Skip to content

Instantly share code, notes, and snippets.

@springcoil

springcoil/Sixnationsmodel

Last active August 29, 2015 14:15
Show Gist options
  • Star 0 You must be signed in to star a gist
  • Fork 0 You must be signed in to fork a gist
  • Save springcoil/6efa806e3a961178559e to your computer and use it in GitHub Desktop.
Save springcoil/6efa806e3a961178559e to your computer and use it in GitHub Desktop.
Download ZIP
SixNationsModel
Raw
Sixnationsmodel
{
"metadata": {
"name": "",
"signature": "sha256:50c3d72f03b2ec97ee24d9e5a4f27faa1a48d7fe209616e378edc70f146f845b"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline\n",
"%config InlineBackend.figure_format = 'retina'\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import os\n",
"import math\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"\n",
"from IPython.display import Image, HTML\n",
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"import pymc # I know folks are switching to \"as pm\" but I'm just not there yet"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"DATA_DIR = os.path.join(os.getcwd(), 'data/')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data_file = DATA_DIR + 'results_2014.csv'\n",
"df = pd.read_csv(data_file, sep=',')\n",
"df.head()\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>home_team</th>\n",
" <th>away_team</th>\n",
" <th>home_score</th>\n",
" <th>away_score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> Wales</td>\n",
" <td> Italy</td>\n",
" <td> 23</td>\n",
" <td> 15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> France</td>\n",
" <td> England</td>\n",
" <td> 26</td>\n",
" <td> 24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> Ireland</td>\n",
" <td> Scotland</td>\n",
" <td> 28</td>\n",
" <td> 6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> Ireland</td>\n",
" <td> Wales</td>\n",
" <td> 26</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> Scotland</td>\n",
" <td> England</td>\n",
" <td> 0</td>\n",
" <td> 20</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 20,
"text": [
" home_team away_team home_score away_score\n",
"0 Wales Italy 23 15\n",
"1 France England 26 24\n",
"2 Ireland Scotland 28 6\n",
"3 Ireland Wales 26 3\n",
"4 Scotland England 0 20"
]
}
],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"teams = df.home_team.unique()\n",
"teams = pd.DataFrame(teams, columns=['team'])\n",
"teams['i'] = teams.index\n",
"teams.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>team</th>\n",
" <th>i</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> Wales</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> France</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> Ireland</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> Scotland</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> Italy</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 21,
"text": [
" team i\n",
"0 Wales 0\n",
"1 France 1\n",
"2 Ireland 2\n",
"3 Scotland 3\n",
"4 Italy 4"
]
}
],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"teams"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>team</th>\n",
" <th>i</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> Wales</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> France</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> Ireland</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> Scotland</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> Italy</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td> England</td>\n",
" <td> 5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 22,
"text": [
" team i\n",
"0 Wales 0\n",
"1 France 1\n",
"2 Ireland 2\n",
"3 Scotland 3\n",
"4 Italy 4\n",
"5 England 5"
]
}
],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df = pd.merge(df, teams, left_on='home_team', right_on='team', how='left')\n",
"df = df.rename(columns = {'i': 'i_home'}).drop('team', 1)\n",
"df = pd.merge(df, teams, left_on='away_team', right_on='team', how='left')\n",
"df = df.rename(columns = {'i': 'i_away'}).drop('team', 1)\n",
"df.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>home_team</th>\n",
" <th>away_team</th>\n",
" <th>home_score</th>\n",
" <th>away_score</th>\n",
" <th>i_home</th>\n",
" <th>i_away</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> Wales</td>\n",
" <td> Italy</td>\n",
" <td> 23</td>\n",
" <td> 15</td>\n",
" <td> 0</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> France</td>\n",
" <td> England</td>\n",
" <td> 26</td>\n",
" <td> 24</td>\n",
" <td> 1</td>\n",
" <td> 5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> Ireland</td>\n",
" <td> Scotland</td>\n",
" <td> 28</td>\n",
" <td> 6</td>\n",
" <td> 2</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> Ireland</td>\n",
" <td> Wales</td>\n",
" <td> 26</td>\n",
" <td> 3</td>\n",
" <td> 2</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> Scotland</td>\n",
" <td> England</td>\n",
" <td> 0</td>\n",
" <td> 20</td>\n",
" <td> 3</td>\n",
" <td> 5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 23,
"text": [
" home_team away_team home_score away_score i_home i_away\n",
"0 Wales Italy 23 15 0 4\n",
"1 France England 26 24 1 5\n",
"2 Ireland Scotland 28 6 2 3\n",
"3 Ireland Wales 26 3 2 0\n",
"4 Scotland England 0 20 3 5"
]
}
],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"observed_home_goals = df.home_score.values\n",
"observed_away_goals = df.away_score.values\n",
"home_team = df.i_home.values\n",
"away_team = df.i_away.values\n",
"num_teams = len(df.i_home.drop_duplicates())\n",
"num_games = len(home_team)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"num_teams"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 25,
"text": [
"6"
]
}
],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>home_team</th>\n",
" <th>away_team</th>\n",
" <th>home_score</th>\n",
" <th>away_score</th>\n",
" <th>i_home</th>\n",
" <th>i_away</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0 </th>\n",
" <td> Wales</td>\n",
" <td> Italy</td>\n",
" <td> 23</td>\n",
" <td> 15</td>\n",
" <td> 0</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1 </th>\n",
" <td> France</td>\n",
" <td> England</td>\n",
" <td> 26</td>\n",
" <td> 24</td>\n",
" <td> 1</td>\n",
" <td> 5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2 </th>\n",
" <td> Ireland</td>\n",
" <td> Scotland</td>\n",
" <td> 28</td>\n",
" <td> 6</td>\n",
" <td> 2</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3 </th>\n",
" <td> Ireland</td>\n",
" <td> Wales</td>\n",
" <td> 26</td>\n",
" <td> 3</td>\n",
" <td> 2</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4 </th>\n",
" <td> Scotland</td>\n",
" <td> England</td>\n",
" <td> 0</td>\n",
" <td> 20</td>\n",
" <td> 3</td>\n",
" <td> 5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> France</td>\n",
" <td> Italy</td>\n",
" <td> 30</td>\n",
" <td> 10</td>\n",
" <td> 1</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 </th>\n",
" <td> Wales</td>\n",
" <td> France</td>\n",
" <td> 27</td>\n",
" <td> 6</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> Italy</td>\n",
" <td> Scotland</td>\n",
" <td> 20</td>\n",
" <td> 21</td>\n",
" <td> 4</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> England</td>\n",
" <td> Ireland</td>\n",
" <td> 13</td>\n",
" <td> 10</td>\n",
" <td> 5</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> Ireland</td>\n",
" <td> Italy</td>\n",
" <td> 46</td>\n",
" <td> 7</td>\n",
" <td> 2</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td> Scotland</td>\n",
" <td> France</td>\n",
" <td> 17</td>\n",
" <td> 19</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td> England</td>\n",
" <td> Wales</td>\n",
" <td> 29</td>\n",
" <td> 18</td>\n",
" <td> 5</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td> Italy</td>\n",
" <td> England</td>\n",
" <td> 11</td>\n",
" <td> 52</td>\n",
" <td> 4</td>\n",
" <td> 5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td> Wales</td>\n",
" <td> Scotland</td>\n",
" <td> 51</td>\n",
" <td> 3</td>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td> France</td>\n",
" <td> Ireland</td>\n",
" <td> 20</td>\n",
" <td> 22</td>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 26,
"text": [
" home_team away_team home_score away_score i_home i_away\n",
"0 Wales Italy 23 15 0 4\n",
"1 France England 26 24 1 5\n",
"2 Ireland Scotland 28 6 2 3\n",
"3 Ireland Wales 26 3 2 0\n",
"4 Scotland England 0 20 3 5\n",
"5 France Italy 30 10 1 4\n",
"6 Wales France 27 6 0 1\n",
"7 Italy Scotland 20 21 4 3\n",
"8 England Ireland 13 10 5 2\n",
"9 Ireland Italy 46 7 2 4\n",
"10 Scotland France 17 19 3 1\n",
"11 England Wales 29 18 5 0\n",
"12 Italy England 11 52 4 5\n",
"13 Wales Scotland 51 3 0 3\n",
"14 France Ireland 20 22 1 2"
]
}
],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"\n",
"g = df.groupby('i_away')\n",
"att_starting_points = np.log(g.away_score.mean())\n",
"g = df.groupby('i_home')\n",
"def_starting_points = -np.log(g.away_score.mean())\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 27
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#hyperpriors\n",
"home = pymc.Normal('home', 0, .0001, value=0)\n",
"tau_att = pymc.Gamma('tau_att', .1, .1, value=10)\n",
"tau_def = pymc.Gamma('tau_def', .1, .1, value=10)\n",
"intercept = pymc.Normal('intercept', 0, .0001, value=0)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#team-specific parameters\n",
"atts_star = pymc.Normal(\"atts_star\", \n",
" mu=0, \n",
" tau=tau_att, \n",
" size=num_teams, \n",
" value=att_starting_points.values)\n",
"defs_star = pymc.Normal(\"defs_star\", \n",
" mu=0, \n",
" tau=tau_def, \n",
" size=num_teams, \n",
" value=def_starting_points.values) \n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 29
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# trick to code the sum to zero contraint\n",
"@pymc.deterministic\n",
"def atts(atts_star=atts_star):\n",
" atts = atts_star.copy()\n",
" atts = atts - np.mean(atts_star)\n",
" return atts\n",
"\n",
"@pymc.deterministic\n",
"def defs(defs_star=defs_star):\n",
" defs = defs_star.copy()\n",
" defs = defs - np.mean(defs_star)\n",
" return defs\n",
"\n",
"@pymc.deterministic\n",
"def home_theta(home_team=home_team, \n",
" away_team=away_team, \n",
" home=home, \n",
" atts=atts, \n",
" defs=defs, \n",
" intercept=intercept): \n",
" return np.exp(intercept + \n",
" home + \n",
" atts[home_team] + \n",
" defs[away_team])\n",
" \n",
"@pymc.deterministic\n",
"def away_theta(home_team=home_team, \n",
" away_team=away_team, \n",
" home=home, \n",
" atts=atts, \n",
" defs=defs, \n",
" intercept=intercept): \n",
" return np.exp(intercept + \n",
" atts[away_team] + \n",
" defs[home_team]) \n",
"\n",
"\n",
"home_points = pymc.Poisson('home_points', \n",
" mu=home_theta, \n",
" value=observed_home_goals, \n",
" observed=True)\n",
"away_points = pymc.Poisson('away_points', \n",
" mu=away_theta, \n",
" value=observed_away_goals, \n",
" observed=True)\n",
"\n",
"mcmc = pymc.MCMC([home, intercept, tau_att, tau_def, \n",
" home_theta, away_theta, \n",
" atts_star, defs_star, atts, defs, \n",
" home_points, away_points])\n",
"map_ = pymc.MAP( mcmc )\n",
"map_.fit()\n",
"mcmc.sample(200000, 40000, 20)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [ 0% ] 236 of 200000 complete in 0.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [ 0% ] 416 of 200000 complete in 1.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [ 0% ] 699 of 200000 complete in 1.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [ 0% ] 965 of 200000 complete in 2.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [ 0% ] 1241 of 200000 complete in 2.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [ 0% ] 1518 of 200000 complete in 3.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [ 0% ] 1814 of 200000 complete in 3.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [ 1% ] 2107 of 200000 complete in 4.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [ 1% ] 2389 of 200000 complete in 4.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [ 1% ] 2673 of 200000 complete in 5.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [ 1% ] 2947 of 200000 complete in 5.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [ 1% ] 3226 of 200000 complete in 6.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [ 1% ] 3502 of 200000 complete in 6.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [ 1% ] 3785 of 200000 complete in 7.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [ 2% ] 4065 of 200000 complete in 7.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [ 2% ] 4346 of 200000 complete in 8.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [ 2% ] 4625 of 200000 complete in 8.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [ 2% ] 4907 of 200000 complete in 9.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [ 2% ] 5185 of 200000 complete in 9.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [- 2% ] 5462 of 200000 complete in 10.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [- 2% ] 5741 of 200000 complete in 10.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [- 3% ] 6012 of 200000 complete in 11.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [- 3% ] 6286 of 200000 complete in 11.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [- 3% ] 6554 of 200000 complete in 12.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [- 3% ] 6828 of 200000 complete in 12.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [- 3% ] 7089 of 200000 complete in 13.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [- 3% ] 7370 of 200000 complete in 13.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [- 3% ] 7648 of 200000 complete in 14.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [- 3% ] 7867 of 200000 complete in 14.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [- 4% ] 8110 of 200000 complete in 15.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [- 4% ] 8348 of 200000 complete in 15.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [- 4% ] 8575 of 200000 complete in 16.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [- 4% ] 8808 of 200000 complete in 16.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [- 4% ] 9055 of 200000 complete in 17.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [- 4% ] 9304 of 200000 complete in 17.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [- 4% ] 9559 of 200000 complete in 18.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [- 4% ] 9792 of 200000 complete in 18.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [- 5% ] 10039 of 200000 complete in 19.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [- 5% ] 10245 of 200000 complete in 19.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [- 5% ] 10507 of 200000 complete in 20.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-- 5% ] 10742 of 200000 complete in 20.