variability.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploration and Skill Acquisition in a Major Online Game\n",
    "\n",
    "* Tom Stafford; University of Sheffield, t.stafford@shef.ac.uk\n",
    "* Sam Devlin; Digital Creativity Labs, University of York\n",
    "* Anders Drachen; Digital Creativity Labs, University of York\n",
    "* Rafet Sifa; Fraunhofer IAIS, Germany"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tracking skill acquisition across 21,543 players of the first-person shooter game Destiny shows that variability in some – but not all – dimensions of practice enhances learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CogSci17: 39th Annual Meeting of the Cognitive Science Society, London, UK,  July 26th – July 29th, 2017\n",
    "### Poster 140, Poster Session 1, Monarch Suite, Thursday, July 27, 1:20pm – 2:50pm\n",
    "\n",
    "Paper: https://mindmodeling.org/cogsci2017/papers/0615/index.html  \n",
    "Full scripts & sample data: https://osf.io/c59n9/  \n",
    "Poster: http://tomstafford.staff.shef.ac.uk/docs/CogSci17_TomStafford.jpg  \n",
    "This notebook: https://github.com/tomstafford/destiny/blob/master/variability.ipynb  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code walks you through the main visualisations of the paper - if you want the full details you need to check out the scripts on the OSF and read the PDF. This is the abridged version, showing that raised variability in some, but not all aspects of the way a player can practice the game are associated with reaching greater levels of performance and/or improving performance faster.\n",
    "\n",
    "Ready? Let's go!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python version = 3.5.2 (default, Nov 17 2016, 17:05:23) \n",
      "[GCC 5.4.0 20160609]\n",
      "working directory = /home/tom/Dropbox/university/expts/destiny_public/destiny\n",
      "Machine = tom-xps\n"
     ]
    }
   ],
   "source": [
    "#libraries\n",
    "# - standard\n",
    "import os #directory and file functions\n",
    "import socket #machine id\n",
    "import pandas as pd #data munging \n",
    "import sys #for getting Python version\n",
    "import numpy as np #number functions\n",
    "import matplotlib #graphing settings\n",
    "import pylab as plt #graphing functions\n",
    "import scipy.stats as stats #statistical functions\n",
    "\n",
    "#Using arrow instead of datetime because @treycausey told me to\n",
    "#http://crsmithdev.com/arrow/\n",
    "import arrow\n",
    "\n",
    "# - bespoke\n",
    "from destiny_funcs import maketime, ranker, tagger, correlate, findspacing\n",
    "\n",
    "#suppress warnings, for tidyiness\n",
    "import warnings; warnings.simplefilter('ignore')\n",
    "\n",
    "print(\"Python version = \" + sys.version)\n",
    "print(\"working directory = \" + os.getcwd())\n",
    "print(\"Machine = \" + socket.gethostname())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parameters\n",
    "logtransform = False\n",
    "\n",
    "# Graph settings\n",
    "%matplotlib inline\n",
    "matplotlib.rcParams['figure.figsize']=[18,12]\n",
    "#all graphs to have same x y range\n",
    "xmin=0.5 \n",
    "xmax=1.1\n",
    "\n",
    "cRmin=60;cRmax=130\n",
    "if logtransform:\n",
    "    cRmin=np.log(60);cRmax=np.log(130)\n",
    "\n",
    "perfvar='combatRating' #'killsDeathsRatio'\n",
    "if logtransform:\n",
    "    perfvar='Ln_combatRating' #'killsDeathsRatio'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#----------------- full data not publically available (sorry!)\n",
    "\n",
    "local=True\n",
    "if local:\n",
    "    filepath='../../destiny/data/validation_dataset/' #location of full dataset on Tom's machine\n",
    "    dfilename='validation-dataset-rldat.csv' \n",
    "    gfilename='grimScore_validationPlayers.csv'\n",
    "    pfilename='validation-dataset-entropyAcrossEventTypesForFirst25Days.csv'\n",
    "\n",
    "df=pd.read_csv(filepath+dfilename) #main player data\n",
    "\n",
    "gf=pd.read_csv(filepath+gfilename) #final grimoire score of each player\n",
    "gf.columns= ['userid','grimscore']\n",
    "\n",
    "pf=pd.read_csv(filepath+pfilename) #entropy across playmodes\n",
    "pf.columns= ['userid','eventEntropy25']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>destinyMembershipId</th>\n",
       "      <th>date</th>\n",
       "      <th>PvPEventCount</th>\n",
       "      <th>totalDeathDistance</th>\n",
       "      <th>activitiesWon</th>\n",
       "      <th>totalKillDistance</th>\n",
       "      <th>deaths</th>\n",
       "      <th>averageLifespan</th>\n",
       "      <th>objectivesCompleted</th>\n",
       "      <th>averageKillDistance</th>\n",
       "      <th>...</th>\n",
       "      <th>assists</th>\n",
       "      <th>resurrectionsReceived</th>\n",
       "      <th>activitiesEntered</th>\n",
       "      <th>score</th>\n",
       "      <th>averageScorePerLife</th>\n",
       "      <th>maximumWeaponLevel</th>\n",
       "      <th>allParticipantsCount</th>\n",
       "      <th>maximumPowerLevel</th>\n",
       "      <th>highestCharacterLevel</th>\n",
       "      <th>winLossRatio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4611686018428705658</td>\n",
       "      <td>2015-12-01T00:00:00Z</td>\n",
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       "      <td>17.868597</td>\n",
       "      <td>...</td>\n",
       "      <td>90</td>\n",
       "      <td>16</td>\n",
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       "      <td>429.410138</td>\n",
       "      <td>0</td>\n",
       "      <td>245</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>2.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4611686018428705658</td>\n",
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       "      <td>3</td>\n",
       "      <td>16.176471</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
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       "      <td>205.277778</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4611686018428705658</td>\n",
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       "      <td>254</td>\n",
       "      <td>1</td>\n",
       "      <td>193.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>13.368421</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>2505</td>\n",
       "      <td>1252.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
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       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4611686018428705658</td>\n",
       "      <td>2015-12-04T00:00:00Z</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>5873</td>\n",
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       "      <td>124.130841</td>\n",
       "      <td>0</td>\n",
       "      <td>30.273196</td>\n",
       "      <td>...</td>\n",
       "      <td>33</td>\n",
       "      <td>42</td>\n",
       "      <td>25</td>\n",
       "      <td>103</td>\n",
       "      <td>0.962617</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4611686018428705658</td>\n",
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       "      <td>62.038095</td>\n",
       "      <td>1</td>\n",
       "      <td>17.490566</td>\n",
       "      <td>...</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
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       "      <td>23625</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   destinyMembershipId                  date  PvPEventCount  \\\n",
       "0  4611686018428705658  2015-12-01T00:00:00Z              1   \n",
       "1  4611686018428705658  2015-12-02T00:00:00Z              2   \n",
       "2  4611686018428705658  2015-12-03T00:00:00Z              3   \n",
       "3  4611686018428705658  2015-12-04T00:00:00Z              4   \n",
       "4  4611686018428705658  2015-12-08T00:00:00Z              5   \n",
       "\n",
       "   totalDeathDistance  activitiesWon  totalKillDistance  deaths  \\\n",
       "0                   0             22               8023     216   \n",
       "1                   0              0                275      17   \n",
       "2                   0              1                254       1   \n",
       "3                   0             18               5873     106   \n",
       "4                   0              7               2781     104   \n",
       "\n",
       "   averageLifespan  objectivesCompleted  averageKillDistance      ...       \\\n",
       "0        75.723502                   32            17.868597      ...        \n",
       "1        39.555556                    3            16.176471      ...        \n",
       "2       193.500000                    0            13.368421      ...        \n",
       "3       124.130841                    0            30.273196      ...        \n",
       "4        62.038095                    1            17.490566      ...        \n",
       "\n",
       "   assists  resurrectionsReceived  activitiesEntered  score  \\\n",
       "0       90                     16                 31  93182   \n",
       "1        6                      0                  1   3695   \n",
