muneebalam/scrapenhl2

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pylab import *\n",
    "%matplotlib inline\n",
    "import matplotlib.colors as mplc\n",
    "\n",
    "from scrapenhl2.scrape import team_info, teams, players, autoupdate\n",
    "from scrapenhl2.manipulate import manipulate as manip, add_onice_players as onice\n",
    "# autoupdate.autoupdate()  # uncomment this to update data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "The purpose of this script is to generate shot counts for skaters after faceoffs.\n",
    "\n",
    "For example, CA after 5 and 10 seconds for Nicklas Backstrom after defensive-zone faceoff wins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "team = team_info.team_as_id('WSH')\n",
    "season = 2017\n",
    "pbp = teams.get_team_pbp(season, team)\n",
    "toi = teams.get_team_toi(season, team)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is how we'll approach the problem:\n",
    "\n",
    "- Filter TOI to 5v5 and go wide to long on players\n",
    "- Isolate faceoffs from the PBP\n",
    "    - Get faceoff zones\n",
    "    - Filter for 5v5\n",
    "    - Get faceoff zones\n",
    "- Join faceoffs to TOI and code how many seconds after a draw that sec is\n",
    "    - Join faceoffs, have an indicator of last faceoff \n",
    "    - Fill forward on last faceoff time\n",
    "    - Calculate difference between time and last faceoff time; filter for time <= 15\n",
    "- Isolate shots from the PBP\n",
    "    - Join to TOI\n",
    "    - Filter for 5v5\n",
    "    - Join to first dataframe on game, player, and time\n",
    "    - Group by player, time since draw, draw zone and type, and which team took the shot, and sum\n",
    "- Repeat for each team (not done here)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5v5 TOI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Game</th>\n",
       "      <th>PlayerID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>20008</td>\n",
       "      <td>8475200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>20008</td>\n",
       "      <td>8475200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>20008</td>\n",
       "      <td>8475200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>20008</td>\n",
       "      <td>8475200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>20008</td>\n",
       "      <td>8475200.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time   Game   PlayerID\n",
       "0     0  20008  8475200.0\n",
       "1     1  20008  8475200.0\n",
       "2     2  20008  8475200.0\n",
       "3     3  20008  8475200.0\n",
       "4     4  20008  8475200.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter to 5v5\n",
    "toi = manip.filter_for_five_on_five(toi) \\\n",
    "    [['Game', 'Team1', 'Team2', 'Team3', 'Team4', 'Team5', 'Time']] \\\n",
    "    .drop_duplicates()  # sometimes these appear\n",
    "toi = toi.melt(id_vars=['Time', 'Game'], value_name='PlayerID') \\\n",
    "    .drop('variable', axis=1)\n",
    "toi.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Link times to time since last draw and faceoff zones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Game</th>\n",
       "      <th>_Secs</th>\n",
       "      <th>Season</th>\n",
       "      <th>ZS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20008</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>NL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20008</td>\n",
       "      <td>330</td>\n",
       "      <td>2017</td>\n",
       "      <td>OL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20008</td>\n",
       "      <td>368</td>\n",
       "      <td>2017</td>\n",
       "      <td>DW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20008</td>\n",
       "      <td>386</td>\n",
       "      <td>2017</td>\n",
       "      <td>NW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20008</td>\n",
       "      <td>441</td>\n",
       "      <td>2017</td>\n",
       "      <td>DL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Game  _Secs  Season  ZS\n",
       "0  20008      1    2017  NL\n",
       "1  20008    330    2017  OL\n",
       "2  20008    368    2017  DW\n",
       "3  20008    386    2017  NW\n",
       "4  20008    441    2017  DL"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get faceoffs\n",
    "draws = manip.filter_for_event_types(pbp, 'Faceoff')\n",
    "\n",
    "# Select only needed columns\n",
    "draws = draws[['Game', 'Team', 'Period', 'MinSec', 'X', 'Y']]\n",
    "\n",
    "# Convert period and mm:ss to time elapsed in game\n",
    "draws = onice.add_times_to_file(draws, periodcol='Period', timecol='MinSec', time_format='elapsed')\n",
    "draws = draws.drop({'Period', 'MinSec'}, axis=1).assign(Season=season)\n",
    "\n",
    "# Get zones\n",
    "directions = manip.get_directions_for_xy_for_season(season, team)\n",
    "draws = manip.infer_zones_for_faceoffs(draws, directions, 'X', 'Y', '_Secs', focus_team='WSH')\n",
    "draws = draws.drop({'X', 'Y'}, axis=1)\n",
    "\n",
    "# Simplify zone notation to just N, O, D\n",
    "draws.loc[:, 'Zone'] = draws.EventLoc.str.slice(0, 1)\n",
    "draws = draws.drop('EventLoc', axis=1)\n",
    "\n",
    "# Combine with team column to change N, D, O, to NW, NL, DW, DL, OW, OL\n",
    "draws.loc[:, 'WL'] = draws.Team.apply(lambda x: 'W' if x == team else 'L')\n",
    "draws.loc[:, 'ZS'] = draws.Zone + draws.WL\n",
    "draws = draws.drop({'WL', 'Zone', 'Team'}, axis=1)\n",
    "\n",
    "draws.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Game</th>\n",
       "      <th>Time</th>\n",
       "      <th>Season</th>\n",
       "      <th>ZS</th>\n",
       "      <th>PlayerID</th>\n",
       "      <th>LastDraw</th>\n",
       "      <th>TimeSinceLastDraw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9410</th>\n",
       "      <td>20008</td>\n",
       "      <td>369</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DW</td>\n",
       "      <td>8468498.0</td>\n",
       "      <td>368.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9415</th>\n",
       "      <td>20008</td>\n",
       "      <td>370</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DW</td>\n",
       "      <td>8468498.0</td>\n",
       "      <td>368.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9420</th>\n",
       "      <td>20008</td>\n",
       "      <td>371</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DW</td>\n",
       "      <td>8468498.0</td>\n",
       "      <td>368.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9425</th>\n",
       "      <td>20008</td>\n",
       "      <td>372</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DW</td>\n",
       "      <td>8468498.0</td>\n",
       "      <td>368.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9430</th>\n",
       "      <td>20008</td>\n",
       "      <td>373</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DW</td>\n",
       "      <td>8468498.0</td>\n",
       "      <td>368.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Game  Time  Season  ZS   PlayerID  LastDraw  TimeSinceLastDraw\n",
       "9410  20008   369     NaN  DW  8468498.0     368.0                1.0\n",
       "9415  20008   370     NaN  DW  8468498.0     368.0                2.0\n",
       "9420  20008   371     NaN  DW  8468498.0     368.0                3.0\n",
       "9425  20008   372     NaN  DW  8468498.0     368.0                4.0\n",
