      <td>23.856209</td>\n",
       "      <td>21.568627</td>\n",
       "      <td>54.575163</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count                      \n",
       "resulttype       Draw  Home loss   Home win\n",
       "Season                                     \n",
       "1963        25.416667  22.083333  52.500000\n",
       "1964        25.833333  23.333333  50.833333\n",
       "1965        22.222222  22.875817  54.901961\n",
       "1966        27.450980  20.915033  51.633987\n",
       "1967        23.856209  21.568627  54.575163"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rs = df_rs.apply(lambda x: 100 * x / float(x.sum()), axis=1)\n",
    "df_rs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bundesliga seasons start late in the summer of one year and end in the spring of the following year. So typically a season is displayed as *1995/96*, the `season_display` function does that. We also set some variables with the colors and data series to use and determine the minimum and maximum season values to show in the title."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def season_display(year):\n",
    "    s = str(year)\n",
    "    return '{0}/{1:02d}'.format(year, int(str(year + 1)[-2:]))\n",
    "\n",
    "colors = ['#993300', '#003366', '#99cc99']\n",
    "alpha = .7\n",
    "\n",
    "c1, c2, c3 = df_rs['count'].columns\n",
    "s1 = df_rs['count'][c1]\n",
    "s2 = df_rs['count'][c2]\n",
    "s3 = df_rs['count'][c3]\n",
    "\n",
    "xmax = df_rs.index.max()\n",
    "xmin = df_rs.index.min()\n",
    "\n",
    "title = 'Wins, losses and draws in the Bundesliga seasons {} to {} in percent'.format(\n",
    "    season_display(xmin), season_display(xmax))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code in the following cell creates the actual graphic. We can use the [Pandas plot](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html) function on the result types data frame, but want some more customization. Specifically the display of the season values as x-axis ticks and the legend.\n",
    "\n",
    "The latter would be displayed above the bars, if we simply called `plot` with the keyword argument *legend=True*. To avoid that and place it below the chart we create a custom legend using [matplotlib Patch](http://matplotlib.org/api/patches_api.html#matplotlib.patches.Patch) objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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47vzsibcXFBYCAOztjDggN+o9WbGIiMhK8OMY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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f791c459b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_rs.plot(kind='bar', stacked=True, figsize=(16, 6), color=colors, title=title, fontsize=13, width=1, alpha=alpha)\n",
    "ax.set_ylim([0, 100])\n",
    "ax.set_xlabel('', visible=False)\n",
    "ax.xaxis.set_major_formatter(\n",
    "    mpl.ticker.FuncFormatter(lambda val, p: season_display(df_rs.index[val])))\n",
    "\n",
    "p1 = mpatches.Patch(color=colors[0], label=c1, alpha=alpha)\n",
    "p2 = mpatches.Patch(color=colors[1], label=c2, alpha=alpha)\n",
    "p3 = mpatches.Patch(color=colors[2], label=c3, alpha=alpha)\n",
    "\n",
    "ax.legend(loc=(.69, -.23), handles=[p1, p2, p3], ncol=3, fontsize=13)\n",
    "ax.annotate(chartinfo, xy=(0, -1.21), xycoords='axes fraction', fontsize=infosize)\n",
    "plt.savefig('img/{}.png'.format(helpers.slug(title)), bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the intentions of the 3 point rule was to increase the incentive of winning and thus reduce the number of draws. Ironically, season 1995/96, when the 3-point rule was introduced in Germany, has the highest percentage of draws in the displayed time frame. To be fair it decreased quite a bit the following year, but nothing suggests that the rule change had the desired impact.\n",
    "\n",
    "## Most common results in the Bundesliga\n",
    "\n",
    "One more thing we'll look at in this notebook are the most common full-time results. Again we'll compare results before and after the point rule change. First we create 2 separate data frames: `df_2points` contains the match records for 2 points for a win and `df_3points` the records 3 points for a win.\n",
    "\n",
    "The series objects returned by the `value_counts` calls are then used to create a new data frame `df_results`. Same as before we want to use ratios instead of totals since the number of matches differs for the 2 series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_2points = df[df.Season < 1995]\n",
    "df_3points = df[df.Season >= 1995]\n",
    "\n",
    "results_2points = df_2points.FT.value_counts()\n",
    "results_3points = df_3points.FT.value_counts()\n",
    "\n",