       "2        1                      0                  1   2505   \n",
       "3       33                     42                 25    103   \n",
       "4       22                      3                 10  23625   \n",
       "\n",
       "   averageScorePerLife  maximumWeaponLevel  allParticipantsCount  \\\n",
       "0           429.410138                   0                   245   \n",
       "1           205.277778                   0                    16   \n",
       "2          1252.500000                   0                     6   \n",
       "3             0.962617                   0                   150   \n",
       "4           225.000000                   0                    85   \n",
       "\n",
       "   maximumPowerLevel  highestCharacterLevel  winLossRatio  \n",
       "0                  0                     40      2.444444  \n",
       "1                  0                     40      0.000000  \n",
       "2                  0                     40     -1.000000  \n",
       "3                  0                     40      2.571429  \n",
       "4                  0                     40      2.333333  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head() #example of what the data look like (nb not all columns shown)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Munging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows in dataset   = 703602\n",
      "Unique player IDs = 12861\n"
     ]
    }
   ],
   "source": [
    "#our headings\n",
    "cols=df.columns.values\n",
    "\n",
    "print(\"Rows in dataset   = \" + str(len(df)))\n",
    "print(\"Unique player IDs = \" + str(len(df[cols[0]].unique())))\n",
    "\n",
    "#create a unique timestamp for each play\n",
    "df['time'] =df.apply(maketime, axis=1)\n",
    "\n",
    "#use this to create a sequential count of each game, for each player\n",
    "df.sort_values('time', ascending=True, inplace=True)\n",
    "df=df.groupby('destinyMembershipId').apply(ranker)\n",
    "#now find some by player stats\n",
    "df=df.groupby('destinyMembershipId').apply(tagger)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#now let's filter players by minimum games played  and remove games beyond this\n",
    "minimum_plays=49\n",
    "sf=df[(df['max_plays']>minimum_plays) & (df['game_n']<(minimum_plays+2))]\n",
    "labeltext=str(len(sf['destinyMembershipId'].unique())) + ' players'\n",
    "\n",
    "if logtransform:\n",
    "    sf['Ln_combatRating']=np.log(sf['combatRating'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 1: Learning Curves\n",
    "\n",
    "Repeating the analysis of Stafford & Dewar (2014), we divide players by their highest ever performance, and then trace the evolution of their performance across time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NOT DONE for nan\n"
     ]
    },
    {
     "data": {
      "image/png": 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St26eqxPOSTFTNPPbmdcNImwxNtlibNabmHaKsEVIkkKr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\nSZKUU4YRkiRJkiQpp/4/nvm/kgOmh8oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f769ba91400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find divisions between thirds on top scores\n",
    "percentiles = [stats.scoreatpercentile(sf['top_cr_'].values, per) for per in range(0,120,33)]\n",
    "\n",
    "#label players according to this\n",
    "#sf.loc[:,'score_cat']=pd.cut(sf['top_cr_'],percentiles) #produces warning \"Try using .loc[row_indexer,col_indexer] = value instead\"\n",
    "\n",
    "sf['score_cat']=pd.cut(sf['top_cr_'],percentiles) #produces warning \"Try using .loc[row_indexer,col_indexer] = value instead\"\n",
    "\n",
    "\n",
    "#this would be better done using seaborn.tsplot\n",
    "plt.clf()\n",
    "for cat in sf['score_cat'].unique():\n",
    "    try:\n",
    "        mask=(sf['score_cat']==cat) & (sf['game_n']<(minimum_plays+2)) & np.isfinite(sf[perfvar])\n",
    "        xvals=list(range(1,minimum_plays+2))\n",
    "        #yvals=sf[mask].groupby('game_n')['killsDeathsRatio'].mean()    \n",
    "        #ysems=sf[mask].groupby('game_n')['killsDeathsRatio'].sem()    \n",
    "        yvals=sf[mask].groupby('game_n')[perfvar].mean()    \n",
    "        ysems=sf[mask].groupby('game_n')[perfvar].sem()    \n",
    "        xvals.append(10000);yvals[0]=0;ysems[0]=0 #see here for why I have to do this  http://stackoverflow.com/questions/35137961/pandas-plotting-with-yerr-gives-key-error-0        \n",
    "        plt.errorbar(xvals,yvals,yerr=ysems,label=cat)\n",
    "    except:\n",
    "        print(\"NOT DONE for \" + str(cat))\n",
    "\n",
    "plt.xlim([0.8,minimum_plays+1.2])\n",
    "plt.ylim([cRmin,cRmax])\n",
    "plt.xlabel('Game n')\n",
    "plt.ylabel('Mean ' + str(perfvar) + ' (SE bars)')\n",
    "plt.annotate(labeltext,xy=(minimum_plays*0.8, cRmin*1.1),xycoords='data')\n",
    "plt.legend(loc=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note how those who go on to have the highest scores (in blue) start off with superior performance on their first play, and increase their performance faster with practice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 2: Spacing (exploration of practice scheduling)\n",
    "\n",
    "\n",
    "Do those who spread their practice/plays outperform those who cramming them together (ie not taking breaks)? \n",
    "\n",
    "Spoiler: yes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "'''\n",
    "now we're going to make a df of player, not game, level statistics\n",
    "'''\n",
    "\n",
    "#get max plays per ID\n",
    "players=sf.groupby('destinyMembershipId')['max_plays'].mean().reset_index()\n",
    "\n",
    "sf=sf.groupby('destinyMembershipId').apply(findspacing,25) #get spacing of first 25 games"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "players=players.merge(sf[['destinyMembershipId','space25']].drop_duplicates(),how='left',left_on='destinyMembershipId',right_on='destinyMembershipId')\n",
    "\n",
    "#figure out quartiles of grimoire score for each play level\n",
    "q1=players['space25'].quantile(0.25)\n",
    "q2=players['space25'].quantile(0.5)\n",
    "q3=players['space25'].quantile(0.75)\n",
    "\n",
    "minplay=50\n",
    "maxplay=500\n",
    "#divide all players according to weather they score above or below expected on their grimscore\n",
    "spacers_=players[players['space25']>q3]['destinyMembershipId']\n",
    "groupers=players[players['space25']<q1]['destinyMembershipId']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZM9w55oy0aooX0lLjiRPStGk2hPjzT+mJJ6QOHaQ8eWx7L79sb+vX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NprJ1MqGhtKLP+YBw2Chx9Olr1sal+FYv13rFHs9SlHfKIlSSpLuQoj2gO/BfqQNLJ8HPhDjPGr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\nkiRJKijDCEmSJEmSVFD/H904FkrpHXwnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f76a77e05c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "groups=[spacers_,groupers]\n",
    "labels=['spacers','groupers']\n",
    "\n",
    "plt.clf()\n",
    "for i,group in enumerate(groups):\n",
    "    mask=(sf['destinyMembershipId'].isin(group)) & (sf['max_plays']>(minplay)) & (sf['max_plays']<(maxplay)) & np.isfinite(sf[perfvar])\n",
    "    xvals=list(range(1,minplay+1))    \n",
    "    yvals=sf[mask].groupby('game_n')[perfvar].mean()    \n",
    "    ysems=sf[mask].groupby('game_n')[perfvar].sem()    \n",
    "    yvals=yvals[:minplay];ysems=ysems[:minplay]\n",
    "    xvals.append(100000);yvals[0]=0;ysems[0]=0 #see here for why I have to do this  http://stackoverflow.com/questions/35137961/pandas-plotting-with-yerr-gives-key-error-0        \n",
    "    plt.errorbar(xvals,yvals,yerr=ysems,label=labels[i])\n",
    "    \n",
    "\n",
    "plt.plot([25,25],[cRmin,cRmax],'-',color='red')\n",
    "plt.xlim([0.8,minplay+0.2])\n",
    "plt.ylim([cRmin,cRmax])\n",
    "plt.xlabel('Game n')\n",
    "plt.ylabel('Mean ' + str(perfvar) + ' (SE bars)')\n",
    "plt.legend(loc=0);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Splitting the groups on the amount of spacing in the first 25 games shows that those in the top 25% for spacing learn faster in the following period"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure 3: exploration of game world\n",
    "\n",
    "The \"grimoire score\" is a measure of how much players explore the game world. I thought it could be used as a proxy for curiosity - a trait measure of a tendency to explore. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#merge with grimoire scores\n",
    "#some players have user IDs but not in rldat.csv\n",
    "players=players.merge(gf,how='left',left_on='destinyMembershipId',right_on='userid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>destinyMembershipId</th>\n",
       "      <th>max_plays</th>\n",
       "      <th>space25</th>\n",
       "      <th>userid</th>\n",
       "      <th>grimscore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4611686018428395647</td>\n",
       "      <td>121</td>\n",
       "      <td>158</td>\n",
       "      <td>4.611686e+18</td>\n",
       "      <td>4490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4611686018428395829</td>\n",
       "      <td>66</td>\n",