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-- 5% ] 10960 of 200000 complete in 21.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-- 5% ] 11180 of 200000 complete in 21.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-- 5% ] 11431 of 200000 complete in 22.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-- 5% ] 11678 of 200000 complete in 22.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-- 5% ] 11930 of 200000 complete in 23.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-- 6% ] 12140 of 200000 complete in 23.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-- 6% ] 12346 of 200000 complete in 24.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-- 6% ] 12573 of 200000 complete in 24.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-- 6% ] 12841 of 200000 complete in 25.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-- 6% ] 13075 of 200000 complete in 25.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-- 6% ] 13343 of 200000 complete in 26.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-- 6% ] 13593 of 200000 complete in 26.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-- 6% ] 13847 of 200000 complete in 27.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-- 7% ] 14109 of 200000 complete in 27.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-- 7% ] 14319 of 200000 complete in 28.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-- 7% ] 14607 of 200000 complete in 28.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-- 7% ] 14865 of 200000 complete in 29.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-- 7% ] 15137 of 200000 complete in 29.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-- 7% ] 15371 of 200000 complete in 30.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-- 7% ] 15614 of 200000 complete in 30.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--- 7% ] 15832 of 200000 complete in 31.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--- 8% ] 16008 of 200000 complete in 31.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--- 8% ] 16265 of 200000 complete in 32.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--- 8% ] 16539 of 200000 complete in 32.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--- 8% ] 16799 of 200000 complete in 33.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--- 8% ] 17026 of 200000 complete in 33.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--- 8% ] 17288 of 200000 complete in 34.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--- 8% ] 17533 of 200000 complete in 34.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--- 8% ] 17767 of 200000 complete in 35.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--- 9% ] 18018 of 200000 complete in 35.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--- 9% ] 18259 of 200000 complete in 36.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--- 9% ] 18520 of 200000 complete in 36.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--- 9% ] 18766 of 200000 complete in 37.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--- 9% ] 19031 of 200000 complete in 37.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--- 9% ] 19252 of 200000 complete in 38.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--- 9% ] 19518 of 200000 complete in 38.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--- 9% ] 19767 of 200000 complete in 39.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--- 9% ] 19971 of 200000 complete in 39.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--- 10% ] 20236 of 200000 complete in 40.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--- 10% ] 20510 of 200000 complete in 40.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--- 10% ] 20783 of 200000 complete in 41.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---- 10% ] 21065 of 200000 complete in 41.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---- 10% ] 21347 of 200000 complete in 42.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---- 10% ] 21517 of 200000 complete in 42.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---- 10% ] 21725 of 200000 complete in 43.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---- 10% ] 21969 of 200000 complete in 43.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---- 11% ] 22229 of 200000 complete in 44.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---- 11% ] 22507 of 200000 complete in 44.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---- 11% ] 22735 of 200000 complete in 45.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---- 11% ] 22997 of 200000 complete in 45.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---- 11% ] 23270 of 200000 complete in 46.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---- 11% ] 23546 of 200000 complete in 46.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---- 11% ] 23814 of 200000 complete in 47.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---- 12% ] 24073 of 200000 complete in 47.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---- 12% ] 24341 of 200000 complete in 48.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---- 12% ] 24594 of 200000 complete in 48.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---- 12% ] 24871 of 200000 complete in 49.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---- 12% ] 25123 of 200000 complete in 49.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---- 12% ] 25388 of 200000 complete in 50.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---- 12% ] 25628 of 200000 complete in 50.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---- 12% ] 25911 of 200000 complete in 51.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---- 13% ] 26165 of 200000 complete in 51.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [----- 13% ] 26441 of 200000 complete in 52.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [----- 13% ] 26716 of 200000 complete in 52.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [----- 13% ] 26979 of 200000 complete in 53.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [----- 13% ] 27234 of 200000 complete in 53.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [----- 13% ] 27514 of 200000 complete in 54.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [----- 13% ] 27793 of 200000 complete in 54.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [----- 14% ] 28065 of 200000 complete in 55.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [----- 14% ] 28334 of 200000 complete in 55.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [----- 14% ] 28614 of 200000 complete in 56.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [----- 14% ] 28842 of 200000 complete in 56.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [----- 14% ] 29122 of 200000 complete in 57.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [----- 14% ] 29297 of 200000 complete in 57.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [----- 14% ] 29479 of 200000 complete in 58.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [----- 14% ] 29763 of 200000 complete in 58.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [----- 15% ] 30041 of 200000 complete in 59.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [----- 15% ] 30312 of 200000 complete in 59.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [----- 15% ] 30512 of 200000 complete in 60.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [----- 15% ] 30759 of 200000 complete in 60.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [----- 15% ] 31024 of 200000 complete in 61.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [----- 15% ] 31265 of 200000 complete in 61.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [----- 15% ] 31517 of 200000 complete in 62.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------ 15% ] 31798 of 200000 complete in 62.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------ 16% ] 32100 of 200000 complete in 63.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------ 16% ] 32385 of 200000 complete in 63.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------ 16% ] 32668 of 200000 complete in 64.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------ 16% ] 32918 of 200000 complete in 64.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------ 16% ] 33136 of 200000 complete in 65.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------ 16% ] 33398 of 200000 complete in 65.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------ 16% ] 33705 of 200000 complete in 66.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------ 17% ] 34016 of 200000 complete in 66.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------ 17% ] 34330 of 200000 complete in 67.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------ 17% ] 34621 of 200000 complete in 67.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------ 17% ] 34918 of 200000 complete in 68.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------ 17% ] 35218 of 200000 complete in 68.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------ 17% ] 35530 of 200000 complete in 69.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------ 17% ] 35841 of 200000 complete in 69.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------ 18% ] 36138 of 200000 complete in 70.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------ 18% ] 36434 of 200000 complete in 70.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------ 18% ] 36728 of 200000 complete in 71.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------- 18% ] 37021 of 200000 complete in 71.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------- 18% ] 37320 of 200000 complete in 72.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------- 18% ] 37615 of 200000 complete in 72.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------- 18% ] 37910 of 200000 complete in 73.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------- 19% ] 38195 of 200000 complete in 73.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------- 19% ] 38481 of 200000 complete in 74.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------- 19% ] 38760 of 200000 complete in 74.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------- 19% ] 39053 of 200000 complete in 75.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------- 19% ] 39333 of 200000 complete in 75.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------- 19% ] 39623 of 200000 complete in 76.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------- 19% ] 39914 of 200000 complete in 76.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------- 20% ] 40204 of 200000 complete in 77.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------- 20% ] 40491 of 200000 complete in 77.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------- 20% ] 40774 of 200000 complete in 78.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------- 20% ] 41058 of 200000 complete in 78.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------- 20% ] 41336 of 200000 complete in 79.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------- 20% ] 41620 of 200000 complete in 79.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------- 20% ] 41904 of 200000 complete in 80.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-------- 21% ] 42187 of 200000 complete in 80.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-------- 21% ] 42475 of 200000 complete in 81.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-------- 21% ] 42781 of 200000 complete in 81.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-------- 21% ] 43089 of 200000 complete in 82.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-------- 21% ] 43399 of 200000 complete in 82.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-------- 21% ] 43700 of 200000 complete in 83.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-------- 21% ] 43987 of 200000 complete in 83.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-------- 22% ] 44276 of 200000 complete in 84.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-------- 22% ] 44566 of 200000 complete in 84.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-------- 22% ] 44855 of 200000 complete in 85.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-------- 22% ] 45144 of 200000 complete in 85.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-------- 22% ] 45434 of 200000 complete in 86.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-------- 22% ] 45728 of 200000 complete in 86.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-------- 23% ] 46019 of 200000 complete in 87.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-------- 23% ] 46305 of 200000 complete in 87.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-------- 23% ] 46596 of 200000 complete in 88.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-------- 23% ] 46887 of 200000 complete in 88.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-------- 23% ] 47158 of 200000 complete in 89.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--------- 23% ] 47451 of 200000 complete in 89.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--------- 23% ] 47743 of 200000 complete in 90.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--------- 24% ] 48036 of 200000 complete in 90.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--------- 24% ] 48328 of 200000 complete in 91.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--------- 24% ] 48621 of 200000 complete in 91.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--------- 24% ] 48913 of 200000 complete in 92.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--------- 24% ] 49208 of 200000 complete in 92.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--------- 24% ] 49494 of 200000 complete in 93.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--------- 24% ] 49796 of 200000 complete in 93.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--------- 25% ] 50096 of 200000 complete in 94.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--------- 25% ] 50384 of 200000 complete in 94.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--------- 25% ] 50672 of 200000 complete in 95.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--------- 25% ] 50954 of 200000 complete in 95.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--------- 25% ] 51236 of 200000 complete in 96.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--------- 25% ] 51505 of 200000 complete in 96.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--------- 25% ] 51783 of 200000 complete in 97.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--------- 26% ] 52050 of 200000 complete in 97.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--------- 26% ] 52321 of 200000 complete in 98.