    "df_results = pd.concat([\n",
    "    results_2points / results_2points.sum(), \n",
    "    results_3points / results_3points.sum()], axis=1).fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After setting a limit, the title, y-axis label and the column headings we add a sum column and sort it in descending order to show the 30 most common results across all years in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "limit = 30\n",
    "title = '{} most common Bundesliga results: 2 vs 3 points for a win'.format(limit)\n",
    "ylabel = 'Relative frequency of result'\n",
    "\n",
    "cols = ['2 points for a win', '3 points for a win']\n",
    "df_results.columns = cols\n",
    "\n",
    "df_results['sum'] = df_results[cols[0]] + df_results[cols[1]]\n",
    "df_results.sort('sum', inplace=True, ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The remaining code creates a grouped bar plot for the most common match results. To make the results better readable and comprehensible the x-tick values are displayed horizontally and the y-ticks as percentages rather than ratios."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OHICzszOuXbuGtLQ0WFhYKHy79p///AcLFiyAq6srjI2N8fDhQ5w6dQqCIEiz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\nkHHLLbdow4YNtbbbbDZ3N11NWlqali5dqhtuuEG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ihebNm1dtvYUL\nF2rYsGHq06ePioqKZLPZlJiYKLvdLj8/P3Xt2rWB/gUAAAAAAMBIli5kSNKsWbN09OhRTZs2Tb6+\nvpo0aZJmzpxZ1b53716tX79eO3bskHTiVpPU1FRNnTpVNptNa9as4QkmAAAAAAA0EZZ+agkAAAAA\nAMCpLD1HhlWkp6erVatW+uyzz+pdd+fOnerQoYNeeuklw/b/5ptv6uKLL1ZISIjOP/98LVmyxOs5\nnO2DxMREnXfeeWrbtq3uuusuFRcXG7J/yfV+MCOL3W7XAw88oHPPPVfnnnuuHnzwQTkctdcCzeoP\nV/ri448/1hVXXKHWrVsrKipKsbGxKisrMyRHUlKSevTooZYtWyo6OlqrVq2q93eMHJuu7t/M8enM\nOWLmsZBcH5+S8e8VrmQwqz9cGRdmH5NK999/vwICApSRkVHnemZ8hjgzNl944QX17dtXLVu2VJcu\nXbRgwQLD9n8qZ/rBCu+b3uiP+vrCrLHpyjlqZj+4cp6ameOrr75SQEBAtZ/6tt9Q5+mpnnjiCQUE\nBGjr1q2G7N/VfjDjPHXn+65kfF+481lqdA5XjodVPse88b6ZlpamG264Qeedd94ZUw2czozz1Nn9\nN/T3zYKCgjPGT0xMjCH7d+e7v2T8eSo1gltLGlJubq4SEhL01FNPqbS0tM51f/vtN61YsUKPP/64\nfvvtN9lsNkMyHD9+XLNnz9aYMWO0aNEibdu2TfPnz1dkZKRuvvlm03O40gcvvvii4uPjlZSUpPbt\n22vKlCkKCAjQE0884VEGyfV+MCvL0qVLlZqaqpUrV6qsrExTpkxR27Ztdf/993stgyt94XA4FBcX\np8suu0wPPvigvv32W8XFxSk0NFQPPPCARzmkEx/2sbGxCg8P13vvvafp06erZ8+euuyyy85Y14xz\nxJX9m3U8nD1HzD4Wkmvj06z3LGczmNkfzo4LbxwTSUpOTtbrr79eZx+bcTxcef8uLS3VnXfeqa5d\nu+o///mP5s+fry5dumjs2LEe56jkTD9Y4X1TMr8/6usLM8emK+8TZvaDK+/fZuaoqKhQUFCQNm3a\nVLXsvPPOq3Hdhj5PK7322mtavny5Ye/bkmv9YMZ56uo5WsmMvnDlHDErh7PHw0qfY2a/b86bN0/P\nP/+8xo0bp9tuu009e/ascT2zvt84u/+G/r4pqepBF2+99ZZatWolSQoNDfVo/6du29n37kpmnKeS\npNLSUgc/Nf/MnTvXccUVVzjefPNNh81mc3z00Ue1rvv00087+vTp49iyZYvDZrM5nn/+ecNyFBcX\nV3t90UUXOSZPnuyVHK70Qd++fR0xMTFVr1etWuUICAhwHD161Ov9YEaW4uJiR7t27RwLFiyoWjZr\n1ixHx44dvd4frvTF6euOGDHCce211xo2Pk/9CQkJcSQlJXllbLq6f7OOhyvniJnHwtXxacbxcDWD\nt8ZmXePC7Azp6emOTp06OT788EOHzWZzfPfdd147Hq6MzdN/evfu7Zg2bZrX+8Eq75tm9oezfWHG\n2HT1HDV7XJz6U9d5amaOtLQ0x3nnnefUulY4T3fv3u0499xzHdu3b3fYbDZHenq61/vBrPPU1XPU\njL5w5xwxI4crx8Mqn2On/xh5nr755puOyMhIx08//VTvumacp67s3wrfNw8cOOCw2WxnjA0zfup7\n7zbrPau0tNTBrSV1mDdvnjZv3qzu3bvXu+5tt92mzz//XP379zc8h4/PycNUXl6u3NxcdenSxSs5\nnO2D0tJS7dy5U4MHD65aNmTIEJWWluqrr74yJIuz/WBWloMHDyo3N1dXXHFF1bIrr7xSmZmZ+vHH\nH72SoZIrY+LUdSUpMzPT8Cf5FBQUaNWqVSotLdWQIUNqXMfMc6S+/Zt5PFx5nzDzWLgyPiVzjoer\nGcwem86MSzMz/Prrr4qJidEzzzyjDh061LmuGcfDlbFZqbCwUK+++qr279+voUOHGpLD2X6w0vtm\nJaP7w5UxYcbYdPUcrWTGuKjkzHlqZo7s7Gy1aNFCeXl59a7b0OdpeXm5xo0bp0ceeUQXXHCBYRkk\n5/vBzPPUlXPUrL5w9RwxK4cr49Iqn2OVzDhPk5KS5Ovrq969e6tFixb6y1/+opycnBrXNeM8dXb/\nVvm+mZOTo+DgYB05csSp26Lc4cx7t5nvWRJzZNSptstfSktLVVxcrOLi4qpLewy/VKYWjzzyiIKC\ngnT77bd7JYczfVBWVqYjR47Ibrerffv2Veuce+65klTrG40nausHM7P8/PPPkqR27dpVLavcR1ZW\nVoP1R31j4lTPPfecDh48qBkzZhi2/xUrVqh9+/aaPn26HnroIUVHR3v1HKlv/2YfD1feJ05l9LFw\ndnyaeTxczXAqo/vD2XFpZoa5c+fqmmuuqfaFRvLeZ4irY/Ott95S27ZtNXbsWE2YMKHaF3lPONMP\nVnzfNKM/XBkTpzJqbLpzjpo1LiTXzlOzchQWFiovL08dO3ZUp06d9NBDD0my1nlaOd/B8uXL1b59\ne40fP97wHM70gzfP07q+50nm9YUr3/XMzOHKuDxVQ36OSeacp2VlZfr88881cOBAvfjii3r11Vd1\n6NAhjRkzRmVlZaafp87u30rfN48ePapmzZopIiJC7dq10/Tp052+dc0Zznz3lsx9z5IoZLhl2LBh\natWqlVq1aqXhw4d7bb+JiYl69tln9dZbbykwMLDBckjV++Daa6/1WiFHqrsfvJ2l0lVXXdUgGVwZ\nEy+//LJiY2P12muvVXuD9dQtt9yid999V9OnT1dcXJxeeeUVr47N+vbfUGOiIY5FbU4dn95+r3Am\ngxn94eq4NDrDvn379I9//EPz5s1TeXl51f2q5eXlGjp0aIMej9r6YdCgQUpPT9f8+fOVmpqqxx57\nzON9OdsPVnzfNLo/3B0T3nq/qO0cNWNcVHLlPDUrx+23364ff/xR+fn5evTRR5WUlKQnn3xS1157\nrWXO02uvvVb5+flKSEjQ4sWLVV5eXvWHQnl5uSH7c6YfvHWe1vc9z+y+qM3p3/XMzOHOuLTC55gZ\n52leXp7Kyso0ZswYDR48WEOHDlViYqK2bt2q3r17m36eOrt/K33fvPrqq5WRkaFffvlFzzzzjDZs\n2KC5c+catk9nvnt74zxlsk83rFy5UseOHZMktWjRwiv7XL58uZKTk5WWllZ1mV1D5Kh0+r7btGkj\nX1/fatXG7OxsSTL0i5cz/WBWlsrq/M8//6yoqChJJ6urGzduVPPmzU3PcCpXxsRrr72myZMnV80I\nbqTQ0FANGjRIgwYNUl5enp544gk9++yzKiwsPCOHGZzZv7fG56m8fSxcGZ9mcSeDWf3hyrg0I8OG\nDRtUUFCg8PDwasujo6O1cuXKqi933n7vlmofm61atdKAAQM0YMAA2e12rVixos7J7ZzhSj9Y7X3T\n6P5wZ0wYPTbdOUfNGBeVXDlPzcwhSc2aNdP48eO1bds2vf7661q9erXXPsdqcvq4fOedd1RQUKC+\nfftWW++aa67RmjVrNG7cOEP2W18/mH2eOnOOmtkXrpwj3jgmzo5Lq3yOmXGeVvZ5fn5+1bLKTGvX\nrpWvr+8ZOYzkyv6t9H1Tkvz9/XX99ddr9+7d+vvf/66EhARD9unMe7c3zg8KGW4wen6B+nz88cda\nsGCB0tPTFR0d3WA5TlXTvi+88EJ9+OGHGj16tCTpgw8+kL+/vy688EJD9ulKP5iRJTIyUu3atdOW\nLVuqPiTS09PVsWNHDRgwwCsZKrnSF/v27dOECRO0bt26OmcUNkJgYKAcDof+8Ic/mLofd/Zv9vg8\nnbePhavj0wyuZvDW2KxrXJiV4fbbb9ewYcOqXmdmZmrkyJF64403dPnllzfIH0aVnPnsqOwzT7na\nD1Z53zydEf3hal+YMTY9fZ8walzUtW1nPj/MzOHr66uKiooG+xyrdPq4bNu2rT7//POq1w6HQ5dc\ncolWrlypa6+91vD919UPZp2nzp6jZvaFK+eIN49JXcfDqp9jRp2nwcHB6tChgz766CNdf/31kqRd\nu3bJz89P559/voKDgz3eh5H7t8L3zdNVjh8z1Pbe7Y3zg0JGI/DQQw9pyJAhKi4u1ieffCLpxKA5\nvcLV0CZPnqwJEybooosuUrt27RQfH6+YmJiqSqanXOkHM7L4+Pho0qRJSkxMVOfOnVVeXq4VK1bU\n+hxpM/vDlb5ISEhQjx491Lp166p1JXk8CVJFRYUWLFigiy66SMHBwfrPf/6j559/XosWLfJou2bt\n3+zx6QyzjoXk+vg0g6sZzOgPV8eFWcckLCxMYWFhVa9bt24tSerZs2eDFjFqs3jxYnXp0kVt27bV\nN998o2XLlmnMmDEeb9fVfrDK+6YZ/eFqX5gxNl09R80aF66ep2blkKSFCxeqZ8+eatWqlb744gut\nW7dOS5YsMWTbRmrTpo3atGlzxvKuXbvWuNxVrvSDWeeps+eomX3hyjliZg5XjodVPsfMPE8nTpyo\nhx9+WN27d1fLli0VFxenMWPGmF7EcGf/Vvi+mZSUpI4dO+qcc87RN998o6SkJP3tb3/zeLuuvHeb\n/Z4lUchwWkPc81Rp+/btKioq0saNG6uWde7cWfv27fNqjvr64NZbb9VPP/2kRx55RMePH9dNN92k\nxMREw/bvSj+YlWXWrFk6evSopk2bJl9fX02aNEkzZ86scV0z+8OVvti+fbv27t1bbaImm82m4uJi\njzL89ttv2rlzp1avXq1jx44pMjJSS5cu1T333OPRds3av9njU6r/HDHrWFRyZXyaxZUMZvSHq+PC\n7GNyqob8HKlv37t27VJKSooKCgrUsWNHTZkyRbGxsV7PYpX3TW/1R119YdbYdOUcNasQjamDAAAg\nAElEQVQfXD1PzcpRXl6u3bt3a/Xq1fr1118VERGhxYsX69577/V42+5oqPcIV/vBrPPUKt93G/qz\n1NXjYZXPMTPfN++//34dOXJE8+bNU1FRkW688UalpKQYsm2j92+F75v79+9XcnKyjhw5og4dOmjy\n5MmKi4vzeL8N/d3/dLbS0lJzrs0DAAAAAAAwGE8tAQAAAAAAjQaFDAAAAAAA0GhQyAAAAAAAAI0G\nhQwAAAAAANBoUMgAAAAAAACNBoUMAAAAAADQaFDIAAAAAAAAjQaFDAAAAAAA0GhQyAAAAAAAAI0G\nhQwAAAAAANBoUMgAAAAAAACNBoUMAAAAAADQaFDIAAAAAAAAjQaFDAAAAAAA0GhQyAAAAAAAAI0G\nhYxalJSUKCsrq6FjwIKysrK0Z8+eqtc7duxQXl5eAyZqGAcPHtR3333X0DEavX/+85965513GjqG\nZXz88cd66aWXGjpGjfhcOFN2drbKysoaOoZ++OEHj37/bH0fbwjufnb89ttv+uWXX0xI1PRY+XPF\n03O1qTLyPaiwsFC5ubmGbAuwMgoZtZg0aZIefPDBho7hVcXFxQ0doUFs3bpVf/zjH9WqVStdeOGF\n9a4/d+5cJSYmVr2++OKLtWvXrhrXLSoqqva6pKREUVFR2r59u2ehLeDOO+/UCy+8UPW6rKxMFRUV\nLm/n66+/1sCBA9W6dWt17txZhYWFRsa0NIfDoRkzZpwxTjzdZmlpqWHb87b58+crOzvbsO3ddNNN\neuKJJ1z+PbvdrvLy8mrLzsbPhbr897//VURERIMXdz744AN16dLFo22c+j7++uuvq1+/fkZEa1A/\n/vijOnXqpMzMTKfWN/J9qC6nf3Y4q2/fvnr99dfd2ufZ9DljxueKUYw4Vz3l6vntre/GdX2XrEtN\n371GjBihlJQUo6K5rabv1+5+VwRqctYWMq688kr9f3vnHRbF1T3+DxhZpeyyS19k6UUEiRXU2KKJ\nLxYsMYKa2GOLxhYTS2wpaowxmphYEkwssSbGhhrUSNDXioiKIkWqiCgaNeILKzq/P3h2viy7S1HT\nfpnP8+wfO+XOmTvn3HPn3HPvyGQyg1+9evXIycnBzMwMMzOzp77OxIkT8fT0xNbWlrCwMOLi4vT2\nz507F2dnZ5ydnZkzZ85TX+9JGTt2LKNHj/7Lrv9XMmjQIDp16kRKSkqNOkk11Y3NmzcTFBSkt00m\nk5Genk6zZs2eWN6/CxXrIS8vD4VCQXZ2dq3LefPNN9FoNJw/f54DBw5gbW39jCWtnpKSEvr370/H\njh31tufk5NC1a1fkcjm+vr5GMwUyMzMZO3Ysfn5+9OrVS9xene0D/Pe//+XOnTt0795d3HbkyBFe\neeUVGjRooBcw03H27FlsbW1Zt26d0XsJCgp64oyGsrIy5s2bh4+PDyqVihdffJELFy7oHVNdm/Xb\nb7/RqVMnhgwZord9/vz5+Pv7I5fLCQkJYdu2bQbn5ubmcvLkSSIjI8Vt58+fx9PTk2+//daozHl5\nebi5ufHBBx8Y3b9161bGjx9f3a0boFAoOHLkiN62Z+UXAM6cOUOvXr1wcHBAoVDQuHFjli1b9kzK\n/rMQBMHkvkGDBqFQKKhXrx4uLi506dKFQ4cO/elyPAl9+vTh2LFjz7TMimzevJmmTZtia2tL48aN\n+fHHH/+Q6zRo0ICcnBzUanWNZKrsr/4onsaOnvS8v8LPPIlfefz4MXPmzMHFxQWVSsWoUaN48OCB\nuH/nzp0Gfdbbt2/rlV/Rr9y+fZsBAwZga2uLRqNh6dKlBnIWFhbyzjvv0KhRI5o2bfqMa0Gfqmy1\nJj7zWVAb+/67941N9b2epa96Gir3r5+mryghYYx/bSBj+/btFBQUcO3aNVxcXFi2bJn4X6PRIAjC\nM+kcdevWjf3793PhwgVatGjB66+/Lu7btm0bK1asYOfOnezYsYMVK1awZcuWp77mk/BPHsF9GgoL\nC7l27RrDhg1DrVbj5eVV7Tk11Q2tVmv0uDp16jyRrH9nHj16xKNHj57IZpKSknj99ddxc3PD19f3\nD5Cuanbt2kVYWBiHDx82cPxRUVEolUqSkpJYsmQJ48eP5/Tp0+L+U6dO0aZNG+RyOZs2beKbb74R\n91Vl+zo2b95Mz549kclkAKxYsYIBAwbQsWNHYmNjDTpQt27dol+/flhbW5vspDx8+PCJ2667d++S\nkZHBxo0bSUhIQKlUMnz4cHF/dW3Wt99+S2hoKBcvXjSQr23btvz4449cvnyZyMhIhgwZQnFxsd4x\nW7ZsoXXr1ri6ulJcXMzs2bMJDw/n9u3bRu+3tLSUPn36YG5ubrI+zM2fzM0Zq8dn5Rd+/fVX2rdv\nj1qtJjY2lgsXLrBkyRL8/Pyeuuy/CwUFBYwcOZJr167x66+/0qxZM3r16kVqaupfLVqN+CPb6cOH\nD/P++++TnJxMv379GDJkyB+WBl7T+zDlr/5uPKmMf7afeVK/smrVKtauXcsPP/zA0aNHycjI4N13\n3xXPLSsro23bthQUFIh9VpVKpVd+Rb8yduxYbt68ycmTJ9mwYQOfffaZXhD5ypUrhIWFce/ePb7+\n+mtiYmL+wFqpmpr4zGdFbezi70xVfa+/2p6N9a+fpq8oIWGMf20gQy6Xo1KpsLOzw9zcHCsrK1Qq\nFSqVSnQ6Bw8exMXFBYVCQWRkJHfu3BHPj4uLIywsDDs7Ozp27Mjly5eNXuell17C398fR0dHrKys\ncHJyEvdFR0czevRoQkNDCQsLY9SoUXovQjouXrxIixYtUKvVKBQKOnXqxMKFCwkODsbGxoYWLVpw\n6dIl8fhr167Rt29fbG1tcXd3Z/HixWKjkZGRQceOHVEoFLi6uvLTTz8B5Q3epk2bxCj/2bNnDeQ4\ndeoUrVu3xsbGBg8PD06dOgXA3r17CQoKwsbGhlatWonbAd555x18fX1FWWbPnk1kZCSOjo44ODgw\na9Ys8diNGzcSFBSEg4MDDg4ODBkyhClTpuDp6YlcLqdv377cu3dPPP7kyZOEhYVhY2NDcHAw+/bt\nE/ctWbKEgIAAVCoVtra29O/f3+jcWl3qZePGjZHJZHTt2pWcnBxkMhmZmZnicZ6enk+UCpufny/W\nqW4kRCaTcfjw4VrXD8BXX31FQEAATk5ODBo0SK8+dKSlpdGxY0fUajVyuVwcqTals4IgMGXKFFG/\ndJ2HX3/9FZlMJqYAlpWVIZPJDEapKxIYGIhMJqN3794ArFu3Dl9fX6ytrQkODjaaollSUkKPHj2Q\nyWQEBgYC5WmRnp6e2NjYEB4eDlSt14cPH+b555/HyckJlUpFr169mDNnDgEBAdjY2NCpUyeT6dUZ\nGRnMmTOHN998U8+53rp1i8TERKZPn46Xlxc9evSgd+/ebNiwQayPwYMH8/HHH7Nw4UKaNWuGvb29\neH5Vtg/lL8rbt28nKipKlGP69OkcOnSIcePGERgYiI2Njd4548aNY8SIETRs2NDkMxAEgdGjRyOT\nybCzswPKR/k++eQT3N3dsbW15dVXXzU6HcDOzo7169fTsmVLvLy8GDp0KGlpaeL+6tqszMxMVq9e\nTffu3Q06Km3btiUoKAgnJycUCgX29vY899xzesds3rxZrA8zMzNu375NXFycQd3pmDNnDi1atOCl\nl14y2THq3LmzmDlSsY3R6VZ+fr7JugwPD0cmk+mNUj4LvzB+/HgiIiL46quvaNKkCRqNhs6dO4u6\nXl1ZxuyjY8eOuLu7Y2NjQ1BQEIsXL6ZDhw7Y2tri6empl222bt06NBoNCoWCVq1a6U11q1iORqNh\n5cqVerIvXboUNzc3HBwceOONN0zWHSD6VT8/P+bPn0/dunXF1Onhw4frZe2sWbNGDOTcvHmTNm3a\n0KBBA6ytrfH19RV9FZTb3owZM3B2dsbFxcUgMygrK0vMdgkODmb37t16916xTSotLTWQe926dXpB\n7aysLLp164ZCocDR0ZFmzZrx66+/AuX6oPNRISEhxMbGVlknUP6y2r17dxo0aMCECRPQarXk5uYa\nPfbw4cM0adIER0dH5HI5nTt31vPPpaWlTJs2DWdnZ+zs7Bg5cqToF7Kzs/V82WuvvYaXlxdyuRwX\nFxfmzp2rdy1j/sqYb6iIMX9TW9+RlJREp06dsLOzo2XLlpw4ccJk3a1cuRI3Nzfs7OwYPXq0XrZC\nVTZjzM9U5VOOHz9Oq1atcHJywtbWlrVr1wLwww8/0LhxY+zt7enZs6fJaVVP6ld+/vlnIiMjadOm\nDYGBgcyZM4fvv/9ePP/u3bs4OjqK/dXKQYzKfuXAgQNMnDgRf39/2rVrx4gRI/TKGzVqFEOHDmXl\nypW0bt0aFxcXo/ezYMEC1Go1KpWKzp07c+XKFeDpbbUi1fnMinz22WcEBASgVCqxt7dnyJAh3Lhx\nQ9xf1bOtaN/VyW+sb1xUVERERAQqlQpHR0e+/PJLA/l27txJ06ZNsbe3x87OjgMHDvD+++/rZecc\nOnRIHMQwRk11DQz7XoIgsG7dOvH6Y8eO1WvrTJV9+/ZtwsPDcXNzw8bGhpEjRwJV26gp3TDWv9a9\nX1WWt6ioiMGDB+Ps7Iy/v79ev+Lrr78mKCgIpVKJk5MTKSkpJutB4t/HvzaQUROef/55Tp48yZEj\nR7h48SIffvghUN456N27N/379+fMmTP4+voyaNAgk+WMHz8elUrFunXr2Lx5s7g9NTWVJk2aiP+b\nNGlidLSqqKiI8+fPc/z4cc6fP0/9+vVZsWIFX3/9NefPn8fJyYnJkycD5S8sr7zyCnXq1CEhIYFv\nv/2WL774ghUrVgDlHX8rKysSExP5+eef9eYJvvLKK2KUv3Hjxnoy5Ofn06VLF7p160ZycjK7d+8m\nICCApKQk+vXrx5QpU0hOTiYiIoLw8HAKCwsBSExMpGvXrly5coXly5ezaNEiPD09OXnyJNHR0Xzy\nySdiil9GRgZWVlZcvHiRQ4cOER8fz6VLl9izZw9HjhwhMTFRnPN37do1wsPD6dWrF8nJyUyaNIlX\nX32Vc+fOAeXBn8DAQNLS0oiPj+fChQt89NFHJp/R8ePHKSgoYOvWrUZfiJ40Rc/FxUWs0zFjxhjs\nr0n9/Pe//wXKHc+cOXNYtmwZcXFxpKam8v777xuUWVBQwLFjxzh48CApKSmMGDGC7OxsevXqZVRn\nY2Nj+eabb9i2bRsXLlzg7bfffqJ7hf+rx/Xr13P//n1GjRrFlClTuHz5MqtXr6ZevXpGz9u6dSsF\nBQUcP34cgH379jF79mzS09P58ssvq9XrvLw8bt26xblz5zh16hT5+fns3r2bTZs2kZiYyL1795g3\nb57Ra0+ePJnevXsbPHeFQoGtrS07duxAEAQePXqETCYTUyKPHDlCdna2+FL4wgsviPLrqGj7lbOt\nYmNjqVOnDp06dQJgw4YN2NjY8Prrr6PRaOjbt69e8GXXrl3k5eUxderUKp+BmZkZn376KQUFBeKC\nel999RXLly/nu+++4/Tp05iZmfHKK69UWQ6Ud6o7d+4s/q+uzfrggw/o0KGDyaDCokWLkMvlzJgx\ng61bt+p14i5evEhqaqool6WlJcuXLzc5enr27Fl27Nih10E1VR86+63YxiQmJlJSUsKkSZNMnqvT\ny4opzk/rFzIyMkhNTWXYsGEmr1tdWZXtA+DYsWMsXLiQjIwM+vTpw8yZMxk7diznz5/n9ddf5403\n3hADie3atePAgQNcuHCBRo0a6bVNunIyMzNZsGABEyZMEIMPGzduZP78+axYsYLTp0/rTYkyhu65\nPHr0iPXr16PVasWgUFWpz8XFxSQkJLB582auXLnCyJEjGTJkCEVFRQB88sknbN26lW3btnH06FFa\ntWolnltWVkbfvn1RKBScPHmScePG8dprr3Hjxg2jbVJVLxJQ/gLcpUsXNBoNZ8+e5cSJExQWFoqB\nh+DgYHbt2sWlS5eIiIhgyJAhtRpt3L9/Py4uLjRq1Mjo/ry8PIqKikhISODcuXM0b96cLl26iD72\nvffeY+/evezZs4f4+HjS0tJMBphOnTrFmDFjyMzM5LvvvmPJkiXs2bNH3F/RX40ePZqff/65Wt9Q\n2d9UzOCqCffu3aNnz560aNGCM2fOEB4eTmRkpNEAE0BAQAAHDx4kNjaW06dPizLVxP4q+pnqfEp6\nejp5eXliv6tbt26cOnWKIUOGMGPGDE6dOsXjx49NTlurrV/JyckBwMPDg7i4OHG6SJ06dSguLhZ1\n//r162RkZLB48WLi4+MNrlvZr2g0Gvbs2UNZWRmCIPDcc8+RlZUFlAeejxw5wuHDh/Hw8KBZs2Z6\nQb+K9O7dmyNHjpCQkEC9evWYNm0a8HS2aoyqfGZFkpOTadiwISkpKRw+fJibN2/y6quvAtX3gytS\nnfxg2DdesmSJqPdHjx7lP//5j0G5586dw9zcnMTERE6fPk1YWFiV912Z2uga6Pe9oLx97dy5M+fO\nnSMmJoZdu3axevXqasu+d+8ev/zyC9HR0aSlpTFr1qxqbdSUblSWrWL/urK8w4cPp7CwkPj4eBYu\nXMjEiRNFv3PixAm8vb1JTk4mPj7+L19jReLvhRTIqAJ7e3s0Gg2NGzdm0KBBHD16FIC1a9fSsGFD\nJkyYgIeHBx9++CHnzp0THVFl5syZw4EDBwgLC6Nnz55iqlpRUZHePE25XK7XeFbGzc0Nd3d3MSU7\nLCwMT09PhgwZIo7QJCYmkpSUxKpVq/Dx8eHFF19kypQpYgOmUqm4c+cO9evXJygoSG/urIWFhRjh\nr5x2t3HjRvz8/Jg5cybu7u4EBwcjl8tZs2YN4eHhDB06FHd3d3GUQRfxNzMzQ6lUYmdnR48ePfD3\n90etVuPp6UlERAT+/v56o0uWlpY4OjrSuHFjunfvTv369WnUqBEhISF0795dPHbTpk34+Pgwbdo0\n3N3dGTZsGF26dGHNmjV69Wlvby8+P2NOX4etrS0qleqZz5s1NzcX69RYh7km9ZOUlASUTzsYMWIE\nXbp0oWHDhrz99tvs3LnT5LUDAgJwdXXFxcWFtWvXEhgYaFRnbW1tKSsro7i4GI1GQ0hIyBPfb8V6\ntLCwwMbGhlu3buHk5FRlB0aXIaVQKMRtHh4eODs74+XlVa1eA9StWxdnZ2d8fHyIiorCzMyMJk2a\n4OvrS1RUlNEso6p47rnn+Pbbb1m1apWoS9u2baNu3bpA+doN7u7uzJo1iwMHDtC+fXsiIiL47bff\nxDIq2n5ERIRe53zz5s307dtXnPpw/vx5mjVrxooVK9izZw+lpaUMHDgQKE9vnT59OsuWLavRVAlr\na2tUKhVKpRIoH9GYOnUqHTt2xNfXl5UrV5KYmEhiYqLJMtasWcPevXv11m2obZtVmVGjRhEfH09k\nZCR9+vTRS6fftGkTL730kihzdbz99tssWLAAS0vLWs0H1rUxvr6+jB49usoMI51eyuVycdvT+gXd\nqKGbm5ve9kuXLomjsjUpq6J96HB2dsbJyYlx48YB4Ofnh0ajYdy4cdy/f5/09HTxXH9/fzQaDZMm\nTdLL6NOV4+DgwMCBA/H19RWDqRs2bGD48OF0794dDw8PevbsabLuBEHgs88+w9HREYVCwYgRIxg2\nbBje3t7i/upe+NVqtRioNzMzE7P91q9fz9SpU2nbti3e3t56LxJHjx4lJSWFL774Ai8vL0aNGoWH\nhwf79+9HJpPVuE3SsWfPHn7//Xc+//xzvLy88PLywsLCQtzv5OREcHAwDRo0YNq0ady6dUsMMlRH\nYmIiEydOJDo6WgzyKpVK0Wfo6r1u3bpoNBo8PT1ZuHAhrq6uREdHIwgC0dHRfPTRRzRv3pxGjRqx\ndOlSduzYYfLrBw4ODtjb29OlSxfatWunp/8V/VW9evVq5Rt0/qYm63FU5KeffkIQBObPn4+Hhwez\nZ8/m/v37JrMyOnbsiL+/P82aNWPevHls2LABQRBqZDMV/UxNfIqFhQVeXl5oNBrs7e1ZuXIlPXr0\nICoqCi8vL2bPnk1MTEytFi405Vd02WkzZ85EoVCgVquxtbUlMjISMzMz0e+8+OKLvP766+Tk5BAZ\nGcngwYP1yq/sV1atWkVcXBwKhQKlUsnnn38u6u/58+eRy+VMmzaN2NhYhg4dSmRkpF4Wno6AgAC8\nvb3x8vJi7NixBm3Gk9iqMYz5zKNHj4p6qcsyhPKgkLOzM8HBwXz33XecPHmSY8eO1ejZVsaU/GDY\nN1apVNy/fx9zc3P8/PzENq0yuqw2Ly8vg+zK6qitrhnrw7q6uuLq6iq+e+h8VU3K9vHxwcXFBTc3\nt2pttDrdMCZbxW1ZWVns379fnF7Zu3dvOnXqxI4dO8TjVSoVrq6u+Pv7i7YgIQFSIKPG2NnZiS8o\nhYWFnD9/HkdHRxwdHQkKCsLMzMxkirK9vT2tW7dm06ZN5OXliQue2dvb662cfe/ePb3UdFPY2try\n+++/6/3XlZOdnY1SqcTW1lbc7+3tLTrz+fPn07JlS0JCQoiKitIb8a3qRSA3N9fo+hG5ubkGjbi3\nt7fJNFmlUqknu1KpNLl6uEKh0NunUCjEOfU5OTkG8vj4+JgMJlV8ftWhq4e/Yg5fVfVz/fp1VqxY\nIerdqFGjavy1gKp0NjQ0lOjoaCZPnkxISIg4R/Zp68HCwoJ9+/bx66+/4unpaXThyppSnV5XpqJN\nGPtfU7p27Upubi7p6elcv34dR0dHAgICgPLggkqlon379vj7+/Phhx/y+PFj8eUDTNt+cXExMTEx\nYvovlI8kN2zYkKZNm9K4cWPmzp3LsWPHuHfvHuvXr6dRo0Z6i8TW5rlUtlNdp8zUgltr1qzhgw8+\nYN++fbi6uurdz5O0WToUCgXNmjVj9erVWFlZ8cMPP4j7tm3bplcfVXHw4EGKi4v10mifRE/t7OwM\nFsqr7fm19Qu6VPDKtnvixAkxw6q2PqYyumCQ7lnp/uvaz82bN9O6dWvc3NyIjIw0+DpLRezt7cU6\nys/Px8PDo0YymJmZMWLECBISEkhMTGTTpk3s2rWLBQsWiPtr+szq1KmDUqkU5bh27Rru7u5Gj71+\n/TqPHz/Gx8dHrL+MjAzy8/OpW7durdukzMxMPDw8THaeDxw4QKdOndBoNLRu3Rqo2bz6xMREIiIi\nWL58uTh6DuUjuUlJSSQlJdG8eXOj5/r7+1NQUMCtW7coLi7Ws23daKWptrEi1flFU76hOmrjO65f\nv05RURHOzs44Ojri4uLCgwcPaqTrHh4elJaWcuPGjVrbTG19CpTbZUxMjHiN8PBwBEGo9Vd7qvIr\nDg4OxMbGcv36dbKzsxk/fjzOzs5ikD8sLIzx48fzxRdfcPz4cbZs2cLJkycB436lZcuWpKSkkJmZ\nSUFBAU2bNtXzYRYWFnTp0gU/Pz/GjRuHn5+f0elRy5cvp3nz5ri5uTFlyhSTbUZtbNUYlX3mL7/8\nQosWLUSbMDUg4eDggEqlIj8//4merSn5wbBvPGHCBPr370+HDh3o2rVrjac61KbNe1a6pkM3iPkk\nZVdnozXVjaruFaBDhw6iTIcOHdJ7P5HW1JAwxXPVH/LvpHLDVbEB0mg0tGjRQpwzqaOq+XxQPqpi\nYWHBo0ePgPIoZmJiovilg7Nnz4oO5kllc3d357fffuP27dtihzkjI0PsfNrY2LBkyRJmz57N4MGD\nGTVqFLt37za68nVF1Gq16CwrotFoDL4Hn56ebvLTVpUb8qoa9qqOdXd3N0jjT09PN9nJro0D0XX6\nCwsLTUbaa4JMJjOZHmuK6u65R48ejB07ttayVKezUVFRREVFER0dTb9+/UhLSxM7AdevX9d7mTWG\nbjSx8v02a9ZMTAHu1q0b7u7u9OvXr9byV6fXlTFWj0+KmZkZzs7OrFmzhvT0dFF+jUZDZmYmjx8/\nxtzcnIcPH4ppwpXR2b5uxGPXrl04Ojrq2YlGo9GbplFSUiKOxP3www8cP34cR0dHoPwF9fTp09y4\nccMg3VsmkxmsRVLZTm/dusXt27eN1t+BAweYPXs2v/zyi8HikzVts2pS35aWlmJ9HD9+nJs3bxIR\nEVHteVA+zerSpUtifTx48ABzc3OKiopq9eWPquQ0Zr/Pwi/4+/vToEEDvv/+ezp06GD02k/qYyrL\nqZOt4v/s7GwGDx7Ml19+yYsvvkh6enqV9V65DTK17ocx5HI5Go0GKM8OiY+PF0fxlEolycnJNS6r\ncl1fvnzZ6OiuRqNBJpNx4sQJvTVYdC+CldskDw8PMSXdGLqXI0EQDJ5/SUkJr7zyClOnTmXlypWU\nlpbW6ItU165do3fv3ixbtkwMxlWUvzqys7PF+fdWVlZkZGSI6z7osm40Gk21wduKdWrKXxnzDdX5\ng9r4Do1GQ4MGDTh48KDe9ooj76ZIS0ujfv36qFSqWttMbX2KTtaIiAjmz5+vt93Z2blaWStjyq/o\nsLW1JT8/ny+//NJkgNfDwwOlUim+UBrzKzqcnJw4cOAAhw4dEgPI7u7u3L59m1u3bon1XVpaajAF\nND4+nnfffZeNGzfSuHFj4uLixICkqXuria1WRcX+skwmq9Yu7t69y507d9BoNJibm9f62ZqS31jf\nuG7dusyePZt33nmHt956i379+hl83csYSqVSbx2Pqqiprpnqe1Wm8jMxVXZeXp5RWUzZaG11w5i8\nuuzEmJgYPXvVZXD8Hb6+IvH3RcrIMEFVL72RkZGcPXuWmJgY8bj79+8bvMCUlpby/fffk5aWRkZG\nBm+//Tb169fnhRdeAMrnhK1atYpTp05x8uRJVq9ezYgRI55K7mbNmhESEsLo0aNJT0/nl19+YcmS\nJeKc2WPHjpGZmUlxcTHOzs5iA+3r68uJEydISUnh4sWLBiuoDxgwgNTUVObNm8eVK1dITU0lPz+f\noUOHsn//ftasWUNWVhbz588nJyeH/v37i/VYsS6N/X8S+vfvz5UrV1iwYAFZWTyoQLcAACAASURB\nVFlER0eLqZFPi27RtiVLlpCVlUVBQQEPHz4U99va2pKRkSFmTsjlcpKSkgzuxdfXl6KiIvbt20dW\nVpbYwaxIbepn0KBBfP/99yQnJ1OnTh1KS0tr3MDrplYY09nc3FwSEhIoLCzE1dWVsrIy7t27h7+/\nP05OTixcuJCrV69y9epVg3q6dOkSpaWlODg4YGtry5YtW8jLy+Ps2bOUlZURGxtLQUEBlpaWWFtb\n1zgrpjLNmzevUq//KO7cucOZM2eYNm0aEyZMYMaMGeL6Md26dQNgxowZZGRkMGvWLJRKJW3btq3W\n9jdv3mzQcR0wYACxsbFs3LhRtLUePXpQv359vvvuO86fP09CQgKnT5+mWbNmzJ07V1yIqyI+Pj7s\n2bOH3Nxc8aXxjTfeYPHixRw+fJi0tDRGjx5N06ZNjX5qb8qUKbzzzjuoVCqKioooKioSR5hr2mYZ\ns+v169dz8eJFsrKyWLRoEdnZ2WLndtOmTeK91oSPPvqI5ORksT66d+/OqFGjjC4k96RtjI+PD9u3\nb+fq1atimvGz8AtmZmZ88sknbNiwgbfffpu0tDSuX7+uN3Jc07KeBN2zVCqV4mKcVVHxnt944w2+\n/fZbNmzYQH5+frUjnA8ePKCoqIicnBy2bt3Kjz/+SJs2bYDydTqOHj1KTEwMN27cqNUUpZEjR/LJ\nJ5+we/durl27ptc2hYaG4uHhwaJFi/jf//7Hc889x82bN1EoFEbbJJ0PNNWOR0REUFJSwrRp08jO\nzubAgQPiYpqPHz+mrKwMhUKBXC43ePn29vbWW1hRx4IFC2jVqhUdOnQQbazyF3wqcvfuXfbv309O\nTg7Lli3j4sWLYvsxfPhw3nvvPRISEsT1onr16lWjTKmK/saYvzLlG6qjNr6jW7duFBcXEx0dzcOH\nDzE3N+f27dtYWVkZLfvatWviujW6dWDq1q1bpY8zxpP4lMGDBxMTE8OxY8cwMzOjrKwMrVZb6y/c\nVOVXHj16REpKCuvWraNz584Gi35v2bKFlJQUUlNTmTFjBg8fPhTXXzDmV4qLi0lKSmLBggUMGDCA\ngQMHimvbtGzZEm9vb7EdWrp0KdevXzdY+6a0tBRzc3OUSiXW1tZ6U0CroypbrXyNqnymMZKTkzlz\n5gzp6em89dZb+Pj40LJly2r7wbXBWN84MTGR1NRUfvvtN1xdXWvcr2nbti1Xrlxh7dq1FBYWGkxB\nq9gG1VTXjPW9wHiWou5/bfW4e/fuBjb622+/YWVlVWvdMCavq6srnTt3ZsGCBdy5c4e6detSWFgo\nDipWvo8ff/yRbt26SVkaEuVotVrh3/5r0KCBEB0drbdt0KBBwsCBA8X/q1atEjw9PcX/hw8fFpo2\nbSrIZDLBzs5OGD9+vEG52dnZQmhoqGBlZSVYWloKnTp1EhISEvSOmTlzpqBSqQSVSiXMmDHDqHwH\nDhwQzM3NhZKSEkGr1Qr79u0TzM3Nxf379u0T6tatK/7PyckRevbsKdSvX19wdnYWFixYIO4bNmyY\nYGNjI9SvX19o1qyZEB