       "      <td>228</td>\n",
       "      <td>4.611686e+18</td>\n",
       "      <td>3110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4611686018428399028</td>\n",
       "      <td>229</td>\n",
       "      <td>97</td>\n",
       "      <td>4.611686e+18</td>\n",
       "      <td>4055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4611686018428401319</td>\n",
       "      <td>147</td>\n",
       "      <td>275</td>\n",
       "      <td>4.611686e+18</td>\n",
       "      <td>4410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4611686018428402622</td>\n",
       "      <td>72</td>\n",
       "      <td>70</td>\n",
       "      <td>4.611686e+18</td>\n",
       "      <td>3685</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   destinyMembershipId  max_plays  space25        userid  grimscore\n",
       "0  4611686018428395647        121      158  4.611686e+18       4490\n",
       "1  4611686018428395829         66      228  4.611686e+18       3110\n",
       "2  4611686018428399028        229       97  4.611686e+18       4055\n",
       "3  4611686018428401319        147      275  4.611686e+18       4410\n",
       "4  4611686018428402622         72       70  4.611686e+18       3685"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#figure out quartiles of grimoire score for each play level\n",
    "q1=players.groupby('max_plays')['grimscore'].quantile(0.25)\n",
    "q2=players.groupby('max_plays')['grimscore'].quantile(0.5)\n",
    "q3=players.groupby('max_plays')['grimscore'].quantile(0.75)\n",
    "\n",
    "minplay=50\n",
    "maxplay=500\n",
    "#divide all players according to whether they score above or below expected on their grimscore\n",
    "explorers_=players[players['grimscore']>q3[players['max_plays']]]['destinyMembershipId']\n",
    "exploiters=players[players['grimscore']<q1[players['max_plays']]]['destinyMembershipId']\n",
    "\n",
    "#Of course, we could do this - it doesn't make a difference\n",
    "#explorers_=players[players['grimZ']>1]['destinyMembershipId']\n",
    "#exploiters=players[players['grimZ']<1]['destinyMembershipId']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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clJKVKwumdXz6qeuoB/2joxfP4+rVbgvH3r3d61bewoicHOnmm92Q\n9ZkzpeOOC629pUvd/XbskJ54QjrvvPDry19zZNEiKT099Jpj5YknpEmTXDBx/fVudxUvRv0QRiQH\nwogoEEYAAADE3po10llnuc5c/frF3yYInVS/24sFr2rcuFHq0MHtvrB9u/vG3Avxfp23bXPTEipU\nkDIziz+v0tqzVpo2za170rKl2x61rAUeC7f35z+7kUH33BNavbFmrQudJk1yAVy0i3XmS4S/FUQv\nKGFESmxLAAAAQKIaNEgaOLDkIALBU7euW9tDctuwfvBBfOvxwqpVLkA45RQ3IiKSgMUYF2Z8/bXb\n7vKMM9zUjd27y77vzJlums8dd4T/uLFijDRxYsF1IBERRgAAAOAQ8+dLH38crA4YQnPUUe7ff/9b\n6tXL7S7x22/xrSlSH3wgtWkj3X67NHq0GxkRjcqVpaFDpSVL3I4YTZu6ERMlfUu8e7d0001unYpo\n12MAUBRhBAAAAIrIzXUd2BEjpCOPjHc1iNSf/uSma2zc6HZgWLQo3hWF59//druDZGa6dRG8lJYm\nvfyy2ylm+HDp/PPdc3WwESOk5s3dIpEAvMWaEQAAAOVcuPvVv/CC9OSTbhHIlDK+uor3WgLxaC8W\nYn3OmZnuG/7+/d2OGxUrxre+0trMyXEjIWbNclMkTjghtjXm5Ej/+Y8bMXHZZW6njpo1XXs1a0pf\nfCEde2z47fohGf9WEL2grBlBGAEAABAw4YYH4SjrQ+ivv0onneS+NW7VKvr2IpEsHSy/X+d166S+\nfd1ikJMmudc5mvaiVVyb27dLPXq441OnStWq+Vfj1q3SkCHuce+/362X8thjLhgJKi9el1i+DxFM\nhBFRIIwAAADJwu+O+dCh0rffSlOmeNNeJJIljIilks7ZWmnsWNfZHjLEjZQoa/RLae15WeN330md\nO7spEZGsD+FVjV9+6bYC/eAD6f/+L/xRJH5Kxvc2okcYEQXCCAAAkCz87JivXSudfrr0+edlb3sY\nq/pi0WYydtjKOudVq6SePaUqVaRx46QGDaJrL9oa58yRrrjCBSR/+1v07UXLWhfSBP19k4zvbUQv\nKGEEC1gCAABAkjR4sHTjjaEHEUhcJ5zgdkvJyJDOOsutExKvTu1zz7ltN194IfIgwmtslwnEHiMj\nAAAAAsyvUQILF7rF+1auLNgaMh71xaLNZPz2OJxz/vxz6eqr3TaXY8dKtWpF1144Nd56q/TGG+5y\n4onRt5ds75tEqBHBw8gIAAAABIK1bivPBx8ML4hA+XDGGdLixW67y9NOk958M/aPuXOn+3fZMheE\nRRtEAEg8jIwAAAAIMD++7X3pJemf/5Q+/TS0xQxjWV8s2kzGb48jPec5c6RrrpEuvFAaNaognIq0\nPWvdLhXff+8WqMy/fPSRlJ3t7QKRyfi+SYQaEQx+75rCApYAAAAJLtYdrN273RaPL74onXtuaG3E\n+kNtMnYqvRbNOe/c6aZPZGVJEydKbdqU3p610qZNRcOGwuGDtdLxxxdcGjVyU0Jatgz265wI75tE\nqBHJiTACAAAgwcW6g/XAA9Ly5dLLL3v3GNFKxk6l17w45xkzpBtukHr1kkaOlNasKT5s+P57qXJl\nFzIUDh3yg4eaNYtfEDLor3MivG8SoUYkJ8IIAACABBfLDtb69W6NgM8+kxo29O4xopWMnUqveXXO\nmzdLf/2r9NprUr16RUOGwtePPjp+NSZKe7GQCDUiORFGAAAAJLhYdrB693YdzIce8q59LyRjp9Jr\nifAcBr3GRHjfJEKNSE6hhBEV/CoGAAAAwbFokTR7tvTNN/GuBACQjBgZAQAAEGCx+LY3N9ctVtmn\nj3Tttd61HY1YLoqZjN8eJ8IoAS/aTPb3TSLUiOTENA0AAIAEF4tO5dSp0ogRbnREaqp3bQdVMnbY\nkiWMiKWg1yclRo1IToQRAAAACS4Wncr0dGn8+NjsLR9EydhhI4yIXtDrkxKjRiSnUMKIFL+KAQAA\nQDCceWbyBBEAgGBiZAQAAECAefnN54YNbveM775z2zEmi2T89piREdELan2xXCcD8ArTNAAAABJc\nNB2i3Fy3W8bChe7y/vvSqlXB7GB5Ldk7bIQR0Qt6fUCQEUYAAAAkuHA6RD/9VBA8LFwoffaZVL26\n1KJFwaV1azpYyYAwInpBrw8IMsIIAACABFdSh2jvXunzzwuChwULpK1bpT/8QWrZ0gUPzZtLtWuH\n1h4SX6Jtcxn092LQ6wOCjDACAAAgwRnjplt8/33R4GHFCunEEwuChxYtpMaNpZQylieng4VIEEYA\nCEcoYUQFv4oBAABA6KyV/vUvd/2YY6TKlQtCh8svl846yx0DACARMTICAAAggEaNkl54wU3FWLfO\n7YLhBb7tRSSSZWREsi98Cngl6mkaxpjDJXWSdK6kepJ+k/SlpDettSs8rDUshBEAAKA8mz9f6tLF\nTclo2DD4CxGi/EuWMAKAN6IKI4wxw+SCiCxJiyVtlnS4pBMltcu7fru1dpmHNYeEMAIAAEQq6N98\n/vSTm4Lx9NNSp06JsSsCyj/CCADhiDaM6GitfbOUxmtL+r21dlF0ZYaPMAIAAHghaJ2h/fuliy+W\nzjhDevhhd4wwAkFAGAEgHJ7vpmGMSZF0lLV2Z7TFRYMwAgAAeCFonaF//EN6/313qZC3zDhhBIKA\nMAJAODwJI4wxUyTdIGm/pM8kVZX0uLX2Ua8KDRdhBAAgWQV9ikGiCVJn6L33pF69pMWLpd/9ruA4\nYQSCwKv3Df8NA5KDV2HEUmvt6caYv0g6U9IgSYuttad5V2p4CCMAAKBT6YWgPIfr17t1Il58UTr/\n/KK/I4xAEPC+ARCOUMKIlBDaqWiMqSjpUkmvW2v3SeI/RQAAAB7IyZF69JBuvPHQIAIAgPKqQgi3\neUZStqQvJM01xqRJiuuaEQAAAOXFPfdIlSu7f4EgKTylom1baehQd50pFQC8UOo0jbwFK7tZa18u\ndMxISrXW5vhQX0l1MU0DAJD0GDYdvXg/h6+/Lg0YIC1ZItWqVfxtmKYBAEg0Xq0Zschae7anlUWJ\nMAIAADqVXvDiOYx0Qb7Vq6UWLaQZM6RWrWJbYyzbAwDgYF6FEQ9L+lnSVEm/5h+31m71oshIEEYA\nAECn0gvx6ujv3Su1aSNddZV0663etBkq3jcAgFjzKoxYXcxha609LpriokEYAQAAnUovxKuj37+/\ntHGj9Mor7j5etBkq3jcAgFgLJYwocwFLa21D70oCAABIbpmZ0qxZ0uLFZQcRAACUV2WOjJAkY8wp\nkppKOjz/mLV2UgzrKqseRkYAAJIe33BHz+9RBytXSueeK82eLZ1xhjdthiLSdS0AAIiEV9M0hkjK\nkAsj3pJ0kaSPrbXdPKozbIQRAAAQRnjBzzBi9263YOXAgdL113vTJgAAQeRVGLFcUjNJn1trmxlj\n6kh6wVr7R+9KDQ9hBAAAdFK94FcYYa3Up4+0f780aVJ40zN4nQEAicaTNSMk/WatzTXG5Bhjqkra\nLKmBJxUCABAgDGVHrIwbJ332mfTpp6wTAQCAFNrIiKcl3S2ph6TbJe2StNRa2yf25ZVYEyMjAAAx\nlQjfRidCjUHnx8iIL76Q2reX5s6VmjTxpk0AAILMk2kaBzWYLqmqtXZZdKVFhzACABBridABTIQa\ngy7WYcSOHdLZZ0vDhklXXeVNmwAABJ1X0zRkjOkqqY0kK+ljSXENIwAAkJhWgWCzVrr2WjcqItIg\nAgCAoMrKzlJWdtaB6xnpGZJ04N+yhDpN43hJL+UdukLS99ba/mU2bszzkjpJ2mStPS3vWHVJUyWl\nScqW1N1auyPvd0/I7dbxq6Te1tqlJbTLyAgAQBF+b9EYBIlQY9DF8n3z+ONuscp586TDDy/9fqG2\nCQBAEJlhRnZIwf+svBoZcb6kJvm9f2PMREkrQqxpvKQnJU0qdGyQpPestSONMXdJGixpkDHmIkmN\nrLUnGGNaSHpGUssQHwcAgKTy9dfxriDxrV7t/t2+XapWzdu2FyyQHnzQ/RtJEFF41E/bttLQoe46\no34AAOVFKGHEd5J+L2lN3s8N8o6VyVr7sTEm7aDDl0hqm3d9oqQP5QKKS5QXWlhrFxpjjjbG1LHW\nbgrlsQAASBYPPywNHhzvKhLb6tVS69buev36Uo0a0imnSKeeWvBvkyZSpUrht71li3TFFdKzz0rH\nHRdZfYQOAIDyrsQwwhgzU26NiCqSvjbGfJr3cwtJn0bxmLXzAwZr7UZjTJ2848dKWlvoduvyjhFG\nAAASktdrWlgr3X23NGOG9N//Sl26SD//LNWq5UW1yWPHDqlTJxfo3HyztHOnlJ0tLV/uLm++6QKf\nH36Q0tNdMFE4pGjYUEpNLbn9nj2lyy+XLr3UrzMCACDxlLhmhDGmbbG/yGOtnRPSA7iRETMLrRmx\n1Vpbo9Dvt1hra+aFHyOstfPzjr8n6e/W2iXFtMmaEQBwkGRfzDHoa0ZE215urjRwoLRwofTOOy6A\nMEa6+mpp8mTv6oxW0N+HOTkuiGjUSBozRkpJKfl12btX+uYbF1B8+WVBWPHzz1LTpkUDilNPlerU\nce2dc4704YdSxYr+nhsAAPESyZoRYW3tGVFRh4YRX0vKsNZuMsbUlfShtbaJMeaZvOtT8263UlLb\n4qZpGGPskCFDDvyckZGhjCB8wgGAgEjGBe+CFh542V5OjtSnj7RmjTRzpnT00QVtNmwoPfWUdNFF\n3tXqlSC+DwcOlL791o1+qFAhshp37JBWrDg0pDDGTdFYu9ZN/QAAIFmY3kZD0gv66MOGDQtEGJEu\nF0acmvfzI5K2WmsfMcYMklTNWjvIGHOxpP7W2o7GmJaS/mWtLXYBS0ZGAEDpgtgJjLUghQdetrdn\nj9Sjh/uWfvp0qXLlom2++67bPvLLL6UqVbyr1wtBex+OGSM9/bQ0f37BgpVe1WittHGjVK9esM4Z\nAAA/BG5khDFmiqQMSTXl1n4YIuk1SdPkFsJcI7e15/a824+R9Ce5rT37FDdFI+92hBEAUIqgdQL9\nEJTwwMv2du1y6w7UqCG98IJ02GHFt9m3r3TkkdKTT3pXrxe8eg69mPrxzjtudMm8eUUXlQzC6wwA\nQKLzNIwwxlS11u4s4Xe/t9b+GE2x0SCMAIDSJWOHKOidynDb275duvhi6aSTpP/8p/gFE/Pb3LbN\nrV3w8stuvYKgiMX7MJI2v/xSOv98t+jnwc9PvF9nAADKg0jCiJRSfpdVqKH3D/rda5EUCAAAyrZ5\ns/vGv3lz6bnnSt+5QZKqV5eeeELq189N60CBzZulzp2lUaOCFdQAAJDsSgsjCqcYNUr5HQAA8Mja\ntdK550qXXCKNHu12ZwjFZZdJTZpIDz4Y2/oSyZ49bprL1Ve7CwAACI7SPuLYEq4X9zMAAIjSqlUu\niPjrX6Vhw9yQ/3CMGSM984y0bFls6ksk+WtpNGjgnksAABAsFUr5XW1jzG1yoyDyryvv52NiXhkA\nAElk2TK3PeewYW66RSTq1ZMeftjtrvHJJ27rymT1wAPS99+7BS9DHV0CAECksrKzlJWddeB6RnqG\nJCkjPePAdRRV2gKWQ4r9RR5rbdy+Z2ABSwAoXTIuohf0hQhLa2/hQunPf3brPlxxRXRtWiu1b+8W\nv7z99sjr9UK8FrDMzJTuuss9r3XrRt+e1/UBAMq3gxdzTAaRLGBZ4ncm8QwbAADwi7XSTz9JP/wg\nrV5dcJGkFSukpk3Dny4Rjg8/lLp3lyZMkDp2jL49Y6Rnn5VatHDrJTRqFH2biWTBAmngQOn998sO\nIgAAQPyUGEYYY66TlGWtXWWMMZKel3SZpDWSrrHWfu5TjQAARGXXLhcwFA4c8q9nZ0uVKknHHSc1\nbOguZ57p7nfRRdIRR0hdu7rL2Wd7G0zMnOmmVEyb5nbP8EqjRtLgwdL110vvvRfbMCVI1qxxr9P4\n8dJpp8W7GgAAUJrSpml8KekMa+0+Y8xVkm6XdKGkMyQNsdae61+Zh9TGNA0AKEWyDRXft0867DDp\n3XcPHeHwww/Sr78WBA35l8LhQ9Wqh7ZpjJSbKy1eLL36qjR9uvTbb1KXLm7ninPOKXvLzYPbK/ya\nTJki3Xab9PrrbgvPSJT2OufkSK1aSTfc4AKPkmRluUv+9fxQJCMj+oDEz2kaO3e61+Taa6Vbbom+\nPa/rAwAkD6ZphDZNo7QwYqm19vS861MkLbTWPp738xJr7ZmeVR4mwggAKF0ydYh++sl9C75xo9Su\nXfFhQ5064Y8OOPg5tFb66isXTLz6qrR+vdt+s2tX6fzzXRgSanvPPiv94x/SO+9Ip5wSXl2l1Xiw\nZcukCy6QvvjCLW4ZbXte1+dVmzk5bs2N3/9eGjs2vNc6Ec4ZAJBYCCOiDyOWSOooaZvc1IzzrbUr\n8n73tbW2iVeFh4swAgBKl0wdor/9zQUBTzzhb6fyhx+k//7XBRNffeXWe+jaVerQQTryyJLbe/RR\n6emn3SiO44+PbY2SdN99bu2LV1/1pr1w+BVG3HKLO8e33pIqVoy+Pa/rAwAkF8KIKBewlHS/pEWS\nUiW9XiiIaCvph+jLBQAgOl984TrZK1e6MMJPxx3ndqu4/XY3SmLGDPetfJ8+bjRC165Sp05StWoF\n97n3Xjfd46OPpPr1/anz3nul0093j3vZZf48pp/GjpVmzXJbmYYbRAAAgOj83/7/U/83+0uSLsm8\nJKz7ljgyQpKMMRUkVbHWbit07Mi8++2KqFoPMDICAEqXDN/OWuumR1xxhVsXISjfcG/ZIr3xhgtJ\nPvzQrWPQtatbTPKMM1zH+ZhjIq8rkjUe5s2TLr9c+vJLqUaNktsOynMYapuzZ0u9ernzi3TXkEQ4\nZwBAYkmmkRGPzX9Mb3/3tj5Y/YFeu+K1A8cvbXJpVNM02lhrPy7xjsZUlfR7a+2XEdYdMcIIAChd\nMnSIpk936y4sWeIWkgxip/KXX9y6EK++KmVmStu2FR0p4acBA6Tdu6Vx40q+jVfPYU6O9O9/u8cc\nMkRq2dJtNVq9evRt59f41VcufJk+XTo3iiW1g/i+AQAktmQJIzbu2qhTnj5F86+dr8ZjGnu6ZsRo\nSS0kvSNpsaSfJB0u6XhJ7SSlSbrdWvuZJ2cSBsIIAChdee8Q/fab1LSp61i3a+eOBb1TGe/X5Jdf\n3GKZzz8vtW9f/G28qHHOHGngQKlmTTdyY9AgacECadEiNzWlVSsXTrRq5V7DcHYkya9x82YXbgwd\n6kZGRMOLc47ljiQAgMSTLGFEnxl9dEzlYzTyjyO9XcAyr4Eaki6TdI6k30n6TdLXkt4sbdRErBFG\nAAGbnZgAACAASURBVOULH+S9F++Ob6wNHy4tXSq98krBsaCHB0F4Td5+W+rfX1q+vPRFNiPxv/9J\nd94pzZ8vPfaY1K2blJJS0F5OjpsmsmCBW99hwQK3A8of/lAQTrRoIdWqVfrjGOOmvrRtKz34YGS1\n8t8cAEAsJUMY8em6T3Vp5qVaOWClqlaq6n0YEVSEEUD5FYQOW3lQnp/H//1PatbMfdPesGHB8aCH\nB0F5Ta6+WqpdWxo16tDfRVLjnj2urVGjpBtvlO66qyDoKKu9LVukhQsLwolPP3XbsOaHEy1bSqee\nKlXIW27bWhdwdOsmTZ3qrgMAEDTlPYzItblq9Xwr3Xj2jbrm9Gskeb+bBgAgwezdK116abyriK1B\ng9x2noWDCITuX/9y0zWuuMKNRIiUtW6hzltvde19+qnbYSQcNWtKF1/sLpK0f7/09dcFoyfGjJF+\n/FE66ywXTvzyi7vdxIkEEQCQTLKys5SVnXXgekZ6hiQpIz3jwHX4Z/IXkyVJPZv1jKodwggAKEcm\nTHCdOUnaurX0nRMS0fz5blj9ypXxriRx1aoljR4t9esnLV4sHXZY+G18+610yy3S999LTz0ldejg\nTW2pqS7YOOUUV58kbd/ugo5PPnGjYiSpcmVvHg8AkBgKhw5mmFFW76y41pPMdu7dqcHvD9ZrPV5T\nionumwG+VwCAcmLfPunhh6WXXnI/9+jh5umXF7m50s03u3M86qh4V5PYevSQ0tLccxmOX35x0zBa\nt3bbqi5f7l0QUZJq1aQLL3S7ckyeHNvHAgAApRs+d7g6HN9BzY9tHnVbZY6MMMZ0LebwDknLrbWb\no64AAOCJF190w+RbtSo49ve/F782QCKaNEmqWFH6y1/iXUniM0YaO1Y680y3/kLTpqXf3lppyhQX\nROSHEL/7nT+1AgCQSMrz2obfbvlW4z4fpy9v/NKT9kKZpnHt/7N313FVX28Axz9HxO7GYuKM2Yot\nArazu3tOMWfMdhs6f85NnTF11uyYrdiNNbt1dk7FmF2gwPn98VXEiXCBW+Dzfr3uixvfe85zEeF+\nn/uc5wClgO1vbntibPWZTSk1VGstn1MIIYSNBQXB8OEwdeq7+xYtguLFjWaPrVvbLjZzePIEBg6E\nVauME2kRfVmywNCh8NVXsHv3x7fYPHrU2Krz5UtYvNioihBCCCHEh5b9vYwu67rYOgyL6bmxJ/3d\n+pMhSQazjGdKMiIu8IXW+g6AUio9MAcoAewEJBkhhBA2tnixsUOCh8e7+1KmNE7ePTwgd+7oNSu0\ntf/9z1gOUKyYrSOJXTp2NJb1TJwI3bu//9j9+zB4MCxfDj/+aCQtPpawEEIIIUKz94aT5o4vWAcz\ndMdQZhydwfce39NlXRcO3zqMa0ZX8wVtY2vPr+Xig4usaLzCbGNGuLWnUupvrXWeULcVcFprnUcp\ndVRrXdhs0ZhItvYUIvayl+0PY5LgYChQAEaPfrd+P/T30cfH2HLxwAHImNH0cX19jcvb656exnVP\nz3fXreHCBWPpSURLA+x9K057/dk+dw7KlHm3VWpgIEyZAt7exo4bQ4caia2osMRrttfvoxBCiLCZ\ne5tLexvv2atntFrRijvP77Cs0TIyJMmAGqLIkCQDO9rsIGfqnGaL1VYCAgPI/3t+xlUdx5c5vgzz\nGEtt7emrlFoDLHlzu/6b+xIDj0yKXgghhMWsXAkJExpN/sJSqxacOAH16hlJhQQJTBs3dNJBqXeJ\nCWv79lvo00d6FFhKrlzQu7dRJQHGNpopUsCWLUaSSwghhBBhu/zwMrX/rE2JTCVYWH8h8ePGD3ns\nf+X/R+W5ldnTbg+ZkmWyYZTRN27/OHKlyfXRRERUmbKbRhdgFlDozWUO0EVr/VxrXc6s0QghhIgU\nrWHYMPjuu/B7KQwaBFmzgpdXzPpUedMmOH3a2EZSWM6338LdNy2pBwyA7dslESGEEEKEZ/uV7ZT+\nozQdinRgWs1p7yUiANoVbkenop2oPK8yD14+sFGU0ef31I9f9vzCr5XN3xE9wsqIN+shlr65CCGE\nsCPr1hnNK2vWDP84pWDmTKP54PjxxhaZ9u71a+jZ01h+Ej9+xMebQ+ilKR4exlIFsP7SFGtzdDQS\nEClTGkszhBBCCBE2rTUTD05k2M5hzK83nwouFT56bN8yfbn34h41FtRgc8vNJI6X2IqRmkf/rf1p\nX6Q9OVLnMPvYpm7t+TOQDlBvLlprnczs0QghhDCZ1kZjwcGDTdthInFio6FlyZKQNy9UrGj5GKPj\n99+NHhe1allvztiedAhPihS2jkAIIYSwb6+CXtFlbRf23tjLX1/9hUtKl3CPV0oxstJI2q5qS8Ml\nDVnVZBWODo4mzWUPTUD33djHlstbONvlrEXGN6VnxC9ATa31GYtEIIQQIkq2boXHj41eEKb67DP4\n809o0gT27IHs2S0W3gci0xDz33+NRIuvb/iJlk+1kkEIIazBHk6GhLAXd57dof7i+qRNnJa9X+0l\nafykJj1PKcX0WtOpt6gebVa1YW7ducRREXdLCP3/TA1R+LbxjUb0kResg+m+vjsjKoww+bVGlinJ\niDuSiBBCfOrsZWeJ0H780egFEdntFj094fvvoXZt2LsXklrm70uY85raEPP776FpU6OCw9QxhRBC\nmJetT4aEsBdH/I5Qd1Fd2hRsww+eP5iUTAgtbpy4LGqwiMrzKtNzQ0/GVh2LMqWs1YZmH5uNQxwH\nmhdobrE5TElGHFJKLQJWAgFv79RaL7dYVEIIYWfsZWeJt3buhJs3jQqHqOjUCY4dg1atYNkyiBO5\nv6kWdeKEEdMZSYMLIYQQwsb+PPUn3dZ34/fqv9MgT4Moj5PQMSGrm67GY5YH/9v1Pwa7DzZjlBGL\nTKXTY//HDNw2EJ8mPpFOvESGKcmIZMALIPSmcRqQZIQQwqx27bJ1BDHHsGHGrgdxTfktHgalYMIE\nKF8ehg59t7zB1rQ2ds744QdIlcrW0YiokKUzQgghYoNgHczgbYNZeGohW1puoWCGgtEeM0WCFGxo\nvgG3mW6kSZQGr6JeZojUNJGpdPpx549U+7waxTIVs2hMpuym0daiEQghBLB6NbRrZ1xfuNAo0Rdh\n278fzp2Dli2jN068eEYFQrFikD8/1K9vnviiY8UKuHcPOnSwdSTm8SmemMfm1yaEEOLT8CTgCc2X\nN+dpwFMOtD9A2sRpzTa2U1InNrXYhPssd9IkShOtagtLOPvvWWYfn82pTqciPDZ0tYWHswfevt4A\nJveU+WgyQinVV2v9i1LqN4xKiPdorbubNIMQQkRgzhzo2xfWroUSJYxtJ93dIVMmW0dmn4YNg379\njGRCdKVPbyQAqlaFHDmgQIHojxlV/v7QuzdMnx71ig97IyfmQggRs0jTTvum9QenpWZ34f4Fav9Z\nG8/PPBlXdZzJu19ERvZU2VnbbC2V51YmZYKU4W4Pak1aa3ps6MEAtwGkT5I+wuOj+/8ivLd7b1fr\nHory6EIIEYGxY+HXX2H7dvjiC+O+rl2NKokNG0zbsvJTcvQoHDkCS5aYb0xXVxg3DurUgQMHIE0a\n840dGaNHQ+HCUME+/h4LIYT4BEnTTvty6+ktDtw8wIGbB9h/cz+HbhmnphXmVCB/uvzkT5efAukL\nkDddXhI5Jor2fJsubaLlipYM9RxKx6Idoz1eeAplKMTSRktpsLgB65qvo2jGohadzxRrzq/h2uNr\ndC3e1SrzfTQZobVe/ebqC631e297lVINLRqVEMKumWNnCa2NHRMWLzZ6RTg7v3ts4EAoUwZ+/x06\ndzZX1LHD//4H334LCRKYd9xmzeD4cWjUCDZuBEfzfwgQrps3jaTUwYPWnVcIIYQQ9uFpwFMO+x1m\n/439HLhlJCBevn5J8UzFKZ6pOL1L9aZYxmKkG5WOPqX7cPLOSXyv+fLbgd84d/8cmZNlDklO5E+X\nn/zp85M9ZXYc4kS87ZjWmjH7xjDyr5EsabgEd2d3K7xicHd2Z1rNadRcWJPtrbeTO01uq8wbloDA\nAHpu7MnEahOJ52CG8lsTmFIIOwD472dwYd0nhPhERHdniaAg6NbN6H2waxekS/f+43HjGks3ypSB\nihUhZ04zBB0LnD5tfL9mz7bM+MOHQ40axlKJ8eMtM8fH9O8PHTuCi4t15xVCCCGE9b0Oes2pu6fe\nq3q48ugKBdMXpHim4jT4ogG/VPwFl5QuYW6BWfXzqlT9vOp74114cIGTd05y4s4JZh+fzcm7J7n7\n/C550uYJqaLIn95IVqRL/P6bz7ar2nLs9jH2fbUP5xTO/53Oomrnrs2Dlw+oMq8Ke9rtIXOyzFad\n/62x+8aSN11eqnxexWpzhtcz4kugGpBJKRX6bWkyINDSgQkhYqdXr4ztJG/fNpZmJEsW9nG5chnN\n/lq1gt27Y08PgegYPhx69oTEiS0zvoOD0Ty0RAn44w/46ivLzPNfe/fCtm1GU04hwvIpNgIVQojY\n5MrDKyFJhwM3D3Ds9jGyJs9KicwlKJ6xOJ2LdSZ/+vxR/kTe0cGRPGnzkCdtHhrnaxxy/5OAJ5y+\ne5oTd05w8u5JVp5byck7J3F0cAxJUAC8eP2CPe32kDiehd5kRaBt4bb8++JfKs+tzK62u0idKLVV\n57/19BYj/xrJ/vb7rTpveG/vb2H0i6gFHA51/1OgpyWDEkLETs+fGzs2JEhg9IOIaKlB586wahWM\nGAGDrbsVs925cAE2bTKWrlhSihTG99zd3ejhUbq0ZecLDjYalo4YAUmSWHYuEXNJ0kEIIWKeV0Gv\naLOyDQBlZpQJSTwMLTcUVydXkidIbvEYksVPRqkspSiVpVTIfVprbj69yck7Jzl59yQAixosCrMC\nw5r6lOnDvRf3qLGwBltabrFqYqT/lv50cO1A9lTZrTYnhN8z4jhwXCm1QGv92ooxCSFioQcPjCUA\nuXLBtGmmVTrEiQMzZ0KRIlCtmvH1U/XTT0Zjz49VkoD5Pj3Ondv4vjdsaCylyWzBasG5c41/5+bN\nLTeHEEIIIaxv9F+jeej/EIBbvW/ZOJp3lFJkTpaZzMky82WOL+m3pZ9ZEhHhbXNp6o4TP1f8mXY+\n7ai/uD4+TX2s0rth7z972XZlG2e7nrX4XP9lSuHzZ0qpn4A8QMjnmFprWdkrhDDJrVtQpQpUrgwj\nRxonn6bKnBnGjIGWLeHwYfM3bowJrl41qhUuXgz/OHN+ely9utHXo25d2LnTPGP+19OnMGCAsbVo\nZH4mhBBCCGHfrjy8wui9ozn49UFcxn8ap43m2P5VKcW0mtOov7g+bVa2YV69ecRRlnuTFKyD6ba+\nGz9X/Jkk8axfomrKK5sJ/I7RJ6IcMAeYZ8mghBCxx8WL4OZm7NYwalTUTjqbNYO8eWHQIPPHFxP8\n/LPR3DFlSuvO268ffP45dOhgmfH/9z+oVMnoUSGEEEJEhe9VX7x9vfH29cZzlmfI9befUAvr01rT\nfUN3epXqRbaU2WwdTowTN05c/qz/Jzef3uSb9d+gtbbYXDOPziR+3Pg0y9/MYnOEx5TKiIRa661K\nKaW1vgZ4K6UOA99bODYhRAx3/LixvOKHH6J3QquU0SuhQAGoWfPTWjt+8yYsWmSb5o5KGY0sy5Y1\nbletCk5OH14yZjS+RrZqZdo0OHnS/HELIYQwrzXn1wDwyP8RKRKkiNZYoUvZfa/6hnySHNVPlUM/\nTw1R+LbxjVZ8IvpWnVvFxQcXWdZoma1DibESOibEp4kPHrM8GLZzmEXmeOT/iMHbB7Om6Rqb9csw\nJRkRoJSKA1xQSnUFbgLSZkwIEa7du41mlRMmGL0Hoit1apg+Hdq0MZIcyS3f88iiQvd38PV9l2D5\n71KLkSOhbVtIm9aa0b2TKJHxb5koEXTvDn5+xuXMGWMHjLe3b982jvlYoiL0JWlSY+xvvzUeF7Yl\nO1UIIcKz8ORCemzsAUDRqUVZ0nAJhZ0KR3k8SR7Ebs9fPeebDd8wq/Ysq/Q7iM2SJ0jOhhYbcJvh\nBkC7Ve3IkSoHOVPnJEfqHHye6nMSOSaK8vhDdwylRo4auGZ0NVfIkaYiKvtQShUDzgApgB+B5MAv\nWut9lg/vozFpS5arCCFMpxT897/junXQujXMn2/0iYjueKF5eUFAgNFg0VYiitFc4925Y+xocfq0\ncRJvSxG9Zq2NJqV+fkaPkLdJirAuShk7q7x8+Wn2ABFCiJhi+pHp/OD7Axuab6DA5AL8Wf9Puq7v\nyvDyw2lfpH20P01VQxT6B/P9QbX38T4F/Tb34+bTm8yr925Vv73/u9j7v/OTgCckH5GcKTWmcOH+\nBS48uMD5++e58ugKaRKlMZITqXK8l6hwSekSbjJIDVGkSZSG051Pky5xOovErZRCax3uL4kIKyO0\n1gffXH0GtH0zcNbohyeEiI3mz4fevWH1aihZ0vzjjxoFhQrBypVQp475x4/I1q3Wm+vXX41+GbZO\nRJhCKaN6JXVqyJfv48dpDU+eGFuISiJCCCHs15i9Yxi3fxy+rX3JkToHAI3zNaZghoI0WNyA3f/s\nZlK1SVbdflDYt1N3TzHj2AxOdTpl61BilWTxja3UOri+v+Y5KDiI64+vhyQnLty/wNYrWzl//zw3\nntwgU7JMYSYqnJM7AzCo7CCLJSJMFW4yQilVCsgE7NRa31VKFQD6A2WBLFaITwgRg0yYYDRb3LrV\naDhpCUmSwOzZ0KABlC4N6T7yO9TUZRCmevUK+vY1dn4AI+Hy44/G0gRLuH/fWJZy9KhlxrcVpWL+\nEhshhIjNtNYM3TGU+Sfns7PtTrImf/8zyNxpcrO//X46re1EieklWNZoGbnS5LJRtMJeaK3pvLYz\nQzyHkD5JeluH80lwiONAtpTZyJYyG5Wzv1+K/CroFVceXglJVPx9729WnVvF+fvnufv8LgBdinWx\nRdjv+WgyQik1EqgBHAP6KaU2Au2Bn4B21glPCBETaA1DhxpVEbt2wWefWXa+MmWM3hFff21USIRV\nJRo66aDUu8REVFy5Ao0bGxUKR48an/7fumVUaIRu8GhO48ZBvXqQVerQhBBCmCi6zSG11vTZ3IdN\nlzaxq+2uj55UJo6XmNl1ZjP9yHTcZrox4csJNM7X2Eyvwj48fPnQbGOZu2mnPZpzfA4vA1/S0bWj\nrUMRQDyHeORKkyvMROGL1y9IPDwxjg6ONojsfeFVRlQHCmut/ZVSKYF/gHxa66tWiUwIEWN07240\nOdy1C9JbKRk+ZAgULw6zZhkNHi1l5UpjJ5ABA6BHj3eJj4ULjSqJxo2NKo2ffoLEZqpUffwYJk2C\n/fvNM54QQohPQ3SaQwYFB9FpbSeO3zmObxtfUiVMFe7xSim+dv0a14yuNFzSkN3XdzOq8ijix40f\njVdgW1pr9vyzh6mHp+JzzgeAH3f8yICyA4gbx5S+/2GL7U07H7x8QL8t/VjTbA0OcRxsHY6IQHSa\nXppbnHAe89da+wNorR8CFyQRIYQI7fVr4+uJE0blQVQTEb6+Rgd/b+933fy9vcOvZogXD+bONZZO\nXL0atXnD8+oV9OxpJCBWrzau/7cCo25dOHUKHj40th3dvt08c0+YYGyJmj27ecYTQgghwvM66DWt\nVrbiwoMLbGm5JcJERGhFnIpwuMNhbjy9gfssd649umbBSC3j/ov7jNk7hryT8tLepz2FMhTiYveL\nAOy6vgu3GW5cuH/BxlHar4FbB9IgTwOKZixq61BEDBNeis9FKeUT6na20Le11rUsF5YQwt7dvWs0\nVwTYsAESJoz6WFHt45A/v5GMaN3aSATECS+9GglXrxoVDxkywJEjkCqc92SpUhlJkTVroGVLqFXL\n6JvxdvvKyHr2zFiisXNn1J4vhBBCRIZ/oD+NlzYmMDiQdc3WkdAx8n/QUyRIwfJGy/l1768Un16c\nmbVnUi1HNQtEaz5aa3Zc28G0I9NYe34tNXPVZHKNyZTNWva9XUI2tNjAxAMTKT2jNEM9h+JV1Cva\nu4jEJvtv7MfnnA9/d/nb1qGIGCi8t+61gdGhLv+9LYT4RO3eDa6uUKKEcTs6iYjo6tXL6FkxZox5\nxlu1ynhdjRsbSzTCS0SEVqOGUSXh728kSTZvjtr8kydDuXKQO3fUni+EEEKY6tmrZ9RYUIP4DvFZ\n0XhFlBIRbyml6F26N8saLaPjmo4M2jqIwOBAM0ZrHvee32PUX6PIPTE3XdZ1oXjG4lz+5jJz687F\n3dn9g0RDHBWHbiW6savtLv44+gfVF1TH76mfjaK3L4HBgXit9WJkpZGkSJDC1uGIGOijlRFa6x0A\nSqlvtNbjQj+mlPoG2GHh2IQQZmKunSW0htGjYeRImDnTWEowfLg5I408Bwejb0SJElClSvjbSobn\n1Svo3x+WLzcSElHZljRFCpgxw6gU+eorI55Ro0zfPeLlS+P7u3Fj5OcWQgghIuOR/yOqL6hO7tS5\nmVpzqtnW+rtldeNwh8M0W9aMynMrs6D+AjIkyWCWsaMqWAez/cp2ph6ZysaLG6mTuw4za8+kVOZS\nJlc55E6Tm71f7WXYzmEUmlKIidUm0iBPAwtHHjZ7aYg58cBEUiZISbP8zSw+V+jX7OHsgbevNxC7\nmoB+ikzpxNIaGPef+9qEcZ8Qwk6ZY2eJR4+MHSz8/ODAAXB2Nl980eXiYjSQbNnSaPoYL17knn/t\nmlEJkS5dxMsyTFG1Kpw8CX36GMmRqVPhyy8jft706UZSpUCB6M0vhBBChOfe83tUnlcZ96zujKk6\nhjjKTOsc30iXOB0bW2xkyI4huE51ZWH9hbg7u5t1DlPcfnabWcdmMe3INJLES0KHIh2YUmNKlD/F\nd3RwZEi5IVTLUY2WK1ric86H3778jeQJrLtntT00xLz19BY/7vyR3e12W2XZiiQdYqeP/uZRSjVV\nSq3mTa+IUJftwAPrhSiEsLUjR4xlGVmzGjtm2FMi4q2vvoIsWYwtRiPDx8fYlaNRI6MiIrqJiLeS\nJzeSEDNnQqdORiLnYTi7hAUEwC+/wODB5plfCCGECMvNJzdxn+VOjRw1GFt1rNkTEW85xHFgaLmh\n/FHrDxotacQve34hWAdbZK7QgnUwGy9upP7i+uSekJuLDy6ysP5CjnU8RpfiXcyynKBE5hIc7XiU\nJPGSUGByAbZfMVMH6xik18ZedHTtSO40MXNdqe9VX7x9vfH29Q6ptPD29Q6pvhDWEV5lxF+AH5CG\n93tEPAVOWDIoIWIScy2BsNR40aG1cUI9eDBMnGicsNsrpWDaNChYEKpXh1Klwj/+9WtjWcbSpUZv\niIiOj6qKFY0qif79jSqJ3383mlz+1+zZxuNFpRG1EEIIC7n88DIV51TEq6gXfcv0tcqcVT+vysGv\nD9JoaSN2X9/N7DqzSZkwpdnG11rz0P8hN5/cBMBlnAupE6WmQ5EOzKw9k2Txk5ltrtASx0vMpOqT\n2HBxAy1XtKRR3kYMrzCcBHETWGQ+e7L50mYO3DzAjNozbB1KlEmlhX0Ir2fENeAaYKG36ELEDuZY\nAmHJ8aLq2TPw8oLjx42Glbly2SaOyEifHiZNglat4NgxSJw47OOuXYMmTSBNGjh61HzVEB+TNKmR\nzGnY0KjgWLQIxo+H1KnfHTNihLErhxBCiE+HNdf+/33vb6rMq8JAt4F0KtbJrGNHJEvyLOxos4O+\nm/viOtWVJQ2X4JrRNdznBAYHcufZHfye+XH72W38nvq9u/7MD7+nxvXbz26TIG6CkL4Uyxoti3Bs\nc6r6eVWOex2n09pOuE51ZV7deRR2Kmy1+a3NP9Cfzus6M/7L8SRyTPTR46THgzBFhD0jlFIlgd+A\nL4B4gAPwXGttmTSjEMLmzpyB+vWN/gX790Oij/+tsTv16hnLLfr0MRIT/7V6NbRvb2wJ2quXkfCx\nFk9POHECBg0yqiAmTjTiBfjsMyhTxnqxCCGEsD1rrf0/4neE6guqM7LSSFoUaGGROSISzyEeY6uO\npUyWMlSdX5X+ZfoDMH7/+DATDQ/9H5ImURqckjiRIUkGnJI44ZTUiTxp81AhWwWckjrhlMSJ9EnS\nh5wUqyHKqomIt1InSs2iBotYcHIBVeZVoWfJnvQt09dsTUHtyS97fiFfunzUyFkj3OMk6SBMYUoD\nywlAE2AJUBRoBeS0ZFBCCNtZsAC++QZ+/hnatbN1NFEzfrzRBHLDBqOZJBjLMgYOhMWLLbssIyKJ\nE8PYsdCggfH9XbTIuF96RQghhLCEPdf3UHdRXabUmELdL+pG6rmW+HS7Yd6GFMxQkD6b+wBw/v55\nnJI44e7s/l7SIW2itDHqZF4pRfMCzXF3dqfNqjasubCGOXXmkD1VdluHFiFTK3QuPrjI+P3jOdLx\niPWDFLGSKckItNYXlVIOWusgYKZS6igwwLKhCSGsKSAAevaEzZthyxaj90JMlTy50TiyVSujEgHA\n3d1YFnHkyPvLI2zFzc1YSvL998btcuVsG48QQojYZ/OlzTRb3ox5dedR5fMqkX6+pT7dzpk6J6ua\nrEINUUyoNsHs49tSluRZ2NxyM+P3j6fkHyUZXn447Yu0t8qOE1FlSoWO1pqu67rSr0w/sibPat0A\nRaxlSvvcF0qpeMAxpdQvSqmeJj5PCBFDXLliLBG4excOHYrZiYi3ypc3qg/q1DFu16tn7JxhD4mI\ntxIlglGjjOt2/B5FCCFEDNV8eXNWNF4RpUSEiLo4Kg49SvbAt7Uvvx/6nVp/1uL2s9u2Ditalp1Z\nxo0nN+hRsoetQxGxiCmVES0xkg9dgZ5AFqC+JYMSQliPj4/RQ2HQIOjePXadFP/0E3z9tXG9Tx/b\nxiLe3ynGwwO8vY3rttgpRgghYquHLx/yx9E/AFjffL1NeigIQ950ednXfh9Ddwyl0ORCMbYK5GnA\nU3pu7MmCegtwdHC0dTifjE+hCWiEyQit9bU3lRGfAcuBc1rrV5YOTAhhWYGBRgJi4UKj4aOtQNNd\nWgAAIABJREFUeihYUsKEMG8ezJ9v60hiJnMnDyTpIIQQlhEUHMTmy5uZdWwWGy5uCKmEkESE7cVz\niMew8sOolqMaXdd1BaDqvKq4O7tTNmtZimUqZvfbgf7g+wMVXSpS1rmsrUP5pMSmpMPHmLKbRnVg\nMnAJUEA2pVRHrfV6SwcnhLCMW7eMrS0TJTJ6KKRJY+uI7N+n+Kl+bH5tQggRG5z79xyzjs1i7om5\nOCV1om2htkyqPolUCVOx+PRiW4cnQimdpTRHOh5BDVF4FfVi57Wd9NzYk7P/nsU1oytls5bF3dmd\nUplLkTR+UluHG+L47ePMOzGP051P2zoUEU32WGlhyjKN0UA5rfVFAKVUdmAtIMkIIWKookWhUyej\nMiKOdIAxiZyYCyFiMlO75Qv799j/MYtPL2bmsZlcfniZFgVasKHFBvKly2fr0GIFa5yw1cldhzq5\njaZWTwKesPefvey8tpNhO4dxxO8IedLmCUlOuGV1I3Ui2zS8CtbBdFrbiWHlh5E2cVqbxCDMxx5/\n35uSjHj6NhHxxmXgqYXiEUKY2atXcO0aXL4M27cb982ZAxUr2jYuIYQQ1mNKt3xhv4J1MNuvbGfm\nsZmsOb+GCi4VGOA2gKqfV5U1/GZm7RO2ZPGTUeXzKiFLa/wD/Tl48yA7r+1k0qFJtFzRkqzJs4Ys\n63B3didTskxWiW3G0RkE62DaF2lvlfnEp+ejyQilVL03Vw8ppdYBiwENNAQOWiE2IYSJHj0ykg2X\nLr37+vb6rVuQKRO4uECuXMbx0U1EfIpLFoQQQghru/TgErOPz2b28dmkSpiKtoXaMrbqWNIkkvWV\nsVWCuAko61yWss5lGcQgAoMDOXb7GDuv7WTx34vptr4byRMkD0lMgLHtprm3Dv33xb8M2jaIDc03\nEEdJGa2wjPAqI2qGun4H8Hhz/R6Q0GIRCSE+EBwMN2+GnWy4dAkCAiB7duPi4gKFCxvbWrq4gLMz\nOIb60GTSpOjHI0kHIYQQwjKevXrGktNLmHV8FmfunaFZ/masarKKQhkK2To0YQNx48SlaMaiFM1Y\nlF6lehGsgzlz7wy7ru9iy+UtADiNdsItqxtuWd0om7UsBTMUJG4cUwrgP67f5n40zdeUwk6FzfEy\nhAjTR39KtdZtrRmIEJ+ioCC4fx/u3n13uXPn3XWA3Lnh6lVIlepdsiF7dqhR410CIm3a2LUlpxBC\nCGFvLNl3I1gHA9B2VVtWnl2Ju7M7PUr0oHrO6sRziBetsUXsEkfFIW+6vORNlxevol4sPLWQ/e33\ns/v6bnZd38X0I9O5/vg6JTKXoGzWsrhldaNEphIkjpfY5Dn2XN/Dxksb+bvL3xZ8JUKYtptGNqAb\nxtaeIcdrrWtZLiwhYq7nzz+eXPjv7QcPIHlySJcO0qc3vr69FC0K06fD0qVGAiJRIlu/MiGEEGGR\n5pCfBkv13Vh/YT3fbPgGgPzp8jOiwgjSJ0lvlrHFp8E5hTPOKZxpXqA5AA9ePmDP9T3svr6bwdsG\nc/zOcfKly4dbFreQCorwGlJ6rfVidOXRJIufzFovQXyiTKnfWQn8AawGgi0bjhCWF7rfga/vu+UG\n0Vl6cOkSdOtmXE+T5v3Ewtvrzs5QrNj796VJA3HD+V/o5QX5pDm2EELYNWkOKaLC76kfPTb24NCt\nQ0yqNomq86vSq1QvW4clYoFUCVNRM1dNauYyVt2/fP2Sg7cOsuvaLqYemUqbVW1wSuIUsqzDLasb\nLildQvpOOCVxolHeRrZ8CeITYUoywl9rPd7ikQhhJaGTDkq9S0xExevXMGoUjB4N/fvD+vXw4oUs\nmRBCCCFszV4rVoJ1MFMPT+W77d/xdZGvmVV7FgkdpR2bsJyEjglxd3YPaXgZFBzEybsn2X19N+su\nrmPA1gEAuGV1A2BitYlmb4gpRFhMSUaMU0r9AGwCAt7eqbU+YrGohIgB9u2DDh2MnSoOHoRs2aBP\nH0lECCGEiP3s9UQ/NHusWDl19xQdVncAYFurbeRPn9/GEcV8oX8WPZw98Pb1BuzrZ9HeOMRxoFCG\nQhTKUIiuxbuitebKoyvsvr6bJX8vIUfqHLYOUXwiTElG5AdaAuV5t0xDv7ktxCfn8WMYOBBWrIBf\nf4XGjSUBIYQpYsLJixDCNPZ4om/PXr5+yY87f2TakWn8WO5HOrh2kO0SzUT+hkSfUgqXlC64pHSh\n9crWtg5HfEJMSUY0BFy01q8sHYwQ9kxrIwHRvTtUqwanT0PKlLaOSoiYQ05ehBCfos2XNuO11oui\nGYtywusETkmdbB2STUklgxDiLVOSEaeAFMBdC8cihN365x/o2hXOn4eFC6FsWVtHJIQQIqqkSkdY\nw93nd+m5sSd//fMXE6tNpFqOarYOyS7I/zMhxFumJCNSAGeVUgd5v2eEbO0pYr2gIJgwAX780aiI\nWLwY4se3dVQiNgi9q4uHB3h7G9ejs6uLEMI0UqUjLClYBzPj6AwGbh1Im0JtONXpFInjJbZ1WEII\nYXdMSUb8YPEohLBDx44ZDSoTJYI9eyBXLltHJCLD3k/27SUOIYQQ5nPm3hk6rulIQFAAm1puolCG\nQladX5ZACCFikgiTEVrrHUqp9ECxN3cd0FrLkg0Raz1/bpy4zpkDP/0EbdtKg8qYSE72RWwlSwxE\ndOy7sQ8wGirKdpLm4x/oz/Bdw5l0cBLent50KtoJhzgOVo9Dfg/YJ0kSCRG2CJMRSqlGwEjAF1DA\nb0qpPlrrpRaOTYj3Pt329X13cmmpE83166FzZyhTBk6ehHTpzD+HEEJEh70vMZBkif269/wedf6s\nA0CG0Rkon608tXLWonrO6qRLLH/womrblW14rfEif/r8HPc6TqZkmWwdkrAz8vtPiLCZskxjEFDs\nbTWEUiotsAWQZISwuNBJB6XeJSbM7c4d6NEDDhyAKVOgcmXLzCOEELGdvSdLPmXfbPiGFgVaMHrv\naC53v8z6i+tZdW4VPTf2JG+6vNTKWYvauWuTK3UulA1KAmNaIuvfF//Se1Nvtl/ZzoRqE6iVS9qp\nCSFEZJiSjIjzn2UZ9wHZGFnEGtOmwaBB0K4d/PGH0SMitrH3/glCCBGTPQ14yroL61hxdgVgNDCM\no+zrrdLqc6s5cPMAJzqdYPTe0aROlJoWBVrQokALAgID8L3qi885HyrNrUTCuAmplasWtXLVonSW\n0sSNY8rbxeiLKYmsF69fAJB3Ul6a52/O6c6nSRo/qY2jEkKImMeUvy4blFIbgYVvbjcG1lsuJCGs\n48wZ4+v06bB5MxQsaNt43rJE4kCSDkIIYV7/vvgXn3M+LD+znJ3XdlImaxnq5q7LotOLGLN3DL1L\n97Z1iCEe+z+m87rOzKkzh0SOH2bc48eNT5XPq1Dl8ypMqDaBY7eP4XPOhx4benD98XWq5ahG7Vy1\nqZy9cqw+6X7x+gV+T/249fQWfs/efH3qx61nt95df3oL/0B/ANY1W4drRlcbRy0+RdKDQsQWpjSw\n7KOUqge4vblrqtZ6hWXDEsIytIa9e2HMGNi+3bjvr7/Awfo9pj5KEgdCCGGfrj++zsqzK1l+ZjlH\nbx+lkkslmuVvxvx680meIDkAHdd0ZMSeEVR0qUjBDPaR5e63pR9ffv4l5bKVi/BYpRSFnQpT2Kkw\nP3j+wD+P/2H1+dVMOzKNtqvaUiZrGWrlrEXNXDXJnCyzFaKPvldBrwDYfX03t57eei/J8DbB8DbJ\n4JTUCackTmRMmpGMSTPilMSJPGnzGNeTGvenTJCSOEPjmCURISeVIirk50PEFqY0sMwGrNNaL39z\nO6FS6jOt9VVLBycsz9oNIm3l9WtYuhTGjoX79+Gbb2DGDEiWzL4SEUIIIezLmXtnWHF2BSvOruDy\nw8vUzFmTXqV6Ucml0kd3oxhdeTTNljfj0NeHbL5jhe9VX9acX8Opzqei9PwsybPQuVhnOhfrzJOA\nJ2y6tAmfcz58t/07nFM4Uyun/fZJePbqGZMPTWb03tEA9N3cNyTBkDFpRvKkzROSYHibZLB2rww5\nqRRCfMpMWaaxBCgd6nbQm/uKhX24iEms1SDSVh48gKlTYcIEyJEDBg6EGjUkASGEECJsWmsO+x1m\n+ZnlrDi7gqcBT6mTuw4jKozA3dkdRwfHCMdoWaAlay+spd+Wfoz/crwVog7bi9cv+Hr110yqPokU\nCVJEe7xk8ZPRIE8DGuRpQGBwIH/98xerzq4CoNLcSvQq2Ysqn1exeb+Mx/6P+e3Ab4zfP55y2cqx\nofkGCk0pxF9f/WXTuIQQQrzPlGREXK31q7c3tNavlFLxLBiTENF29iyMGwd//gm1a8Pq1VC4sGXm\nkuaQQggRswUGB7L7+m5WnDEqIBLETUC9L+oxq/YsimUqFumTa6UUk6tPpuDkglTLUY2qn1e1UOTh\n8/b1xtXJ1SK7PMSNExd3Z3fcnd35dd+vtCrQioHbBtJ7U296luxJiwItrF4Vcv/FfcbuG8vvh36n\nWo5q7Gizgy/SfmHVGISIiWS5kLAVU5IR95RStbTWPgBKqdrAv5YNS4jI0xq2bDH6QRw+DB07Gk0q\nM2Sw7LySdBBCiJjpiN8RAJxGO5ElWRbq5q7L+ubryZM2T7TL9VMmTMnsOrNpsaIFxzoeI23itOYI\n2WSHbh1i9vHZnOx00irztSzYkhYFWuB71Zdf9/3K4O2D8XL1onOxzqRPkt6ic99+dptf9/7K9CPT\nqf9Fffa330/2VNktOqcQsYkkHYStmJKM8ALmK6UmvLl9A2gZ3YmVUt8A7d/cnKa1Hq+USgksApyB\nq0AjrfXj6M4lYreXL2H+fKMfhFLQowcsXw4JEtg6MiFEWE7fPW3rEITg1N1TVJ1nVCwcaH+AbCmz\nmX2OctnK0Tx/c9qvbs/Kxiut1o/gVdAr2q1qx+jKo0mXOJ1V5gSjIqRctnKUy1aOs/+eZdy+ceSe\nmJt6uevRs1RP8qXLZ9b5bjy5wcg9I5l7Yi7N8zfnmNcxsibPatY5hBBCWE6EdYda60ta65JAHiCP\n1rq01vpSdCZVSuUFvgKKAoWAGkqp7EB/YIvWOhewDRgQnXlE7ObnB999B599BitWGMmIEyfgq68k\nESE+Tb5XffH29cbb1xvPWZ4h19+WXtqDAzcPUGFOBUCSEsJ2/J76UWNBDcZUGQNgkUTEWz+W+5Hr\nj68z7cg0i83xX7/s+YXMyTLTPH9zq835X7nT5Ob3Gr9zodsFsqXMRqW5lag6ryqbLm1Cax2tsa88\nvILXGi8K/F4ARwdHTnc+zW/VfpNEhBBCxDCmVEYAoLV+ZsZ5vwD2a60DAJRSO4F6QC3A880xswFf\njASFECGOHjUSDz4+0LQp7NwJuXLZOiohbC90maUaovBt42vTeP5r+5XtNFraiJm1Z1JzYU16b+rN\nhhYbbB1WjBXdE7pP1bNXz6ixsAbti7SneYHmtFjRwqLzxY8bnwX1FlB2Zlk8P/MkZ+qcFp3v73t/\nM3bfWI50PGL1nSHCkiZRGga7D6ZP6T4sPLWQbzd9S7AOplepXjTL34wEcU3/9OD8/fMM3zWc1edX\n4+Xqxflu50mTKI0FoxdCCGFJtmp3fAooq5RKqZRKBFQDsgDptdZ3ALTWtwHr1RYKuxYcbHz19IRa\ntSBPHrh0CSZNkkSEEDGBzzkfGi1txJKGS6iRswYAVx9dZcNFSUZExfn758n0ayZbhxGudRfWAfA0\n4KmNI3knMDiQJkubUDB9QQaVHWS1eb9I+wVDPIfQfHlzXge9ttg8QcFBtPdpz9ByQ+2uSiB+3Pi0\nKdSG417HGVt1LEv/XspnYz9j6I6h3Ht+L9znnrp7iqbLmlJmRhmyp8zOxW4X+V+F/0kiQgghYjib\nJCO01meBn4HNwDrgKMaWoR8cas24hH168QIaNzaue3nB5cvQrx+kSmXbuIQQpllwcgEdVndgXbN1\n7zXIGlV5FL039SYwONB2wcVAL16/oP7i+lTKXgmABy8f2DiiD/3z+B/armoLQOV5lXnsb/v2T1pr\nvln/DQFBAUypMcXqVQOdi3UmXeJ0DNkxxGJzTDw4EYc4DngV9bLYHNGllKKiS0XWNV/HttbbuPHk\nBjkn5KTD6g6cuXfmvWMP3zpMvUX1qDinIoUzFOZy98t85/EdKROmtFH0QgghzMmkZIRSqrRSqplS\nqtXbS3Qn1lrP1FoX1Vp7Ao+Ac8AdpVT6N3NmAO5+7Pne3t4hF9+3+yqKWMfPz9gu820PiCZNwDHi\nLd6FEHbi94O/03dzX7a22kqxTMXee6x6juo4JXFi2mHrraWP6bTWdF7bmUIZCjGr9iwA2q1qZ1dL\nNgKDA2m6rCk9S/YEoFjGYlScW5GHLx/aNK4x+8aw8/pOljZciqOD9f+QKKWYUWsGfxz9g93Xd5t9\n/KuPrjJ0x1Cm15we6a1IbSVP2jxMrTmVc13PkTlZZsrNLke1+dWYe3wuALX/rI2HsweXv7lM3zJ9\nSRo/qY0jFkII8TG+vr7vnaObQkX0BkYpNRfIDhzjXfWC1lp3j0asKKXSaq3vKaWyAhuAksAg4IHW\n+melVD8gpdb6g54RSiltT2+8YguljO0x7cWxY8aSjI4dYeBAiBPH/PHZ22sWwlzUEIX+IXo/3KH3\nHfe96htS1WDqFmAjdo9g6uGpbGm1BZeULmHGd+LOCSrPrczZrmdJkSBFtOI1h+i+Zkv748gf/Lrv\nVw60P0DieIlRQxSuTq60LtiabiW62To8AAZvG8zBWwdZ33w9DkMdCP4+mG83fcv2q9vZ3HIzqROl\ntnpMy/5exjcbvuGvr/76YPmCtf+vrDm/hm7ru3Gs4zGSJ0gerXnf0lpTZV4VymcrT3+3sFttWfJn\n2xzfQwD/QH8WnFzAotOL2HRpE/6D/IkfN360xwXzxWip8YQQIrZRSqG1DrcM0ZRkxBmMXTTM+hv3\nTdPKVMBroKfW2lcplQpYjNE/4hrG1p6PwniuJCMswJ5OzH18oH17mDgRGjY07rNEfPb0moUwJ1u+\n8dZaM2DrAFafX82mFpvIlOzD3gahx/va52tSJEjByMojzRavOdjbycax28eoNLcSO9vs5Iu0XwBG\njBe7XaTUH6XY2GIjhZ0K2zTGLZe30Hpla450OEL6JOlDvodaawZuHcjaC2vZ2moraROntVpM+27s\no+bCmmxssZEiTkU+eNwW/86d1nTi2etnzK071yzjzTo2i/H7x7O//X7bVH1Y4Hto78kDe/v9IIQQ\n9saUZIQpu2mcAjIAfmaJ6g2ttXsY9z0AKppzHhGzaA2jR8OYMbBmDRQvbuuIhBCREayD6bK2C4f8\nDrGjzQ6TGsz9WP5H8k3Kh1dRL7Knym6FKGOex/6PabC4Ab99+VtIIuKt7KmyM/7L8TRe2pjDHQ7b\nrJT9zrM7tFrRirl155I+Sfr3HlNKMbzCcBwdHPGc7cnWVlvJkCSDxWO69OASdRfVZVbtWWEmImxl\ndJXRFJlShD9P/UmTfE2iNdbtZ7fpu7kvG1tstEki4lMSurrEw9kDb19vwH4qp4QQIqYxJRmRBvhb\nKXUACHh7p9a6lsWiEjGWr69xeXvd09O47un57vrHvHoFXbrAwYOwbx9kyWL5GD084O2SJlNiFEJ8\n3Oug17RZ1YYbT26wtdVWksVPZtLzMiTJQK9Svei3pR9LGy21cJQxj9aatqvaUvXzqh89cW2Srwlb\nLm+hy7ouzKk7x8oRGkmoFita0L5Ieyq4VAjzGKUUQ8sNxTGOI56zPNnWehsZk2a0WEz3X9yn2oJq\nfOf+HdVzVrfYPFGRyDER8+vN58v5X1I6S+kwd74wdVlF13VdaV+kvc2rYj4FknQQQgjzMiUZ4W3p\nIETsEfqEXql3J/0RefAAGjSAJElg927jq6VI0kEI8/MP9KfRkkYE6SA2NN9AQseEkXp+z5I9yT0x\nN7uu7aKsc1kLRRkzjdk3hhtPbrCw/sJwjxtXdRzFphVjzvE5tCoY7T7TkTJi9wgCAgP43uP7CI/9\nzuM7HB0c8ZjlwbZW28iSPOLMc2T7HfgH+lNnUR1q5axF52KdI/NSrMY1oyu9SvWi1YpWbG21FYc4\nDu89Hvq1qSEK3za+H4yx7O9lnLx7knn15lkh4vfFhCqBmBCjEEJ8yiJMRmitd1gjEGE7AQHQps27\n6/HN0yvKZOfPQ40aRrPKn38GB4eInyOEsB9PA55S+8/apEucjjl15xDPIV6kx0jomJARFUbQc2NP\nDnx9IMbsBmBpu6/v5uc9P7O//f4IG/kljpeYRQ0WUX5OeUpkKkGuNLmsFuP4/eM51OEQceOY8hkH\n9Hfrb1RIzPZkW6ttOKdwDvd4U07M3wrWwbRd1Zb0idPzc6WfTX0ZNtGndB/WX1zPqL9G0c+tX6Se\n+/DlQ7qt78bihotJEDeBhSL8uJhwQh8TYhRCiE9ZhO/2lFIllVIHlVLPlFKvlFJBSqkn1ghOWMfI\nkXD7tnE9e3ajX8Pz59aZe/t2KFsW+vaFUaMkESFETPPg5QMqzq1IjlQ5mF9vfpQSEW81ydcERwdH\n5p+Yb8YIY667z+/SdFlTZtSawWcpPjPpOfnT52dYuWE0WdYE/0B/ywaIsRSi2bJm/FHrDzInyxyp\n5/Yu3ZtvSnyD52xPLj+8bLaYvtv2HdceXWNu3bl2n9RyiOPA3LpzGb13NEf8jkTqub039abeF/Vw\ny+pmoeiEEEIIyzLlr/QEoClwAUgItAcmWjIoYT0XLsDYsTB7tnHbxwf27AEXFxg+HB4/ttzc06dD\nkyawcKGxc4YQImbxe+qHxywPPJw9mFxj8gdl5pGllOLXyr8ycNtAnr+yUkbUTgUFB9FsWTNaFWgV\n6X4HHVw78Hmqz+mzqY+FojO87WXRME/DKPdk6F6iO31L98VzlicX7l+IdkzTj0xn0elFrGqyKtyl\nQr5XffH29cbb1zukfN/b1zukpN+asibPyriq42i2rBkvXr8w6TmbL21m65Wt/FThJwtHJ4QQQliO\nSfWUWuuLSikHrXUQMFMpdRQYYNnQhKVpDV5eMGgQZH3TO6tIEVi6FP7+G376yaiU8PKCHj0gTcRN\n8U0SFAT9+8OqVbBrF+TMaZ5xhRAQEBhAhTlGA8Gph6dS0aUiLildzD7PlYdXqDS3Eu0Kt2OA2wCU\nCnfnJpOVylIKt6xujN472qT+A7HV0B1DCdJBDCk3JNLPVUoxreY0Ck8pTIWzFaiTu44FIoRx+8dx\n+9ntaDcd7VSsE44OjpSfU57NLTeTO03uKI2z8eJGBm8bzM62OyPcOtTeyveb5m/Kmgtr+HbTt0yq\nPincY5+9ekaHNR2YXH2yzXZOEUIIIczBlMqIF0qpeMAxpdQvSqmeJj5P2Ll58+DhQ+jW7cPH8uSB\nuXPhwAG4d89IGPTuDbduRW/OZ8+gXj04dMjYMUMSEUKY1+zjs0N6C+y8tpPSf5TGZZwLX/t8zaJT\ni7j3/F605zhz7wzus9zpWbInA8sONFsi4q0RFUYwfv94bj65adZxY4oNFzcw/eh0FtZfaHIPhv9K\nkSAFC+svpOOajlx/fN3MEcKhW4cYvms4fzb4M1pLc95qX6Q9w8oNo8KcCpy+ezrSzz9++zgtVrRg\naaOl5EwdM/+wTKw2kXUX1rHm/Jpwjxu8bTBls5blyxxfWikyIYQQwjJMSSq0fHNcV+A5kAWob8mg\nhOXdvw99+sDUqRA3nPe6Li4wZQqcPAnBwZAvH3TqBFevRn7Of/4BNzejwmLjRkiVKsrhCyHCEBgc\nyC97fsHbwxuAefXm4dfbj9VNV5MvXT7mn5zP5799TqHJhei9sTfrL6zn2atnkZrj8K3DlJ9TnuHl\nh9OleBcLvApwTuFMB9cODN4+2CLj27Prj6/TZmUbFtZfSIYkGaI1VsnMJeldqjfNljUjMDjQTBHC\nY//HNF7amInVJoZZdRPVJRCtC7VmZKWRVJxbkRN3Tpgcz80nN6m5sCa/fflbjO6fkCJBCubUncPX\nq7/mzrM7YR6z95+9LDq9iDFVxlg5OiGEEML8lNY64oOUSghk1Vqfs3xIEVNKaVPiFh/31VfG9pnj\nxr27Tylj6UZ47t41ekxMmQI1a8KAAZDrIw3bQ4938CDUrWss9+jd23gsskyJT4hP2Z+n/mTCgQns\nbrcbNUShf/jwP8zroNccunWILZe3sPXKVg7dOkQRpyJUyFaBii4VKZ6pOI4Ojh88Tw1R7GizgwaL\nGzC15tQolf5HZnvGJwFPyDUhF2ubraWIU5FIz2UOH/seWsqroFe4z3Sn3hf16Fum70ePi8z3MVgH\nU21+NYpmLMqw8sOiHaPWmqbLmpIyQUp+r/F7tMcLy+LTi+m+vjvrm6+nsFPhDx4P/e/yNOApZWeW\npUm+JvR362+ReKxt4NaBHL9znDVN14RUHakhCv9B/hSeUhhvT28a5W1k4ygtz9r//4QQQpiXUgqt\ndbhnfREmI5RSNYFRQDytdTalVCFgqNa6lvlCjRxJRkTPjh3QogWcPg3Jkr27PzIn+48ewYQJMH48\neHrCwIFQqND7x7wdb8kS6NzZaFhZu3bkYvX1NS5vr3t6Gtc9Pd9dF0IYJ4mFphRiePnhVM9Z3eQ3\n8s9fPWfX9V1svbyVLVe2cPnhZcpmLUtFl4pUyFaBfOnyoZRCDVGkSZSGhfUXUtGlohVeEUw7PI15\nJ+fh29rX7EtBIrLz2k48ZnlE+2QoMomDHht6cPnhZVY2WWnWXSDuPr9L4SmFmVNnDhVcKkRrrGmH\npzHh4AT2fbUv3AaR0bX8zHI6re3EmqZrKJap2HuPvf3ZDgwOpObCmmRJloUpNaZY/WfEUl4FvaL0\nH6VpV7gdnYt1BozX/J37d5y4c4IVjVfEmtcaHklGCCFEzGauZMRhoDzgq7Uu/Oa+k1rr/GaLNJIk\nGRF1AQFQsKDRnLJu3fcfi0rlwbNnxlKPUaPA1dVohlmy5Lvxhg0zqihWrYLCH37AJYT/UCQcAAAg\nAElEQVQwk7Xn1zJw20COdTwWkjyIyhv5e8/vsf3q9pDkxPNXzymdpTQrzq7gr3Z/USpLKQtEH7ag\n4CAKTynMEM8h1P2ibsRPMJMJByYwfNdw/J75UTNnTUZUHEGetHmiPW54/yZLTi+h35Z+HO5wmJQJ\nU0Z7rv/acnkLrVe25mjHo6RLnO69x0xNmJy8c5Lyc8qzq+2uKDeZjAyfcz6092mPT1MfSmYuGXK/\nGqII/j4YrzVeXHt8jdVNV4dZzROTnfv3HGVmlGFX2118kfaLkGTgca/jZEya0dbhWYUkI4QQImYz\nVzJin9a6pFLqaKhkxAmtdQEzxhopkoyIuqFD4fBhWLnyw6US0VkG4e8PM2fCzz8bO3D06wdVqkDR\nokYiIuOn8d5JCJtxm+FG1+JdaZKvCWC+N/JXHl7B96ov7Xza2eTEYMvlLXit8eJ059MhjTktJVgH\n029zP1afX8365utxGe/C6MqjGbF7BLVy1WKI5xAyJcsU5fE/9m9y7t9zuM10Y0PzDbhmdI3OSwjX\noK2DOOx3mHXN13208uJjMT5/9Zxi04rRr0w/WhdqbbEY/2vdhXW0WdmG5Y2Xh/SDUEMUIyqMYMGp\nBexqu4tk8ZNFMErMNOXQFKYcnsLudrtJPDwx02pOo32R2L0PdmQqiYQQQtg3cyUj/gC2Av0xGld2\nBxy11l7mCjSyJBkRNefPQ+nScPQoZMny4ePm6Mnw+jUsWAAjRxrLQJ4/h0SJojemECJ8u67tou2q\ntpztejZk9wVzf6poy08pay6sSbnPytGrVC+LzREQGEDrla258eQGPk19SJUwVchrfuT/iBG7RzDt\nyDQ6unakX5l+JE+QPNJzhPU9fPH6BSWml6Brsa50LNrRXC8nTIHBgXjM8qBOrjr0KdPH5BgB2q1q\nR5AOYnad2RaNMSybLm2i+fLmLG24FI/PPFBDFJmTZWbvV3vJnCyz1eOxFq01dRbV4fLDy5y6e4rg\n74M/ieUZQgghYgdTkhGmLErtBuQFAoCFwBOgR/TDE9akNXh5weDBYScizMXREVq3hlOnjNuSiBDC\n8n7a/RN9y/SN8jaQ9m5kpZH8tPsn/n3xr0XGf/jyIZXnVSZIB7Gl1RZSJXx/q58UCVIwouIIjnU8\nxu1nt8nxWw7G7B1DQGBAtObVWtN5bWcKpi9IB9cO0RrLFHHjxGVh/YWM2juK/Tf2m/y8eSfm8dc/\nfzGx2kQLRvdxlbNXZlGDRTRc0pCRe0YCsKbpmlidiADjTdz0mtNJkSBFyG0hhBAiNokwGaG1fqG1\nHqS1Lqa1Lvrmur81ghPmM3cuPH4MXbvaOhIhhDkd9TvK8TvHaV3QeqXz1pY7TW6a5mvKEN8hZh/7\n2qNrlJlRBlcnVxY1WESCuAk+emyW5FmYUXsG21pvY+uVreSemJv5J+YTrIOjNPeMozM4eOsgk2tM\nttqJZtbkWZlcfTJNlzXlkf+jCI8/f/88PTf2ZFGDRSSJl8QKEYatfLbyLG20lJF/GcmIghkK2iwW\na0qbOC272u6ydRhCCCGERXw0GaGU8gnvYs0gRfT8+y/07Ws0mowbOz84FeKTNWLPCHqV7GXxfgq2\n9oPHDyw6vYgz986YbcyjfkcpM6MMHV078muVX03ewSJfunysabaGWbVnMf7AeIpOLcrmS5sjNfex\n28fov7U/SxsutfpJft0v6lItRzU6rO5AeEse/QP9aby0MUM9h9rFyb+7szt3vr1j6zCEEEIIYSbh\nnZqWAv7BWJqxH5D6wBiqTx9o0sTY7UIIEXtcuH+BbVe2Mb3mdFuHYnGpE6VmgNsA+mzuw5pma0Lu\nj2rDu40XN9JiRQsmV59M/Tz1oxSTx2ce7PtqH0v/XkrndZ3JliIbP1f8mcJO4W8d9Nj/MQ0WN+C3\nL3/ji7RfRGnu6BpVeRQlp5dk2pFpH10i8u2mb8meMjteRW3WIuoDslRBCCGEiD3CS0ZkACoBTYFm\nwFpgodb6tDUCE+bh6wtbtsDff9s6EiGEuf2y5xc6F+1M0vhJbR2KVXQp3oVJhyax+dJmKmWvBLyf\ndFBDFL5tfCMcZ+bRmQzYOoCVjVdSJmuZaMWklKJh3obUyV2HaUemUW1BNSpkq8Cw8sP4LMVnHxyv\ntabtqrZUyV4lZOcTW0gQNwGLGizCbaYbpbOUJl+6fO89vvzMctZdWMeRjkckASCEEEIIi/hoTarW\nOkhrvUFr3RooCVwEfJVS0nUghggIgI4d4bffIOlHzlV8fcHb27h4eLy77utrrSiFEFFx88lNlp1Z\nRvcS3W0ditXEc4jHyEoj/9/encdpOe9/HH9/2mjXXqSSSqQVJeV0JwmFivZdks4hRzgnyzk1frIL\nRzgUNSekvSja1I0WldKmDRkSorQqLTPf3x8zarRMM8099/deXs/HYx5z3dd93df1vtOle95zfb+X\nBswaoOSU5Cy/3jmnwcHBevSTR/VRz4+yXUSklzd3Xv31sr9q450bVaV4FV3y2iUaMHOAtu/b/qft\nnvv0OW3evVlDWwwN2bFP1wUlL9AzzZ9RhwkdtO/QviPrk3Ym6Y5pd+idW945MnkiAABAqGV4a08z\nO0NSS6VeHVFJ0ruS3nDObQlLupPn4taemZCQIK1YIU2e7Of4obhVKIATGzBzgJxzeu7a546sO90h\nC5nh89ae6Tnn1DSxqTrX7Hzc8IKMMh5KPqS+0/pq9c+rNa3TNJUpVOaUx8rOe/5p70965KNHNH7t\neN3b8F7d3eBuFXisgEoXLK3Fty0+4VUTvnSf3F1n5D5Dw28cLkswXV7+ct184c2674r7fEc7oUj5\nuxhO8fieAQDRLTO39jzpMA0z+5+kiyW9LynBObcmxPmQgzZsSL0i4vPPfScBEGrb923XqBWjtKrf\nqj+tD0XpEOnMTENbDFXLt1uq48UdVeSMIqd8zZ4De9RufDvlyZVHwR5BFcxXMMdzli1UVi+3fFl/\nv/zvevDDB1X1xaqSpDdufCOiighJeun6l3TJa5fonTXvSJKK5y+uAQ0HeE4FAABiXUZTh3eVVFXS\n3ZIWmtnutK89ZrY7PPFwOpyT7rhD+te/pHPP9Z0GQKi9uORFtb2wrcoXKe87ihf1ytXTtVWu1eOf\nPH7KbX/c86OajGqiikUrakrHKWEpItKrVqKaJrSfoAntJ0iSWlZrGdbjZ0bhMwrrnVve0V+n/1WS\nlNg6MdN3FgEAADhdJ70ywjnHJ5EIEwwencshGJQCgdTlQODosiQlJkp79kh3MrsHEHP2Htyrl5a+\npAW3LvAdxashVw1RrVdqqe+lfU96pcHaX9bq+reu1+2X3K4HGj/gdSLGy8tf7u3YmVGvXD2NuHGE\nbh53s0oWKOk7DgAAiAMZ3U0DESZ96WB24kkmt22T/vlP6f33pdy5wxgOQFi8tuw1Na3UVNVKVPMd\nxauzC5+t/g36a+CcgXrnlneOe/6jpI/UfkJ7PdP8GXWr3S3T+00/70aTik00ODhYUnwMgWl7YVvf\nEQAAQByhjIgx990nde4sXXKJ7yQAQu3A4QN6dtGzmtZpmu8oEeG+K+7TBcMu0MLNC3XFuVccWf/O\nmnfU/4P+GnPzGDWr3CxL+4yH0gEAACASUEbEkHnzpLlzpS++8Jch/VCSP24VKh0/lARA1v1v5f9U\nq0wt1S1XNyzHi/SrBArkLaDHrnpM98y8R4t6L5IkPbPwGb2w+AXN6T5HtcrU8pwQAAAAJ0MZESN+\n/z110sphw6TChf3loHQAckZySrKeWviUXr/x9bAdM1JKh4x0qdVF/1nyH7216i1JUuLKRC3qvShu\nJ/cEAACIFpQRMeLxx6UaNaQbb/SdBEBOmLB2gkoXLK0rK1zpO0pEyWW5NPSaoWqa2FSSNL/XfBU9\ns6jnVED2RfqVSQAAZJc553xnyDIzc9GYO5TMUm/hKUnr10uNG0srVkjl+WUgEHOcc6r7al09etWj\nalWtle84EWnW17PU4s0WcoMi998GS7CIzidFbsb0P5gHk4JHfhjnB3MAACKTmck5l+GtzLgyIso5\nJ/XtK/373xQRQKz64KsP5OTUsmpL31Ei1jXnX+M7AnIQpQMAALGHMiLKjRol/fab9Le/+U4CxDaf\nv5l9fP7jGthooMwyLJcBAACAqEEZkYPS31kiGDw6sWOoJnn85Rdp4EDpgw+k3Lmzvz8AJ5e+dLAE\nU7BnMCzHnf/dfP2w5we1q9EuLMcDAAAAwoEyIgelLx3MjhYToXLffVKXLlK9eqHdL4DI8fj8x/WP\nK/6hPLn43zUAAABiB59uo1gwKH3xhe8UAHLKip9WaMVPKzSx/UTfUXCauCMCAADAiVFGRKHt21O/\nDxsmFSrkNwuAnPPE/Cd0z+X36Mw8Z/qOgtMUDaUDhQkAAPCBMiJK/P67NH269Oab0ty5qetuuMFv\nJgA556tfv9KH33yo4TcM9x0FMY7SAQAA+EAZEcFSUqSPP04tICZNkurWlbp2Tb2Dxlln+U6HaOLz\nThA4sVP9N3lqwVPqd2k/FT6jsL+QAAAAQA4x55zvDFlmZi7acptJmY28Zk1qAfH221KxYqkFRKdO\nUvnyp7c/ID