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--------- 26% ] 52602 of 200000 complete in 98.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---------- 26% ] 52868 of 200000 complete in 99.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---------- 26% ] 53142 of 200000 complete in 99.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---------- 26% ] 53366 of 200000 complete in 100.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---------- 26% ] 53611 of 200000 complete in 100.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---------- 26% ] 53887 of 200000 complete in 101.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---------- 27% ] 54174 of 200000 complete in 101.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---------- 27% ] 54440 of 200000 complete in 102.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---------- 27% ] 54716 of 200000 complete in 102.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---------- 27% ] 54961 of 200000 complete in 103.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---------- 27% ] 55204 of 200000 complete in 103.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---------- 27% ] 55428 of 200000 complete in 104.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---------- 27% ] 55674 of 200000 complete in 104.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---------- 27% ] 55894 of 200000 complete in 105.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---------- 28% ] 56135 of 200000 complete in 105.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---------- 28% ] 56357 of 200000 complete in 106.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---------- 28% ] 56580 of 200000 complete in 106.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---------- 28% ] 56816 of 200000 complete in 107.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---------- 28% ] 57016 of 200000 complete in 107.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---------- 28% ] 57263 of 200000 complete in 108.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---------- 28% ] 57532 of 200000 complete in 108.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---------- 28% ] 57815 of 200000 complete in 109.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [----------- 29% ] 58100 of 200000 complete in 109.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [----------- 29% ] 58384 of 200000 complete in 110.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [----------- 29% ] 58671 of 200000 complete in 110.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [----------- 29% ] 58960 of 200000 complete in 111.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [----------- 29% ] 59241 of 200000 complete in 111.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [----------- 29% ] 59497 of 200000 complete in 112.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [----------- 29% ] 59752 of 200000 complete in 112.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [----------- 30% ] 60036 of 200000 complete in 113.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [----------- 30% ] 60296 of 200000 complete in 113.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [----------- 30% ] 60544 of 200000 complete in 114.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [----------- 30% ] 60796 of 200000 complete in 114.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [----------- 30% ] 61069 of 200000 complete in 115.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [----------- 30% ] 61290 of 200000 complete in 115.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [----------- 30% ] 61567 of 200000 complete in 116.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [----------- 30% ] 61790 of 200000 complete in 116.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [----------- 31% ] 62020 of 200000 complete in 117.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [----------- 31% ] 62260 of 200000 complete in 117.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [----------- 31% ] 62474 of 200000 complete in 118.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [----------- 31% ] 62731 of 200000 complete in 118.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [----------- 31% ] 62963 of 200000 complete in 119.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------------ 31% ] 63214 of 200000 complete in 119.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------------ 31% ] 63492 of 200000 complete in 120.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------------ 31% ] 63765 of 200000 complete in 120.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------------ 32% ] 64043 of 200000 complete in 121.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------------ 32% ] 64295 of 200000 complete in 121.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------------ 32% ] 64505 of 200000 complete in 122.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------------ 32% ] 64740 of 200000 complete in 122.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------------ 32% ] 64921 of 200000 complete in 123.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------------ 32% ] 65129 of 200000 complete in 123.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------------ 32% ] 65352 of 200000 complete in 124.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------------ 32% ] 65597 of 200000 complete in 124.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------------ 32% ] 65855 of 200000 complete in 125.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------------ 33% ] 66084 of 200000 complete in 125.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------------ 33% ] 66306 of 200000 complete in 126.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------------ 33% ] 66576 of 200000 complete in 126.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------------ 33% ] 66862 of 200000 complete in 127.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------------ 33% ] 67129 of 200000 complete in 127.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------------ 33% ] 67385 of 200000 complete in 128.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------------ 33% ] 67662 of 200000 complete in 128.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------------ 33% ] 67951 of 200000 complete in 129.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------------ 34% ] 68230 of 200000 complete in 129.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------------- 34% ] 68497 of 200000 complete in 130.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------------- 34% ] 68736 of 200000 complete in 130.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------------- 34% ] 68999 of 200000 complete in 131.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------------- 34% ] 69279 of 200000 complete in 131.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------------- 34% ] 69483 of 200000 complete in 132.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------------- 34% ] 69696 of 200000 complete in 132.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------------- 34% ] 69887 of 200000 complete in 133.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------------- 35% ] 70103 of 200000 complete in 133.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------------- 35% ] 70336 of 200000 complete in 134.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------------- 35% ] 70561 of 200000 complete in 134.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------------- 35% ] 70814 of 200000 complete in 135.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------------- 35% ] 71060 of 200000 complete in 135.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------------- 35% ] 71329 of 200000 complete in 136.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------------- 35% ] 71598 of 200000 complete in 136.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------------- 35% ] 71862 of 200000 complete in 137.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------------- 36% ] 72121 of 200000 complete in 137.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------------- 36% ] 72385 of 200000 complete in 138.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------------- 36% ] 72646 of 200000 complete in 138.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------------- 36% ] 72919 of 200000 complete in 139.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------------- 36% ] 73175 of 200000 complete in 139.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [------------- 36% ] 73440 of 200000 complete in 140.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-------------- 36% ] 73696 of 200000 complete in 140.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-------------- 36% ] 73958 of 200000 complete in 141.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-------------- 37% ] 74212 of 200000 complete in 141.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-------------- 37% ] 74473 of 200000 complete in 142.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-------------- 37% ] 74766 of 200000 complete in 142.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-------------- 37% ] 75028 of 200000 complete in 143.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-------------- 37% ] 75302 of 200000 complete in 143.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-------------- 37% ] 75597 of 200000 complete in 144.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-------------- 37% ] 75889 of 200000 complete in 144.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-------------- 38% ] 76153 of 200000 complete in 145.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-------------- 38% ] 76367 of 200000 complete in 145.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-------------- 38% ] 76615 of 200000 complete in 146.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-------------- 38% ] 76866 of 200000 complete in 146.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-------------- 38% ] 77137 of 200000 complete in 147.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-------------- 38% ] 77413 of 200000 complete in 147.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-------------- 38% ] 77675 of 200000 complete in 148.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-------------- 38% ] 77948 of 200000 complete in 148.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-------------- 39% ] 78220 of 200000 complete in 149.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-------------- 39% ] 78485 of 200000 complete in 149.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-------------- 39% ] 78738 of 200000 complete in 150.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--------------- 39% ] 79003 of 200000 complete in 150.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--------------- 39% ] 79275 of 200000 complete in 151.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--------------- 39% ] 79539 of 200000 complete in 151.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--------------- 39% ] 79801 of 200000 complete in 152.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--------------- 40% ] 80061 of 200000 complete in 152.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--------------- 40% ] 80318 of 200000 complete in 153.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--------------- 40% ] 80604 of 200000 complete in 153.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--------------- 40% ] 80883 of 200000 complete in 154.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--------------- 40% ] 81184 of 200000 complete in 154.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--------------- 40% ] 81482 of 200000 complete in 155.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--------------- 40% ] 81766 of 200000 complete in 155.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--------------- 41% ] 82052 of 200000 complete in 156.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--------------- 41% ] 82338 of 200000 complete in 156.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--------------- 41% ] 82633 of 200000 complete in 157.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--------------- 41% ] 82921 of 200000 complete in 157.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--------------- 41% ] 83204 of 200000 complete in 158.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--------------- 41% ] 83487 of 200000 complete in 158.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--------------- 41% ] 83766 of 200000 complete in 159.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [--------------- 42% ] 84045 of 200000 complete in 159.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---------------- 42% ] 84327 of 200000 complete in 160.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---------------- 42% ] 84627 of 200000 complete in 160.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---------------- 42% ] 84903 of 200000 complete in 161.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---------------- 42% ] 85142 of 200000 complete in 161.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---------------- 42% ] 85393 of 200000 complete in 162.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---------------- 42% ] 85650 of 200000 complete in 162.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---------------- 42% ] 85918 of 200000 complete in 163.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---------------- 43% ] 86175 of 200000 complete in 163.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---------------- 43% ] 86428 of 200000 complete in 164.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---------------- 43% ] 86685 of 200000 complete in 164.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---------------- 43% ] 86954 of 200000 complete in 165.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---------------- 43% ] 87230 of 200000 complete in 165.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---------------- 43% ] 87505 of 200000 complete in 166.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---------------- 43% ] 87778 of 200000 complete in 166.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---------------- 44% ] 88038 of 200000 complete in 167.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---------------- 44% ] 88272 of 200000 complete in 167.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---------------- 44% ] 88537 of 200000 complete in 168.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---------------- 44% ] 88807 of 200000 complete in 168.