8fL2i1WuHmzZtCeHi4IJPJhAYNGgh79+41kOPw4cNCkyZNBAsLC8HBwUHY\ntGmToNVqhR07dgj+/v6ChYWF0Lx5c+Ho0aPiOe3bt9e7rzZt2gizZ8/W+//+++8LWq1WmDVrlvDC\nCy+I+6ZPny506NBB73/nzp3F/0eOHBGaNWsmWFhYCAEBAcLOnTtr/Px0v7S0NMHc3Fy4fPmy3vak\npCShdevWgpWVlSCXy4WGDRsKsbGxglarFc6cOSM0bNhQmDRpkqDVaoVFixYJ9vb2wunTpw3KnzRp\nkmBjYyMolUrxPs3MzISff/651vWj1WqFL7/8UvDw8BAsLCwEV1dXYe3atdXqS3U6+9NPPwmurq5C\n3bp1BVdXV2HevHl654SEhAj16tUTbG1thZCQEOHcuXOCVqsVfv75Z8HT01P47LPPBK1WK2zYsEFw\ncnISLC0tha5duwpZWVlCQECAULduXUGlUgn9+/cX7t69ayBvxfqoapsxvS4tLRW0Wq3wzTffCG5u\nbiaf96pVqwQfH58q24HK+qfVaoVx48YJDg4OQnh4uLBjxw6Dc44ePSq0aNFCsLS0FEJCQkT7rsr2\nCwoKBAsLCyEpKcmgvNWrVwu+vr5C/fr1hR49egg3btwwKmv79u0N2izd78yZM0KjRo0ECwsLwd/f\nX7h27ZpQWloqzJ8/X3B2dhbq168v9OrVS8jNzTU4t6CgQDAzMzP4VbxWTdqsyvZ3//59oUOHDoJc\nLhdkMpkQGhoqHDx4UNBqtcKDBw8ER0dHPfut/PPw8BBWr15tcv+gQYP07KZyXenkrPyMK7elFX+x\nsbGCh4eHUK9ePaFJkyZCSUnJM/ELFa/dtm1bwcrKSpDJZIKHh4cwZMiQGpVVnc08ePBAMDMzEw4d\nOqT3/9dffxW0Wq3w4YcfCq6ursJzzz0n2rWpsiu3SWvXrhWCg4OF+vXrCyqVSggNDRUKCgoM7i8q\nKkqwsLAQzMzMBEtLS6FRo0bC3Llz9Y6ZMWOG0KBBA0EmkwnOzs5Cv379BK3WeLtc0U+XlpYKixcv\nFvz8/ASZTCbY29sL7dq1E4+9cuWK0L17d8HKykqwsbERQkNDBa1WK2RmZppskyq245Xbk0OHDglN\nmjQRrKyshOeff16wtLQUfvjhB7Ht8fLyEurWrSvY2NgIgYGBQn5+vqDVagW5XC762Iq/oKAgAxsb\nPHiwUT355ptvBGtra6FFixZC/fr1BV9fXyEmJkbPtiZPniyoVCrBxsZGGDp0qHDr1i2j9VjZjgYM\nGKB33cr+aseOHSZ9g+5Xlb+pqe84e/as0K5dO6F+/fqCQqEQevfubbQuoqKiBGdnZ0Emkwmenp7C\nzJkzhQcPHjyxzdTGp+h+27ZtE/s7Tk5OwsKFC03auLE2R6ut2q+cOHFCsLa2Fpo0aSLMnTtXuHPn\njrjv1q1bQtu2bQVra2uhXr16Qps2bYS4uDhBqzXtVxYtWiQolUqhQ4cOwpo1awzkS05OFjp27CjY\n2NgIPj4+Yj+n4q+0tFSYMGGC4ODgIDz33HOCSqUSunbt+kxsVferSX+54u/1118XvL29BR8fH6Fe\nvXpCu3bthJSUlFo/2+rkr9w3jomJEWbNmiUolUpBJpMJgYGBwvbt22v03LVarfDFF18Inp6egoWF\nheDo6Kh3TOW+ZE11rXLfS6s17FtW7k+bKttUn9iUjdZWN0zJW1hYKLz22muCQqEQLC0thaCgIKGw\nsFDQag37E0uXLhXc3d2F4uLiKm1P+v07fmZarVYKaUlISEj8SaxatYqvv/6ahISEv1qUvwX79+9n\n2LBh5OXl1XpkU0LizyAxMREnJyfMzMyIi4vjzTffJDk5ucppE5cvX6Z9+/bk5uY+VSbNunXrmDt3\nrt7nwCUkKvNv8yvDhw/n8ePHfPvtt3+1KBISEn8h0hoZEhISEn8iW7ZsqfGilv8GtmzZIn4qT0Li\n70ZJSQljxowhLS0NMzMzAgIC2LhxY7VrP1haWvLVV189k+lA0hxxier4t/kVySYkJCQApIwMCQkJ\nCQkJCQkJCQkJCQmJfwzSYp8SEhISEhISEhISEhISEhL/GKRAxjPm/v37Bl/8kHgyKn9CUkJCombc\nvn1bXL38n8CNGzeq/VSkKQoLC/W+KvR3IT4+no0bN9b4+L/qmeXk5LBw4cI//bp/R27fvs2DBw/+\najEkJCQkJCQkaoAUyHgKHj58yOPHj/W29erVi+XLl/9FEj17tm/fbvSb5H80jx8/JjIyku++++5P\nv/ZfzbJly+jbt+9fLcbfguq+jS5hnOXLlzN58uS/Wgyg/HOuU6ZMEf//73//MzjGzc2N06dP17rs\nnJwcNBoNeXl5TyXjH8GcOXO4fv06n3/+eY3s+Vk9s9q2H2vWrOH48eNPfd1/Oo8ePcLFxYUzZ87U\n6NiysrI/QSoJCQkJCQkJU0iBDOCDDz7A0tKSnJycGp+Tl5eHQqEgOztbb7uZmdmfsgjRjh07UKlU\nyGQy7OzseP7555k/f/4z71z16dOHY8eOPbPytFot8+fPp1GjRlhbW6NWq+nZs6dB3b///vvUr1+f\n11577Zld+5/ChAkT2Lp1618txlOzfPly5HI5MpkMpVJJaGgoixYtqnFwolu3bsyfP/+ZyvTrr78i\nk8kMApB/BzIyMhg0aBBqtRobGxuCgoJYunTpXy3WU/Ppp5/y6aefArB582aCgoKeWdl/1+/I5+bm\ncvLkSSIjI3nrrbf+VHuubfuxdevWf9UigaaojS4pFAqOHDnyB0ojISEhISEhUR3/+q+W/O9//2PF\nihWoVCo+//xzscNdHY8ePeLRo0dGOz9/Ruf63r172NjYkJaWhlarJTk5mQkTJnD37l0+/vjjZ3qt\nZ/U1gcePH9OnTx9SUlL4+OOPadmyJXfv3uXIkSM4OTnpHTt48GA0Gs2/9ksG5ub//Bjj7du3ady4\nMbt27aKoqIjExETmzp3Ljh07OHjwIJaWllWer9Vq/yRJ/3ouXbpE+/btadu2Ldu3b0ej0ZCWlvb/\nXZq7Vqs12T4+Tbv5dwtobNmyhdatW4tftviz7bmm1zt16hTXr1+nZ8+ef7BE/3/x8OHDv53OSUhI\nSEhI/Nv4578tPSWbNm3C0tKSjz/+mLVr1+rN0x4+fDhDhgwR/69ZswY/Pz+98wMDA5HJZPTu3Rso\n71CvW7cOe3t77OzsGDt2rN4I9N69ewkKCsLGxoZWrVpx6tQpcd8HH3xAQEAACoWCBg0acPfuXVq1\nasW0adOMyl6nTh3s7e1Rq9W8/PLLDB8+nIMHDwJw69YtAgMDUSgUeHl58cknn4jnLVmyhICAAFQq\nFWq1mkmTJjFixAhcXV1RKpWMHj1anHO+bt06vLy8gPIX0/DwcNzc3LCxsWHkyJEAXL58mZdeegkb\nGxt8fX1Zt26dUXl//PFHDh06xM6dO+nbty8ajYbg4GDGjh1LvXr1gPKpBNOmTaNVq1Y4OjoycuRI\n7t27B8DNmzdp06YNDRo0QC6XExoayuLFi2nZsiVyuZzAwECOHj0qXq+oqIjBgwfj7OyMv78/33zz\njbhPJpPp/fz9/Y3KbIqdO3fStGlT8TkfPHiQixcv4uvri1wuJyAggPXr14vHv/POO/j6+mJra4u7\nuzuzZ88mMjISR0dHHBwcmDVrlnjs+++/T8eOHQFIS0ujY8eOqNVq5HI5H3zwAQAnT54kLCwMGxsb\ngoOD2bdvn0lZa6pzrq6u3Lt3j7KyMmbPni3Ws6+vL9OnT69V/eioW7cuKpUKPz8/oqKiOHToEKmp\nqXz++ecAJCcnm6wzQRBYuHAhMpmMevXqcfv27Sr1Oioqiv79+9dKvgULFqBWq1GpVHTu3JkrV64A\nz1bX9u3bR1BQENbW1vj5+RnN/HrnnXdo2LAh27dvJywsDLVaTYcOHejatStQrq+HDx8Wj+/QoYOo\nC5V18cCBAwblm5IvLi4Oa2triouLxWPbt29f44CujqysLLp164ZCocDR0ZFmzZrx66+/AobtaH5+\nvmh3FTNOxowZg0KhQKPR8OWXX4rba9IODx8+HAcHBzQaDcuWLTMp56lTp2jdujU2NjZ4eHiItlCV\njdTGdnVs3rxZzHKoaM8AzZs3R6lUotFoePvtt03KWpVuvvDCC7i6umJtbU1QUBDbtm0Tz6t8vcr3\nfPLkST05u3fvLgYV4+LiCAsLw87Ojo4dO3L58mXx2I4dO+Lu7o6NjQ0ajYaVK1ca3HNAQIC4/+WX\nXwbK7XjKlCmo1WoUCgWvv/66eE5V7ZggCHz66ad4eHggl8vFe3r8+LHR7WVlZcybNw9vb29cXV2Z\nNGmS6MeOHz9Oq1atcHJywtbWlrVr1wKwceNGfHx8UKlUhIeH693PunXr0Gg0KBQKWrVqZTDlJDw8\nHJlMRtOmTQE4ePAgnp6eyOVyQkJCiI2NNflsJSQkJCQkJJ6ef30gY9WqVQwZMoS+fftSp04dNmzY\nIO6ryTSR48ePU1BQIL6AmZmZ0blzZ86dO0dMTAy7du1i9erVACQlJdGvXz+mTJlCcnIyERERhIeH\nU1hYCJSnves6j7GxsSgUCnx9fWnQoEG195GZmcnOnTsJDAwEQKVSER0dTUpKCp9++ilz5szhwoUL\nAFy8eJHAwEDS0tLYtm0b69ato6ysjLi4OHbv3s22bduMpibfu3ePX