1LMLlB/OXJjlD/GR67vy27t6jmKzW18a6NKlmgZMiOE+v4uw0AABAZzEzOuQzvS8+V\nERFiyxZpzJjUEmL79tS7ZEyfLtWs6TsZgHAbumioetTuQREBAACAmEUZ4dHu3anDL958U1q+XGrb\nVnr+eekvf5Fy5fKdDoAP2/dt18gVI7Wq3yrfUQAAAIAcQxkRZocOSTNnphYQM2ZITZtKd9whtWol\nncmE+UDcG7ZkmNpUb6PyRcqfemNwJwgAAIAoRRkRBn/M7XDnndK4cdIFF6TOA/Hyy1Lx4n6zAcia\nnb/vzLF97z24Vy8tfUmf9Pokx44RaygdAAAAohNlRBj06JH6vVw5afFi6bzz/OYBcHp27N+h+iPq\nS5KueP0K3XTBTWpdvbUuKHlBSPY/fNlwBSoFQrY/AAAAIFIxM0EOe/ddadGi1OWHHqKIAKJVckqy\nukzqopZVW0qS/t3k30ramaSr/neVqg+rrn/O/qcWbl6oFJdyWvs/cPiAnl30rB5o/EAoYwMAAAAR\niTIiB+3aJf3tb9KIEb6TAMiuf837l/Yf3q+nmz8tSbq2yrV6pdUr2nzPZo1uM1p5c+fV7e/drnLP\nllOfd/to2sZp2n9of6b3P3rVaF1c+mLVLVc3p94CAAAAEDHMuei7J7uZuWjI3bevZCb997+p30MZ\nOdT7Q/ywBJMbxF+erJiwdoLum3WflvZZqlIFS2X4Z/j1r19r6oapmrphqlb8tELNzmum1tVbq2XV\nlipRoMQJX2MJpqrFq2r4DcPVpFKTnHwrAAAAQI4zMznnLKNtmDMihwSD0vvvS2vW+E4CIDtWb12t\nftP7aWbXmSpVsNQptz+/+Pka0HCABjQcoG37tmn6xumavH6y7vrgLtUtW1c3XXCTbqp+kyoXq/yn\n15UsUFJ/qfiXnHobAAAAQEShjMgB+/ZJffqk3i2jaFHfaYBU3+/+Xm3GtpEkOedklmFRCUm/7v9V\nbca20fMtnle9cvWy/PqSBUqqR50e6lGnh/Yf2q85m+Zo6oapemLBEypTsMyRYkKSHrzyQf6bAAAA\nIG4wTCMH/OMf0ubN0pgxR9eFYlhFMJj69cdyIJC6HAgcXQZOZO/Bvbpy5JVqUrGJXlj8glpXb63X\nWr2Wqd/0x6vklGRd//b1qlGqhoa2GPqn57I71CU5JVmLtyzWlPVTNHXDVG3cvlEp/06hjAAAAEBM\nyMwwDcqIEPvsM6lVK2nVKql06aPrmeMBviSnJKvtuLYqmb+kRtw4QrkeyaV/XPEPjV41WsNvGK6W\n1Vr6jhiRBs4ZqKU/LNXMrjOVJ9efLyIL9bwbzOMBAACAWMKcEWF26JDUu7f0zDN/LiIAn/4555/a\nfWC3xrcbf+Q37082f1LXV71ePab00LSN0/TMNc+oYL6Cmd5nMCmoYFLwyHKgUkCSFKgUOLKcFaHe\nX3aNXTNWY78Yq6V9lh5XRAAAAADIPq6MCKEhQ6QFC6Tp01OvhEiPKyPgw/Blw/X0wqf16W2fqnj+\n4pL+/Fv4Xb/v0l0f3KXFWxZrdJvRqn9O/SwfI9auElj500pdPfpqze42W3XK1jnhNrH2ngEAAIBQ\nysyVEbnCFSbWrV8vPf/80dt4Ar59uOlDPTzvYU3vPP1IEXGsomcW1f/a/E+PNn1UN4y5QY989IgO\npxwOc9LIsX3fdrUZ20b/ufY/Jy0iAAAAAGQfZUQIpKSkDs8YPFiqUMF3GkBav229Ok/qrLG3jFXV\nElVPuX27Gu20/Pblmv/dfDV+o7G+3P5lGFJGlsMph9VxYkfdfOHN6lSzk+84AAAAQExjMHQIvPxy\n6tUQ/fr5TgJI2/ZtU6u3W+mJZk9kab6Fc4qcoxldZ2jYkmFq+HpDPdbsMfWp1ydu7vDwwJwHJEmP\nX/34CZ9PP69Fk4pNNDg4WJK/eS0AAACAaEYZkU3ffpt6RcT8+VKuY64zSX8rziZNUreTuBUncs6B\nwwfUZmwb3XLRLepVt1eWX5/Lcql/g/66uvLV6jqpq6ZtnKYRN45Q6YKxPSPrmNVjNHHdxAwnrKR0\nAAAAAEKHMiIbnJPuuEMaMECqXv345ykdEE7OOfV5r49KFyytx5o9lq19XVTqIn1626caHBys2v+t\nrddavaYbLrghREnDI7N36Fjx0wr1n9Ffc7rNUYkCJcIfFAAAAIhDlBHZ8Oab0o8/Svff7zsJID32\nyWNa+8tafdzrY+Wy7E8Hky93Pj3W7DFdX/V6dZvcTe9tfE9DWwxVoXyFQpA256UvHSzBFOwZPG6b\nbfu2qc3YNnrp+pdUu2zt8AYEAAAA4hgTWJ6mn3+W7rtPev11KW9e32kQ78Z9MU6vLntV73Z6VwXy\nFgjpvhtXaKyVd6zUoZRDqvtqXX36/ach3b8vh1MOq8OEDmp/UXu1r9HedxwAAAAgrnBlxGnq31/q\n2VO65BLfSRDvFn+/WH97/2+a3W22zi589gm3ye7ki0XOKKKRN43UpHWT1Pqd1up7SV89/JeHlTd3\n9DZx/5j9D+XJlSfbQ1oAAAAAZB1lRDrpJ5wMBo/O93Ds3A/vvistWyaNHBnOdMDxvt35rdqMbaM3\nbnxDdcrWOel2oZp8se2FbdWwfEPd+u6tavRGI73Z9s0Tbuec0/7D+7XnwB7tObgn898P7pEkjV0z\nVq2qtVLBfAWznflE3lz1pt7d8K6W9Fmi3Lly58gxAAAAAJycOed8Z8gyM3M5ndssdYLKY+3aJV18\ncep8EU2a5GgERInMTpQY6v3tPrBbjd5opF51emlAwwGn/wZOg3NOLy99WYOCg7R9/3ZddvZl2nNw\nj/Ye3Ks9B1K/582dV4XzFVbhMwqf8HuhfIVOuL712NZqcX4LLfp+kVqc30Lta7TX9VWvz9bwE0sw\nuUGpJ/TyH5erxZstNLf7XNUsUzNUfyRZFuq/NwAAAECkMDM55yzDbSgjTnaME5cRffumPvff/+bo\n4RGl0v/Qm5P7O5xyWDeOuVEVilbQKy1fkVmG53mO+WbHN6r8n8pa1HvRcUXD6Q7h+OM9b9+3XZPX\nT9a4L8ZpyZYluq7qdWp/UXtdW+Va5c+b/7T2+ctvv+iy4Zfp6eZPq12NdqeVDwAAAEDGMlNGMEwj\nC4JB6f33pTVrfCdBvBswc4AOpxzWi9e96K2IkKTzip0nSbq8/OUh33eJAiV0W73bdFu92/TLb79o\n0rpJGrZ0mHpN7aWW1Vqq/UXt1aJKC52Z58xM7e9Q8iG1n9BenWt2pogAAAAAPONuGpm0b5/Up4/0\n8stS0aK+0yCevbTkJc3ZNEfj2o2L6gkks6JUwVLqe2lffdj9Q224c4Man9tYzy9+XuWeLafuk7tr\n2sZpOnD4QIb7uH/2/cqfJ7/+r+n/hSk1AAAAgJOhjMikwYOlSy+VbrjBdxLEsxlfzdCjnzyqaZ2n\n6awzz/Idx4syhcqo32X9NK/HPK3961rVP6e+nlrwlMo9W049p/TUB19+oIPJB4973fQvp+uttm8x\nYSUAAAAQARimkQmffSYlJkqrV/tOgni25uc16j65uyZ3mKzKxSr7jhMRyhUupzvr36k769+pLbu3\naOK6iXr0k0fVdXJXtb6gtdrXaK+iZ6ZeyjSlwxQVy1/Mc2IAAAAAEmXEKR06JPXuLT37rFS6tO80\niFdb927VDWNu0HMtnlOjCo18x4lI5xQ5R/0b9Ff/Bv21eddmTVg7QYM/GqylW5ZKkmqUruE5IQAA\nAIA/UEacwlNPSeecI3Xp4jsJQiXabqm4/9B+tR7bWt1qdVOXWvxFzIxzi56rexreo3sa3qMDhw/o\nzCGZm+QSAAAAQHhQRmRg3Trpueek5ctTb+eJ2JC+dLAEU7Bn0GueU7n13VtVsWhFJQQSfEeJSmfk\nOcN3BAAAAADHoIzIwG23SQkJUoUKvpMgniXtTNLc7nO93sITAAAAAEKJMiIDZlK/fr5TINrsPbj3\nyNeeA3v+9Hjvwb3ac/D4dSdbL6VOvJg/b37P7woAAAAAQocy4gS+/Tb1+4gRUi5ufooMpLgULdq8\nSJPXT9aU9VMkSWWfKatC+Qod91X4jMKpy3mPritb6PhtC+crfGT57KFnq0yhMp7fZc5LP49Hk4pN\nNDg4WFLkzuMBAAAAIHsoI47h3NGrIapX95sFkelg8kHN+2aeJq2bpKkbpqpUwVJqU72NJrSfoLqv\n1tXeB/f6jhh1KB0AAACA+EIZcYzZs6WvvvKdApFm78G9mvHVDE1eP1kffPmBqpesrjbV22j+rfNV\npXgV3/HCjisZAAAAAGQHZUQ6KSnSwIHSY49J7dr5TgPftu3bpvc2vKfJ6ycrmBRUw3Mbqk31Nnqm\n+TMqV7ic73heUToAAAAAyA7KiHTGjZPy5JFuvtl3EviyeddmTVk/RZPXT9ayH5fp6spXq32N9kps\nnahi+Yv5jgcAAAAAMYEyIs3Bg9JDD0nDh6feRQPxY90v6zR5/WRNXj9Z3+z4Rq2qtdLdDe7WNedf\nw10sAAAAACAHUEakGT5cqlpVuuoq30kQDikuRY9/8rgkqfno5mpdvbWevPpJXVnhSuXNnddzOgAA\nAACIbZQRkvbulR59VHr/fd9JEC73zbpPS7YskSR9d893ymXcwzXWMMkmAAAAELkoIyQNHSo1bSrV\nres7CcLh2YXPatbXs/RJr09U/KniFBExitIBAAAAiFxxX0b88ov0wgvSkiW+kyAc3lr1ll5Y/IIW\n3LqACSkBAAAAwJO4LyOGDJE6d5bOP993EuS02V/P1oBZAzS3+1ydW/Rc33EAAAAAIG7FdRnxzTfS\n6NHS2rW+kyCnLf9xubpM6qKJ7SeqRukavuNkiLkOAAAAAMQ6c875zpBlZuZCkbtbN6lyZSkh4UTH\nkKLwjwYnsGnHJjV+o7GGXT9MbS9s+6fnLMHkBmXvP3T68iCYFDxSGFAeAAAAAIhHZibnnGW4TbyW\nEatWSc2bS19+KRUpcqJjUEbEgp9/+1mN3mikAZcPUL/L+h33fCjKCAAAAADAUZkpI7zdRsDM7jGz\nNWa2yszeMrN8ZlbJzD41s41mNsbMcmwYyQMPSA89dOIiArFh78G9avV2K3Wo0eGERQQAAAAAwA8v\nc0aY2dmS7pJU3Tl30MzGSuok6XpJzzrnxpvZK5J6S3o11Mf/6KPUeSImTQr1nhEpQxYOJR9S+/Ht\nVbN0Tf1f0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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f768d50d208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "groups=[explorers_,exploiters]\n",
    "labels=['explorers','exploiters']\n",
    "plt.clf()\n",
    "\n",
    "for i,group in enumerate(groups):\n",
    "    mask=(sf['destinyMembershipId'].isin(group)) & (sf['max_plays']>(minplay)) & (sf['max_plays']<(maxplay)) & np.isfinite(sf[perfvar])\n",
    "    xvals=list(range(1,minplay+1))\n",
    "    yvals=sf[mask].groupby('game_n')[perfvar].mean()    \n",
    "    ysems=sf[mask].groupby('game_n')[perfvar].sem()    \n",
    "    yvals=yvals[:minplay];ysems=ysems[:minplay]\n",
    "    xvals.append(100000);yvals[0]=0;ysems[0]=0 #see here for why I have to do this  http://stackoverflow.com/questions/35137961/pandas-plotting-with-yerr-gives-key-error-0        \n",
    "    plt.errorbar(xvals,yvals,yerr=ysems,label=labels[i])\n",
    "\n",
    "plt.xlim([0.8,minplay+0.2])\n",
    "plt.ylim([cRmin,cRmax])\n",
    "plt.xlabel('Game n')\n",
    "plt.ylabel('Mean ' + str(perfvar) + ' (SE bars)')\n",
    "plt.legend(loc=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Players who start off better at the game explore more, but there's no indication that exploring more faciliated a greater learning rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Calculate, for each player, a  grimscore Z score \n",
    "(normalised against distribution for all players with that number of plays)\n",
    "'''\n",
    "\n",
    "grim_mu=players.groupby('max_plays')['grimscore'].mean()\n",
    "grim_sigma=players.groupby('max_plays')['grimscore'].std()\n",
    "\n",
    "players['grimZ']=(players['grimscore']-grim_mu[players['max_plays']].values)/grim_sigma[players['max_plays']].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 4 :  Exploration in play style: social play\n",
    "\n",
    "Destiny records both your 'kills' but also your 'assists' (when you are near when another player makes a kill). By dividing players on their number of assists in their first 25 games, perhaps we can index propensity to explore the social aspects of the game. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sf['assistsX']=sf['killsDeathsAssists']-sf['killsDeathsRatio']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "players=players.merge(sf[sf['game_n']<26].groupby('destinyMembershipId')['assistsX'].sum().reset_index(), how='left',left_on='destinyMembershipId',right_on='destinyMembershipId')\n",
    "players['assists_rate']=players['assistsX']/25\n",
    "\n",
    "mask=~players['assists_rate'].isnull()\n",
    "q1=players[mask]['assists_rate'].quantile(0.25)\n",
    "q2=players[mask]['assists_rate'].quantile(0.5)\n",
    "q3=players[mask]['assists_rate'].quantile(0.75)\n",
    "\n",
    "minplay=50\n",
    "maxplay=100\n",
    "#divide all players according to weather they score above or below expected on their grimscore\n",
    "packplayers=players[players['assists_rate']>q1]['destinyMembershipId']\n",
    "lonewolves_=players[players['assists_rate']<q3]['destinyMembershipId']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rryy4ORQSEqT77gvtXBfp80YEK5g41hNPuGE0114rXXBBaI7hq6y9dwJBzwkA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\n+A4QLQ4cC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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f769ac9cc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "xvals=list(range(1,minplay+2))\n",
    "\n",
    "mask=(sf['destinyMembershipId'].isin(packplayers)) & (sf['max_plays']>(minplay)) & (sf['max_plays']<(maxplay)) & np.isfinite(sf[perfvar])\n",
    "yvals=sf[mask].groupby('game_n')[perfvar].mean()    \n",
    "ysems=sf[mask].groupby('game_n')[perfvar].sem()    \n",
    "yvals=yvals[:minplay];ysems=ysems[:minplay]\n",
    "yvals[0]=0;ysems[0]=0 #see here for why I have to do this  http://stackoverflow.com/questions/35137961/pandas-plotting-with-yerr-gives-key-error-0        \n",