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---------------- 44% ] 89065 of 200000 complete in 169.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [---------------- 44% ] 89333 of 200000 complete in 169.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------44% ] 89584 of 200000 complete in 170.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------44% ] 89842 of 200000 complete in 170.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------45% ] 90107 of 200000 complete in 171.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------45% ] 90360 of 200000 complete in 171.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------45% ] 90616 of 200000 complete in 172.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------45% ] 90848 of 200000 complete in 172.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------45% ] 91103 of 200000 complete in 173.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------45% ] 91358 of 200000 complete in 173.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------45% ] 91630 of 200000 complete in 174.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------45% ] 91893 of 200000 complete in 174.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------46% ] 92152 of 200000 complete in 175.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------46% ] 92421 of 200000 complete in 175.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------46% ] 92687 of 200000 complete in 176.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------46% ] 92956 of 200000 complete in 176.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------46% ] 93232 of 200000 complete in 177.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------46% ] 93497 of 200000 complete in 177.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------46% ] 93774 of 200000 complete in 178.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------47% ] 94035 of 200000 complete in 178.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------47% ] 94302 of 200000 complete in 179.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------47% ] 94572 of 200000 complete in 179.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------47% ] 94803 of 200000 complete in 180.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------47% ] 95008 of 200000 complete in 180.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------47% ] 95215 of 200000 complete in 181.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------47% ] 95447 of 200000 complete in 181.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------47% ] 95672 of 200000 complete in 182.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------47% ] 95890 of 200000 complete in 182.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------48% ] 96168 of 200000 complete in 183.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------48% ] 96398 of 200000 complete in 183.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------48% ] 96627 of 200000 complete in 184.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------48% ] 96908 of 200000 complete in 184.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------48% ] 97166 of 200000 complete in 185.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------48% ] 97411 of 200000 complete in 185.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------48% ] 97696 of 200000 complete in 186.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------48% ] 97959 of 200000 complete in 186.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------49% ] 98231 of 200000 complete in 187.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------49% ] 98481 of 200000 complete in 187.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------49% ] 98729 of 200000 complete in 188.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------49% ] 99014 of 200000 complete in 188.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------49% ] 99293 of 200000 complete in 189.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------49% ] 99579 of 200000 complete in 189.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------49% ] 99839 of 200000 complete in 190.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------50% ] 100123 of 200000 complete in 190.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------50% ] 100392 of 200000 complete in 191.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------50% ] 100653 of 200000 complete in 191.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------50% ] 100922 of 200000 complete in 192.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------50% ] 101144 of 200000 complete in 192.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------50% ] 101381 of 200000 complete in 193.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------50% ] 101643 of 200000 complete in 193.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------50% ] 101922 of 200000 complete in 194.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------51% ] 102131 of 200000 complete in 194.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------51% ] 102381 of 200000 complete in 195.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------51% ] 102638 of 200000 complete in 195.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------51% ] 102898 of 200000 complete in 196.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------51% ] 103123 of 200000 complete in 196.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------51% ] 103396 of 200000 complete in 197.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------51% ] 103591 of 200000 complete in 197.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------51% ] 103839 of 200000 complete in 198.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------52% ] 104043 of 200000 complete in 198.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------52% ] 104264 of 200000 complete in 199.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------52% ] 104522 of 200000 complete in 199.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------52% ] 104763 of 200000 complete in 200.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------52% ] 105050 of 200000 complete in 200.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------52% ] 105332 of 200000 complete in 201.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------52% ] 105555 of 200000 complete in 201.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------52% ] 105814 of 200000 complete in 202.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------53% ] 106067 of 200000 complete in 202.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------53% ] 106260 of 200000 complete in 203.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------53% ] 106494 of 200000 complete in 203.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------53% ] 106759 of 200000 complete in 204.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------53% ] 106992 of 200000 complete in 204.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------53% ] 107249 of 200000 complete in 205.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------53% ] 107524 of 200000 complete in 205.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------53% ] 107805 of 200000 complete in 206.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------54% ] 108066 of 200000 complete in 206.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------54% ] 108322 of 200000 complete in 207.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------54% ] 108596 of 200000 complete in 207.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------54% ] 108870 of 200000 complete in 208.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------54% ] 109130 of 200000 complete in 209.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------54% ] 109355 of 200000 complete in 209.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------54% ] 109589 of 200000 complete in 210.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------54% ] 109866 of 200000 complete in 210.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------55% ] 110130 of 200000 complete in 211.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------55% ] 110362 of 200000 complete in 211.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------55%- ] 110638 of 200000 complete in 212.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------55%- ] 110905 of 200000 complete in 212.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------55%- ] 111182 of 200000 complete in 213.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------55%- ] 111447 of 200000 complete in 213.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------55%- ] 111723 of 200000 complete in 214.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------55%- ] 111979 of 200000 complete in 214.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------56%- ] 112250 of 200000 complete in 215.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------56%- ] 112509 of 200000 complete in 215.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------56%- ] 112771 of 200000 complete in 216.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------56%- ] 112997 of 200000 complete in 216.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------56%- ] 113182 of 200000 complete in 217.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------56%- ] 113457 of 200000 complete in 217.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------56%- ] 113721 of 200000 complete in 218.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------56%- ] 113993 of 200000 complete in 218.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------57%- ] 114258 of 200000 complete in 219.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------57%- ] 114530 of 200000 complete in 219.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------57%- ] 114799 of 200000 complete in 220.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------57%- ] 115062 of 200000 complete in 220.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------57%- ] 115333 of 200000 complete in 221.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------57%- ] 115591 of 200000 complete in 221.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------57%-- ] 115862 of 200000 complete in 222.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------58%-- ] 116129 of 200000 complete in 222.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------58%-- ] 116403 of 200000 complete in 223.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------58%-- ] 116677 of 200000 complete in 223.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------58%-- ] 116954 of 200000 complete in 224.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------58%-- ] 117231 of 200000 complete in 224.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------58%-- ] 117512 of 200000 complete in 225.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------58%-- ] 117749 of 200000 complete in 225.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------58%-- ] 117990 of 200000 complete in 226.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------59%-- ] 118239 of 200000 complete in 226.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------59%-- ] 118493 of 200000 complete in 227.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------59%-- ] 118745 of 200000 complete in 227.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------59%-- ] 118952 of 200000 complete in 228.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------59%-- ] 119234 of 200000 complete in 228.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------59%-- ] 119469 of 200000 complete in 229.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------59%-- ] 119675 of 200000 complete in 229.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------59%-- ] 119885 of 200000 complete in 230.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------60%-- ] 120119 of 200000 complete in 230.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------60%-- ] 120372 of 200000 complete in 231.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------60%-- ] 120627 of 200000 complete in 231.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------60%-- ] 120894 of 200000 complete in 232.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------60%--- ] 121091 of 200000 complete in 232.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------60%--- ] 121264 of 200000 complete in 233.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------60%--- ] 121520 of 200000 complete in 233.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------60%--- ] 121737 of 200000 complete in 234.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------60%--- ] 121961 of 200000 complete in 234.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------61%--- ] 122179 of 200000 complete in 235.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------61%--- ] 122402 of 200000 complete in 235.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------61%--- ] 122593 of 200000 complete in 236.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------61%--- ] 122792 of 200000 complete in 236.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------61%--- ] 122995 of 200000 complete in 237.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------61%--- ] 123175 of 200000 complete in 237.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------61%--- ] 123368 of 200000 complete in 238.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------61%--- ] 123590 of 200000 complete in 238.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------61%--- ] 123861 of 200000 complete in 239.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------62%--- ] 124130 of 200000 complete in 239.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------62%--- ] 124391 of 200000 complete in 240.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------62%--- ] 124663 of 200000 complete in 240.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------62%--- ] 124933 of 200000 complete in 241.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------62%--- ] 125206 of 200000 complete in 241.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------62%--- ] 125475 of 200000 complete in 242.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------62%--- ] 125743 of 200000 complete in 242.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------63%--- ] 126015 of 200000 complete in 243.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------63%--- ] 126285 of 200000 complete in 243.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------63%---- ] 126550 of 200000 complete in 244.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------63%---- ] 126805 of 200000 complete in 244.