375hejoaNLS0pg1axYPHjyg\na9euBAYGcv78eRYtWsTEiROJiYkxOH/Pnj20bNmyytTy9957j71797Jnzx7i4+NJS0vjjTfeAKC4\nuJiEhAQ2b97M5cuXady4MfPmzWPhwoVcuHCBNm3aiMdC+ctNYWEh8fHxLFy4kIkTJ3Lu3DkACgoK\nKCgo4OzZs9jZ2fHWW29VW8cVOXfuHObm5iQmJnL69GlCQ0PRaDRs2bKFy5cvM2HCBMaOHcutW7cA\nSExMpGvXrly5coXly5ezaNEiPD09OXnyJNHR0XzyySd6U3h0eldQUMCxY8c4ePAgKSkpjBgxgmvX\nrhEeHk6vXr1ITk5m0qRJvPrqq+K9VaQ2OnfgwAHkcjmzZ8/mhx9+YOvWrVy+fJnOnTuTkJBgUPbp\n06fx8/NDo9Gwf//+GtWbq6srXbt2FY93d3c3WWdmZmZMmjSJgoICrl27hkqlqlKvPTw88PDwqOET\nLKd3794cOXKEhIQE6tWrJwYNn4WunT9/HoCRI0fSu3dvUlJSWLdunThKr6OkpITDhw8zbNiwGstd\nsW2qrIthYWEGx5uSr3379qhUKjH48fvvv5OQkCAGUGpCSUkJXbp0QaPRcPbsWU6cOEFhYSG5ubkG\nsgK4uLiI9jdmzBhx++TJk7l8+TILFixg8uTJYv3VpB3u2bMnp0+fZvny5Xz00Uds3rzZ4Jj8/Hy6\ndOlCt27dSE5OZvfu3QQEBFRrIzWx3f/+97/idS5evEhqaiqvvPKKUVm//PJLkpOTWbt2LWvXrmX3\n7t1Gj6tKN0+fPs33339PWloa77zzDiNGjDD64mzsnhs2bAiUZxX+8MMPYsAlOzub3r17079/f86c\nOYOvry+DBg0Syzp27BgLFy4kMzOTBQsWMGHCBPEZ/fzzz4wZM4Z58+aRmprK+++/LwayYmNj+eab\nb9i2bRsXLlwQgzfVtWNffPEFX3zxBV9//TWXL1/ms88+A8qnrBnbvmzZMtauXcuGDRvYvXs3MTEx\nYrAlPT2dvLw8jh8/zvnz5+nWrRvHjh1j5MiRzJ07l7NnzzJ06FC9umvXrh0HDhzgwoULNGrUSE9X\noXxKTkFBAXFxcQAEBweza9cuLl26REREBEOGDJGyNiQkJCQkJP5A/tWBjMTERJKSkhgwYAAymYw+\nffqIQQcoHxGqriNia2uLSqXC2tpa3Obq6oqrqythYWH07NlTHLlds2YN4eHhDB06FHd3d6ZPn46X\nlxfff/89UN5hd3JywsXFRQxIrFu3jnHjxhm99rVr13B0dMTGxoaGDRuiVqvF0UgzMzNatWqFWq2m\nd+/ePP/881y6dEk8Vy6XY29vT5s2bXjhhRews7PD19eXF154gRdeeIGkpCST9+zj44OLiwtubm7s\n3buX0tJSlixZgqenJ71792bo0KF8/fXXBufduHEDNzc3g+3r1q0jNTUVQRCIjo7mo48+onnz5jRq\n1IilS5eyY8cO8eUWQK1Wo1arGTlyJFqtlqZNm+Lu7s7IkSPJzMzk999/Jysri/3797NkyRL8/Pzo\n3bs3nTp1YufOnUB5oMfW1pbJkyfzn//8hzfffFNPptzcXJRKpfjyfPXqVQO5dSOPXl5e2NjYYGNj\nQ9OmTVGr1YwZMwa5XE5GRob4PJRKJXZ2dvTo0QN/f3/UajWenp5ERETg7++v98WCynoXEBCAq6sr\nLi4ubNq0CR8fH6ZNm4a7uzvDhg2jS5curFmzxkDG2uqcIAisWrWKjz76iNatW6NWq3FwcDCqBxs2\nbCAnJ4fCwkKjz9sUrq6u4pd9qqozgHr16onPQCdvZb2+ePEiAAsXLmTBggU1lgPK69Xb2xsvLy/G\njh2rZyPwdLq2Y8cOAJRKJbdv38bOzo6wsDCee05/Rt+tW7coKyszCHDUhsq6WJGq5DMzM6NPnz5s\n374dKP/Shqurq/iyq6NHjx7ic1i0aJHevj179vD777/z+eef4+XlhZeXFxYWFuL+yrpsbm4uliWT\nycTtfn5+uLi4MHDgQHx9fcXgQE3a4Z49e+Lh4SG+QFbM7NGxceNG/Pz8mDlzJu7u7gQHByOXy2tk\nI7Wx3U2bNvHSSy+hVCqNyhoaGoqrqysdO3akc+fOpKSkGD2uOt3UaDSo1WoGDRrE0KFDWbJkSY3v\nGeCXX37h4cOHdOnSBYC1a9fSsGFDJkyYgIeHBx9++CHnzp3TyyBydnbGwcHB4Bl99dVXDBkyhMjI\nSNRqtV47b2trS1lZGcXFxWg0GkJCQsR6qqod+/rrr5k6dSovvfQSarWa559/vsrtK1as4N1336VV\nq1Y0bdqUkSNHiu09gIWFBV5eXmg0Guzt7Vm/fj0RERG89tpruLu7069fP7268/DwwN/fH41Gw6RJ\nkwzqXy6Xo1KpxPp0cnIiODiYBg0aMG3aNG7duiUGwyQkJCQkJCSePf/qQMaaNWto3ry5OHUiMjKS\nixcviqPPZmZmTz2iolKpuHPnDlC+wr63t7fefm9vb3HksrY4OTmRkJDAyZMncXJy4vnnnxdf+K5c\nuUK/fv3w9vbGz8+P9PR0k2sO2NraitM3dP8rpppXhe6rARXXsvD29jZYBBXAzs6OgoICg+1z5szh\n1KlTFBUVUVxcrFdHPj4+4nWMyQ3lo8gV/9+/f1/sQHbo0AFHR0ccHR05dOgQ165dE89fvHgx165d\n46uvvjIo29XVlXPnzpGUlERSUhIuLi7V1sWZM2fo3r07np6eBAYG8vvvv5usc6VSKcqt+1/Tz0/m\n5OSIOqvDx8fHaB3l5ubWSucKCwu5f/9+jabaDBw4EA8PD5ycnBg+fHiNZAe4evUqjo6OQO3qDIzr\n9ZN8elM3wr98+XKaN2+Om5sbU6ZMMblY7tPo2vbt2yksLMTDw4Pp06cb3J9SqcTc3FxPN43xpG1R\ndfL179+fXbt2ce/ePXbv3k23bt0Myvjmm29EWxg1apTevszMTDw8PKhbt+4TyWcMe3t7bt++DdS+\nHfbw8DCq37m5uQZ2o9teGxsxZrsV28tt27aZXDzz5s2bDBs2DH9/f7y8vIiPjzep7zXVTSgPAuXn\n5xu9N2P3DOVTQfr06SMG1goLCzl//ryoI0FBQZiZmRktF/SfUWZmpsk2IzQ0lOjoaCZPnkxISIiY\nrVddO5aXl2fwXKrafv36daZPny7K/+GHH1ZpU9euXcPd3d3k/s2bN9O6dWvc3NyIjIysdiHtAwcO\n0KlTJzQaDa1btwb+Xev8SEhISEhI/Nn8axf7LCkpYcuWLZSWlqJWq4H/e1H47rvvxHnMycnJRs+v\nuKZDVVTshLu7u+uNNkN5yuuTft60Tp06aDQaoLzT+9prr/Hqq6/i4+PD6NGjUSgU7Ny5EwsLC715\nyVXJaOx/Vbi7u5Obm0tZWZnYIc7IyDCa4t+pUyfGjRtHZmamXgdW91Lp4OCAlZUVGRkZYkZKeno6\nUD76WPlFX3eeTtaK/3UjgjExMXoLiVpZWQHliysuXLiQX375RXyWFalYtzWlf//+vPTSSyxevBhB\nEPjPf/5j8tinrfPKn0tMT083WucajaZWOieXy6lTpw45OTniMzBFy5YtSU1NrZHMOq5evcrevXt5\n9913garrrF69epSUlOidXxu9NkZRURHW1taYmZkRHx/Pu+++y8aNG2ncuDFxcXEmMzqeRtd8fHz4\n4YcfSEtLo3v37qhUKqZOnSoeZ2lpSWhoKN9//z2DBw82en2lUsn169drda86qpMvNDSUZs2a8c03\n37Bnzx69UWwdlRfjrYidnR35+fkIglDtFBCZTFajr9ZUtAeVSiVOH6oJaWlpRrNb1Gq13voQOmpr\nI1XZ7vHjx7l58yYRERFGz50+fTrp6els3rwZGxsbk2tk1EY3oTwoYOyl3NQ9l5SUsGvXLjETB8rr\noUWLFuL6ETpMPfvKz8hY8FpHVFQUUVFRREdH069fP1JTU6ttx9RqNenp6QbtqKntGo2GsWPH6tV9\n5eynysdXXAOkItnZ2QwePJgvv/ySF198kfT0dL1yK+txSUkJr7zyClOnTmXlypWUlpbSrFkzk9eW\nkJCQkJCQeHr+tRkZO3fupKSkhBMnTpCQkCD+Jk6cyLZt29BqtbRr146jR48SExPDjRs3KCoqEs93\ncHDA1taWLVu2kJeXJ6YWG0uD1v0fOnQo+/fvZ82aNWRlZTF//nxycnLEBQqNnTtkyBC9he9MERER\nwYsvvsiMGTOA8pEgS0tL5HI5SqVSL9W7MsbkrelLddeuXZHJZEyZMoXMzEy2b9/Od999p7d+gI6B\nAwcSEhJCt27d2L9/PwUFBWRmZuqNWg0fPpz33nuPhIQEcd50r169sLe3r5E8OlxdXencuTMLFizg\nzp071K1bl8LCQjFjZcKECQwcOBA3NzeKior0nu2TotVqsbGxQS6XY2dnp5elUrlOjf2vKf379+fK\nlSssWLCArKwsoqOjiY2NFed4T58+nZkzZwK11zlLS0v69u3LrFmzSEpK4tKlS5w5c+aJPyn88OFD\nbt26RWpqKps2baJTp074+/uLa5JUVWc+Pj4cOnSIrKwszp49y/3796vU64r3raO0tJTFixdz+fJl\nLl26xBdffCEuDlhaWoq5uTlKpRJra2sUCsUT3WN1uvbzzz+Tn59PnTp1xGkmlVm4cCEnTpxg0KBB\nJCcnc/36dU