       "9430  20008   373     NaN  DW  8468498.0     368.0                5.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Join to TOI\n",
    "draws_joined = draws.rename(columns={'_Secs': 'Time'}) \\\n",
    "    .merge(toi, how='right', on=['Game', 'Time'])\n",
    "    \n",
    "# Add last faceoff indicator and fill forward\n",
    "draws_joined = draws_joined.sort_values(['Game', 'PlayerID', 'Time'])\n",
    "draws_joined.loc[pd.notnull(draws_joined.ZS), 'LastDraw'] = draws_joined.Time\n",
    "draws_joined.loc[:, 'LastDraw'] = draws_joined[['Game', 'PlayerID', 'LastDraw']] \\\n",
    "    .groupby(['Game', 'PlayerID']).ffill()\n",
    "draws_joined.loc[:, 'ZS'] = draws_joined[['Game', 'PlayerID', 'ZS']] \\\n",
    "    .groupby(['Game', 'PlayerID']).ffill()\n",
    "\n",
    "draws_joined.loc[:, 'TimeSinceLastDraw'] = draws_joined.Time - draws_joined.LastDraw\n",
    "\n",
    "draws_joined = draws_joined[pd.notnull(draws_joined.TimeSinceLastDraw)] \\\n",
    "    .query(\"TimeSinceLastDraw > 0\")\n",
    "\n",
    "draws_joined.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shot counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Game</th>\n",
       "      <th>Team</th>\n",
       "      <th>Time</th>\n",
       "      <th>PlayerID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20008</td>\n",
       "      <td>CF</td>\n",
       "      <td>46</td>\n",
       "      <td>8476344.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20008</td>\n",
       "      <td>CF</td>\n",
       "      <td>46</td>\n",
       "      <td>8475758.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20008</td>\n",
       "      <td>CF</td>\n",
       "      <td>46</td>\n",
       "      <td>8474291.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20008</td>\n",
       "      <td>CF</td>\n",
       "      <td>46</td>\n",
       "      <td>8468498.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20008</td>\n",
       "      <td>CF</td>\n",
       "      <td>46</td>\n",
       "      <td>8474590.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Game Team  Time   PlayerID\n",
       "0  20008   CF    46  8476344.0\n",
       "1  20008   CF    46  8475758.0\n",
       "2  20008   CF    46  8474291.0\n",
       "3  20008   CF    46  8468498.0\n",
       "4  20008   CF    46  8474590.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get shot attempts\n",
    "cfca = manip.filter_for_corsi(pbp)\n",
    "\n",
    "# Select only needed columns\n",
    "cfca = cfca[['Game', 'Team', 'Period', 'MinSec']]\n",
    "\n",
    "# Convert period and mm:ss to time elapsed in game\n",
    "cfca = onice.add_times_to_file(cfca, periodcol='Period', timecol='MinSec', time_format='elapsed')\n",
    "cfca = cfca.drop({'Period', 'MinSec'}, axis=1).rename(columns={'_Secs': 'Time'})\n",
    "\n",
    "# Add on-ice players\n",
    "cfca = cfca.merge(toi, how='left', on=['Game', 'Time'])\n",
    "\n",
    "# Change Team to CF or CA\n",
    "cfca.loc[:, 'Team'] = cfca.Team.apply(lambda x: 'CF' if x == team else 'CA')\n",
    "\n",
    "cfca.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ZS</th>\n",
       "      <th>PlayerID</th>\n",
       "      <th>TimeSinceLastDraw</th>\n",
       "      <th>TOI</th>\n",
       "      <th>CA</th>\n",
       "      <th>CF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DL</td>\n",
       "      <td>8468498.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>87</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DL</td>\n",
       "      <td>8468498.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>87</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DL</td>\n",
       "      <td>8468498.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>85</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DL</td>\n",
       "      <td>8468498.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>83</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DL</td>\n",
       "      <td>8468498.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>84</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ZS   PlayerID  TimeSinceLastDraw  TOI   CA   CF\n",
       "0  DL  8468498.0                1.0   87  0.0  0.0\n",
       "1  DL  8468498.0                2.0   87  1.0  0.0\n",
       "2  DL  8468498.0                3.0   85  3.0  0.0\n",
       "3  DL  8468498.0                4.0   83  1.0  0.0\n",
       "4  DL  8468498.0                5.0   84  7.0  0.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Join to faceoffs df\n",
    "joined = draws_joined.merge(cfca, how='left', on=['Game', 'Time', 'PlayerID'])\n",
    "\n",
    "# Get counts of time after each draw\n",
    "time_counts = joined[['ZS', 'PlayerID', 'TimeSinceLastDraw']] \\\n",
    "    .assign(TOI=1) \\\n",
    "    .groupby(['ZS', 'PlayerID', 'TimeSinceLastDraw'], as_index=False) \\\n",
    "    .count()\n",
    "\n",
    "# Get counts of shots\n",
    "shot_counts = joined[['ZS', 'PlayerID', 'TimeSinceLastDraw', 'Team']] \\\n",
    "    .dropna() \\\n",
    "    .assign(Count=1) \\\n",
    "    .groupby(['ZS', 'PlayerID', 'TimeSinceLastDraw', 'Team'], as_index=False) \\\n",
    "    .count() \\\n",
    "    .pivot_table(index=['ZS', 'PlayerID', 'TimeSinceLastDraw'], columns='Team', values='Count') \\\n",
    "    .reset_index()\n",
    "shot_counts.loc[:, 'CA'] = shot_counts.CA.fillna(0)\n",
    "shot_counts.loc[:, 'CF'] = shot_counts.CF.fillna(0)\n",
    "\n",
    "alljoined = time_counts \\\n",
    "    .merge(shot_counts, how='left', on=['ZS', 'PlayerID', 'TimeSinceLastDraw']) \\\n",
    "    .fillna(0)\n",
    "\n",
    "alljoined.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can replace Player IDs with names, export, and graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ZS</th>\n",
       "      <th>TimeSinceLastDraw</th>\n",
       "      <th>TOI</th>\n",
       "      <th>CA</th>\n",
       "      <th>CF</th>\n",
       "      <th>Player</th>\n",
       "      <th>CF60</th>\n",
       "      <th>CA60</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DL</td>\n",
       "      <td>1.0</td>\n",
       "      <td>87</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Brooks Orpik</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DL</td>\n",
       "      <td>2.0</td>\n",
       "      <td>87</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Brooks Orpik</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.379310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DL</td>\n",
       "      <td>3.0</td>\n",
       "      <td>85</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Brooks Orpik</td>\n",
       "      <td>0.0</td>\n",
       "      <td>127.058824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DL</td>\n",
       "      <td>4.0</td>\n",
       "      <td>83</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Brooks Orpik</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.373494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DL</td>\n",
       "      <td>5.0</td>\n",
       "      <td>84</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Brooks Orpik</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ZS  TimeSinceLastDraw  TOI   CA   CF        Player  CF60        CA60\n",
       "0  DL                1.0   87  0.0  0.0  Brooks Orpik   0.0    0.000000\n",
       "1  DL                2.0   87  1.0  0.0  Brooks Orpik   0.0   41.379310\n",
       "2  DL                3.0   85  3.0  0.0  Brooks Orpik   0.0  127.058824\n",
       "3  DL                4.0   83  1.0  0.0  Brooks Orpik   0.0   43.373494\n",
       "4  DL                5.0   84  7.0  0.0  Brooks Orpik   0.0  300.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alljoined.loc[:, 'Player'] = players.playerlst_as_str(alljoined.PlayerID)\n",
    "alljoined = alljoined.drop('PlayerID', axis=1)\n",
    "alljoined.loc[:, 'CF60'] = alljoined.CF * 3600 / alljoined.TOI\n",
    "alljoined.loc[:, 'CA60'] = alljoined.CA * 3600 / alljoined.TOI\n",
    "alljoined.to_csv('time_since_last_draw_data.csv', index=False)\n",
    "alljoined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/muneebalam/anaconda/lib/python3.6/site-packages/matplotlib/axes/_axes.py:545: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
      "  warnings.warn(\"No labelled objects found. \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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CCCGEEFbTtoLktBgoyDBm2RPmc+sE964w8rtXPA1zxxrpLFf9C1x8rV06IYQQ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\nhGhEgmQhhBBCCCEa+f8ti5XJIDNxUAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1145c07f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "comp_players = ('Nicklas Backstrom', 'Evgeny Kuznetsov', 'Lars Eller', 'Jay Beagle')\n",
    "\n",
    "def plot_cumulative_shot_lines(df, zone, metric, ax, *comp_players):\n",
    "    for p in comp_players:\n",
    "        df1 = df.query('ZS == \"{0:s}\" & Player == \"{1:s}\"'.format(zone, p)).sort_values('TimeSinceLastDraw')\n",
    "        df1.loc[:, 'TOI'] = df1.TOI.cumsum()\n",
    "        df1.loc[:, metric] = df1[metric].cumsum()\n",
    "        df1.loc[:, '{0:s}60'.format(metric)] = df1[metric] * 3600 / df1.TOI\n",
    "        ax.plot(df1.TimeSinceLastDraw, df1['{0:s}60'.format(metric)], label=p)\n",
    "    ax.set_title('Cumulative {0:s}60 after {1:s}'.format(metric, zone))\n",
    "\n",
    "def plot_comparison(df, metric, *comp_players):\n",
    "    fig, axes = subplots(2, 3, sharex=True, sharey=True, figsize=[12, 8])\n",
    "    axes = axes.flatten()\n",
    "    df2 = df.query('TimeSinceLastDraw <= 15')\n",
    "    for i, zone in enumerate(['OW', 'OL', 'NW', 'NL', 'DW', 'DL']):\n",
    "        if metric == 'N':\n",
    "            plot_cumulative_ns(df2, zone, axes[i], *comp_players)\n",
    "        else:\n",
    "            plot_cumulative_shot_lines(df2, zone, metric, axes[i], *comp_players)\n",
    "        legend(loc=1)\n",
    "        \n",
    "plot_comparison(alljoined, 'CF', *comp_players)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/muneebalam/anaconda/lib/python3.6/site-packages/matplotlib/axes/_axes.py:545: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
      "  warnings.warn(\"No labelled objects found. \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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oBWF+7sxesY9T5t/54JZe+Hk2jJq9R9KOsCt5F4/1eqxRzK0uzUE5MLrtaIZG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\nkAH8CJwE/E4pdRWwAqO1eXcr21wPXA/Qr1+/rihGt6n3+Pj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yI4OXI6r1R6vDuILe1fxe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yQHBgayYsWKauu5urqybNky7r77boqKigBYsGCBJMnV0JRSzR0DwcHBStbuE+I8TdMilFLBzR1H\nTeSeFaKqq/mevd7v10OHDtG1a9fmDkM0k+p+/nW9X2VMshBCCCGEEBeQJFkIIYQQQogLSJIshBBC\nCCHEBSRJFkIIIYQQ4gKSJAshhBBCCHEBSZKFEEIIIYS4gCTJQgghhBBNyNbWtsFt6PV6goKC6NGj\nB7169WLHjh2NEFlV8+fPZ+HChY3ebkslm4kIIYQQQlzlrKys2L9/PwA///wzzz77LH/88UczR3Vt\nk55kIYQQQogmlpuby/Dhw+nVqxcBAQF8//33AMybN4+33367ot5zzz3HO++8c8m2srOzadWqVcX7\nN954g5CQEAIDA3nxxRcryseOHUvv3r3x9/dn8eLFFeVLliyhU6dOhIaGMm3aNGbOnHnRNWJjYxk5\nciS9e/dm4MCBHD58uN6fvaWSnmQhhBBCiCZmaWnJmjVrsLe3JzU1lT59+jBmzBimTJnC+PHjmTNn\nDkajkVWrVrF79+6Lzi8oKCAoKIjCwkISEhL47bffANi0aRMxMTHs3r0bpRRjxoxh69atDBo0iKVL\nl+Lk5ERBQQEhISHccccdFBUV8fLLL7N3717s7OwYNmwYPXr0uOh606dP58MPP6Rjx47s2rWLf/zj\nHxXXvF5IkiyEEEKI60Lif/5D0aHG7RG16NoF97lza62nlGLu3Lls3boVnU5HfHw8SUlJ+Pj44Ozs\nzL59+0hKSqJnz544OztfdH7l4RY7d+7kvvvu4+DBg2zatIlNmzbRs2dPwNRjHRMTw6BBg1i0aBFr\n1qwB4MyZM8TExJCYmMjgwYNxcnICICwsjKNHj1a5Vm5uLjt27CAsLKyirKioqH5fUAtWa5KsadpS\nYBSQrJTqXl72BjAaKAZigQeVUpmapvkAh4Aj5aeHK6UeaYK4hRBCCCFajJUrV5KSkkJERAQGgwEf\nHx8KCwsBeOihh1i2bBmJiYlMmTKl1rb69u1LamoqKSkpKKV49tlnefjhh6vU+f3339m8eTM7d+7E\n2tqaIUOGVFyvNkajEUdHx4qk/HpVl57kZcB7wIpKZb8AzyqlSjVNew14Fni6/FisUiqoUaMUQggh\nhGiguvT4NpWsrCxat26NwWBgy5YtnDp1quLYuHHjmDdvHiUlJXzxxRe1tnX48GHKyspwdnZmxIgR\nvPDCC0yaNAlbW1vi4+MxGAxkZWXRqlUrrK2tOXz4MOHh4QCEhIQwZ84cMjIysLOzY/Xq1QQEBFRp\n397eHl9fX7755hvCwsJQShEZGVntsIxrWa1JslJqa3kPceWyTZXehgN3Nm5YQgghhBAtX2lpKRYW\nFkyaNInRo0cTEBBAcHAwXbp0qahjbm7O0KFDcXR0RK/XV9vOuTHJYBq6sXz5cvR6PTfffDOHDh2i\nb9++gGm5uc8//5yRI0fy4Ycf0rVrVzp37kyfPn0A8PT0ZO7cuYSGhuLk5ESXLl1wcHC46HorV67k\n0UcfZcGCBZSUlDBx4kRJkuthCvBVpfe+mqbtB7KA55VS2xrhGkIIIYQQjUophTE3F83CAp25eZNc\n4++//6Z9+/a4uLiwc+fOausYjUbCw8P55ptvamynrKysxmOPPfYYjz322EXlP/30U7X177nnHqZP\nn05paSnjxo1j7NixgGmd5HN8fX3ZuHFjjde8HjRoCThN054DSoGV5UUJgHf5cIvHgS80TbOv4dzp\nmqbt0TRtT0pKSkPCEEJcAXLPCtFyyP16aUopSjMzKTp2jOJTpyg+cQJVWtro1/nwww+5++67WbBg\nQY11oqOj6dChA8OHD6djx46NHkN15s+fT1BQEN27d8fX17ciSRZVaUqp2iuZhlusPzdxr7zsAeBh\nYLhSKr+G834HnlRK7blU+8HBwWrPnktWEeK6omlahFIquLnjqIncs0JUdTXfs9f7/Xro0CG6du0K\ngDIaKcvMpDQlFVVSjM7CAp2jI6XJyehtbTF4e6NpWjNHLBpT5Z//OXW9X+s13ELTtJHAv4DBlRNk\nTdNcgXSlVJmmaX5AR+B4fa4hhBBCCNEYVFkZZekZlKalokpL0VlZYfDwRmdnh6ZpaJpGSWIiurQ0\nzFxcmjtccZWoyxJwXwJDABdN0+KAFzGtZmEB/FL+F9e5pd4GAS9pmlYCGIFHlFLpTRS7EEIIIUSN\nSjMyKMvOpvDIETAa0dnaYnBxQWdjU6XHWO/sjDE/n5LEJDQra/Q21s0YtWgsdRktcSl1Wd3i7mqK\nl9RQdzWwukERCSGEEEI0QElSMunLlpHx1VeomTPI6tsXV19f9DY21dbXNA2DpyfGwlhK4s6ga98e\nzUz2W2vJlFKkpaVhaWlZ7zbkN0AIIYQQ14SS5GRS3/8fWd99hzIasb/tVhz79yfZ3Jyjp0/Xer4q\nKaE0JQUtJQUzJyeQ8cmNRhmNpt77K/idWlpa4uXlVe/zJUkWQgghxDUh8cX55G3fjsMd43GeOhXz\ntm0B8L2MNjJWrSJx/r9xnTMHl0cerv0EcUmqrIzEBQvI/HIV6PUY3N0xeHlh8PLEvG1bDJ7lr728\n0Lu4XFUTJyVJFkIIIUSLZywsJG/nThwnTsT9ufrvrOc4YQL5u/8iZdEirHr1xCY0tBGjvL4Yi4o4\n++RT5PzyC45hYeidnSg5E0dJXBy5f2ylLDW1Sn3NygqDZxvMPb3KE2kvDB7uGNzdMfPwwMzFBa2G\nzVaagiTJQgghhGjx8vdEoAoLsR04oEHtaJqG+0svURgdTfwTT+C3Zo2seFEPZdnZxM2YSf5ff+H2\n7DM43X//RXWMBQWUxMdTHBdXkTwXx8dREhdPfkQExtzcqieYmWFo3RozDw9Tj7SHO2buHhjaeFQk\n0npHx0brjZYkWQghhBAtXt62bWjm5liHhDS4Lb2tDZ7vvMPJu+4i/smn8F7yyRXtwWzpSpKSOTNt\nGkUnTtBm4UIcRt1WbT2dlRUWHTpg0aHDRceUUhizsylJTKQkIYHSxERKEhIpSThLaUIiBZGR5Gza\nhCopqXKeTb++eC9d2iifQ5JkIYQQQrR4udu2YR0Sgs7KqlHas+zcCfd5L5Dw3POk/u8DXGfNbJR2\nr3VFx09w5qGHKMvMpO2HH2Dbv3+92tE0Db2DA3oHByw7d662jjIaKUtPpyQhoSKR1rdyakj4VUiS\nLIQQQogWrTgunuLjx2k14a5Gbddh/Hjy/9pD6v/+h1WvnvVO+K4XBZGRnJn+MOh0eK9YgVV3/ya9\nnqbTYebigpmLC1YBAY3evq7RWxRCCCGEuILytm8HwGbgwEZtV9M03Oe9gEWH9px96l+UJCU3avvX\nktytWzl1/wPo7Ozw+fKLJk+QrwRJkoUQQgjRouVu34ahTRvMfS9nsbe60Vlb4/n22xgLC4l/4nFU\naWmjX6Oly1y7ljP/mIG5jw8+X6zEvF275g6pUUiSLIQQQogWSxUXk78zHJuBA5tsjV2L9u3xmP8i\nBXsiSHlnUZNco9mkxUJiFBjL6nf6kqUkPPMs1sHBtPtsBWauro0cYPORMclCCCGEaLHy9+/HmJfX\n4KXfauMwZgz5f+0h7eOPserdC7shQ5r0elfEsV/hi7vAWAoW9uAVAt59wbsPePYGc+saT1VGI8mv\nv0H6smXY3TKSNq+9hs7c/AoGfwGjEbLjoKQQXDs1SpOSJAshhBCixcrbth3MzLDu04flfy9ny5kt\njPAZwQifEThZNt5KBwBuz82lICqKhKefwfjCC1gFBWHwbHNV7RJXZ2f3w9f3gWsX6DcLzuyC0+Gw\nZYHpuM4MPHqcT5rb9gFbUy+xKi7m7HPPk/3DD7S6917c5j6LprtCgxPKSiHjJKQegZTDkHLU9Jwa\nAyV50H4YTF7TKJeSJFkIIYQQLVbu9u1Y9+xJkYWOjw58RKkqJSIpgtd3v05/z/6M8hvF4LaDsTJr\n+NJwOktLvN7+LyfvnczZJ58EQO/sjFWPHlgFBmLVIxDLgAD0trYNvlaTSj8BK+8EKyeY9C3Ye0CP\niaZjBRlw5i84vdOUNO/+GHa+Zzrm1J5i6x4kbkwk7+BJXOc8hvPDDzfNHwmlxZAWcz4RTj0CKUcg\n7RiUFZ+vZ+8JLp2g132mHmT3Ho0WgiTJQgghhGiRSpKTKTp0CNcnHmfjyY3klOSwfORybAw2bDix\ngQ3HN/BH3B9Ym1lzY7sbGeU3ilD3UPS6+m8MYu7jQ8ctv1F49CiFkZEU7D9AQWQkub/9ZqqgaVh0\naI9lReLcA4sOHa6ezUjyUuHzO0xDLO5dbUqQK7NqBZ1uNj0ASosg4QCFf64n7dtfyP57J5oOPEIz\ncUydD0tWQ5ue0CbI9OzSCS73+81NgaQoSDwISQdNz6lHTDECoEErH3DtDB1vApfOph5wl45gad/A\nL6RmkiQLIYQQokXK2/4nALYDB/L1kfl0cOxAz9Y90TSNzk6deaznY0QkRbD++Hp+OfUL62LX4Wrl\nyq2+tzKq/Sg6t+qMlpMIlg6XHH97Ic1gwMrfHyt/f1rdfTcAZVlZFERGURBZnjT/spmsb1eb6ltb\nY+Xvj0Xnzlh07Fj+6IDezq7xv5RLKc4zjUHOjof71tVp7G5+VDRpH31K7u+/o7OxwXnqNJxGD8Ss\n6KRpyMbZfbDvc9j9kekEgzW4B55Pmj2CTMmsTm8aKpEWU54MV0qKc5POX9DOA9y6m5L01v7Qugs4\ndwBD42wSczkkSRZCCCFEi5S3fRt6VxdinUv5e9ffPBv6bJV//et1ekI9Qgn1CGXuDXP54/QW1h9e\nxcroz1gevZwOpYrbsrMYX2KGU/BDEDq9Ytzt5dI7OGA7cEDFBEKlFCWnT1Nw4AAFByIpOBhF1nff\nYczPrzjHzMMDi44dKiXOHbFo3x6dpWXDvpjqlJXCt1NMSe1dn4H3DTVWVUqRv3MnqR8tJn/XLvQO\nDrjMnoXTpEnoHRzKa4VCYPnmLcYy0zCIs/vOJ857V8CuD03HDTbg6A3px6GsyFSmM5h6g9sPMyXF\n7t3BLQBsnBv/s9dTrUmypmlLgVFAslKqe3mZE/AV4AOcBO5SSmWUH3sWmAqUAbOVUj83SeRCCCGE\nuG6psjJy/9yB3bBhfBTzDVZmVoxuP7pqpcIsiPsLTu/C8kw4I+IiGFGSR6ZOxybnNqy3t+cdJ43v\nNQs+3/4mDjsWQdAk6DcTnPwaFJ+maZi3a4d5u3Y4jBljitlopORsAkUxRymKOUZRTAxFMTHk7wxH\nlZSYTtTpMG/bFotOHbHq1RuneyehGQwNigWlYMM/4ehGuO0t6Dqq+mpGI7lbtpD60WIKIyMxa92a\n1s88TauwMHQ2NjW3r9ObhkK4dj4/ttlYBqlHzyfOmaeh442mRNjN3zQsw6wZV8Oog7r0JC8D3gNW\nVCp7BvhVKfWqpmnPlL9/WtO0bsBEwB9oA2zWNK2TUqp+i+8JIYQQQlSjMCoKY1YWur69+enEa9zq\neyt2CjjwFZwJh9O7IDkaUKDpTL2VPSdB2xtwbHsDdzm25S5gd8JuHtn8CLODbmSxcsFi32cQ8Sl0\nHQP9HwPPXg0LtCjXFE/mabSAuzD38sTcyxO7oUMrqqjSUopPn6boaExF4lx09Cg5v2wm+8cf8Vz4\nRsM26Pj9VVPP7qCnIGTqRYdVaSnZP20kbfFiimJiMLRti/u//43DuLH1X9ZNp4fWXU2PoHvqH3sz\nqjVJVkpt1TTN54Li24Eh5a+XA78DT5eXr1JKFQEnNE07BoQCOxsnXCGEEEIIyN22HXQ6/nDLoCCj\ngLDOYbB6Ghz9CcztoG0IdBsDbW8Ar2CwqH78b6hHKK8MeIV/bf0Xz/mM4PXHDqDbvRj+WgrRa8Fn\nIPSfAx2GQ11WcSjKNS2ndnI7nNxm6kk9NwFt9ycwcSU4Vd0ZUDMzw8LPDws/Pxg5oqI8++dNJMyb\nx4lx43Gb9wIOt99++StJRCyDP16FoHth6HNVDqnSUjLXrCFt8ceUnDmDRccOtHnjDexvGYlmJiNy\n6/sNuCmlEspfJwJu5a89gfBK9eLKy4QQQgghGk3u9m1YBgTwZcJ6ujt3x19nY4izGlwAACAASURB\nVBpO0P8xGP7iZa2wcIvvLSTmJfJWxFu4W7vz5I3zYcDjsHc57PwfrLzD1BPd/zHwHwf6SsMfivPO\nJ8UntsHZvaakWGdm2pCj/2PgMwDKSmDNw7B4CNy51JR018J+xM1YBQZw9ql/kfDMs+Rt2477/Bfr\nPuHvyE+w/p/Q4SYY/XaVJD932zaSX3+dophjWAYE4PbM09gOHXrl1jtuARr8Z4JSSmmapi73PE3T\npgPTAby9vRsahhCiick9K0TLca3fr6UZGRRGRlH8wHhis77npX4vQcRyUxIY+vDlL0EGPOD/AGdz\nz7I8ejketh5M6jrJtMlG6MNw8Fv48x34bhr8+hKEPARF2abEOD7ifFLcphf0m21Kir37gPkF43in\nbYFVk0xrFA9/0ZRA19IzbPDwwHv5MtIWLyblvfcp2L+fNgvfwLpnz0t/oDN/wTcPmjYECVtWkdgX\nHjlC8utvkPfnnxjaeeO56B3sbrqpZW6I0sTqmyQnaZrmoZRK0DTNA0guL48H2laq51VedhGl1GJg\nMUBwcPBlJ9lCiCtL7lkhWo5r/X7N+3MHKMUm92TsDHaM8B4Oa/8FHUeAQ/3+ga1pGs+EPkNSfhKv\n7X4Nd2t3hrcbbppcFnQPBE6EY7+YkuXNL4KmN41X7jfLlBS37QMWtWwi4uQLD/0C388wtZFwAG5/\n7+Jk+sLY9HpcHn0U6z59OPvkU5y6dzKuM2fgPH169esvp8aYlnqzc4d7vgELW0qSk0lZtIis79ag\ns7PDbe6ztJo4Ea05t5K+ytU3SV4H3A+8Wv78faXyLzRNewvTxL2OwO6GBimEEEKIlkspRdHRo1h0\n6tQoPZZ527ahOTqwir8I6zAB62O/QV4yBD/YoHb1Oj2vDXqNhzY9xNPbnuYTq08Iah1kOqjTQacR\npkf6cbBxrXGc8yWZ28Cdn5p6eDf/27QCxMSVps0yamHdsye+a9eQOP/fpLyziLw/d9DmjdcxeFTa\nECQnET4fb5qsOPk7jDob0t5/n7QlS1ElJTjddx8ujz5SaSk3UZNaB55omvYlpol3nTVNi9M0bSqm\n5PgmTdNigBvL36OU+hv4GogGNgIzZGULIYQQ4vpWEBHBidvHkvXddw1uSxmN5P75Jynd21BMKWGd\nwkyrUTi0hQ43Nrh9KzMr3h32Lm7Wbsz6bRYns05eXMnJr34J8jmaBgP+Cfd+C1lnTOOUY7fU6VS9\nnR1tFr6Bx6v/R2F0NMdvH0v2z5tMBwuzTUM58tJQd39F5u8HiB15C6nvvoftgAG037Aet2eelgS5\njmpNkpVSdyulPJRSBqWUl1JqiVIqTSk1XCnVUSl1o1IqvVL9V5RS7ZVSnZVSPzVt+EIIIYS42uVH\n7AUg5Z1FVTbTqI+iw4cpS01lk3sKvd16074MOP479LqvXmORq+Nk6cQHN36Ahsajmx8lrSCtUdq9\nSIcbYfrvpl3mPh8Pfy4yrWlcC03TcBw7Ft8132Hu3Zb4xx4j4YGbMb7TB5Kiyev8HCdm/4eEuXMx\n83Cn3Rcr8Vr0DubX4Pj0piRTGIUQQgjRpAqiItHZ2FCanEz6ihW1n3AJudu2A7DFI4MJnSeYVqDQ\n9NBzcmOEWsHb3pt3h79LakEqs36bRX5Jw5L7Gjn5wdRfoOto+OUFWP0QFNdyraw42LMU8z//hU/A\ndpy75pAZfpoT68w5EzOM08+/hzErizZvLsRn1SqsezVwrefrlCTJQgghhGhShVEHsR06FNsbh5P2\n8SeUptW/ZzZv2zZS2tqhd3FmeJsBsO9z6HwL2HvUfvJl6uHag1cHvcrB1IM8vfVpSs+td9zYLGwh\nbDkMnwcHV8PSmyHj1PnjxjI4HW4aw/xBf/ivv2lpt+RDaCGTaf36Mrw/WYxR70D+4VO0fvIJ/H76\nEYfbbpNVKxpAVooWQgghRJMpSUqiNCkJq8AAbAYM5Pjo0aT+7wPcX3j+stsqy8khf98+tt+gGNvh\nbsyP/gz5aQ2esHcpw72H80zoM/zf7v/j1d2v8twNzzVN4qlpMPAJcA+Eb6eaxikPesq0GcmxzVCQ\nbuox9+4LN71kWsnDtXPFEnI2HaH9Tz+ijMa6r6MsLkmSZCGEEEI0mcKoKAAsuwdg4eeL411hZHz1\nFa3unYSFr28tZ1eVt3MnlJWx30/Pm53uhNWPgmM78BvWFKFXuKfrPSTmJfLp35/iYePB1ICLt3Zu\nNB1vgulbYNU98POzYO0MHW+GTjdD++Fg5VjjqTqbSy8lJy6PJMlCCCGEaDIFUQdBr8eyW1cAXGfM\nIOv7daT89228Fr1zWW3lbNtKgYWGS0g/2hYVmrZ9Hj7PtDxbE5vTew6JeYm8vfdt3G3cuc3vtqa7\nmHN704S+9BOm3uJGmpAoLo8kyUIIIYRoMoVRkVh06oTO0hIAMxcXnKdOIfXd98jft6/2nePKKaVI\n//03In3gzq4TIWKZaZe7oHubLvhKdJqOBQMWkFyQzPN/Ps/GkxuxN7fHwcIBB3MHHCwczr+v9NrW\nYIu+PkmuwQrcujX+BxF1JkmyEEIIIZqEMhopOPg39iNHVil3fvBBMlatIvmNhbRb+XmdxvgWx8Zi\nlpLB8X6O3OsWCl9OgS63gZ1bU4V/EXO9Oe8MfYeXw1/mVPYpjqQfIasoi/zSmlej0NCwM7ejtXVr\nngx+kv6e/a9YvKJhJEkWQgghRJMoPnUKY3Y2VoEBVcp11ta4zppF4rwXydm8Gfubbqq1rbhffgDA\n5+ZxmB35EQoyoHfTTdiriYOFAwsHL6xSVlJWQlZxFtlF2WQXZ5NVlEVWcZbpuSiL7OJswhPCeWTz\nIzwU8BAzgmZgppMU7GonPyEhhBBCNInCgwcBsAwIuOiY4/jxpC9fQcqbb2E3ZAiawXDJts7+uoFC\nF43b+twPXz0ArXzBd3BThH3ZDHoDLlYuuFi51FinoLSA13a/xidRnxCRFMHrg17H3cb9CkYpLpes\nkyyEEEKIJlEQGYVmZYVF+/YXHdPMzGj9xBMUnzxJ5rffXrKdwpxMHA7FkxbUDre8DDi9w7Ts2xWY\nsNdYrMysmN9vPq8OfJUj6Ue484c72Rq3tbnDEpfQcn67hBBCCNGiFEZGYunfDc2s+n9c2w4dgnVw\nMCnvvU9Zbl6N7ezY8DGGMvAbcYdpwp7eHIImNVHUTes2v9v4atRXuFu7M+PXGSz8ayElZSXNHZao\nhiTJQgghhGh0qqSEwkOHsOp+8VCLczRNo/W/nqIsLY30pUtqrHd283qKDBq9hoyHA1+YtnC2qXlo\nw9XOx8GHlbetZELnCSyPXs4DGx8gPje+ucMSF5AkWQghhBCNrvDoUVRx8UWT9i5kFRiI/a23kPbp\nMkqSki86HpMRQ5u/k8kP8MMsdiMUZjXLhL3GZqG34Pk+z7Nw8EKOZx0n7Icwfj39a3OHJSqRJFkI\nIYQQja4wquZJexdynTMHVVpK6nvvXXRsw9YleGSA9823w55Pwbkj+Axo9HibywifEXw9+mu87byZ\ns2UOr+5+leKy4uYOSyBJshBCCCGaQEFUJHpHRwxeXrXWNff2ptXdE8lcvZqiY8cqyvNL8kn+/WcA\nWgf4Qtxu6P0A1GFd5ZakrV1bVtyygnu73svKQyuZ/NNkzmSfadRrGJWRwtJCsouzSS1IJT43nuNZ\nxzmSfoTC0sJGvda1QpaAE0IIIUSjK4yMwjIwoE4bhQC4PPooWWvWkvzmW7T94H8A/HTiJ7rFFGH0\ndMM8cSPoLSDonqYMu9mY6815OvRpQtxDeP7P5wlbH8Y/evwDW3NbCkoLKCoroqi0iIKyAopKiygq\nK6ooLywrpLC00FRuLKK4rJiiMtPzudclxponBzpbOjOl+xTCOodhZWZ1BT/11U2SZCGEEEI0KmNe\nHkWxsdjVYZOQc8xatcJ5+jRS3nyLvN27sQ4J4du/v+SZU+B0x2CIXArdbgdrpyaMvPkN8x7Gt07f\n8tTWp3hjzxsXHTfXmWNhZoGl3hJLM0ss9OdfO1g6YKGzwMLMwlRPb4G53vRsobfAoDdUvDbXm2Ou\nM0ehWHNsDW/seYOlB5dKslxJvZNkTdM6A19VKvID5gGOwDQgpbx8rlLqx3pHKIQQQogWpTA6GoxG\nLCtN2sspziGnOMfUI1r+ONfLea5ntLivA77O9kT/+2m2zx+NduAQ5iUK27bAmWzT2sjXgTa2bVgx\ncgXxufFVklwLvQV6nb7Rr3eb321EJEXwwYEPJFmupN5JslLqCBAEoGmaHogH1gAPAv9VSi28xOlC\nCCGEuEYVlE/asyqftBeTEcOdP9yJURlrPXdwHyMzNmRz6Nsl9EuxAkMxNkV/gEtn8O7bpHFfTfQ6\nPd723lfser3devPJzZ9IslxJYw23GA7EKqVO1XXskRBCCCGuTQVRkRjatMHM2RmAqNQojMrIk8FP\n4mrlWnUYgNn5f/9b6i0x3KEn69g0norIR2cwxxBoiy75Zxj52jU3Ye9qJMnyeY2VJE8Evqz0fpam\nafcBe4AnlFIZF56gadp0YDqAt/eV+0tJCFE/cs8K0XI09/1qmrQXWPH+WOYxLPWWTO42GZ1W+8Ja\nVk89zZmHHgLA0b8zmFlCjwlNFq+4WF2T5VJjKTnFOWQVZZFVnGV6Lsoiuzi74nVWcRZGo5Hh7YYz\ntO1QzPXmzfzp6kZTSjWsAU0zB84C/kqpJE3T3IBUQAEvAx5KqSmXaiM4OFjt2bOnQXEIcS3RNC1C\nKRXc3HHURO5ZIaq6mu/ZK32/lqanE9OvP62fehLnqVMBeOSXR0gvTOfr0V/XuZ3TU6aSt2MHfmNy\nseg7CsZ90FQhizo4lyzvStiFg4UDtgZbsouyySnJqfEcDQ1bc1sczB0oLCsktSAVRwtHRvmNYmyH\nsXR26nwFP0GluOp4vzZGT/ItwF6lVBLAuefyID4G1jfCNYQQQgjRAhRGRQFVNxE5lnmMEPeQy2rH\n4+WXyFnxKuaFK66bCXtXs8o9y98c/QYdOhwsHLC3sMfB3AEHi/JHpde2BtuKiYZlxjLCE8L5LuY7\nVh1ZxeeHPsff2Z9xHcZxi98t2JvbN/MnvFhjJMl3U2mohaZpHkqphPK344CDjXANIYQQQrQABVEH\nQdOw7OYPQG5xLkn5SbR3bH9Z7Rg8PXFyOgBl/uB1eQm2aDq93XrT2633ZZ+n1+np79mf/p79ySjM\n4McTP/JdzHcs2LWAN/a8wY3tbmR8h/EEuwfXaUjOldCgJFnTNBvgJuDhSsWva5oWhGm4xckLjgkh\nhBDiGlYQFYlFh/bobW0AiM2KBaC9w+UlycRHQMIBuHWhTNi7xrSybMWkrpO4p8s9RKdHsyZmDT8e\n/5ENxzfgZevF2A5jub3D7bjbuAOmXujMokzSCtNIK0ir9jm9IJ20gjQCXQP579D/NkqcDUqSlVJ5\ngPMFZZMbFJEQQgghWiSlFIWRUdgOHVpRdjzzOMDl9SQXZMKaR8HSEQLvauwwxVVC0zT8nf3xd/bn\nyeAn2Xx6M2tj1vLe/vd4f//7+Dr4klWURUZRRrXLBxp0BpytnHGydMLFyoXOTp3xd/ZvtPhkxz0h\nhBBCNIqS+LOUZWRgFdC9oiw2MxYLvQWetp51a6S0GL66F9KPw+Q1YOnQRNGKq4mlmSWj/EYxym8U\nZ3LO8P2x7zmacRQnSyecrZxxtnSu+mzljJ3Brs7bnteHJMlCCCGEaBSFUZEAWAZUWv4t6xi+Dr51\n2ylOKfhhNpzcBuMWg+/ApgpVXMXa2rVlZs+ZzR0GV8fIaCGEEEK0eAVRB9EMBiw7dawoO555vO5D\nLf54DQ58CUOfk3WRRbOTJFkIIYQQjaIwMhKLbl3RzE2bReSV5JGQl1C3SXv7v4Tf/w+CJsGgp5o4\nUiFqJ0myEEIIIRpMlZVREB2NVaWhFnWetHdiK6ybBb6DYNTbspqFuCpIkiyEEEKIBiuKjUXl51eZ\ntHcs8xhQS5KcfBhW3QvO7eGuz8CsZWxZLK59kiQLIYQQosHO77RXqSc56zjmOnO8bL2qPyknCVaG\ngcESJn0DVo5XIlQh6kRWtxBCCCFEgxVERaGztcXcp11FWWxmbM0rWxTnwZcTID8VHtgAjt5XMFoh\naic9yUIIIYRosMLIKCwDuqPpzqcWsZmx+Dn6XVzZWAarp8HZ/XDHEvDsdQUjFaJuJEkWQgghRIMY\ni4ooPHq0yqS9/JJ8zuadpYNjh4tP2PQ8HNkAt7wGXW69gpEKUXeSJAshhBCiQYoOHYLSUiwrTdo7\nnlW+ssWFy7+Ffwjh/4M+/4AbHr6SYQpxWSRJFkIIIUSDFESaJu1ZBZ7vSY7NjAUuWNni8AbY+Ax0\nGQU3L7iiMQpxuSRJFkIIIUSDFByMwszVFYObW0VZbGYsBp0BL7vylS3iI+DbqdCmJ4z/GOqyTbUQ\nzUiSZCGEEEI0SGFkFJaVepEBYrNi8XHwwUxnBrnJ8MVEsHWFe74Cc+tmilSIupMkWQghhBD1Vpad\nTfHJk1gFBFQpj82MpYND+aS9ne+blnq7+yuwbd0MUQpx+SRJFkIIIUS9FR48CFBl0l5+ST7xufGm\n5d+KcmDPp9B1DLh1a64whbhsDdpMRNO0k0AOUAaUKqWCNU1zAr4CfICTwF1KqYyGhSmEEEKIq1HF\npL3u55PkE9knAEzLv+1dAUVZ0G92s8QnRH01Rk/yUKVUkFIquPz9M8CvSqmOwK/l74UQQghxDSo4\nGIW5jw96B4eKsnMrW/jZeUP4B9CuP3j1bq4QhaiXphhucTuwvPz1cmBsE1xDCCGEEFcB0057F49H\nNtOZ4X1mH2SdgX6zmik6IeqvoUmyAjZrmhahadr08jI3pVRC+etEwK26EzVNm65p2h5N0/akpKQ0\nMAwhRFOTe1aIluNK3a8lSUmUJidXO2nPx94Hs53vgUsn6DiiyWIQoqk0NEkeoJQKAm4BZmiaNqjy\nQaWUwpRIX0QptVgpFayUCnZ1dW1gGEKIpib3rBAtx5W6XwujTOORK0/aA1OS3N7MDhIjoe9M0Mk6\nAaLladBvrVIqvvw5GVgDhAJJmqZ5AJQ/Jzc0SCGEEEJcfQoio8DMDMuuXc+XlRYQnxtP+7TTYNMa\nAic0Y4RC1F+9k2RN02w0TbM79xq4GTgIrAPuL692P/B9Q4MUQgghxNWn8GAUlp06obO0rCg7kXUC\nhaJ94mG4YToYLC/RghBXr4YsAecGrNE07Vw7XyilNmqa9hfwtaZpU4FTwF0ND1MIIYQQVxNlNFIQ\ndRD7W2+tUn5uZYsOygyCpzZHaEI0inonyUqp40CPasrTgOENCUoIIYQQV7fiU6cw5uRgFXjBpL3k\nA5gpRdvuE8HaqZmiE6LhZCS9EEIIIS5bxaS97hckyaf/wKekFEO/mc0RlhCNpkE77gkhhBDi+lQQ\nGYVmbY1Fh/bnCwuzic2Np6tVa2jl02yxibr59VAS//fTYTQg1NeJG/yc6ePrRGt7GUcOkiQLIYQQ\nF8kszGRf8j72Ju9lpO9I/J39mzukq05hVBRW3bqh6fXny/YsJU6vY3S7wc0YmahNQlYB89f9zc9/\nJ9GhtS1tHK1Yuy+elbtOA+DrYsMNvk4VibOno1UzR9w8JEkWQghx3UvITSAiOYK9SXvZm7SXzDPH\n6HZK0f2Mxul7zfAfLUlyZaq4mMJDh2g1adL5wrISTkR8jHLU4dd2QPMF14QKS8o4cCaTqPgsAjwd\nCPFxQqfTmjusOistM7Jsx0n++8tRypTiXyM789AAP8zNdJSWGYlOyGbX8XR2nUjjx6gEVv11BgCv\nVlbc4OvMDX5O3ODrhLeTNeULN1zTJEkWQghxXVFKcTzrOBFJEexNNiXFxQln6XZKERRnxjNxOhxS\nywDQ2dvhjl8zR3z1KYyJQRUXV5209/caYovTARfaO7Sv8dyWJLeolIhTGew+kcZfJzLYfyaT4jJj\nxXGvVlaM6+nJuJ6e+LnaNmOktdt7OoPn1hzkUEI2Qzu78tLt3WnrZF1x3EyvI9DLkUAvR6YN8qPM\nqDicaEqad59IZ8uRZFbvjQPA3d6SXu0c6eHlSI+2jgR4OmBjce2llNfeJxJCCCEqMSojMRkxhCeE\nE5EUwb7kfeiTM+h2WtH7rAXjTmvYVyTFlliHhmATEoJ1aCgWnTpVGU4gTM7vtFeeJCsFOxZx3MEd\nMw3a2bdrxujqLz2vmL9OmpLCv06mczA+C6MCvU6ju6cD9/drR6ivM9097dl1PJ3v9sXz/pZjvPvb\nMXq0dWR8T09G92iDk415vWPIzC9m3+lM9p3OICO/hL7tnenf3gUHa0O92svKL+G1nw/z5e7TuNlZ\n8uG9vRjh715rT7Bep+HfxgH/Ng5MGeCL0ag4lpLLruNp7DqRzoG4TH6MSgRAp0EnNzt6eDkS5G1K\nnju52WKmb9nrQ0iSLIQQ4poTlxPHroRdhCeEsztxN+mF6QScMHJzjA33njZil1KeFDsYsA4JwSY0\n9HxSLFso16ogKgp9q1YYPD1NBcd/h8QojvUYhrdmxKCvX0LXHMKPp/HDgbPsPpFOTHIuAOZmOnq2\ndWTm0A6E+DrRy7vVRT2lY3t6MranJ8nZhXy//yzf7YvnxXV/8/L6aIZ0dmV8Ly+GdWmNpaHmP7JK\ny4wcScph3+lM9p7OYP/pTI6n5gGmxNPSoOez8FPoNAhq68jgTq0Z1MmFQC9H9LUM81BKsXZ/PK9s\nOERGfglT+/sy56ZO2Nazx1en0+jkZkcnNzsm9/UBIC23iANxmew/k8WBM5n8HJ3IV3tMQzQsDToC\nPB0Iamvqbe7h5YhXK6sWNUxDkmQhhBAtXnphOrsTdxN+NpxdCbuIyzX9W7i1VWv6t+nP8NP2tPlq\nBTp7HdYhoZIUN1BhZBSWgQHnE54d74JNa46rYjq16ty8wV2GbTEpPPDpX1gZ9PRu14qxPT25wdeJ\nAC8HLMzq9h+E1vaWTBvkx7RBfhxKyGbNvnjW7otn86Fk7CzNGBXowbieXoT4tCI1t5h9pzPYdyaT\nvacyiIzLoqDE9Aebs405Pb1bcUdvL3p5tyLQywELMx37z2Tyx9EUth5N4e1fj/LfzUdxtDYwoIML\ngzq5MriTK24XrEYRm5LL82sOsvN4GkFtHVk+pTv+bRwa/ftztrVgWBc3hnVxA0yJ+am0/PLE2fRY\nvvMUxdtOAGBnYUYXDzu6etjT1cOeLu52dHa3w9r86kxHr86ohBBCiEvIL8lnb/JeU1KcuIvD6YcB\nsDPYEewezORuk+nj0QdfB18K/47m1OzJWHTvTrsVy9FZXZ8z9RtLaUYGRbGx2I0YYSpIPAixv1I0\nZC5nTn/BLX63XrqBq8Sx5Bz+sXIvHVxt+fbRvthZNrz3+1zy9/TILuyITWXN3njW7jvLl7vPYGdp\nRk5hKQBmOg3/NvZMCGlLT29Henm3qrGXNdjHiWAfJ564uTPpecVsi0lh69FUtsaksD4yAYAu7nYM\n6uTKoI6u7D6Rxod/HMfCoGPB2O7cE+p9xSYXapqGj4sNPi423B5k+i9DcamRI4k5RMZncighm0MJ\nOayOiCOvuKz8HPBxtqGrhx1d3M8nz1dDr7MkyUIIIVqEM9ln2Bq/lW1x2/gr8S+KjcX/3959x0lR\n3g8c/8z23q5X7o529DsEQcBuQlQUIcaWmJioaAzRJGqMmkST+IumGDUxsSTGlhgbKgpYEBuIgBSp\nR7vC9b63u7d7W2d+f8x5tKMIV+F5v17zmi1TnrnbZ/c7zzzzfdBr9BSnFnNz8c1MyZjC6KTR6DR7\nf9piNTVU/fBGdB4POY/9QwTIPSDw7rsgy9jP6xxc97NHQW+lYsTZyHv+w1DXwL9przUY5QfPrMWo\n0/LUNZN6JEDel1YjcfrwFE4fnsLvLonz7tZ6VpW1MDzVTnGui7FZzsN2wzgUj9XA7KIsZhdloSgK\nJXUBPtnVxMc7mnj603Ke/KQMgEuKMrn7wtGk2I09elzHwqDTMC7bybjsvS3ZsqxQ7e2gpN5PSZ2f\n7XUBttb6u/o4g9rqPDLdztAUG8NSbQxNtTI0xUa223LEriY9RQTJgiAIwoAUS8RY17iO5dXL+aT6\nEyr8FQDkOfK4vPByZmTOoDitGLOu+8A3EQhQdeONKOEIOU8/jS45uQ9Lf+LyvbUIw7ChGEeOBF8N\nbH4FJl/H7nATwIDPbBGJJ7jh+bU0+MO8OG8q2W7LkVc6DlajjrkTs5k7MbtHtytJEqMzHYzOdHDj\nmUMJRuKsLm/BZTEwMdfdo/vqaRqNRG6ShdwkCzPHpHe9HozE2V4fYHtn8Lyzvp33Sxq6+jmDGnQX\nJKsB89BUG0NT1McFKdYe77YhgmRBEISTgKzIfF7/OQk5wdiUsTgMjv4uUreaQk0sr1GD4s9qPyMU\nD6HX6JmcPpkrCq/g9KzTyXXkHnE7SixGzS23ECmvIPdf/8Q4bFgflP7EF6upoWPdOlJ+8hP1Uvjq\nx0GRYepNlJYvRCtpB3RmC0VR+MWCzXxe4eXRq4opHuDB5FdhNeq6+gYPVlajjlOGuDllyP7/F28w\nSmlTe+cUZHdjO1tqfby9pQ5Z2btclsvMeaNS+c3ssT1SHhEkC4IgnMC8YS9v7H6DV3a+QlVAbY2R\nkChwFlCUWsSElAlMSJlAnjMPjdT3N7ApikJJawnLKpexvHo5Ja0lAKRZ0rig4ALOyDqDKRlTsOiP\nvrVPURTq7r2X4MrPyLj/fqxTp/ZW8U86viVLAHDMuhDCflj3DIy+BNxDKPuijFxHLgbtsac/622P\nfrCb1zfU8LOvjWDW+Mz+Lo5wlNxWA5Osat/sfYVjCSpagpQ2BruC6J7sOiOCZEEQhBOMoihsbNrI\nSzte4r2K94jKUSamTmR+0XzcJjcbmzaysWkjS/csZcGuBQDYDXbGp4zvCprHJ4/HZuidwRG+DIzf\nrXiX9yreo7q9Go2koSiliFsm3sLpWaczwj3imG/aaXniCXwLXiP5pptwN8QLNQAAIABJREFUzbmk\nh0t/cvMvWoy5qAhDdraa0SLih2k/BqC0rZRhroHbYr9oUy0PLt3JnOIsfnzOwC2ncPRMei2F6Q4K\n03vnypgIkgVBEE4QoViIRWWLeHnHy+zw7sCqtzJ3+FwuG3kZw93Du5Y7LfM0QO2CUeGvYGPjxq7A\n+bGax1BQkJAY6hrKhJQJjE4aTaGnkOHu4Yfs/3sk3QXGWknLlIwpXD/+es7JOQeXyXXcfwPfW4to\nevgRHBdfRPKP5x/39oS9wjt3Etmxg7Rf/hISMVj1GOSdDlkTiSaiVAYqmZk3s7+L2a0NlV5ufXkj\nk4a4eeCb4/o9a4IwOIggWRAEYZDb5d3FSzteYlHZIoKxIIWeQn592q+5MP/Cw3ZT0EgaCpwFFDgL\nmDN8DgCBaIDNzZu7gub39rzX1dqskTQMcQxhpHskIz0jKfQUUugpJNnc/Q1xfRUYfym0di11d92F\nZfJkMu67TwRCPcy/aDFotTjO/wZseQ38NTDrYQDKfeXIijwgM1tUe0Nc/9w60hwmnrj6lKPOfywI\nxxwkS5KUAzwHpAEK8KSiKI9IknQvcD3Q1LnoXYqiLDneggqCIAh7xRIxlu5Zyks7XmJ943oMGgMz\n82Zy2cjLmJAy4ZgDRLvBzrTMaUzLnAaogW5tsJbtrdvZ0bqDHa072Ny8mXcq3ulax2PyUOgpVANn\ndyEplhRW1Kzok8D4S5Hycqp/NB99djbZj/4NjWHg9osdjBRFwb9oEdZp09B5PPDy3yClEIadB0CZ\nT009NtCC5EA4xnXPriUST/DivCkk2fo/JZoweBxPS3IcuFVRlPWSJNmBdZIkLe187yFFUf58/MUT\nBEEQDiQrMjd/eDMralaQY8/h1lNuZfaw2bhNPX+nviRJZNmyyLJlcW7uuV2v+yI+dnp3sqN1B9tb\nt7PTu5Pntz1PXFYHS+iLwPhL8dZWqm64EbRacp58Aq2z50cWO9l1bPiCWG0tKbfcDKUfQMNmuPhR\n6BytcHfbbjSShjxHXv8WdB/xhMzN/9vArsZ2nv3+qQxLtfd3kYRB5piDZEVR6oC6zscBSZJKgKye\nKpggCILQvX9t/hcralZw+6Tb+c7o7/RLVgqn0cnk9MlMTp/c9Vqoopw9Lz5NbOVqbKnZWIbkYKgN\noM1ZTyQnB312do8P5iGHw1Tf9CPiDQ0MefYZDDk5Pbp9QeVf9BaSyYTt3PNgwZVgz4Dxl3W9X9ZW\nRq59YGW2uG9xCR/uaOL/5oxlxnCRI1v46nqkT7IkSXlAMbAamA78WJKk7wJrUVubvT2xH0EQhJPd\nmro1/P2Lv3NB/gVcPfrqfu93K4fDBJa+T9uCBYRWrQJJwjFxInKLF9+GjcjB4H7L61JS0OfkYMjJ\nUee5OeizczDkZKNNSkLSHH3Ar8gytb+4k46NG8l6+GHMRUU9fXgCas5p/9vvYD/nbLT+nVD+MXzt\nt6Db23Wh1Fc6oLpaPP9ZBc+srODaGfl8e8rAzdssDGzHHSRLkmQDFgA/URTFL0nSY8DvUPsp/w54\nEPhBN+vNA+YB5OYeOTG8IAx6ibh6mXL3+3D+H9QB6wcRUWf7X3NHM3csv4MhjiHcc9o9/Rogd2zd\nim/Ba/gWLUL2+9FnZZF8849xzZmDPiMDUPuxJtraiFVVEa2sIlZVSbSqmlhlJcHVq4kvXLjfNiW9\nHl16OvqMDPQZ6egyMtCnZ6DPzECfkYEuIwOtbW9auqaHHiLwzjuk3n47jplf79PjH+h6sr4GP/uM\nhNeLY9Ys+PQRMDrglO93vR9NRKn0V3Je7nnHtZ+e8vHOJu59axvnFqZy1wWj+rs4wiB2XEGyJEl6\n1AD5v4qivAagKErDPu//E1jU3bqKojwJPAkwadIkpbtlBOGE0LCV6Ibn2bn9dbYl2tlltnFX23wk\n9+AKNEWd7V8JOcEdn9xBe7SdJ7/25FcaXKPHyuDz4XtrEW0LFhApKUEyGLB//eu4Lv0mllNPPagV\nWJIkdG43Orcb8/jxB21PjkSIVVcTraoiVlVNrL6OeF09sfp6gms+J97YCInEfuto7Hb06elo3W5C\na9bguvIKPD/4/kHbPtn1ZH31vbUIjdOJbUwWPL4Qpt0Mpr15aff495BQEgMiR/KO+gDz/7ue4ak2\nHrmyGK1mcDVGCAPL8WS3kICngBJFUf6yz+sZnf2VAeYAW46viILQ+5pCTWxq2oReqyfNkkaqJRWX\n0XVMLXXRRJSdtWvYtvV/bKtZpQbGBj1xtwHw4DQ4mWc0k9LzhyGcwB7f9Dhr6tfwu+m/2y/ncW9T\nZJnQ6tW0vbqAwNKlKNEoxtGjSPvVL3HOmnVcN8lpjEaMQ4diHNr9ZXolHife1ESsrp5YXS3x+npi\ntXXE6tXnrm9dSvrdd/d7l5MTmRwKEVi2DOesWUhrnwSNDqb+cL9lSttKgf7NbCHLCv9dvYf7396O\n1ajj39dMxmYUWW6F43M8n6DpwNXAZkmSvuh87S7gSkmSilC7W1QANxxXCQWhhymKQoW/gvUN61nf\nuJ71Deupbq8+aDmDxkCqJZU0qxo0p1vS93ueZknDYXBQ5itjW8s2tjVtZlvdanaF6omjNtw4tRKj\nHUO5Ovt0UqxjMcq5BAL2Q+aVFYTurKxZyRMbn2D20NlcMqzvRpCL1dRQOe8GoqWlaBwOXN/6Fq5v\nzsU0enSf7F/S6Tq7XmSg3vYi9LXAhx+ihEI4zp0By78DE64Ae/p+y5T6StXMFs68filjZUuIny/Y\nyKqyVs4YkcIDc8eR6erZG0SFk9PxZLdYAXR3+i5yIg8yMTnG8urlLClfggYN+c588l355DvyGeIY\ngkln6pdytXS08Gntp6yoXkFdsI40axrplnQybBmkW9JJt6qTx+Q5bEtSXI6zvXV7V1C8oXEDreFW\nANxGN8WpxVxReAVFqepNPw3BBhpDjTSEGtQp2MDmps28H3qfmBw75H4cssKYSJjvJHS4LBNoMl3A\nto5CtpeHWLahg4SsAGowflFRJqn2/vm7CoNLQ7CBO1fcyVDXUO6eenef7TfW0Miea75Pwucj809/\nxP71r6MxihyzJxv/osXo0tKwxFZDPKJ2tThAaVspOfYcjNq+/XzIssJzn1Xwh3d2oNNI/PGb4/nW\npGxxZUHoMeJaxEmstK2UN3a/wZulb9IabiXJlIRJZ+KdindQOltCJdQcqfnOfPKd+RQ4C7oe93RO\n1oScYHPzZlbUrGBFzQq2tmwF1IEKCpwFbG/dzoeVHxKVo/utZ9AY1ADamk6GNYM0i/q4paOFdY3r\n2NS0iY54BwDZtmxmZM1gYupEitOKyXfkd32hRisrkYNBRicVocv2IOn2rx6KotAWaWNXSzWbq3fQ\nWvo2uta1jAk1MDyqsCVaxIL4GfxDHkcCLVaDlvyUKOOzncwuyiQvyUpespX8ZCtui75H/3bCiSku\nx/n5Jz+nI97Bg2c+eMxDQn/l/ba0UPn975NoaSH36X9jnjChT/Y74ETaoewjyCwG58mX4TTu9dK+\nfDmeb1+hdrUovBCSD+7qU9pWSoGz4LDbUhSFnQ3t5CdbMeiOP2VhRXOQny/YxJryVs4amcL9c8eR\n4RStx0LPEkHyIBCX4zR3NJNmSTvuM+RANMA7Fe/wxq432NS8CZ2k48ycM5kzbA7Ts6aj0+joiHdQ\n6a+k3FdOma+Mcl855b5y1tSvIZKIdG3LbXST58wj25ZNpi2TLFsWmbZMMm2ZpFvS0WuPHAi2dLSw\nsnYly2uWs7J2Jb6ID42kYXzyeOYXzWdG9gxGeUZ15YFVFAVvxEt9sJ76YD11wToagg1dj9fUr6Ep\n1ERCSSAhMdIzkkuGXcLEtIkUpxSTZk3rthxtr75K3a/vAVlWX5AkJJeLmMNF0OKk1WCjTmshIEcY\nrtvNxZbt2M0d7DTm8pHrGt7OPp+0tDQuSLZyU7KVvGQLKTajaNEQjsujGx5lfeN67j/9fgpchw9C\nekqirY3Ka68jVltL7j+fHFABcqM/zJZaH1tq/Gyp8eEPx0iyGUm2Gki2GUmyGUmyqY+TO+cWg/ar\n1cPWMtj5Hux6FypWQCIKM++H027qvQMboALvvgfxOM4CGUraYPotBy0TS8So9FfuN9BMd15YU8nd\nr2/BadZz/th0LpqQydSCpK98Y50sKzyzsoI/vrsdvVbDny4dz6WniNZjoXeIIHmAUxSFu1bcxdvl\nb2PT2xjhHsFw93BGuEd0PbbqrYfdhqzIrGtYx+u7XmfpnqWEE2GGuYZx26TbmFUwiyRz0n7Lm3Vm\nRnpGMtIz8qDt1LbXdgXN5X51vrZhLQ3lDciK3LWsRtKQYk7ZL3D+8rFeo2dV3SpWVKutxQoKHpOH\nM7PPZEbWDKZlTsNp7P5mIEmS8Jg8eEweRid13y/yy5MKq96K3XD4EZYURaHy708QevQRWkdPZN24\nMwjWNxJrbsES9OGOtOMOeMmM7CYvEkSKqy3szdhpxo6eMF9jIRrbMrQeD1q3C53LTdzjodHtRudx\no3XvnXRuN1qPB43dLr7UhcP6pPoTntryFJeOuJRZBbP6ZJ+J9nYqr59HtLSU7McfwzJ58pFX6gWK\nolDnC7OlxqdOtWpQ3BhQT9IlCfKTrXgsBkpq/TS3R/CH491uy6TXkGQ1kmxXg2mP1YDbasBtMeC2\n6PGYJXICm0hv+Bh71QfoWnepKyaPgFPnwYiZkHtaXx36gOJftAhDQQHGmhchdxrknHrQMnv8e4gr\n8cOexJU1tXPfohIm57nJcVt4a2MtL35eRbLNyKzxGVw0IYOJue4jfieWNwf5+asb+bzCyzmFqfx+\nzjjSnaLbmtB7RJA8wC3YtYC3y99m9tDZmHQmdnl3sbhsMS/FXupaJtuWrQbNnhFdwXOOPYfGUCML\ndy/kjd1vUN1ejU1v4+KhFzNn+BzGJI35ykGaRtKQbc8m257N6dmn7/deTI7REGygtr2WmvYa6oJ1\n1LTXUNtey/qG9SwpX3JQED0ueRw3Fd3E6VmnMyppVI+NGqbT6Ei3ph/0ejQus7uxnZI6f+fko2jJ\nf5hV8gEfZhfzl2GXkWS0MmzyBIal2Mh1x5niXUz27hfQBqrBkY087rvEc84nHpKJtzSTaGkh4fUS\n93pJtHrVx01NhHfuJNHaihKJdFNCGP7ZSnTunh9CWDgx1LXXcdeKuxjpHskdk+/ok33KoRBVN95I\nuKSE7L/+Fdv06X2yX4A6XwcbKtu6AuKtNT5agmq3Ko0Ew1PtzBiezNhMJ+OynYxyJbCVLgFfFZg9\nYEkianLhx06LbKchYaUxrKc5GKWlPUJLe5Sm9gi1vjBba/0QamK6vIGztRuYqtmEQ+ogouhYKY/i\nA/m7rNFPItSRi2u3AU+dge9Na+PMESdXPppYXR2htWtJufwcJP8KuPDBbpcr9amZLQ6V/i2WkPnp\nyxsx6DT87cqJpDtNhGMJPtzeyJsba3lhTSXPrKwgy2Vm1oQMLp6QyegMx36/TwlZ4elPy/nTuzsw\n6jQ8+K0JzJ2YJRoahF4nguQBbKd3Jw+seYDTMk7jt9N/u1+Xg7pgHTu9O/ebPqr+qCsQNevMhONh\nFBSmpE/hR8U/4tzcc3u+T2OkHfRm9Bp9VwDdnZgcozHUSG17Le3RdopTi3GZXD1bln20tEfY1hUM\nByip87O7sZ24rLYEmzUKd5a8weSST6k/5yIm/ux21mW6cFr0ULsB1vwDPn4VEhHIPwMueABGnI9G\nq8MAHO3Aq3Io1BlEt5HwtpLwqoG01uE48srCSSmWiHHbJ7cRl+M8eNaDfXLjrByJUD1/Ph3rN5D1\n4J+xn3N2r+0rlpApqfOzbo+XdXu8rN/jpdYXBkCnkRiRZue8UWmMzXIwJsvJqHQHZoNW/a7Z8Tas\nfBV2L4MDbqI1AMmd00gAjR4sSZ2TB+weSHFBw1aUmnVIKCSsafiyZ7Mt7UzKHJNoiRpwBqNMDkXx\nhmJ4Q1EaA2E6oglONv7FiwFwmNaBoxCGdz9YS2lbZ2YLR1637z/6wW42VrXx6FXFXa2+Jr2W88dl\ncP64DALhGEu3NfDWxlqeWl7OEx+XUZBi5aLxmVxclAnA7a9sZH1lG+cWpvL7ueNIc4jWY6FviCB5\ngArFQtz28W3YDXZ+X3ApmsW3gjMbPAVIngIy3flk5pzFWTlnda0Tjocp9ZWys1UNmh0GBxcNveiQ\ngesxScSgag3sXqqOHFe/GSQt2NLUtED2jIPnjgz09gyyrGqXi54WjMTZUuNjU7WPL6rb2FjVRrW3\no+v9dIeJURl2zilMpTDDwSiPAeP99xAs+ZTk+fMp/OGNSK2lsP1tWP8cVH8OeitMvBomXwepxz5i\nk8ZiQWOxoM86+W76EY7Nw+sfZlPTJv585p8Z4uj94XSVaJSaW35CcOVnZNx/P47zz+96LxqXeX1D\nNZG4TJJ1//6+TrP+qFryvMEo6yu9XUHxxuo2wjH1ZD7LZeaUPA/zcl0U57opzLBj1Gn3rhyPwK63\nYcursOMdiHeAIwum3ghjL4X08RBug1ArdLRCqGWfqXXvvKMVGkvU5+48pLPuhBEz0aaPx6PR4AH6\nJqnd4OFbtBjzyDwMkZUw/TE4xHDhpW2lZNuyuz2Z21Dp5dEPdzOnOItZ4zO7Xd9u0jN3YjZzJ2bT\nGozyzpZ63txYw18/2MUjy3ah1UjYjDoeunwClxSJ1mOhb4kgeYD6/erfU+Gr4J9Ft+J+7hoCDRp0\nxjAGWxytoXPwJGsKeAq6JpOngDGefMZknwXD5/RcYXzVakC8+30o+xgifjUwzp0KZ92pBs6BegjU\ngbcCKj9Tf5QOpDWogbMtXS27NfnguaXzsSUJtAd/PGMJmR31ATZ2BsObqn3sbAjQ2UBMlstMUY6L\nq6cOYVyWk8IMBx7r3nbfhN9P9Q3XEvxiK2mXTsBjXQJ/uB+iAXWBpGFw/h/VXKCmYx8kQRCOxbLK\nZTy37TmuLLySmXkze31/SjxOzc/voP2jj0i/9x5cc/bmYN5W6+fWVzZSUufvdl2dRiLJZugKnlM6\nb5pLshkx6zRsqfWzrtJLWVOwa/kxWU6uOnUIpwxxM3GIq/tsBIk4lH8MW16Dkrcg4lO/F4q/rQbG\nOVP2D9gsHnUSekxk1y4i27eTdl4y2DPVv/shlLaVdtsfORSN89OXviDdYeI3s8cc1X49VgNXTcnl\nqim5NPjDLNpUR11bB/POKCBVtB4L/UAEyQPQm6VvsrB0ITcOncvEV39N+XsuYj4FUG9C01hM6JOs\nGJwa9GY/esPHGLSvobcm0FvjaHSA2Q2uXLXVxZGpTvbOuSMLHBlgOMQNf/GIGujufh92vQ9NJerr\njiwYcwkM+xoUnHn4IDIWhvaGvcFzoB4CtfsH0zVrIdgMyiEuZZrdxM3J+DROGuI2qsMmKkIGWhJW\n2rARNzg5MyWdq6ZkMjQ3m5H5uSS7XOpdPQCKAm2VULEBajcQ3/U5lf/ZTcSrIXNqG07jB5AYrwbE\nmcXqlFJ4yBYTQehNVYEqfrXiV4xJGsNtk27r9f0pskzd3XcTeOcdUn9xB+4rrgDUE9F/fFjK3z7Y\nhcti4ImrT2FirpuWYITmQFSdt6t9fZs7+/s2B6OUNbYzJLiJq6S3malZS0zS06F1oCS50NuTsbmS\n0Vo9ah/igBtK3er3lMWjzju8amC87Q0INoHRAaMugrFzIf+sbk+ahd7hW7QYNBocti1w2m9B130H\ns5gcY49/D2fnHtw9577FJexpDfG/66fiMH31lJdpDhPXzsj/yusJQk8S3zoDTJmvjPtW3cckzxiu\nfe8F9rxtIYGFrL/dD4pCrLqGWHU10ZpqIlXVtO+qQYkA7M1QoXVaMLgM6K0RdIYSdNo16HRBdOYE\nOrOMzpRAY1CQzM69QbQ9Q+0y0bAVyj+BWFDt0zdkGhRdBcPOU7sdHO2lLr0J3EPU6XBkWb1cGmyG\nYBNtzbXsqaqkqb6a9tZ6dE0tJEt+kqR6JmlDnKMNoNN09kVUgMbO6csxH7VG9QfX7FaD9M4W7WjI\nSOUnqcSDBnJ+/k1s51+qBsRHkaZOEHpbNBHlto/VwPjPZ/4Zg/Zoe70fG0VRqP/Nb/EtfJOUn9xC\n0jXXALC93s+tL29ka62f2UWZ3HvRGNydV2JS7EY4+H5Y9YR462uw6jGIbEIxuego/B5mkxlLuE0N\nfju80LwDqrxqnZS7z0SBzgwjvwFjv6mejOtF62FfUxQF/6JFWAvs6Jx2mPi9Qy5b5a9SM1sckCN5\nWUkDL6yuZN4ZBUwtSDrE2oIw8IkgeQAJx8Pc/vHtmDQG/m/dFqoWa5E1NnKffgbz2O4vVymyTLy5\nWQ2ea6qJVlV1BdIdDfXEK5pRQgffaibptOjsBrSWKDrTbnSGLei0QXQeJ9q8s9COPQ1t4eno0rLQ\nulxI+oODSUVRqGnrQKfR4DTrMek1X7m/WI0/wuqyEKvLwqwuj1LRYgEKsRvHMinPzZSCJLLyPeRl\nOdFrNWrrcKyj84e3de8P8IFTqBWyJ0FmMeFIMlV3PYisRMl97jEsxWJ4W2Hg8Ia9/GrJT0heuYWb\nJ16LZ2cDEXdUTRvodCL18JUNRVFofOAB2l56iaR580i+8UbiCZnHPy7lkWW7cJr1PP6dU/jG2O4i\n4n3462DtU7D2aQg1Q8oomPUw0vjLsRgshysARIMH119JC0PPBuPh0zYKvavjiy+I1dSQPLUNJt8I\npkPfZLy7bTcAQ11Du15rbo9wx4JNFKbbufXrI3q9vILQm0SQ3A86ooluA8o/ff4ndnp38kS1juDC\nGIrByZBnn8NUWHjIbUkaDfrUVPSpqTCx++Av0R4k3tRIvKmJRHMz8aam/aZYUxMddc0k2jSADJ9s\nADYA/+jahsZmU3+0XS6CZhs1soGdHVpqZAMBvZmg3kzEaAG7DewO9E4HBqcTq92C06Le5OM063GY\n9MiKwto9XlaVtXTdYOcw6Tg138O3pwxhakESozLs6LTdBAeSBAaLOh3FCFihDRuomv9DNEYjef95\nHuPwg0eLEoT+sr11O488NY/vvNyEJwAs+id7+OfeBTQSWqsRrcOKzuVQ62BSCrrkNLRpmWjdSWid\nDjR2O1rH3rlkPPRANk2PPELrs8/h/u7VpPz0J+yoD3DbKxvZXONj1vgMfjt77H79+PejKOqNrasf\nh20LQU7AyAtgyg1qFpijOUmWJDDa1MmV+9X/aEKv8i9ajKTTYM9NwJQbD7tsqa8UCYl8p9otQlEU\nfrFgM/6OOP+5bsr+N2EKwiAkguQ+1BqMct+ibby2oQaDVkOqw0i6w0Sa00TUsIFVwZe5oUpH8ish\nEmYXmc88j6nw+M/EtTYrWls+xvzD9++So1ES3jYSbd595l6iLa3UVjZQV1lPe2MzxlAVzmiI02Mh\njLHwYbcZ0+gI6k206820600E9WaCOhMFJjMTk1ykpLnJzEwiPT0JnS2CJuZHU91IzGslYbWi+XKy\nWL5yi1r78uVU33wLutQUcp/6N4ZskWFCGDje3r2YdX+8k/nLY0i2BEOuHYfGYiXequbeTvgCxP0h\nEqF2EhEv8UYNkSoNibCGRFQDHDoglbQaNBYDWqsJjdWC1m5D43CgyBLtKz7HNfdikn52C499tJuH\n39+NzaTjH9+eyAXjMrrfYDwCW99Qg+Pa9WB0qgHU5OvAI/qNniiUWAz/ksXYMjvQTroC7N2PUPql\n0rZSsmxZXalFX15bxfslDfzywlEUpos0l8LgJ4LkPqAoCm98UcPvFpXg74hxzbQ8jHoNDb4w9f4w\nW+pLaXU9xvRq+NqrIdo0Vn466YfUPLMLl6WCdIeJFLuxa4Qo1z5zl0Xf+boBl1WP3ag75hQ5GoMB\nTVoq+rRUwrEEn+5s4l1/A++3NODT5GEaruGsC1OZOTaN4sI0nGY9ciSCHAiQ8AeQA/795omAH7lz\nnvAHiPp8xNr8KO1+tMEG5MYg8togKAqNR1E+yWhEYzYjWcxoTGY0ZvNBzyWLGY3ZAokErf/9L8YR\nw8l98kl0ycnH9DcRhJ6WkBM8vuz3pPz5f3xzj4JxqMyQ3z+IdsJFh1gh1pnOrLkrvZkSaCTRVE+i\npQHZ5yPh96n1sD1IItiBHAyT6AggxzQkghKJNg2xqEQipsE9LEya/nGkBx7nekXLNUYzRosT7Sd2\nWNXZwmuwqd0eDFaQNGqAHGyEpOFwwZ9hwpXqcsIJJbhqFQlvG84xQZh28xGXL20r7RpEZE9LkN+8\ntY3TCpL4wXRx4iScGESQ3MuqWkPc/cYWPtnZRHGuiwfmjmdk+t4+d9FElKuXPET25gi3vBoFu4vY\nA0/yE7OHBr8aRDf4IzQGIlS2hvAGo4ccfhXUNEuuzgDaadZjM+qwmXTYjTpsRh12k37vc5Nuv/dN\nei3rK728s6Wej3Y00RFL4DDpOG9UGjPHpnPG8BQ1qf8+NEYjGqPxmINQRVFQOjqQg0HkYJBE51yd\nQnsfh0LIHSF12Y4wckeH+jzUQcLbRqyjFiXU0fl6B0o4jHX6dLIefgitXfRxFAYGX8TH3//xPc55\nfgfWqELqRdl4fv0Ckj310Ctp9WqL3j6teoqs8OnuZrbV+rEYtJgNWqwGHRaDtnPSYdGDlRB2OYg5\n0Y4+5ifR0cYnm8p4cms5bl2Ub4ywMcypIEWDEG1Xp0g7tDeq82hAvQcg/wy15bjgbJH95QTmX/gG\nGoOC9exzIbn7EfS+FJNjVPgrOCP7DOIJmZ++9AVajcSDl01AoxG5jIUTgwiSe0k8IfP0pxX8ZelO\nNBL85uIxfGfqELQHfHk8tPYhEhu38euX4ujdbob8bwEjjzDwRDwh4+uI0dYRoy0UxRtUR4Zq6xwh\nyhtSX/eH1XlVa4hAJE57OE5H7MgjR6XYjXzzlCxmjklnakGSesNcL5EkCalzwA1Sem7YV0WWe/yG\nJ0E4HjuatrH0zquZsyJE2CMz4p5rMV58+9FnjEEdOOe19dU8s7KKxvD0AAAH60lEQVSC0s78w0dL\nr5XQaw2EosOZOWYGP7xknJqxQhAAuaODwPtLcWSH0Jz5syMuXxWoIi7HGeYaxmMflbK+so1Hrigi\n09XDo7oKQj8SQXIv2FLj487XNrO5xse5han87pKx3X5xfFj5ARvefY57Xk5gSnKR+/JC9GmH7wMG\noNNqSLIZSbJ99R+4eEImGEkQiMRoj8QJhNXgORCJE4zEGZFmozjHPehbAkSALAwky1Y+S/BXf+Dc\nGoXEOCMTHvoPmuyxR71+VWuI5z6r4MXPqwiE44zPdvLQ5RM4b1Qa0bhMKJronOLdP47ECcUSdEQT\nTM7zcMG4dDFymbCf9mXvI4djOE4dqmYGOoKytjIA5EgqjyzbxUUTMpldJO77EE4svRYkS5L0DeAR\nQAv8S1GUB3prXwNFRzTBw8t28q/l5bgtBh69qpgLx2V0+2NU117H//79M+56JYE5xcGQl99El3qY\nS649RKfV4LRocFpEfmBB6G0JOcHLf7uWYU+vJklWsH9vEtm3//uQgzPsS1EUVpe38vSn5Szd1oAk\nSXxjbDo/mJ7HxFz3ft8rIhOtcLx8Lz6FzpzAcvkdR7X8l+nfHn67jWSbkftmH/1JnyAMFr0SJEuS\npAX+DnwNqAY+lyTpTUVRtvXG/gaCFbuauev1zVS2hrh8Ug53XTDqkIFoTI7x5F++xfxXIhhTreQt\neAedRwyrKggnEp+vgSW3zKJoVTutqTD697/FOuOyI64XjiV4c2Mtz3xawbY6Py6LnhvOHMrVU4eI\nS9lCr0h4vbSv34FnggWp8OiGQy9rK8OiSaW8Kc5/rztFNLwIJ6Teakk+FditKEoZgCRJLwKzgRMu\nSPYGo9y3uIQF66vJT7byv+unctrQw7frvPLHy5j7YguJdCPDXl2KVgTIgnBC2f3Zq5T/4tcUNSg0\nT3Ey/a9vonEe/kpRgz/Mf1bt4YXVlbQEo4xIs3H/3HFcUpR10A2zgtCT/P95BGRwXPbdo+4jv7Fx\nO36fhx9Mz2f6MJE9SDgx9VaQnAVU7fO8GphyrBt7cf7XGLa8+rgL1Vuu7JwAeB7WHmH5CRFozdIx\n7dX30bpFgCycWNYuexF+9pv+Lka/MsbAbYCFZ4/lhbTr4KENR1wnGE0gKwrnFqby/en5TBuaJPoN\nC33is4UvY/LAZa0vwL9fPLqVtB04defx82+M7N3CCUI/6rcb9yRJmgfMA8jNPfyoS9bhYyhr8fZF\nsb46ScKo0xyUteJwdFYzZ9zzLFq3OPsWBo+jrbPu1FzWjbb2VbEGJFmnpWbG9YQzTmPuUa7jMOmY\nOzGbvOST+28n9Iyjra9KIkHbyAwaDRJDrd2P2todnaTl9rN/gEkvrnIIJy5JUZSe36gknQbcqyjK\nzM7ndwIoinJ/d8tPmjRJWbv2SO2vgnDykCRpnaIoR77FvJ+IOisI+xvIdVbUV0HY39HW197Kk/U5\nMFySpHxJkgzAFcCbvbQvQRAEQRAEQehRvdLdQlGUuCRJ84F3UVPA/VtRlK29sS9BEARBEARB6Gm9\n1idZUZQlwJLe2r4gCIIgCIIg9BYxLJkgCIIgCIIgHEAEyYIgCIIgCIJwABEkC4IgCIIgCMIBRJAs\nCIIgCIIgCAfolTzJX7kQktQE7OnvcvShZKC5vwvRj8TxH/n4hyiKktIXhTkWos6edMTxD+I6K+rr\nSUccfw/V1wERJJ9sJElaO1CTzvcFcfwn9/EPRif7/0wc/8l9/IPNyf7/Esffc8cvulsIgiAIgiAI\nwgFEkCwIgiAIgiAIBxBBcv94sr8L0M/E8QuDzcn+PxPHLwwmJ/v/Sxx/DxF9kgVBEARBEAThAKIl\nWRAEQRAEQRAOIILkPiZJUoUkSZslSfpCkqS1/V2e3iZJ0r8lSWqUJGnLPq95JElaKknSrs65uz/L\n2JsOcfz3SpJU0/kZ+EKSpAv6s4zCoYn6KuqrqK+Di6izos72ZJ0VQXL/OFtRlKKTJEXLM8A3Dnjt\nF8AyRVGGA8s6n5+onuHg4wd4qPMzUKQoypI+LpPw1Yj6KuqrqK+Di6izos72SJ0VQbLQqxRF+QRo\nPeDl2cCznY+fBS7p00L1oUMcvyAMSKK+ivoqDC6izvZunRVBct9TgPclSVonSdK8/i5MP0lTFKWu\n83E9kNafheknP5YkaVPnpaIT9lLYCUDUV1FfQdTXwUTUWVFnoYfqrAiS+94MRVGKgPOBH0mSdEZ/\nF6g/KWp6lZMtxcpjQAFQBNQBD/ZvcYTDEPV1H6K+ivo6CIg6uw9RZ4+vzooguY8pilLTOW8EXgdO\n7d8S9YsGSZIyADrnjf1cnj6lKEqDoigJRVFk4J+cnJ+BQUHUV0DUV1FfBxFRZwFRZ3uszooguQ9J\nkmSVJMn+5WPg68CWw691QnoT+F7n4+8BC/uxLH3uyy+vTnM4OT8DA56or11Efd1L1NcBTNTZLqLO\n7nVcdVYMJtKHJEkqQD2zBdABLyiK8n/9WKReJ0nS/4CzgGSgAbgHeAN4GcgF9gCXKYpyQt4sc4jj\nPwv1MpACVAA37NN/TBggRH0V9RVRXwcVUWdFnaWH66wIkgVBEARBEAThAKK7hSAIgiAIgiAcQATJ\ngiAIgiAIgnAAESQLgiAIgiAIwgFEkCwIgiAIgiAIBxBBsiAIgiAIgiAcQATJgiAIgiAIgnAAESQL\ngiAIgiAIwgFEkCwIgiAIgiAIB/h/QQXwUddvdi8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a38c9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_comparison(alljoined, 'CA', *comp_players)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/muneebalam/anaconda/lib/python3.6/site-packages/matplotlib/axes/_axes.py:545: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
      "  warnings.warn(\"No labelled objects found. \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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jEt1I8rUP8/CF8Q8YxfL5T4KywLJH4Lkh8PZM2Pi2LEzSS5TuZAuCUuoy4AKt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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bd39518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_cumulative_ns(df, zone, ax, *comp_players):\n",
    "    for p in comp_players:\n",
    "        df1 = df.query('ZS == \"{0:s}\" & Player == \"{1:s}\"'.format(zone, p)).sort_values('TimeSinceLastDraw')\n",
    "        ax.plot(df1.TimeSinceLastDraw, df1.TOI, label=p)\n",
    "    ax.set_title('Post-faceoff time in {0:s}'.format(zone))\n",
    "    \n",
    "plot_comparison(alljoined, 'N', *comp_players)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It also might be interesting to take a look at what fraction of a player's 5v5 TOI is accounted for by the time after faceoffs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Game</th>\n",
       "      <th>PlayerID</th>\n",
       "      <th>ShiftIndex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>281384</th>\n",
       "      <td>41</td>\n",
       "      <td>20008</td>\n",
       "      <td>8468498.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281385</th>\n",
       "      <td>42</td>\n",
       "      <td>20008</td>\n",
       "      <td>8468498.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281386</th>\n",
       "      <td>43</td>\n",
       "      <td>20008</td>\n",
       "      <td>8468498.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281387</th>\n",
       "      <td>44</td>\n",
       "      <td>20008</td>\n",
       "      <td>8468498.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281388</th>\n",
       "      <td>45</td>\n",
       "      <td>20008</td>\n",
       "      <td>8468498.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Time   Game   PlayerID  ShiftIndex\n",
       "281384    41  20008  8468498.0         0.0\n",
       "281385    42  20008  8468498.0         0.0\n",
       "281386    43  20008  8468498.0         0.0\n",
       "281387    44  20008  8468498.0         0.0\n",