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------63%---- ] 127069 of 200000 complete in 245.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------63%---- ] 127326 of 200000 complete in 245.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------63%---- ] 127593 of 200000 complete in 246.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------63%---- ] 127851 of 200000 complete in 246.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------64%---- ] 128131 of 200000 complete in 247.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------64%---- ] 128394 of 200000 complete in 247.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------64%---- ] 128670 of 200000 complete in 248.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------64%---- ] 128937 of 200000 complete in 248.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------64%---- ] 129218 of 200000 complete in 249.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------64%---- ] 129489 of 200000 complete in 249.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------64%---- ] 129751 of 200000 complete in 250.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------65%---- ] 130013 of 200000 complete in 250.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------65%---- ] 130270 of 200000 complete in 251.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------65%---- ] 130534 of 200000 complete in 251.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------65%---- ] 130770 of 200000 complete in 252.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------65%---- ] 131035 of 200000 complete in 252.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------65%---- ] 131301 of 200000 complete in 253.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------65%---- ] 131568 of 200000 complete in 253.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------65%----- ] 131837 of 200000 complete in 254.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------66%----- ] 132108 of 200000 complete in 254.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------66%----- ] 132366 of 200000 complete in 255.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------66%----- ] 132632 of 200000 complete in 255.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------66%----- ] 132889 of 200000 complete in 256.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------66%----- ] 133150 of 200000 complete in 256.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------66%----- ] 133366 of 200000 complete in 257.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------66%----- ] 133629 of 200000 complete in 257.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------66%----- ] 133896 of 200000 complete in 258.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------67%----- ] 134167 of 200000 complete in 258.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------67%----- ] 134449 of 200000 complete in 259.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------67%----- ] 134721 of 200000 complete in 259.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------67%----- ] 134995 of 200000 complete in 260.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------67%----- ] 135266 of 200000 complete in 260.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------67%----- ] 135534 of 200000 complete in 261.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------67%----- ] 135806 of 200000 complete in 261.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------68%----- ] 136080 of 200000 complete in 262.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------68%----- ] 136367 of 200000 complete in 262.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------68%----- ] 136637 of 200000 complete in 263.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------68%------ ] 136900 of 200000 complete in 263.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------68%------ ] 137163 of 200000 complete in 264.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------68%------ ] 137431 of 200000 complete in 264.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------68%------ ] 137700 of 200000 complete in 265.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------68%------ ] 137968 of 200000 complete in 265.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------69%------ ] 138224 of 200000 complete in 266.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------69%------ ] 138476 of 200000 complete in 266.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------69%------ ] 138682 of 200000 complete in 267.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------69%------ ] 138897 of 200000 complete in 267.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------69%------ ] 139103 of 200000 complete in 268.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------69%------ ] 139305 of 200000 complete in 268.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------69%------ ] 139518 of 200000 complete in 269.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------69%------ ] 139736 of 200000 complete in 269.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------69%------ ] 139997 of 200000 complete in 270.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------70%------ ] 140248 of 200000 complete in 270.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------70%------ ] 140511 of 200000 complete in 271.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------70%------ ] 140787 of 200000 complete in 271.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------70%------ ] 141058 of 200000 complete in 272.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------70%------ ] 141332 of 200000 complete in 272.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------70%------ ] 141532 of 200000 complete in 273.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------70%------ ] 141783 of 200000 complete in 273.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------71%------ ] 142023 of 200000 complete in 274.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------71%------- ] 142290 of 200000 complete in 274.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------71%------- ] 142531 of 200000 complete in 275.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------71%------- ] 142809 of 200000 complete in 275.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------71%------- ] 143094 of 200000 complete in 276.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------71%------- ] 143383 of 200000 complete in 276.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------71%------- ] 143669 of 200000 complete in 277.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------71%------- ] 143956 of 200000 complete in 277.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------72%------- ] 144210 of 200000 complete in 278.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------72%------- ] 144479 of 200000 complete in 278.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------72%------- ] 144724 of 200000 complete in 279.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------72%------- ] 144949 of 200000 complete in 279.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------72%------- ] 145227 of 200000 complete in 280.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------72%------- ] 145457 of 200000 complete in 280.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------72%------- ] 145603 of 200000 complete in 281.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------72%------- ] 145847 of 200000 complete in 281.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------73%------- ] 146098 of 200000 complete in 282.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------73%------- ] 146375 of 200000 complete in 282.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------73%------- ] 146657 of 200000 complete in 283.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------73%------- ] 146939 of 200000 complete in 283.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------73%------- ] 147207 of 200000 complete in 284.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------73%-------- ] 147491 of 200000 complete in 284.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------73%-------- ] 147776 of 200000 complete in 285.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------74%-------- ] 148008 of 200000 complete in 285.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------74%-------- ] 148269 of 200000 complete in 286.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------74%-------- ] 148517 of 200000 complete in 286.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------74%-------- ] 148722 of 200000 complete in 287.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------74%-------- ] 148860 of 200000 complete in 287.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------74%-------- ] 149100 of 200000 complete in 288.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------74%-------- ] 149335 of 200000 complete in 288.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------74%-------- ] 149591 of 200000 complete in 289.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------74%-------- ] 149811 of 200000 complete in 289.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------74%-------- ] 149980 of 200000 complete in 290.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------75%-------- ] 150198 of 200000 complete in 290.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------75%-------- ] 150399 of 200000 complete in 291.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------75%-------- ] 150589 of 200000 complete in 291.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------75%-------- ] 150871 of 200000 complete in 292.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------75%-------- ] 151109 of 200000 complete in 292.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------75%-------- ] 151315 of 200000 complete in 293.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------75%-------- ] 151477 of 200000 complete in 293.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------75%-------- ] 151744 of 200000 complete in 294.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------76%-------- ] 152018 of 200000 complete in 294.6 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------76%-------- ] 152291 of 200000 complete in 295.1 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------76%-------- ] 152526 of 200000 complete in 295.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------76%--------- ] 152788 of 200000 complete in 296.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------76%--------- ] 153022 of 200000 complete in 296.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------76%--------- ] 153250 of 200000 complete in 297.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------76%--------- ] 153509 of 200000 complete in 297.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------76%--------- ] 153751 of 200000 complete in 298.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------77%--------- ] 154007 of 200000 complete in 298.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------77%--------- ] 154241 of 200000 complete in 299.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------77%--------- ] 154414 of 200000 complete in 299.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------77%--------- ] 154616 of 200000 complete in 300.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------77%--------- ] 154861 of 200000 complete in 300.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------77%--------- ] 155109 of 200000 complete in 301.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------77%--------- ] 155330 of 200000 complete in 301.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------77%--------- ] 155530 of 200000 complete in 302.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------77%--------- ] 155735 of 200000 complete in 302.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------77%--------- ] 155974 of 200000 complete in 303.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------78%--------- ] 156188 of 200000 complete in 303.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------78%--------- ] 156396 of 200000 complete in 304.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------78%--------- ] 156678 of 200000 complete in 304.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------78%--------- ] 156955 of 200000 complete in 305.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------78%--------- ] 157236 of 200000 complete in 305.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------78%--------- ] 157502 of 200000 complete in 306.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------78%--------- ] 157773 of 200000 complete in 306.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------79%---------- ] 158026 of 200000 complete in 307.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------79%---------- ] 158264 of 200000 complete in 307.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------79%---------- ] 158505 of 200000 complete in 308.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------79%---------- ] 158672 of 200000 complete in 308.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------79%---------- ] 158908 of 200000 complete in 309.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------79%---------- ] 159172 of 200000 complete in 309.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------79%---------- ] 159399 of 200000 complete in 310.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------79%---------- ] 159615 of 200000 complete in 310.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------79%---------- ] 159842 of 200000 complete in 311.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------80%---------- ] 160100 of 200000 complete in 311.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------80%---------- ] 160293 of 200000 complete in 312.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------80%---------- ] 160565 of 200000 complete in 312.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------80%---------- ] 160819 of 200000 complete in 313.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------80%---------- ] 161088 of 200000 complete in 313.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------80%---------- ] 161314 of 200000 complete in 314.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------80%---------- ] 161531 of 200000 complete in 314.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------80%---------- ] 161728 of 200000 complete in 315.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------80%---------- ] 161946 of 200000 complete in 315.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------81%---------- ] 162152 of 200000 complete in 316.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------81%---------- ] 162363 of 200000 complete in 316.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------81%---------- ] 162585 of 200000 complete in 317.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------81%---------- ] 162820 of 200000 complete in 317.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------81%---------- ] 163021 of 200000 complete in 318.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------81%----------- ] 163242 of 200000 complete in 318.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------81%----------- ] 163506 of 200000 complete in 319.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------81%----------- ] 163756 of 200000 complete in 319.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------82%----------- ] 164019 of 200000 complete in 320.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------82%----------- ] 164285 of 200000 complete in 320.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------82%----------- ] 164543 of 200000 complete in 321.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------82%----------- ] 164802 of 200000 complete in 321.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------82%----------- ] 165010 of 200000 complete in 322.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------82%----------- ] 165236 of 200000 complete in 322.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------82%----------- ] 165450 of 200000 complete in 323.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------82%----------- ] 165722 of 200000 complete in 323.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------82%----------- ] 165995 of 200000 complete in 324.2 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------83%----------- ] 166267 of 200000 complete in 324.7 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------83%----------- ] 166466 of 200000 complete in 325.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------83%----------- ] 166658 of 200000 complete in 325.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------83%----------- ] 166932 of 200000 complete in 326.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------83%----------- ] 167193 of 200000 complete in 326.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------83%----------- ] 167465 of 200000 complete in 327.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------83%----------- ] 167725 of 200000 complete in 327.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------83%----------- ] 167975 of 200000 complete in 328.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------84%----------- ] 168238 of 200000 complete in 328.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------84%------------ ] 168518 of 200000 complete in 329.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------84%------------ ] 168805 of 200000 complete in 329.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------84%------------ ] 169090 of 200000 complete in 330.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------84%------------ ] 169382 of 200000 complete in 330.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------84%------------ ] 169662 of 200000 complete in 331.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------84%------------ ] 169942 of 200000 complete in 331.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------85%------------ ] 170224 of 200000 complete in 332.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------85%------------ ] 170476 of 200000 complete in 332.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------85%------------ ] 170726 of 200000 complete in 333.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------85%------------ ] 170965 of 200000 complete in 333.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------85%------------ ] 171204 of 200000 complete in 334.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------85%------------ ] 171415 of 200000 complete in 334.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------85%------------ ] 171577 of 200000 complete in 335.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------85%------------ ] 171757 of 200000 complete in 335.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------85%------------ ] 171996 of 200000 complete in 336.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------86%------------ ] 172194 of 200000 complete in 336.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------86%------------ ] 172383 of 200000 complete in 337.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------86%------------ ] 172620 of 200000 complete in 337.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------86%------------ ] 172871 of 200000 complete in 338.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------86%------------ ] 173154 of 200000 complete in 338.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------86%------------ ] 173438 of 200000 complete in 339.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------86%------------- ] 173723 of 200000 complete in 339.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------87%------------- ] 174003 of 200000 complete in 340.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------87%------------- ] 174286 of 200000 complete in 340.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------87%------------- ] 174573 of 200000 complete in 341.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------87%------------- ] 174859 of 200000 complete in 341.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------87%------------- ] 175148 of 200000 complete in 342.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------87%------------- ] 175436 of 200000 complete in 342.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------87%------------- ] 175721 of 200000 complete in 343.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------88%------------- ] 176020 of 200000 complete in 343.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------88%------------- ] 176331 of 200000 complete in 344.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------88%------------- ] 176618 of 200000 complete in 344.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------88%------------- ] 176904 of 200000 complete in 345.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------88%------------- ] 177176 of 200000 complete in 345.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------88%------------- ] 177409 of 200000 complete in 346.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------88%------------- ] 177687 of 200000 complete in 346.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------88%------------- ] 177970 of 200000 complete in 347.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------89%------------- ] 178258 of 200000 complete in 347.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------89%------------- ] 178545 of 200000 complete in 348.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------89%------------- ] 178841 of 200000 complete in 348.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------89%-------------- ] 179125 of 200000 complete in 349.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------89%-------------- ] 179406 of 200000 complete in 349.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------89%-------------- ] 179686 of 200000 complete in 350.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------89%-------------- ] 179966 of 200000 complete in 350.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------90%-------------- ] 180247 of 200000 complete in 351.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------90%-------------- ] 180528 of 200000 complete in 351.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------90%-------------- ] 180809 of 200000 complete in 352.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------90%-------------- ] 181092 of 200000 complete in 352.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------90%-------------- ] 181373 of 200000 complete in 353.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------90%-------------- ] 181654 of 200000 complete in 353.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------90%-------------- ] 181936 of 200000 complete in 354.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------91%-------------- ] 182224 of 200000 complete in 354.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------91%-------------- ] 182510 of 200000 complete in 355.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------91%-------------- ] 182791 of 200000 complete in 355.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------91%-------------- ] 183072 of 200000 complete in 356.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------91%-------------- ] 183353 of 200000 complete in 356.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------91%-------------- ] 183640 of 200000 complete in 357.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------91%-------------- ] 183927 of 200000 complete in 357.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------92%--------------- ] 184218 of 200000 complete in 358.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------92%--------------- ] 184502 of 200000 complete in 358.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------92%--------------- ] 184794 of 200000 complete in 359.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------92%--------------- ] 185073 of 200000 complete in 359.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------92%--------------- ] 185345 of 200000 complete in 360.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------92%--------------- ] 185630 of 200000 complete in 360.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------92%--------------- ] 185908 of 200000 complete in 361.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------93%--------------- ] 186165 of 200000 complete in 361.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------93%--------------- ] 186445 of 200000 complete in 362.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------93%--------------- ] 186675 of 200000 complete in 362.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------93%--------------- ] 186942 of 200000 complete in 363.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------93%--------------- ] 187214 of 200000 complete in 363.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------93%--------------- ] 187466 of 200000 complete in 364.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------93%--------------- ] 187735 of 200000 complete in 364.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------94%--------------- ] 188015 of 200000 complete in 365.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------94%--------------- ] 188278 of 200000 complete in 365.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------94%--------------- ] 188552 of 200000 complete in 366.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------94%--------------- ] 188767 of 200000 complete in 366.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------94%--------------- ] 188990 of 200000 complete in 367.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------94%--------------- ] 189256 of 200000 complete in 367.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------94%--------------- ] 189462 of 200000 complete in 368.3 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------94%---------------- ] 189743 of 200000 complete in 368.8 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------95%---------------- ] 190018 of 200000 complete in 369.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------95%---------------- ] 190279 of 200000 complete in 369.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------95%---------------- ] 190495 of 200000 complete in 370.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------95%---------------- ] 190762 of 200000 complete in 370.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------95%---------------- ] 190969 of 200000 complete in 371.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------95%---------------- ] 191196 of 200000 complete in 371.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------95%---------------- ] 191422 of 200000 complete in 372.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------95%---------------- ] 191676 of 200000 complete in 372.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------95%---------------- ] 191922 of 200000 complete in 373.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------96%---------------- ] 192207 of 200000 complete in 373.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------96%---------------- ] 192486 of 200000 complete in 374.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------96%---------------- ] 192652 of 200000 complete in 374.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------96%---------------- ] 192932 of 200000 complete in 375.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------96%---------------- ] 193195 of 200000 complete in 375.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------96%---------------- ] 193453 of 200000 complete in 376.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------96%---------------- ] 193686 of 200000 complete in 376.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------96%---------------- ] 193917 of 200000 complete in 377.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------97%---------------- ] 194172 of 200000 complete in 377.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------97%---------------- ] 194441 of 200000 complete in 378.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------97%---------------- ] 194704 of 200000 complete in 378.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------97%----------------- ] 194949 of 200000 complete in 379.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------97%----------------- ] 195225 of 200000 complete in 379.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------97%----------------- ] 195492 of 200000 complete in 380.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------97%----------------- ] 195703 of 200000 complete in 380.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------97%----------------- ] 195969 of 200000 complete in 381.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------98%----------------- ] 196178 of 200000 complete in 381.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------98%----------------- ] 196405 of 200000 complete in 382.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------98%----------------- ] 196653 of 200000 complete in 382.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------98%----------------- ] 196913 of 200000 complete in 383.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------98%----------------- ] 197105 of 200000 complete in 383.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------98%----------------- ] 197340 of 200000 complete in 384.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------98%----------------- ] 197598 of 200000 complete in 384.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------98%----------------- ] 197858 of 200000 complete in 385.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------99%----------------- ] 198129 of 200000 complete in 385.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------99%----------------- ] 198355 of 200000 complete in 386.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------99%----------------- ] 198566 of 200000 complete in 386.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------99%----------------- ] 198800 of 200000 complete in 387.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------99%----------------- ] 199055 of 200000 complete in 387.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------99%----------------- ] 199279 of 200000 complete in 388.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------99%----------------- ] 199483 of 200000 complete in 388.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------99%----------------- ] 199686 of 200000 complete in 389.4 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------99%----------------- ] 199897 of 200000 complete in 389.9 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------100%-----------------] 200000 of 200000 complete in 390.2 sec"