6fPi2OXrdt25aVK1eSmppKYWFhtRlTtra25OXlUVRUVK180fkLnAAABwRJREFUAO++\n+y7z5s0TF+qsDREREZSUlDBt2jSys7M5cOCAXoYX/J9++/r6UlRUxL59+8jKyhIDlZWpqJNt27Y1\n2Q7ryM7OJj8/n/Xr1/Ptt98yadIkoDzAq7OLAQMGkJqayrx587hy5Qqpqank5+fX2kaqst1NmzbR\no0cP6tevryefzn4q6q+tra1eEFWhUIjPrCa6uWvXLrKzs4mJieG7775j4MCBBseYuuc9e/ZgY2ND\n27ZtxWOjoqI4e/YsMTEx4j3dv39fb/pPRSre96BBg1izZo34XHWLTkN5kCUhIYHCwkJcXV0pKyvj\n999/r7YdGzp0KIsXL2bv3r1cvXqVEydOVLl98ODBrFy5kuzsbOrWrcv9+/erzBIaNmwYsbGxfP75\n5+Tm5uoFs3Q+SalUYmVlpWcrUN42bd++natXr3L69GkeP35MWVkZCoVCbMskJCQkJCQk/lj+tYGM\nTZs28eqrrxIYGCjOg3d1dWXChAkUFxezZ88eevTowZQpUxg/fjze3t4sX76cli1bAuUj9l988QXR\n0dEEBwczb948wHBhuor/mzRpwtatW/nss88IDg5mz5497N27F2dnZ6PnQvnoYl5entF7qHzsBx98\nwJ49e7hw4QKLFy8mPT2doKAgfHx8uHHjhjiyVpWMpv6buqalpSX79u0jJSWFkJAQpk2bxtKlS42m\np1tYWBAbG0v37t1588038fb2plWrVri7u+Pp6QnARx99RNeuXenevTvt27c3+AJE5etX9X/Dhg3Y\n2trSqVMnGjZsyKhRo7hz5w65ubnEx8ezevVq8bnXZEHVytepfO0VK1awb98+/Pz88Pf3p379+uIL\nSG3ruKo6V6vV7Nu3j127dhEcHMyyZcvYtm2bOPc8KyuLzMxM4Ml0btmyZQQGBtKlSxe6du1Kdna2\nOA+8Ntjb25OcnIxarSY0NJQlS5YwdOhQDh8+LL7oVVVnU6ZMwdLSkuDgYKKiosjOzq5Sryvet46i\noiLi4+N54YUXaN26NXZ2dmJgsHPnzowePZr+/fvj7u7O2LFj9bJQnoWulZaWMnv2bAICAggNDcXF\nxYXJkycb1FVYWBhxcXHcvXuXjh074uPjw+uvvy5+PWPp0qUoFAratGlDw4YNuX//vjjtytgz7Nu3\nL46OjowfP96kfL/99pt4/Msvv4yrq6vBgoc1wcHBgR9//JG4uDiaNGnCzJkzxZe6yvI1bdqUiRMn\n8tprrxEWFsaPP/5otMyK91NVO2xtbU2LFi0YMGAAgYGBLFu2jK+//lpc9+Hq1aukpaUB5ZlMe/fu\nZe/evYSEhPDiiy9y4sSJWtuIKfssKytj+/btBtNKKmarzZkzh0ePHtG8eXM8PT05ffq0mDHz6quv\nis+sOt0E2LFjB82bN2fUqFG88847YuClonzG7vn48eNs3rzZ6JoQMTExrF27lqCgIHFKiCkq1sGw\nYcMYN24cY8eOJSwsjIMHD4rPPzk5mVdffRUvLy/GjBnD3LlzxTWdqmrHpkyZwujRoxkzZgz+/v68\n8cYbPHjwwOT2qVOnMmDAAAYNGoSvry+9e/cW18+pLC+Ut407duxgy5YthISE0LZtW0JCQnBwcMDP\nz48PPviAt99+G09PT7p160bjxo3Fc5csWUJcXByBgYGMGzeOevXqsXLlSr766iu8vLwICgqiYcOG\nBgEtCQkJCQkJiWeHmVarlZbVlpCQENEFA6ysrLh69SojR47ktddeE0e5Jf7/QZfZsWvXLt577z2S\nk5Of6OUrMTERJycnzMzMiIuL48033yQ5OfmpFjD9p7F//36GDRtGXl4ederUobS0lOvXrzNgwACa\nNGnC8uXLn8l1srOz8ff3JyUlxeT6F1Vx584dNBoN8fHx4kKZT0tRURE3b95EpVJx9+5dPvroI+7e\nvSsueCshISEhISEh8az512ZkSEhIGGf9+vW0bdsWHx8foqKiePnll2v9eVqJfwarV6/G2dmZ5cuX\n8+OPPz5REKOkpIQxY8YQFBREUFAQy5cvZ+PGjf+qIAbAli1beOWVV8TpUYcPHyY4OBiZTCZ+OvVZ\n8aRTvQB++uknPDw8nlkQA+DEiRP06NEDb29vOnXqxKNHj1ixYsUzK19CQkJCQkJCojJSRoaEhISE\nhISEhISEhISEhMQ/BikjQ0JCQkJCQkJCQkJCQkJC4h+DFMiQkJCQkJCQkJCQkJCQkJD4xyAFMiQk\nJCQkJCQkJCQkJCQkJP4xSIEMCQkJCQkJCQkJCQkJCQmJfwxSIENCQkJCQkJCQkJCQkJCQuIfgxTI\nkJCQkJCQkJCQkJCQkJCQ+McgBTIkJCQkJCQkJCQkJCQkJCT+MUiBDAkJCQkJCQkJCQkJCQkJiX8M\nUiBDQkJCQkJCQkJCQkJCQkLiH8P/A+/n5RRb3OrtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f791c1b2c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_results[cols].head(limit).plot(kind='bar', figsize=(16, 6), title=title)\n",
    "ax.set_xticklabels(df_results.index[:limit], rotation=0)\n",
    "ax.set_ylabel(ylabel)\n",
    "ax.yaxis.set_major_formatter(\n",
    "      mpl.ticker.FuncFormatter(lambda val, p: '{}%'.format(int(val*100))))\n",
    "\n",
    "text = '''The 30 most common full time results from {} to {} in the Germand Bundesliga, 2-points rule before 1995/96 and 3-points rule thereafter.\n",
    "{}'''.format(season_display(xmin), season_display(xmax), chartinfo)\n",
    "ax.annotate(text, xy=(0, -1.16), xycoords='axes fraction', fontsize=infosize)\n",
    "plt.savefig('img/{}.png'.format(helpers.slug(title)), bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 3 results that stand out most prominently are all home losses, whereas we do not see big differences in draws. This matches the impression of the first chart, i. e. there is no clear decrease in the number of draws after the point rule change.\n",
    "\n",
    "Similar to the EPL the trend towards fewer home wins has started before the 3-point rule was introduced. While there could be a positive impact here, I assume this is rather due to football having become more professional and more of a business in that time frame and the teams have assimilated regarding their strengths.\n",
    "\n",
    "As far as I can tell from these observations the 3-point rule change has hardly affected results. But we only looked at the surface yet. By summarizing over single seasons in the first chart and over many seasons in the 2nd one we may have covered a different story the data could tell.\n",
    "\n",
    "I'll leave it at that for today, but intend to explore this dataset further in future notebooks. To be informed of new posts you can subscribe to the [notebooks RSS feed](https://ramiro.org/notebook/rss.xml)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "Author: <a href=\"https://ramiro.org/\">Ramiro Gómez</a>\n",
       "            • Last edited: August 05, 2015"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "signature"
   ]
  }
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