    "plt.errorbar(xvals,yvals,yerr=ysems,label='social players')\n",
    "\n",
    "mask=(sf['destinyMembershipId'].isin(lonewolves_)) & (sf['max_plays']>(minplay)) & (sf['max_plays']<(maxplay)) & np.isfinite(sf[perfvar])\n",
    "yvals=sf[mask].groupby('game_n')[perfvar].mean()    \n",
    "ysems=sf[mask].groupby('game_n')[perfvar].sem()    \n",
    "yvals=yvals[:minplay];ysems=ysems[:minplay]\n",
    "yvals[0]=0;ysems[0]=0 #see here for why I have to do this  http://stackoverflow.com/questions/35137961/pandas-plotting-with-yerr-gives-key-error-0        \n",
    "plt.errorbar(xvals,yvals,yerr=ysems,label='lone wolves')\n",
    "\n",
    "plt.plot([25,25],[cRmin,cRmax],'-',color='red')\n",
    "plt.xlim([0,minplay])\n",
    "plt.ylim([cRmin,cRmax])\n",
    "plt.xlabel('Game n')\n",
    "plt.ylabel('Mean ' + perfvar + ' (SE bars)')\n",
    "#plt.annotate(labeltext,xy=(minimum_plays*0.8, .55),xycoords='data')\n",
    "plt.legend(loc=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If anything, propensity to social play predicts *decreased* learning on subsequent games"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 5: Exploration in play mode\n",
    "    \n",
    "Destiny has several play modes: raids, quests, player vs player etc. We calculate the entropy of each players distribution of their time over these play modes and then use this as an index of their propensity to explore the play modes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "players=players.merge(pf,how='left',left_on='destinyMembershipId',right_on=['userid'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask=~players['eventEntropy25'].isnull()\n",
    "q1=players[mask]['eventEntropy25'].quantile(0.25)\n",
    "q2=players[mask]['eventEntropy25'].quantile(0.5)\n",
    "q3=players[mask]['eventEntropy25'].quantile(0.75)\n",
    "\n",
    "minplay=50\n",
    "maxplay=100\n",
    "#divide all players according to weather they score above or below expected on their grimscore\n",
    "mode_explorers_=players[players['eventEntropy25']>q1]['destinyMembershipId']\n",
    "mode_exploiters=players[players['eventEntropy25']<q3]['destinyMembershipId']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZMzdhYHZbanPKFBeyTJrkuuQjOD786UNN2TBFq7quOi2YSEn3a7tr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\npDYVoIxRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f76a773f2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "xvals=range(1,minplay+2)\n",
    "\n",
    "mask=sf['destinyMembershipId'].isin(mode_explorers_) & (sf['max_plays']>(minplay)) & (sf['max_plays']<(maxplay))  & np.isfinite(sf[perfvar])\n",
    "yvals=sf[mask].groupby('game_n')[perfvar].mean()    \n",
    "yvals=sf[mask].groupby('game_n')[perfvar].mean()    \n",
    "ysems=sf[mask].groupby('game_n')[perfvar].sem()    \n",
    "yvals=yvals[:minplay];ysems=ysems[:minplay]\n",
    "yvals[0]=0;ysems[0]=0 #see here for why I have to do this  http://stackoverflow.com/questions/35137961/pandas-plotting-with-yerr-gives-key-error-0        \n",
    "plt.errorbar(xvals,yvals,yerr=ysems,label='mode explorers')\n",
    "\n",
    "mask=sf['destinyMembershipId'].isin(mode_exploiters) & (sf['max_plays']>(minplay)) & (sf['max_plays']<(maxplay)) & np.isfinite(sf[perfvar])\n",
    "yvals=sf[mask].groupby('game_n')[perfvar].mean()    \n",
    "yvals=sf[mask].groupby('game_n')[perfvar].mean()    \n",
    "ysems=sf[mask].groupby('game_n')[perfvar].sem()    \n",
    "yvals=yvals[:minplay];ysems=ysems[:minplay]\n",
    "yvals[0]=0;ysems[0]=0 #see here for why I have to do this  http://stackoverflow.com/questions/35137961/pandas-plotting-with-yerr-gives-key-error-0        \n",
    "plt.errorbar(xvals,yvals,yerr=ysems,label='mode exploiters')\n",
    "\n",
    "plt.xlim([0,minplay])\n",
    "plt.ylim([cRmin,cRmax])\n",
    "plt.xlabel('Game n')\n",
    "plt.ylabel('Mean ' + perfvar + ' (SE bars)')\n",
    "#plt.annotate(labeltext,xy=(minimum_plays*0.8, .55),xycoords='data')\n",
    "plt.legend(loc=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although those who start of with higher performance also have higher game mode entropy, there is no indication that exploration of the game mode leads to greater learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model: which factors predict learning\n",
    "    \n",
    "We fit a linear regression to each players play number vs performance data, extracting the slope and the intercept as crude gauges of both their performance on game 1 and the rate of improvement in their performance.\n",
    "\n",
    "We then enter all the variables considered into a single regression model to try and predict these players characteristics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "players['KDRslope']=np.NaN\n",
    "players['KDRintercept']=np.NaN\n",
    "players['KDRstd_err']=np.NaN\n",
    "players['CR_slope']=np.NaN\n",
    "players['CR_intercept']=np.NaN\n",
    "players['CR_std_err']=np.NaN\n",
    "\n",
    "\n",
    "total_n=len(players)\n",
    "countn=0\n",
    "\n",
    "for i,group in sf.groupby('destinyMembershipId')[[perfvar,'game_n']]:\n",
    "    countn+=1 \n",
    "    \n",
    "    #if countn%100==0:\n",
    "    #    print(\"Now on \" + str(countn) + \" of \" + str(total_n)) #progress indicator\n",
    "\n",
    "    if (i in players['destinyMembershipId'].values):\n",
    "        yKDR=group.sort('game_n')['killsDeathsRatio'].values\n",
    "        yCR_=group.sort('game_n')[perfvar].values\n",
    "        x=range(1,len(yKDR)+1)\n",
    "                \n",
    "              \n",
    "        #plot regression lines for subset\n",
    "        slope, intercept, r_value, p_value, std_err = stats.linregress(x,yKDR.tolist())  \n",
    "        players.loc[players.destinyMembershipId == i,'KDRslope'] = slope\n",
    "        players.loc[players.destinyMembershipId == i,'KDRintercept']=intercept\n",
    "        players.loc[players.destinyMembershipId == i,'KDRstd_err']=std_err\n",
    "        \n",
    "        slope, intercept, r_value, p_value, std_err = stats.linregress(x,yCR_.tolist())  \n",
    "        players.loc[players.destinyMembershipId == i,'CR_slope']=slope\n",
    "        players.loc[players.destinyMembershipId == i,'CR_intercept']=intercept\n",
    "        players.loc[players.destinyMembershipId == i,'CR_std_err']=std_err "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "players[['destinyMembershipId','CR_slope','CR_intercept','max_plays','space25','assists_rate','grimZ','eventEntropy25']].to_csv('forR.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#now it is easiest to run this in R\n",
    "os.system(\"sudo Rscript regmodel.R\"); #(except it doesn't work in jupyter for some reason)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4fae1Npxkr75VBAAAAAyMXlbl+GFVnZfkE6P7K5L8sH8lAQAAAIOilxETZyY5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\nptbRvTIzH9nm+Q7ebxBILJcJnma01aj12k8G7kU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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f76a3cd6710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now we can load data from regression\n",
    "slope_betas=pd.read_csv(\"slope_betas.csv\")\n",
    "slope_se=pd.read_csv(\"slope_se.csv\")\n",
    "intercept_betas=pd.read_csv(\"intercept_betas.csv\")\n",
    "intercept_se=pd.read_csv(\"intercept_se.csv\")\n",
    "\n",
    "yvalsS=slope_betas[1:]['x'].values\n",
    "ysemsS=slope_se[1:]['x'].values\n",
    "yvalsI=intercept_betas[1:]['x'].values\n",
    "ysemsI=intercept_se[1:]['x'].values\n",
    "\n",
    "xvalsS=range(len(yvalsS))\n",
    "xvalsI=[xv+0.2 for xv in xvalsS]\n",
    "plt.clf()\n",
    "plt.errorbar(xvalsS,yvalsS,yerr=ysemsS,marker='o',color='b',fmt='none',label='Slope')\n",
    "plt.plot(xvalsS,yvalsS,marker='o',ls='',color='b')\n",
    "plt.errorbar(xvalsI,yvalsI,yerr=ysemsI,marker='s',color='g',fmt='none',label='Intercept')\n",
    "plt.plot(xvalsI,yvalsI,marker='s',ls='',color='g')\n",
    "plt.plot([-1,5],[0,0],'k')\n",
    "plt.ylabel('standardised regression coefficient (beta)')\n",
    "plt.xlim([-0.25,4.45])\n",
    "plt.legend(loc=0)\n",
    "\n",
    "labels=['games played','spacing','social play','grimoire','event entropy']\n",
    "plt.xticks([x+0.1 for x in xvalsS], labels, rotation=23);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Graph shows the standardised regression coefficient (beta, with SE bars) for each factor\n",
    "\n",
    "Slope (learning rate) predictors are in blue\n",
    "\n",
    "Intercept (initial performance) predictors are in green\n",
    "\n",
    "Only spacing predicts enhanced learning (social play predicts slower learning)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thanks for reading this far.\n",
    "\n",
    "Reminders:\n",
    "\n",
    "Paper: https://mindmodeling.org/cogsci2017/papers/0615/index.html  \n",
    "Full scripts & sample data: https://osf.io/c59n9/  \n",
    "Poster: http://tomstafford.staff.shef.ac.uk/docs/CogSci17_TomStafford.jpg  \n",
    "This notebook: https://github.com/tomstafford/destiny/blob/master/variability.ipynb  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}