       "281388    45  20008  8468498.0         0.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find shift starts\n",
    "shifts = toi.sort_values(['Game', 'PlayerID', 'Time'])\n",
    "\n",
    "# If shift yields diff time in same game and player, then it's a shift start\n",
    "shifts.loc[:, 'PrevT'] = shifts.Time.shift(1)\n",
    "shifts.loc[:, 'PrevP'] = shifts.PlayerID.shift(1)\n",
    "shifts.loc[:, 'PrevG'] = shifts.Game.shift(1)\n",
    "shifts.loc[(shifts.PlayerID == shifts.PrevP) & (shifts.Game == shifts.PrevG) & (shifts.Time != shifts.PrevT + 1), \n",
    "           'ShiftIndex'] = 1\n",
    "shifts.loc[:, 'ShiftIndex'] = shifts.ShiftIndex.fillna(0)\n",
    "shifts.loc[:, 'ShiftIndex'] = shifts.ShiftIndex.cumsum()  \n",
    "\n",
    "shifts = shifts.drop({'PrevT', 'PrevP', 'PrevG'}, axis=1)\n",
    "\n",
    "shifts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TimeSinceLastDraw</th>\n",
       "      <th>TOI</th>\n",
       "      <th>ZS</th>\n",
       "      <th>Player</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>725</td>\n",
       "      <td>Other</td>\n",
       "      <td>Brooks Orpik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>589</td>\n",
       "      <td>Other</td>\n",
       "      <td>Alex Ovechkin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>501</td>\n",
       "      <td>Other</td>\n",
       "      <td>T.J. Oshie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>452</td>\n",
       "      <td>Other</td>\n",
       "      <td>Matt Niskanen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>323</td>\n",
       "      <td>Other</td>\n",
       "      <td>Taylor Chorney</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TimeSinceLastDraw  TOI     ZS          Player\n",
       "0                  1  725  Other    Brooks Orpik\n",
       "1                  1  589  Other   Alex Ovechkin\n",
       "2                  1  501  Other      T.J. Oshie\n",
       "3                  1  452  Other   Matt Niskanen\n",
       "4                  1  323  Other  Taylor Chorney"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate amount of time at each point in shift\n",
    "starttimes = shifts[['Time', 'ShiftIndex', 'PlayerID']] \\\n",
    "    .groupby(['ShiftIndex', 'PlayerID'], as_index=False).min() \\\n",
    "    .rename(columns={'Time': 'StartTime'})\n",
    "\n",
    "shifts2 = shifts.merge(starttimes, how='left', on=['ShiftIndex', 'PlayerID'])\n",
    "shifts2.loc[:, 'TimeSinceLastDraw'] = shifts2.Time - shifts2.StartTime + 1\n",
    "counts = shifts2[['TimeSinceLastDraw', 'PlayerID']] \\\n",
    "    .assign(TOI=1) \\\n",
    "    .groupby(['TimeSinceLastDraw', 'PlayerID'], as_index=False) \\\n",
    "    .count() \\\n",
    "    .assign(ZS='Other')\n",
    "    \n",
    "counts.loc[:, 'Player'] = players.playerlst_as_str(counts.PlayerID)\n",
    "counts = counts.drop('PlayerID', axis=1)\n",
    "counts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player</th>\n",
       "      <th>TOI</th>\n",
       "      <th>TimeSinceLastDraw</th>\n",
       "      <th>ZS</th>\n",
       "      <th>TotalTOI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aaron Ness</td>\n",
       "      <td>0.051948</td>\n",
       "      <td>1.0</td>\n",
       "      <td>DL</td>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aaron Ness</td>\n",
       "      <td>0.052174</td>\n",
       "      <td>2.0</td>\n",
       "      <td>DL</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Aaron Ness</td>\n",
       "      <td>0.052174</td>\n",
       "      <td>3.0</td>\n",
       "      <td>DL</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aaron Ness</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>4.0</td>\n",
       "      <td>DL</td>\n",
       "      <td>228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aaron Ness</td>\n",
       "      <td>0.052863</td>\n",
       "      <td>5.0</td>\n",
       "      <td>DL</td>\n",
       "      <td>227</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Player       TOI  TimeSinceLastDraw  ZS  TotalTOI\n",
       "0  Aaron Ness  0.051948                1.0  DL       231\n",
       "1  Aaron Ness  0.052174                2.0  DL       230\n",
       "2  Aaron Ness  0.052174                3.0  DL       230\n",
       "3  Aaron Ness  0.052632                4.0  DL       228\n",
       "4  Aaron Ness  0.052863                5.0  DL       227"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add to original\n",
    "alljoined2 = pd.concat([alljoined[['Player', 'ZS', 'TimeSinceLastDraw', 'TOI']], counts])\n",
    "alljoined2 = alljoined2.sort_values(['Player', 'ZS', 'TimeSinceLastDraw'])\n",
    "\n",
    "# Convert to percentages\n",
    "totals = alljoined2.drop('ZS', axis=1) \\\n",
    "    .groupby(['Player', 'TimeSinceLastDraw'], as_index=False) \\\n",
    "    .sum() \\\n",
    "    .rename(columns={'TOI': 'TotalTOI'})\n",
    "alljoined3 = alljoined2.merge(totals, how='left', on=['Player', 'TimeSinceLastDraw'])\n",
    "alljoined3.loc[:, 'TOI'] = alljoined3.TOI / alljoined3.TotalTOI\n",
    "alljoined3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/muneebalam/anaconda/lib/python3.6/site-packages/matplotlib/axes/_axes.py:545: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
      "  warnings.warn(\"No labelled objects found. \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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NtU0/McQyB9wesa5OfCOlO2Cv1yqsA5L9wBn2sI8Cfxlp3p7o1yTeToZcZsq43wJusd8f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\nLIiIiIiISFNKFkREREREpKn/H+o1wUcBHGCdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ba02780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_stacked_area(df, p, ax, limit=20):\n",
    "    zones = ['OW', 'OL', 'NW', 'NL', 'DW', 'DL'][::-1]  # for DZ at bottom\n",
    "    \n",
    "    # Set colors\n",
    "    color_cycle = plt.rcParams['axes.prop_cycle'].by_key()['color']\n",
    "    colors = [mplc.to_rgba(color_cycle[0], alpha=0.5), color_cycle[0], \n",
    "             mplc.to_rgba(color_cycle[1], alpha=0.5), color_cycle[1], \n",
    "             mplc.to_rgba(color_cycle[2], alpha=0.5), color_cycle[2]]\n",
    "    \n",
    "    \n",
    "    struct = pd.DataFrame({'TimeSinceLastDraw': range(1, limit+1, 1)}).assign(Player=p)\n",
    "    struct = struct.merge(df[['Player', 'TimeSinceLastDraw', 'ZS', 'TOI']], \n",
    "                          how='left', on=['Player', 'TimeSinceLastDraw'])\n",
    "    struct = struct.pivot_table(index=['Player', 'TimeSinceLastDraw'], columns='ZS', values='TOI').reset_index()\n",
    "    struct = struct.fillna(method='ffill')\n",
    "    ax.stackplot(struct.TimeSinceLastDraw, [struct[zone] for zone in zones], labels=zones, colors=colors)\n",
    "    ax.set_title('TOI into shift by 5v5 shift\\nstart for {0:s}'.format(p))\n",
    "    ax.set_xlabel('Seconds into shift')\n",
    "    legend(loc=2)\n",
    "\n",
    "def plot_tois(df, *comp_players):\n",
    "    fig, axes = subplots(1, 4, sharex=True, sharey=True, figsize=[12, 4])\n",
    "    axes = axes.flatten()\n",
    "    for i, p in enumerate(comp_players):\n",
    "        plot_stacked_area(df, p, axes[i])\n",
    "    legend(loc=2, bbox_to_anchor=(1, 1))\n",
    "    axes[0].set_ylabel('% of 5v5 shifts')\n",
    "\n",
    "plot_tois(alljoined3, *comp_players)"
   ]
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}