]
}
],
"prompt_number": 31
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pymc.Matplot.plot(home)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Plotting home\n"
]
},
{
"metadata": {
"png": {
"height": 370,
"width": 599
}
},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAABK4AAALlCAYAAADt1a7qAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npE76H8j27GAZmdOFAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0xa98ae70c>"
]
}
],
"prompt_number": 32
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pymc.Matplot.plot(intercept)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Plotting intercept\n"
]
},
{
"metadata": {
"png": {
"height": 370,
"width": 607
}
},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAABL8AAALlCAYAAAAluUQ8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAWJQAAFiUBSVIk8AAAIABJREFUeJzs3Xd4FFX3wPFvAiEhEJoJAlJUxAFRaSJNBMsPwf7asSIK\nvoi9915eGyJNighIUWlKR4r03mtyA4EESEJ672V/f8xuyCa7ydbsJjmf5+FZdvbOzJ1tmTl77rk+\nBoMBIYQQQgghhBBCCCFqIl9Pd0AIIYQQQgghhBBCCHeR4JcQQgghhBBCCCGEqLEk+CWEEEIIIYQQ\nQgghaiwJfgkhhBBCCCGEEEKIGkuCX0IIIYQQQgghhBCixpLglxBCCCGEEEIIIYSosST4JYQQQggh\nhBBCCCFqLAl+CSGEEEIIIYQQQogaS4JfQgghhBBCCCGEEKLGkuCXEEIIIYQQQgghhKixJPglhBBC\nCCGEEEIIIWosCX4JIYQQQgghhBBCiBpLgl9CCCGEEEIIIYQQosaS4JcQQgghhBBCCCGEqLHqOrsB\nTdNaAJ8AdwDNgWRgHfCRUuq0Det3AT4H+gMBQDgwQSk1zULb2cBjVjb1jVLqXUeOQQghhBCiOtA0\nrRUQin6e9VOZxwLRz8keBFoBicAy4H2lVJKFbd0BfAB0BnKMbd9VSiVYaNsH/XytB1AMrAfetuVc\nTwghhBDC03wMBoPDKxsDX7uB1sAa4BDQEbgTSAF6K6VOVrB+N2Ar4AfMB+KAe4HLgG+VUu+UaX8Y\nCAYmW9jcVqXUvw4fjBBCCCGEF9M0rSH6D4zXA68opcaVeqwOsAnoC+wx/v9aYBBwAuiplEov1X4o\nMBeIABYB7dCDZqeB65RSaaXaDkA/z0sCfgeaAI8Cmca2UW46ZCGEEEIIl3A28+sT9MDXa0qpsaaF\nmqY9BswGfgDuqWD9L4H6wL1KqaXGdT8C9gFvaJo2WSkVaVzuhx5YW6qU+szJfgshhBBCVBuaprUD\nFgPdrDS5Hz3wtVgp9UCp9b4E3gVeAT4zLmsITEQPfHVTSmUal68BpqNng71pXOYLTOFCoCvGuHwu\nsBb4Hj1oJoQQQgjhtZyt+fUfIL504AtAKTUXOIX+a2NFugPJpsCXcd0s4A9j33qWatsJPVh32Mk+\nCyGEEEJUG5qmvQIcAa4BrGW5dzfeziyzfKrxtlepZUPRs7d+NAW+AJRSMwAFDNM0zce4+BbgSmC6\nKfBlbPsvevDrXk3Tmtl7TEIIIYQQVcnh4Jfxl8Av0bO/LMkD6hkztqyJAxprmtakzPJLjLela05c\na7yV4JcQQgghapOX0Ycj3oieWW9JnPH20jLLWxtvS59T3Wi83WBhO5uAi4CrbWi7EagD3GClT0II\nIYQQXsHhYY9KqWJgnKXHNE3riD5EMUIpVVDBZsYAM4B5mqa9CMSjp84/hT70cVOptqbgV0dN07YZ\n72cDK9ALucY6eixCCCGEEF5sJLBOKWUwnmNZMhd4C/hI07QIYDP6udgU9B8kJ5Zq2x4woGfplxVp\nvL0SPdusvfF+RAVtO9h0FEIIIYQQHuLssMdyjBlhEwAfLqTaW6SUmgU8gZ5SfwJIA35BT+n/P6VU\n6Wr8puDXh+gnYJPRZ4YcBuzWNO0ShBBCCCFqGKXU2jLnRJbaxAN9gPPoPwxmoBe+bwncqpTaU6r5\nRUCeUirPwqZMhe4bl2oLkGpDWyGEEEIIr+TS4JexPsQU4Gb0E66xlbS/FT17LA+YBfyEPn33/6FP\np11aNnqw6zql1JNKqTeVUv3Ri7JegpUsNCGEEEKImk7TtKbo51JXo/+I+D2wHL2211RN09qUau6H\nfu5liWl5QKm2Bivty7YVQgghhPBKzs72WELTtLrANPQhixHAPUqpwgraN0WftagA6K6UOmlc7oee\nuj9a07TjSqmfAZRS91nZ1NfAs8CdmqYFKqWyXXVMQgghhBDVxHigP/CWUup700JN0/4DLAIWAL2N\ni3OAi61sx994m1WqrQ9Qz4a2QgghhBBeySXBL03TAtFPqoagZ2fdqpQ6X8lqdwENgU9NgS8ApVSB\npmkvAA+gD2n8uaKNGOtfHEIv8NrauH9HVDicQAghhBDVnk/lTaof4w+QDwGnSwe+AJRSf2matgoY\nomlaR6VUGJCCXkPVz0JtVtMQRtOQxpRSyxMqaesoOQcTQlTIx8f869tgkK8NIaoZj5+DOR38MmZw\nrQKuB/YDg5VSiTasaqrRFVr2AaVUvKZpSUAb4z4C0Kf3zlVKHbGwrfrG21w7u28mISHDmdWrpZCQ\nIKB2HjvU7uOvzccOtfv45dhr57FD7T5+07HXUCHo53TKyuPH0X+gbAuEof9Q2Bf9h8MTZdpeZrw1\nbSu81PKTlbR1WG1+T9bGY4faffxy7M4fe3V97uS1r53HDrX7+L3lHMypml/GoNRy9MDXRmCgjYEv\ngBjTZixstyl6gVVT9tglwC5gjoW2gUB3IF4pdcae/gshhBBC1ADJQD76DI2WmGZjNJ1XbTHeDrTQ\ndiCQqpQKtbFtEbDb5p4KIYQQQniAswXvv0KfWWg7MEQplWnHusvRa0S8qGma6ZdDNE2rA4wx3v0d\nQCkVARwCrtE07dFSbX2A/wHBVDI8UgghhBCiJjLO2vg3cLmxdEQJTdP+D73UxHGl1GHj4r/RZ4N8\ny/iDo6ntcPRA2S+lNrEJOAM8p2lau1Jtb0GfoOgvpVSS649KCCGEEMJ1HB72qGlaC2C08W4Y8K6m\nlUviAvhaKZWnadongEEp9SmAUipJ07RRwEzgoKZpC9FrRtwMXIueSVZ6tsj/os9eNFvTtPuBKPTC\nrj3QT8y+cvRYhBBCCCGquVeBXsA4TdPuBg4AVwD3oge6njI1VEqlaJr2FvoPhwc1TVuAnmX/IPoQ\nxq9KtS3WNO15YAmwV9O0eeg1Wx8D4oE3q+DYRC01adIYs/vPP/+ah3oihKgq8rkX7uJM5ldvLkx/\nPRz4yMK/D7kwE5BpWQml1Bz0YNd24D7geeM2PwBuK12EVSm1C3145WJggLFtA2PbQRYKtgohhBBC\n1DQGLBSIV0rFAj2BCeglJV5Fr+s1D7hOKbWvTPspwCPoReyfB25A/0FyoFIqtUzblcBg9DqtzwC3\nowfD+imlolx4bEIIIYQQbuEjM2WUMNTm4nO18dihdh9/bTr2wqJiDp5IpHnT+rS9WD/u2nT8Zcmx\n185jh9p9/MZj9/hMQ8IiOQerhWrz8cux23/szZs3MrsfH5/usj5VJXnta+exQ+0+fm85B3N6tkch\nhPB2k5ccY394Ar4+Prz7eHfaX9LY010SQgghhBBCCFFFnC14L4QQXm9/eAIAxQYDv64MraS1EEII\nIYQQQoiaRIJfQohaJTYp29NdEEIIIYQQQghRhST4JYQQQgghhBBCCCFqLAl+CSGEEEIIIYQQQoga\nSwreC1GDFBYVs+lgDPXq+tLv2pb4+nh8Ug0hhBBCCIdMmjTG7P7zz7/moZ4IIaqKfO6Fu0jwS4ga\nZP6/J1m37xwAGTkF3N67nYd7JIQQQgghhBBCeJYEv4SoQUyBL4CFGyMk+CWEEEKIaksyPoSofeRz\nL9xFan7VQLuOxzFt2XGORyZ7uitCCCGEEEIIIYQQHiXBrxrmfHI2U5YeY8ex8/y08DC5+YWe7pIQ\nHmUwGDzdBSGEEEIIIYQQHiTBrxpmxfbIkv8XFBZzIDzRc50RQgghhBBCCCGE8DAJftUwxWWyXAxI\n1ouo3eQTIIQQQgghhBC1mwS/ajgZ8SWEEEIIIYQQQojaTGZ7rHF8PN0BIYQQQgghnDZp0hiz+zIL\nnBA1n3zuhbtI5leNI6letUVmTgGrd53hcITUdauQfCSEEEIIIYQQolaTzC8hqqnxiw5z4lwaAG88\n0pWrLm3m4R4JIYQQQriOZHwIUfvI5164i2R+1Tgy7LE2MBgMJYEvgF+WH/dgb7ybTPrgPoVFxRQX\ny/MrhBBCCCGE8G6S+SVEDZCame+R/WZk5xOfmsOlLYKo4+t4LD09O595a8PJzCng4Zs70KZ5Qxf2\nsvqIiE7jbHwm13dqTmCAn6e7U6EN+88xd+0JgpsE8MYjXQluXN/TXRJCCCGEl/hnzT/Enj9fbnnD\nhgEAZGbmOrX9mb/Nsrmtj48PDz/4EAEBAU7tUwhRvUnwywX69+/J8OEjefrpETavs2zZ30RFRfLC\nC6+4sWeud/jwQWbPnsF33/1ktU18fBzfffcVr7/+Di1atKzC3omqlJKRx6czdpOeXUDXK4J56YFr\nHd7Wki2n2R0aD8DUpcf4/NlerupmtRF5Pp2v5uzDYIBtR2J5/8nrPN2lCs1eEw5AfEoOf/57ktH/\nucbDPRKWFBYVk19Q5PXBVCGEEDXL5Om/wcX9LDyS5ZLtL9lr+3aSTqznztvvkOCXELWcBL9cYMqU\nGTRvfrFd68yaNZ3u3b374taSZcv+JjLydIVt9u7dzc6d25EhmDXbkq2nSM8uAODgyUSS03Np1six\nk4oNB6JL/h+d6JqTIhNDNRmVN/uf8JK+RsSkk5lTQMP61SNgsU8leLoLwoL07Hy+mbuf2KRshvRq\ny4M3XeHpLgkhhKgl6vkH0KBVR7dtv6kd286LP+y2fgghqg+p+eUCV111NcHBIZ7uhheqJlGHasgb\nntmImHSz+9m5hR7qSc2QlJZjdr9IamkJJy3fFklsUjYAq3adISdPPqNCCCGEEKJ2kswvF+jfvydP\nPz2C4cNHsn//Xl5+eRRjx05izpyZHDlyiAYNGjB48J0899xofH19eeCBu4iLO8+qVctZtWo5CxYs\no0WLFpw/f56ffx7Hnj27yM/Po3Pna3nhhZfp0EEDIDY2hoceuocXXniFJUsWEx8fx+uvv8OQIXdy\n9OgRpk+fzMHDhyk2+BIY3IGQq+4o6WN6ehqTJ09g69bNZGZm0qHDlYwc+TyDB99sdhwvvvgaYWHH\n2bJlE/Xr1+eWWwYxatSL1KtXjy+//ITVq1eUtH3vvY8ZMuROs+di5cplfP31ZwA8+ODdDBlyJ++9\n9zEPPHAXAwbcxMmTJzh69DCDBg3h7bc/4OTJE/z661QOHz5IZmYGTZs2Y8CAmxk16kX8/f0BKCgo\nYObMX1izZjXJyYlccklrhg59wmzfW7ZsZObM6Zw+fYqgoIbcfPMgnntutKQ3CyEcZgoW1fevnn8q\n96p4s/u5+UUe6okQQjhm0qQxZvdlFjghaj753At3cckZvaZpLYBPgDuA5kAysA74SClV8Rg5ff0u\nwOdAfyAACAcmKKWmWWgbCLwLDAVaAaeBiUqpSa44Fkf5+JgP8fvssw+4776HeOKJp9m2bQvz5v1G\nq1aXcM899/H119/zxhsvo2mdGDbsGS666CJSU1MZNWo49evX57XX3iIgIID5839n9OiRTJs2i3bt\nLi3Z9q+/TuWVV94kMLABV13VmfDwMF58cSSdO1/DDXc8R1hUEolhqzi3azrF/+lJXl4eL700ipSU\nZEaOfJ7g4BCWL1/C66+/SJMm0+ndu7fZtq+55lo+//x/REaeYtq0n0lOTuTTT79m2LBnSUtLRakw\nvv76e1q1al3ueejbtz9PPfUMs2ZN56uvvqN9+w4ljy1aNJ+hQ5/g8ceHERjYgMTEREaPfparr+7C\n++9/Qr169dixYxt//jmX4OBgHn98GACffvoBO3du46mnnqVz56vZvn0rX331KX5+ftx6622sWbOa\nzz//kEGDhvDcc6OJiYlm6tRJnD4dwdixHn1b1CqSpyRqkgMnEpi85Bi+Pj68cP81dL60mae75DRD\ndRkDLIQQQgghhIs5HfwyBr52A62BNcA8oCPwKDBE07TeSqmTFazfDdgK+AHzgTjgXmCKpmntlVLv\nlGpbB1gADAFWGNvfDkzQNO0ypdSbzh6Pq9x993089dQzAHTvfh1btmxk+/Yt3HPPfXTooFGvXj2a\nNGnCVVddDcCff84lIyOdyZN/5eKLWwDQu3c/HnvsAX75ZTKff/6/km3ffPMgs6ynn376niZNmvLj\njxOZufoE0fnnqRvQmPMH5hEXc4Z/ovYQEXGCqVNn0qlTZ+O2+/LCCyP5/vvvWbhwYcm2mjVrxjff\n/Iivry+9e/fF17cO48eP4Zln/kvbtu1o3LgJ9erVK+l3WU2aNKFVq0sA6NChIy1atCh5rEWLVjz3\n3OiS+7t37+TKKzvyxRffUL++PlNcjx492bNnFwcP7ufxx4dx6tRJNm36l5dffp0HHnik5Pk8fz6W\nAwf2ceuttzF58nh69+7Lhx9+VrLtNm3a8sorz7Njx1b69LnB3pdPCFHLjV90pOT/Py04xNQ3b/Jg\nbxxT9kcZIYSobiTjQ4jaRz73wl1cUfPrE/TA12tKqcFKqbeVUvcATwLNgB8qWf9LoD7wgFLqcaXU\n68C16Nlfb2iadmmptg+jB76+U0rdpZR6D7gO+Bd4TdM0yxEZD+jc2Xzms5CQ5uTm5lltv2/fHjp0\nuJLg4BAKCwspLNSH2/Tq1Yc9e3aZte3Q4Uqz+4cPH6J37774+flhutap37Qtl938Di1bX8a+fbtp\n1uwirryyY8m2CwsL6du3P0ePHiUjI6NkW7fcMghf3wtviwED9Au+gwf32/8klFG239df35vx46fg\n5+fH6dOn2Lp1E7NmTSc1NZmCggLjsR009uNms3W/+OIb3nzzPaKiIklIiKdfv/5mx9alSzcCAwPL\nPXc1hhcmcHjrZbYku9gmIiaNNyZt441J24iISfN0d7xKYZG8iYQQQgghhKjOXDHs8T9AvFJqbOmF\nSqm5mqZ9CgyqZP3uQLJSammpdbM0TfsD+AjoCUQaHxoNFABflWpbqGnaB8B24BngVecOxzXK1pry\n8fHBYCi22j49PY3o6HMMHNi73GM+Pj7k5V0InJmypEqv26RJU4vbNRggLS2N5OQkq9uOj4+nUaPm\ngB6kK61p02Yl+3CGj49PuX4XFxczZcpEFi9eQG5uDs2bX0ynTp2pVy+gZHhOWlqaWT/KSktLBeCH\nH77hhx++KbfPpKREp/otbCfhgept4uIjpGbmAzDpr6P8MNrS9OTW5eYXElCvetbGEt7NYDCQlVtI\noH9dfH1tD7Nn5xYSl5JNm+YNqVtH5vcRQgghhKjNnLpS0TTNFz1zK99KkzygnqZpfkqpAitt4oCr\nNE1ropRKLbX8EuNtgnFf/sD1wH6lVNlIzB4gB7jRgcPwCkFBQXTr1oPRo18xW24KAvn5+Vldt2HD\nIFJTUwDz7JvMuFAy0loQFNSI1q3b8MknX5mtZzAYaNo0kNatW5Oerr+EpmCSSXJyMlA2+OSaHJ85\nc2Yyf/483nrrfQYMuInAwAYAjBjxpNmxAaSmppjNqBkVFUl6ehpBQY0AGD36Zbp1u67c8QUFBbmk\nr6I8b830Eo4xBb4AUjKsZ6laczY+kw6tm7iyS8LFqmsW5JSlx9gdGk/7Vo1469Fu+NWtU+k62bkF\nfPTrbpLT8+jUrilvDu1WBT0VQgghhBDeyqmfQpVSxUqpcUqpyWUf0zStI3rtr4gKAl8AY4A6wDxN\n09prmhakadpw4ClgH7DJ2K6dsV2EhX4UAWeBK8s+5i1SMvIIP5vKmD8PkptfWK4WS9euPYiKiqRN\nmzZoWseSf2vXrmbFiqVmQxHL6tKlK7t376SwsLAk+yY37Rwxe2YSfTaCrl27Ex8fR5MmTdC0jrRs\nczmXXt6Bfft2M336dOrWvRAD3bx5o9m2N25ch4+PD92764ElvR8VX0FV1NfSDh8+yGWXtWfIkDtL\nAl8JCfFERERQXKxnyV17bVcAtm7dZLbupEk/MW7cD7RrdylNmzYjJiba7HkLCQlh6tSJnDwZblNf\nqlJ+QRGz/1F8M3c/YVEpnu6Ow6rPdbR39fRsfCaTlxxlydbTFBZZzwatboqLvet5FlUvNimLDQei\nSU7Pddk2o85nsDtUn7UyIiadbUfP27TeP7vPkpyuB3FDo1KIOp9RyRqiuouNjaF//568++4bDm/j\nzJkoNmxY58JeeY+ioiIWLfqT3NzKP5/6LOaP2rTdF14YSf/+PcnKyrS7TxkZGcydO9fu9YQQQghH\nuGWMijEjbAJ6csjUitoqpWZpmlYI/AqcKPXQGuARpZTpiuoi4615atIFaUAHTdN8lVJedUWZk1dI\nfEoO+MDR08lsOBBNUFAjwsMVBw7s46qrOvPww4/xzz8refnl5xk69AkaNWrE+vVrWb78b1566fUK\ntz9s2LM899xw3nzzZRq160tGTDyJ6h8CmrShQ6du9OoUzOLF83n11dFc2+cuDkblU5R2iji1gSef\nfII6dS78iq5UKJ988j5DhtzJyZPh/PrrVO6++z5atmwFQFBQI5KTk9ixYxsdOmgEBweX648pW2vT\npvX07t2Pdu0utTjL2FVXXc2sWdOZPXsGqcUhhEec5tzRtQQGBpKTkwPodcJuuulWJk4cR25uLldc\ncSU7dmxlx45tfPXVd/j6+jJy5Ci+++5rfH3r0K/fDWRkZDJr1nQSE+PRtE4Ov27usuVwLBsORAPw\n44JDTHljoN3bMHhZQEfYbsrSY8QkZgHQvGl9+nRuUcka1UORHcGvc/GZhEal0K1DMMFN6le+gvB6\nGdn5fDpzD/kFxSxtUI/vnu9rcahhTl4hc9Yo4lNyuH9Aezq2szxk3yQmKcvsfviZVAZ2vcRK6wui\n4syDXamZ9mcziuolKKgRTz89gnbtLnNo/RMnwhk58inuu+9BbrrpVhf3zvM+/fQDNmxYx+DBd9jU\n3tYJM+6442569OiJn189u/t02223cfHFFzNo0N12ryuEEELYy+XBL03TfIApwM3owxHHVtL+VmAc\n+hDJeejBrUHA/wGfAy8am5rG/Vk7gzUtDwCyHey+S5Q9YUjNzDOOEdOXbz4Uy9ChjzNu3BjeeOMl\nxo6dxDXXdOHnn6czZcpEvv/+a/Lz82jbth3vvvsRt99+V4X769BBY/z4KUyZMoFNSyZi8PWnQfNO\nhHS6nTp16hAQEMCECdOYMmUCq/6eSXFBLn6Bzejc5z7effdds23df//DJCcn8f77b9KkSVOeeuoZ\nnnji6ZLHb7/9Lnbu3MZ7773Bs8/+l8cee6pcf3r06Ml1113PlCkT2bdvD99+O9biSdQTTzxNWloq\nv//xOxkZ6fg3akVwh//joV7N+GXaJLKyMmnQoCEfffQ506dPYf7830lLS+XSSy/niy++5YYbBgBw\n5533EhjYkHnzfmPp0r8IDKzPNdd05eOPv6BFi5a2vGRVau7aC9loBYVeFae1S3UZ9uhtQ71MgS+A\nacuOWw1+ZecW8PNfR4iKy+T+AZdz63VtqqqLDrE18ys9K5/PZu2lsKiYFTsi+X50P6+rx5SdW+jp\nLrhEVU72uGbPWfIL9O+ztKx8Dp5I5LqOzcu1W7vnLDuOxQEwZv4hpr45sOo6KWq0hg0bMnz4SIfX\nz8hIL5lsqCZKTk5yywywpWcft1dycjIXX3xxhW0mTRpjdl9mgROi5pPPvXAXlwa/NE2rC0xDH7IY\nAdyjlLJ6JqFpWlNgMXoR++5KqZPG5X7AXGC0pmnHlVI/o9f0ArD205I/YFBKORz4CglxrD5UWFhY\nyf9vu+0mQkNDzR7PM0CbPv8tuV+3jg9Dhz7A0KEPlNl/J37+eUIF/dPM9lXawIF9GDiwDz/+vp9/\n954tWR4UFEBISBAhIUH88MO3hL++5EK/zLYdZLxtyhdffFJBH7ryzz+rrT5u3Ctz5vxmtmTjxg0W\nW3711eckNxpAdMKFgMB9D9/MSy/+16zdBx+8wwcfvGN1jw8//B8efvg/lfTLOkdfe1dwZN9FFobL\nWdqOLdt29Njrlqm707RpoMueR1e+HnkFRRVu35Ovfen9lx0uvPFQLOHn9PKG89ad4IH/02yqdeTI\nvu19zJKgRvVtWmf5zmMlwz3Tsws4FZdFvy6t7NqXK1jrq8Fg4JUxm8ot9/T7xBFl31PNmjUo+b+r\njye/zIyYderVtbiPv7eeLvl/YVFxpf1oFGSe7O3v72dT3+uVmXyhcWPJMBS2sZSpXpPU8MMTQggh\nrHJZ8EvTtEBgATAECAduVUpVVpzjLqAh8Kkp8AWglCrQNO0F4AFgGPAzYCqO1NjKthoD9hccEF5H\nTsyEM/ILijgVk0bbi4MIDLA+UYS3KTssa+3uM2b3c/KKXB788oS0LPPjzMmrqCSk4zKy81m57TQX\nX9SAAd0usTnj4UxcBqdinJvd1ls4muRRVGxg2ZYIImPTuW/gFbRt0cjubbjre1yGfAtrYmNjeOih\ne7jhhgF8/fX3AHz55SesXr2ClSv/ZcqUCWzevJHMzEwuu+xynnzyaQYMuBmA6dOnMHPmLwAsWPAH\nCxb8wfjxU+jatTsASoUxc+Y0Dh06SF5eLm3btuOee+7n3nvvN+tD//49GTLkTlq3bsO8eb8BPgwf\nPoKHHtLrZ61YsZS//15EVFQk9esH0LVrV15++WWaNTP/AeDff9cxf/48IiJO4OvrS8eOnXnqqeEl\n9VdLH++TTw6nffsO/PrrVGJjo2nRoiV33XUvDz/8WMn3Xv/+PUvWGzLkJrp27c748VMqfU6PHj3C\ntGk/c/z4Efz86tG9+3W88MIrZln1L7wwkkOHDrB69QYaNGgIwO7dO5k7dxYRESfJycmmdes23Hrr\nYIYOfZy6deuyf/9eXn55FAChoaHGGmMjLGbuScaHELWPfO6Fu7gk+GXM4FqFcTZGYLBSKtGGVU2F\nO0LLPqCUitc0LQkwjfWJRJ9VslwxB03T6hjbHbW786UkJLinIG5ysnnNksLCYrftKy/X/EIyIyPX\npn2Z2mRn57utb9YUlskYSEnJIqAKRkEZDAaKfOvQJMifzPScyldwk/j4dLuHIlgqlG7pdavotTRl\nTzj6ehcWmmdUpaRk09DPNS+co30yGAx88ds+TsemE9w4gM+f6WVxfGZCQobTx+8qtu4/KSmTvGzX\nBPNsOXZ7n5e0tByb1skt8x2VbuN3lL1++OMAxyL130yyMnO5vpM+tKayY4+NS7e4vCrfJ+nZ+cxZ\nE05WTgFosz2LAAAgAElEQVSP3NKBNs0bOrSd4jLfE0lJmTRvFghUfDx7w+KZvvQYAMdPJfHliN6V\n7qvs65qZad/fHmsyMsyLc+flFdq03fx886Tz1DTPfceLqmXp7+mrr44mLS2NW24ZRE5ONmvWrObD\nD9/hhx/G0bNnb7p3v464uPOsWrWczp2voVevPiUBnh07tvH++29Sr149brzxJpo2bcbOndv54Yf/\nER4exltvvW+2r127trN5cx63334XSUlJdO58LQDffvsly5b9TYsWrRg8+HYKCgpYt+4fdu7cyaRJ\n02nf/goAfvllMrNmTadly1bccYdeC2vDhnW8+upo3n//EwYNGmK2v507tzN79gz69etPr1592L59\nCxMn/sSpUxG8997HADz99AhWrVrO+fOxPP74MNq2bVfp8xgTE8NLL/2XLl26cv/9DxMaeoxNm/4l\nNPQYc+cuJCAgwOJzfujQAd5++1WaNm3Grbfehr+/P3v27GTq1IlER5/lnXc+pFWrS3j66RHMmDGN\n4OBg7r77Prp161Fpn4QQQghnOB380jQtAFiOHvjaCNytlLI1AyvGtBkL222KXuT+IIBSqlDTtF3A\n9ZqmNSyzj+uB+sAOhw7Cyx08kUh0Yib9u7SiUaB9BUXt+ZV8y5Y99natWpu1OozNh2IJaVqfdx/r\nTpOG/p7uknBSaFQKp2P14EViWi4bDkRzU/fKi2O7W0FhETuOxdGkYfnP789/H+VwRFKl26jpQ3Fc\nzRT4Api85FhJ8Ks6WLLlNHvD9BkOpy49xufP9qrS/U9ddrzk/7FJ2RQVF1Onkll8q+rdWVRsoKCw\nGL+63lUnTni3OnXqMGfOn/j76wGbHj168tlnH7J8+VJ69uxNt249MBgMxuDX1Tz99AgAcnNz+fLL\nTwgKCmLKlFm0aKHXaPzvf1/go4/eZdmyv+nffyB9+vQr2VdKSgr/+98Y+va9oWTZvn17WLbsb7p0\n6ca3344lMFAPQj/22CM8+uij/PLLz3z99Q8cP36UWbOm0737dXz77Vj8/fXzkuHDRzJy5NN8991X\nXH99H5o0aVKy7fDwMEaPfplHHnkcgBEjRvHKK8+zatVyhgy5k27dejB8+Ej2799LXNx5Hn/8qZIM\nrYpkZWUycuRonnhiWMmyd999g61bN7F7905uvHGgxfUWLPidoqIiJk36pSSAOGLEKEaOfIrVq1fw\n0kuv06JFS4YPH8mMGdMICQkpeb6FEEIId3LF2eNXQB9gOzDEjsAX6EGzLOBFTdNKMrqMmVymSne/\nl2r/G3ptr09LtfVDL4xvQK835v3syPI5FpnMuEWHWbTpFOMXHrZh2070y0PKdrkqrvEzcwrYfCgW\ngISUHJaWqkPjTgs3RlTJfjzBG4IzSenmWSLRiZlVd1VegRmrwpi5KoyxC8p/hveExVusS1aZ4mID\nKRl5FBVX30kTvJGPh79Ez8ZnlswGCxCdmFVBa/fw5vfU3rB4nh+zic2HYipvXEo1/NMoXOj++x8q\nCXwB9O6tB6vi4iquzrF16ybS0lIZOvSJksAX6JlOzz03GoCVK5eZrePv728WDANYt+4fAP773xdL\nAl8A3bp147XXXqNv3/6APiwS4PnnXy4JfAE0atSYxx9/ktzcXP79d63Ztlu1uqRkWCVAQEAAI0bo\nQwrXrKmsRqt1AQEBDB36uNmyfv30gF5sbLSlVQD9XMBgMHDs2IXBGHXr1uWHH8azcuV6s+MXQggh\nqpJTmV+aprUARhvvhgHvalq5JC6Ar5VSeZqmfYJelP5TAKVUkqZpo4CZwEFN0xYCaegzRV6LnklW\nerbIGcDTwKuapl2DcYilse13SqljzhyPN5q+/MIv8BEx6RQXG/D1reA03gsu9N3p6Kkkpi47Tn3/\nOrx4/7W0DnFsOFB2mSE6xyKTXdG9Sq3cGVVumQG5MKvpdhpnt3MVg8HAD38eJDQqhStaN+adR7tX\n/L1QS2Rk5zN7TTjpmdYmBfZ+U5e58s+YY+8JH3zsrq3lqndfaGQyf289TcuLGjD01g4W2xQVG5i5\nKowbPTBRgqie2rRpa3a/YUP93CE/P7/C9ZTSJxkKCwtl+vTyNbJ8fX05cSLcbFnz5heXG3p58mQ4\nderUoVOnq8ptY8SIESVDeU3727hxPVu3mk+8kZCgZ4OW3V+XLt3KTW5h2k9ExIkKj68iF1/cgrp1\nzS8TGjXSM85ycqwPI77rrv+wZcsmPvnkPaZN+5k+ffrRu3dfevToWW57QgghRFVy9q9Qb8AP/fp9\nuJU2BvQsrjzgI+P9kswtpdQcTdPOAO8C96EPX4wAPkAPaBWUalusadpg4/oPATcAJ9EDcJOdPBaL\nzsZnsmH/OS5v1Zh+17RwzTTRNmbIFBUXk5pZ8YlZbTNm/iFAz9z6dUUoHw3rWckaoqq4Ywp1YdmR\nU8mERunD+k6eS2Oviq9Ww/rcZdm2yJLhglXJYDCQnl1Aw/p1Kx0eWJnSM9+6mqO/jTiS1OloJuiY\n+YcoKjZw4lwarYIb0Ciw+kxaIbyXn5/5kPMLf68qfp9mZupBqfXr11h83MfHh8xM8zqBpTO2TDIy\nMvD396dOnYonLTHtb86cmVb3l5Fhvr/g4JBy7QIDG+Dv709WluPzQNWrZ73MRkWf7969+zJu3GTm\nzZvNvn27WbjwDxYu/INGjRoxfPhI7r//YYf7JIQQQjjDqeCXUupv7Bg6qZSy2FYptRnYbOM2MoHX\njf/cqthg4Mf5B0nNzGfjwRiaN63PlW2aVL5iGY4GBf7892S5ZYbK8oRqUfwh8rzrClC7Y8TembgM\nMnIKuKpd02oTGMrOLeTo6SQubdmI5k3q27WuNwx7tKQmzg53LsH8gibqfEa1DX7NWBmG1rap3e83\nS9btO+eCHtlvwuIjHDiRyGUtG/HOY929ph6Vo187Pj54LIu4qPjCjv9Yf4KRd5XPlCkrJ68Qg8FQ\nrWZ3FdVD/fr699JPP/1sNtOiI9s5fz6P4uLicllaubm5Zu3q1KnD+vXbKg2UmeTllc90zc/PJy8v\nj8aN7T9ndYWuXbvTtWt3cnNzOXToANu3b2HVqhWMHfs9l1zSht69+9q8rUmTxpjdl1nghKj55HMv\n3MU7ztC91PmkbLPMq3lrwytobZ/s3EIWbDjJwo0R5OQVWmyzbq9nLuRE5QqLisnMKbD6+JFTSXw6\ncw8//HGQP9aXD2J6I4PBwNdz9zF5yTE+/nU3KRmVDR2rHgE9UZ7BYGB/WDz7wxMo9oKg5axVYZ7u\ngsMiYtI4cEKf3Ph0bDo7jlVcQ8iTqvKVrqqAvzqTwusTt/HK+K3s8UDWn6g5LL1nr7jiSgDCwo6X\neywzM5Px439kzZpVlW67ffsOFBUVoVS5yc0ZNWoUgwffRF5ebkm78PDy34mhoceYMmUihw4dLLe8\nrOPH9XpbV111dcmyqvpMzpkzk2nTfgb0umG9evXh1Vff4vXX3wbg8OGDFa0uhBBCuI0EvypQNpOl\noMh1RYAXbYpg1a4zrNwZxWsTtnEmLqNknws3RvDBL7ssrmd3MeYqvq5Nychj+vLj/PaPIivXenCo\ntLLnY56/FK9YZk4Bn8zYw0s/bbEaEB2/6HBJNtnavWcr3qCXHHBETHrJkKu8/CKWb4+sZA0v6Xgt\n5GyW3eodkXw8bQcTFh/h7y1VM9lDRUKjUsjNL2TS30d5b+pOdoe6tkaaPey9PowpU5D+xLlUohMy\nGbvgENNXHLf5e9AbJKbmEJ9avpaPK4Y9FhcbOByR6GjXrBq36Ai5+UUUFhn4+e+jla8ghBWmelQF\nBRc+szfeeBMNGjRg7txZnD17xqz9xIljmT9/HtHRlf9QedttQwCYOnWSWabWgQMH2LVrF9de2wV/\n/wBuv/0uAMaNG0N29oXvluzsbL777ivmzJmJwWB+LnrkyCE2blxfqm0WU6ZMwNfXlyFD7jQ7PoPB\nQH6+e7+Tdu7czuzZM8wK3gPExuqTVJhmgDT1qfTzbcnzz79m9k8IUfPJ5164i1SetCIlI48pS91X\nP7/0bF55BUV8PmsvX47oRWpmvsWi6CaVDnv0sD/Wnyj59b1eXV8eucVyweLqbNXOqJIL3nX7znFX\nv0sJCjSvjVFY5N7AkDuSdbJzzTMQE9KsF7StTrwgscnrTFp0YdbJ5dsjue/Gy8u1ycsvop6fb5Vl\nC2zYH11Sr2vykmNcpzW3WsQ/N7+QjQdiaNTAjz6dXVSL0YWmLTvOmXh9aGqjwHo8eNMVHu5R5faG\nxTN5yTEMGCx8Zir/EFX2EsxYFcq2Iw5kxVWyXWuZ02XJ14CoTEiIPnT833/XEhAQwJAhd3HZZZfz\n9tsf8OmnHzB8+GPceONALroomAMH9hMWdpxOnTozdOgTlW67Z8/e3HHH3axYsZRhw4bSq1cfsrOz\nWb9+DUFBQbz2mp4V1b37dTzwwCMsXPgHjz/+EH369MPPrx6bN28gISGee+99gK5du5ttu0GDhnz0\n0bvceONAgoObs337FmJjYxg27Fnat7/w3RMS0hyAr7/+jOuv78UDDzziqqfOLNg9YsTzvPLKKF56\n6TluuulWgoNDiIw8xfbtW7nssstLAoEAF198MREREXz//f/o06cf/fr1d1mfhBBCiLIk+GXF/A0n\nOefGwsNlFRUbWLTpFIEBnn9JioqLSUrPo1mQP3Xr2JccWHrYyZo9Z2tk8OvgSfPshYzsgnLBrxqh\n0qtF7wo4ADX6Crf0oVVFsGfUmE30v7YlT9/eye37AliyzTwDraCwGP96lmvezFgZVvJdU1RkoL8n\nZ/2z8J4zBb4AVu06YzX4dSA8gT1h8XS/MoTrOjZ3Vw9tMsnNWVMOBb6EcJKPj4/N35ctWrRgxIhR\nzJ//O3/9tZDLLmvPZZddzk033UpIyMXMmTODnTu3k5ubS8uWlzBs2LMMHfoEAQEBNm3/nXc+pGPH\nTixZspilS//G39+fgQMH8uqrr9KwYXBJu5dffp1Ona7ir78WsGbNKurUqUvbtu0YMWIUgwffUW67\nPXv2onfvvsyePZOdO7dz6aWX8+yzoxg0aLBZu6eeeoaoqEj27t1FdPRZh4Jflp7Kss9xly5dGT9+\nCr/9NoN9+/aQlpZKcHAIDz30KE899Qz+/heer48++ogvvviClSuXUVRUJMEvIYQQbuX5SIuXKCgs\n5rfVYZyMTmNI73bsOl5+2I27M0iSM3IJDGjo1DbsHhZZhsFg4Ic/DhJ2JpV2Fwfx/pM97A6AVQtl\nzuBy84swGAw2nSS7+n3gLdl8XpY84xLOfh68VVVNLrDlcCy3Xd+WVsENqmR/tiodZJ+xKsylwa+y\nwxjtZet7Lj0rnwl/HcFggJ3H4/jgyev4fZ3r6kpaVIVpkGfjM1m0KYJrLr/IoYliXK1mfhOI0lq2\nbMWWLXvMlr333se8997HFtuXbQvw5JPDefLJ8pOXX331Nfzvf2PKLbdlm6Xde+8D3HvvAyX3Q0KC\nAEhIMJ/AZ9CgIQwaNARb3XHH3dxxx90VtrnkktZMmTLDpu1ZO47+/QeWe2z8+Cnl2l199bV8++2P\nle5nwIABDBgwoNzxCyGEEO5QA6MajtlyMJqNB2M4l5DFtGXlC5uC65JKnNmOu69djkelEHYmFYCo\nuAyvLt7sSpk5BUz666hDQYWaGDRytSOnkvjgl138tOBQhRMFuJo3zPRYJYEqN74HY5Oy3bdxL/TX\nllMWl384fRdvTtpGWFSKS/az+VCM2ff5F7/tJSIm3SXbNnHVd5Mjb+F/90ezYkcU38zbb8PkGab9\nuO+z4vlvAiGEEEII4UkS/DJavOFE5Y3KnJifi89kw/5zLNh4kt9WhxGf4uRFohecnceWyXo4cz7T\nSks38lCRpn3hCZw4l1ZpOy94mfCWXtjqx/mHiEnM4lBEkg2F9J3ghYHI7Uc9G0AuKi5mwuIj/Pf7\njfyx3obvuXIce6/Fp+aQmmlb0MOb5OUXWVwenZBFUnoeYxcccsl+PPEJ9sg+DfDP7jOVNxRCCCGE\nEMKNZNijgxLTcvhs1h6zwuanz2fw8bCela7rhdfnVnlD5kxVOhufWfkQHamg7pSqrgXn6Zdr+ory\nU9s74pNfd/PO4z1o3qS+XevtDUtgf3gCoD/3A7q6vz7Wur1nmbfuBH51fXn1wS50bNe05DFrwaWy\nPPXdU9nQ5/xCF836WwVvTFcN+3W2p3kF1l/zj3/dzRODNK5o3djJvVSsOv3dFUJcMGmS+XBTmflN\niJpPPvfCXSTzyw6lLwCWbDldbka/qPPeV7PA3osWb5s1rTbwdHDGmqro1rmETFbsiKyCPVV/qZn5\nzFtrf02oAycSzO5Hxdn/PVVsMNg1JG3eOj3DrKBQzzorbX94oqVVnArUVOVQ2lrJjV8GZ+MzGWec\nfdRLvwqF8EqmGmeff/4/T3dFCCGEqBYk88sepc7MU5wYzmPtBN8lJ/4Su6py6/aeY+itHUomBigb\nbPB2Z+M9MLQVyMkr5Mvf9lWYFWKruORs1u87Z+VYas4l9eGIpCrf52//KCb+dZTOlzbl5Qe72D0B\nRnZeodl9d9R1em/qTt59vLvLt+tOBUU1532pcy54OXuN4tYerS1sVf6oCVGbScaHELWPfO6Fu0jw\ny8imWf4cvIi259S9sraVXje6fCZCUZkNB6JpUN+P+268HIPBwPhFR8q1mbEylGgnZ5ErzVXxg7W7\nohg3/yAA13dq7pqN2mjrkViXBL4MBgNjFxwiLiXHBb2q+ewNJmRk61lVxyJT2HU8jn7XtHRHt5wa\n5piZU8CcNW6eKdEKR3ud5WS2WlJaLg3r++Ffr47tKznY2aoYgrphf7TLJhOwRP6WCSGEEELUbhL8\nspPBYGDr4ViOR9p+ki4n3RUrG3i09ny5YmjTsdPJrN93jqZB/g5vw1L/lm+P5L4bL7e6zpbDsQ7v\nz51MgS+A3aHxZo8dO51MVm4BDQL83LLvXBtrP1UmKT1XAl8VKBsoXbw5wuFtHTmV5LbgV1n2BulC\nXRQ4cXrkdxUkKs1cFcrmQ7E0DfLn/Sd60KxRgFN9qey7taqGZte2mUWFEEIIIUTVkZpfdigsMnAq\nJp0Zq8Kc2o616xHbLjAqaVSDR4ikZtg/1PRUTDoR0foMjgWFxYxbdJiDJxPZcCDa8Y7Uomjmyh1R\nZvcPnEhg6dbTpGbmOR8kcJE/1p+0+pgPPh55uSKi09inEigu9r43S0Jqrmc7YOP7piZNtpGY5rrg\nbFJaLpsP6cH0lIw8Fm2yPZhp7Rkdb6y55S7eUNfQS76uhBBCCCGEh0jmlx1SMvL4c4P1C21becF1\nQK2w6WA0s1YrAB77vytp1yKIAlfN1OZhRXYGVfLyi1i0OYLUzHzuveEyWgU3sGm9VbvO8OBNVwCg\nzqSUDOncF57g9AWtqy5GTTMZeotDJxP5aaEeTKiKmRXBPEsqO7eQRZsiyMwp4L4B1rMRS4s8n+6u\nrtnMW2o7OduLgyfKF/T/cPpuGgTUJSiwHs/fezUhds7YWdqiMpl7O47FMeKuzhbb2nosJ86lVfi4\nwQC7Q+MA6NmxebWcGEX+7gohhBBC1G6S+WWnokqKFM9YGcqpGPMLSdsvE9xzep6bX8javWctXpRV\nytU1xAwG0rPzKSq2LwiVmJbDpoMxdq1jCnwBzF0bXiVXPwmp7h9+l56Vz8e/7ra5/YlzqYwas4l1\ne8+xNyye6StCHdrvzFIZj2fjM8kpU8i8NsgvKGLbkVhOVhAsGFcqi8be96wrrN59hg0HotkTFs8s\nG7NUv/xtn03tdofGc/R01Rfdr1JOBnYsDSHMyy8iOT2PqPMZ/G6cCdNRoXYMuXeV5TsimbzkGJOX\nHGNFmWxQW5Sd9EAIIYQQQoiqJplfLrblcCz7VAJjX7rB6qxolQXQKuJIts3Upcc5eNK2wJc9132b\nDkYzf8NJWgU34MX7r6VRYL0K2xsMBiYsPsKBE4m0v6QRbz/a3aaZ4/IKivh0xh6ycr37AioxLYeP\n7AhKmdj7mi7bFklimu1D1yYsNi/AfzrWsSwfU+HzC5yLJlZF8ogBg0uHXE1ecoyDJxPxAd56tBta\n26bl91kFQdaK9rF8e2TJ/8POpHJdx8onMrAnk3DMn4f47z2WM41E5UzfxY6+TYpd9AY7eDKRsKgU\nm2q4rd51puT/izef4s6+l9q1r71h8ZU3ckBBofW6gWVnRq1+uWpCCIBJk8aY3ZdZ4ISo+eRzL9xF\nMr/cIDuvkBNnU60+npTueM0dA/rwkxU7IsnOta0AvK2BL7v6YTAwa7UiJ6+IiOh0VmyvPBsgIjqd\nA8bss4jodHYe04fRlLsoKXNtt/1IrFcFvqzVInrr5x3kF7h/WOXO4+ftal8+aOUatXEYUenAxbTl\nxz3bGQ+avOSYy7fpipk/awNXfJ7PxmUwbuFh1uw5y7fz9rugV57x3PebLC6PSylfOL82fl8JIYQQ\nQogLJPPLTRw50bblB/3Sw9bCz6bx6kNdzB6399ft/IKiCjNwrHWpbF/X7j3L0Fs7VLiv6MRMs/sn\no1O54drKsw4yXDDLY3Vx9FQSh04mcf1VzenQuomnu1PCYDDUmqFLeQVFxCVn0/KiQPzq1rHaLjk9\nj5SMPBrW98OvbtX+jmBX1pw3VBuvYRJTcwh2om6Xvc7GZzLp76MUOlmz0GAwkJyey4QFF2Z5ddUP\nC+7K5LR3u+cSMjkbn1l5QyFEtSAZH0LUPvK5F+4imV928+yFZOl6TUdOOV9759GPVvHEJ/8QXkGm\nmq3emLSt3LJv5u4nIkavj1RTLsFdH0vQN5iSkceP8w+xfv85vvv9oNWaWp4oNn0yunyNq+R0+2ff\n9HaFRcV8MWsvn8zYw1dz9lc6W+PrE7fx2cw9VV7/TOJZ7mPLp+tnN2S+VWTasmPEJWfbnzVc5rti\nb1g8wz9fw/HTyS7snXdxtKahEEIIIYSo2ST45SYOZX5B1RRCKiUvv4isnAIm/nWk8saVsBQMUWdT\nmbjY+W27woETzs0KeDgiiXELD9tVb8se/+w+U/K+KSwqZtfxOLfsxxGWgl+WpGbmMXnJUSYsPkJi\nFRT/d7UdR88TnZgFQNT5DA6dTKSwqJiI6DSyrAwzjk7MYu3es1XZzXKq4eR7XsuWmlql6+ZlZOe7\nszsAnEvIcsl2/tpy2u6ZYqubqPMZNeeXFiGEEEII4TIy7NFN5vyjqFPHl9uub8OVbhy+VlhUzMET\niVzUOIDLWjZyeDumOjI2X0PbcbGdmun+i0NbrCpVtNleeflFjF902K0XjgVF5kOaqttFqsEACzZE\nsDtUL25dWFTMKw92qWQt9/LBB4MdaVKmwJdJXEoO//x+gPBzaTQN8re6XlhUCnf3u8zhfjrLmzLB\nKipCXh3YW1Nr0aZTju3Im140Ua1omtYKCAU+Ukr9ZOHxwcA7QHcgD9hnbFtuRhZN0+4APgA6AznA\nMuBdpVS5X4s0TesDfA70AIqB9cDbSqnTLjo0IYQQQgi3kcwvI9szJ2xrGJeSQ0xiFjNWhpGT774h\nUTNWhjLp76N8MWsvoZFVOJTFJddtPmY3Lt20i4WdSXFbMKrkGtjGzXsiy8fHxvf9jmMXivGXnW2t\nOoo8n074OT3rLSWjeg7zrOrP09Yj9k3IUF0lpuaw7Uisp7shahlN0xoCi4EgLHy8NU0bAawE2gO/\nAEuBG4Etmqb1KtN2KHqwKxiYBPwLDAO2a5rWuEzbAcBG4CrgV+Bv4C5gt6Zp7Vx2gEIIIYQQbuJ0\n5pemaS2AT4A7gOZAMrAO/VdGq78Gapo2EP1Eq0JKqZIAnaZps4HHrDT9Rin1rs0dr0LRtg5ZceAq\ndYdxxkQDMHnpMbpeEWy+SWezC1yUnZCZU1Bp/SRv5k3DyrJyvLPwvDc9RyaFxcX8tdn2zJyyx+At\nWYuV8fHRP+umGVQ9afY/qsLHffCxOmNqaa5OjMrNLySgnuuSnT+e4WStN2/8wDjBm47GlvdXdWQM\nMi0Gull5vC3wE3AcuFEplWxcPgXYDvwPuMm4rCEwEYgAuimlMo3L1wDT0bPB3jQu8wWmAJnAdUqp\nGOPyucBa4HvgQdcfsRBCCCGE6zh1JWAMfO0GWgNrgHlAR+BRYIimab2VUietrH4aPWhmSS9gCFB2\nHvMuwHlgsoV1ttrVeS/k7Am7vcN1LHLTBdlLP21xy3bL2nnsPMcjU+jfpfJZJO3j/ku7+JTscsti\nk7JIzsijU7um+ProQ/is1SRa5+G6U/YEK778bS+R5zPc1xmj3cfjbarhVBOs33eOeetOeLobLpOb\nX4RfXV/q1nFNgnLU+Qy0tk1dsi3AqcBXcbGBjQeiXdYXS9Ky8olLLv+d4i7e/inzpuCcIzRNewX4\nDAhA/+HwZgvNnjE+/pIp8AWglNqtadq3QL1SbYcCTYAPTIEvY9sZmqa9BQzTNO0tpZQBuAW4Evje\nFPgytv1X07S1wL2apjUrvU8hXGXSpDFm92UWOCFqPvncC3dx9mfwT9ADX68ppcaaFmqa9hgwG/gB\nuMfSikqpKPQTOTPGVPsjQALwcKnlfuiBtaVKqXLrOet0THrljQC3nuJ74dWDqUsFhXoGTUxSFnf1\nvdSp+mKVORyRSOuQBnbPanguPpOpy44DsFfFu7RP7k7SSE7P5VhkitmyiJg0/lh/gqJiAzd2acmw\nIZ04X8HFrL2Bj/gqvDAuK8Lmz5tznA18uWIW1KpSkwJfoM+k2bxJfd56tBvNGgU4vb3s3EK+nL2X\nqPOZPD64Izde08IFvXTM+v3n3L6P6SuOu23baVn5NG5Qr/KGXsQL/7za62X0Hw2fAzQsB7+GAMlK\nqXJZ9Uqp98osutF4u8HCdjYBI4Gr0c/HKmq7ERgE3IA+xFIIIYQQwis5G/z6DxBfOvAFoJSaq2na\np+gnRPb6Hj2g9qhSZhGMTuj9PexoZz3F1sDJmfhM2l/SuPKGLtiXrc7EZXA8MpmE1BxW79YLxkee\nz0qBaikAACAASURBVOCH0X2d3ra1vi7adIrs3EIevOkKu7a3uNTwttx81xXd3nnsfElQzR0MwNJt\n5UcIlx7CtvlQLMOGdCK/oLhcO0cUFBbz5vhqnyzpcrbWNvOkwiI73wPV+Ko/PjWHRZtOMeKuq5ze\n1j97zhIRrQddZ644Ti8tBP96dZzeriN+d3GgMje/kNW7zlCnji+Dr2+DX906HD3lviScpVtP88Rt\nmtkyb/rkHAhP9HQX3GEksE4pZdA0rWPZBzVN80Gvx3XQWBD/a+B2oD56ZvzbSqlDpVZpj/7tYGlc\neKTx9kr04Fd74/2ICtp2sOdghLCVZHwIUfvI5164i8PjSYw1IL7E+tDFPKCeMWPL1m1eDQwHtiil\n/ijz8LXG22oX/LKH3Re2bnY6NoPv/zjIrNUX6vikZ+Xz9xbXTe5k6aLJkZkZ3TW8zZ2BL5OE1Fy3\n76O0ZdsjSc+qHvWsLCksKmbcwsM89/1GT3elyn0770C1i2cVFTv+vbbzuGsK6JfN4svKdcEwcS8x\nf0MES7dF8tfmUyzdFun2/R086d3BpX3h5SYqrPaUUmuNQxCtaQwEoge7dgPXA3OAFejDFrdqmtaj\nVPuLgDyllKWZPNJKbdPUFsBSKmzZtkIIIYQQXsnhzC+lVDEwztJjxl8lOwIRSil7rjC+Qo+FvG3h\nMVPwq6OmaduM97PRT+zeV0pVybRbp2Ptr1NkT0zG00Xhbf31fsWOKLf2Q7jX8u2Rnu6CU3aHxrnn\nAtyb0lesOBmdhm+Zfv6z23qw2BsCZfkFxdT3d/C3FjcfQNT5DA6cSKBbhxDatQhy787cpHT9sBU7\noriosfPDRO3m5QX8F2w4SUZuEffZmVFcjTQw3nZDn3ToLlNgS9O0u4AlwFTAFADzQ/+R0hLT8oBS\nbQ1W2pdtK4QQQgjhlVw39ZWRMSNsAvpl5FQ71usA3Ime9bXTQhNT8OtDYBH6zEW90afl/j9jcX33\nVhB2kF1D8Dx4/bB8e6TZ0EFvUdFTsu1ILIs3n6JN84ZkOzPzmicZvPu68WR0mk2TMYSdSam0javs\nU85lduw6HsedA6pnoAMg/Fya2f3CIs+HuH5acIhvRzk/HNqagycSSUzL4YZrXTeZRXZuIV/N2UdB\nYTErd57hxxf7uWzbnvTb6opn3HQLL59YIjYpmxnLj9Xk4JcpvdIAvF46o0sptUzTtI3AQE3T2iul\nIoAc4GIr2/I33pqmqs5B/1NsqdBb2bYOCwmpvt/JzqrNxw61+/hr8rH7+blmwhpX8PX1ITi4IcHB\n3vN81+TXvjK1+dhBjt+TXBr8MtacmIJeiHUPMLbiNcy8YLz91srj2UA48B+lVGipfb4HfIGehXa/\nvX2uCqt3VY8sqaoOfO04dp6nBpcrXVJORZdU01fob4WUDGs/YFcPXhz74qvZ+2xq9+e/lid2NXjh\nRfGUpcfo1701TYNck6wQdiaVv7ecot81rp5ltPowAH+sr7yWlY8Pdmdz7VMJTPzrCAChUa4Lsm45\nHENBoR4zKCwqdvsMjEK4kSkiXqCUOmLh8UPAQPT6XRFACnomvZ+FDH3TEEbTNlNKLS/7y0PZtkII\nIYQQXsllwS9N0+oC04Cn0E+s7lFK2ZSKo2laHeBRIFoptcJSG6XUfVZW/xp4FrhT07RApZTnprCz\nIind9sCMv7/NJdIsCggw/2G2YUPvHYmQX1BMSEgQdetaLjptiorvPB5n8XF3aNjQv8qj8cHBDfGr\nV/lHMSQkiPQ8+wr5e/qXhZCQIIeCX5X129/f+a+uTfujuXdAe7N9BdZ3fAa7pdsiq6Tekr1c8VzZ\nylqtpeDghgQGOPjd5kNJ4AvgwAnXDHdt2qwBdct87ur6Vd1zVZ35+vqU+4zWd+KzU5FGjeq7Zbs1\njVIqW9O0WKC5pml1lFJl/1iYPoCmc6RwoC9wKVA2an2ZabOl2pqWl/2Vo2xbhyUk2F9WorozfY5q\n47FD7T7+2nDsBQXFFtNFPaG42EBiYiYGg3/ljd2sNrz21tTmY4faffyeviY1ccmZvqZpgcAC9Gm2\nw4FblVL2VCnui15Q1WINsYoYZz46hH4C15oLJ2m1kjcPn7PEYDBUWnj6fJLXxTNdztaXzftyqCrn\nSOLX+j1nuKVnW6uP+7jgjZ6QWv59Vd0+P7bwtsw77+qNEDXKZuBh9Ayv9WUe6wEUAKYZXLagl40Y\nSPng10AgtVSW/ZZSy9daaFuEXmRfCJebNGmM2X2ZBU6Imk8+98JdnA5+aZrWFFiFPrPQfmCwUsre\nn+ZvN94utLKPAOAaINdKOr/pp+GqnTLPDfLynJuBLCfHfP2MTO9+Sr6fvYeElByLj3kiKp6ZmWe2\n37PxmW7fZ1hEAgdsmJ0sISGD1BT7AoGe/mUhISHDoUkcxv5xgFZNAwhubDnrIybB+ddl6eZTjLjn\nGrPnKDu7+s6AaU2eF9TCi4lN+3/27jxOjqrc//inZ6ZnXzMzyUy2yX6y74QsZIdAEvY1EJBNFsEr\nAoKgyKJX9IrmgssoeK+KXNQLqKgoXllkUfkByr4dIIQ9Ifu+J/P7o2qS7p7u6a2qu6fn+3698upU\n1amqU9NdPdNPP+c83Plny/KPNyX9evArdrd+3Ta2bQvPyn3i+Q/9OVmeWbdpJ5f+xyN89sQxNNQ6\n9+gOn6pnbtmS27/DcswdOMGvbxljZltrtwIYY04DDgV+Y61d77a9H2dqiquNMfdZaze4bc8DhgLf\nDjnu48D7wEXGmDuste+5becDRwD3WWvX+X95IiIiIqlLK/jlBqUewAl8PQYc2/7HVpKmAruBp2Ns\n7+NuexkYF9GHcmAisNpaG7vkmeSkJ17svEjnOx9vzlBPovtorf/Br0TmSeqO/vbSSo6fOSjqtvdW\ndb904a7s8Rc/9qc6Zxre+mgjn6wPD7yv7AZZpl55f/VWfvnIW/zbSWPjN5aMsNb+1RjzXeBzwCvG\nmN/gZMSfCKwCLg9pu8EYczXwQ+AFY8y9OH9rnYIzhPHmkLb7jTGX4FSM/Kcx5hdAJbAUWA1clYnr\nk+5JGR8i3Y/ue/FLumU4bgam4VReXJhi4AtgPPBalElXAXArE70IjDHGnNG+3p1g/5tAA84fcF3e\n9p3Zz9LIFe+u2szX7/pntrvhu1ffzVyVxGxIpFJkNq3fvJPn3lzj3Ht5OOwxFzz+wsfZ7kIHd/z+\nNZ56NZnR+RLJq/nXOvPD+1/x/RxdUBsxRhBbaz8PnIszMf3FwCzgbuBQa+0HEW1vB5a4bS8BDgN+\nBsyx1m6MaPsn4CjgdeB8nIz93wEz2jPBRERERHJZyplfxpgm4FJ38Q3gWmNMtKbfsNbuMsbcCLRZ\na2+KOE49TrWgeJ+OLgYeBe4yxpwEvAfMxJnH4nFCvqXsyjz/MJHbcYdO3fV/1rchT/G8/M467vo/\ny9pNXXvIzc3/k1ilRr/89E+vM29i35T2bWtz5qt68qWVvPbueuaM78PwljpP+7d5225u+MkzbNu5\nl+b6csYPafD0+CLdheLGmWOtvRO4M9XtEW3vAe5JsO0jdJxLTERERKRLSGfY41Sc6kFtwHkx2rQB\ny4BdwPXu8k0Rberdx07LZFtrnzbGTAFuBObipNy/A1wH3BIra0y6rtUx5gLLhP+858WsndtLb3+Y\n3erzT760kidf6nxoa2fe+2QLP3vwDQBeWr6Of//0odz32HKvusf9j7/NNjfbcuW67ZQW53cWnoiI\niIiISHeUcvDLWns/SQybtNZGbWutfTPR41hrX8WZj0Kk29nf1saq9d1rTqJfP/7Ogf/v3L2Pb979\nnKfZeCvXbgtb3rI9/2LoOVbsUUREREREJOPSrvYouSWQR2NPtmn+szA//O0r/CuBqpD5pC0icuP1\nMNTIuFA+3T/tcm2ieRERERERkUxT8Euki+hugS/I/DxCAc1cJCIikjNaW5eFLasKnEj+030vfkm3\n2qOIiC/awPdUrE/WbYvfSERiev6tNR0yNEVEREREco0yv0QkJ33wyRbf87CyXRBApKv73q9fZvG0\nlmx3Q0TylDI+RLof3ffiF2V+5Zn9+/UNvOSHbbv2ZmPco4gk6Y9Pvad7R0RERERymoJfeebJl1aG\nLSsUJl1ZpkdT6fO7iIiIiIhI/lHwK89t37kn210QSUlbWxuvrlif7W6IiIiIiIhIF6fgV5779ePv\nZLsLIilZ/tHmjJ9zzcadGT+niIiIiIiI+EvBLxER135VrRMREREREck7qvYoIiIiadF8eSLih9bW\nZWHLqgInkv9034tflPklIiK+2rBlV7a7ID5TzqSIiIiI5DJlfomIiIiISM5RxodI96P7XvyizC8R\nERFJi4Y9ioiIiEguU/BLRERERERERETyloJfIhFWrNyc7S6IiIiIiIiIiEcU/BIJsW9/G/95z4vZ\n7oaIiIiIiIiIeETBL5EQ763awtYde7LdDRERERERERHxiKo9ioRpy3YHRES6nICmvBcRH7S2Lgtb\nVhU4kfyn+178oswvkRAvLV+X7S6IiHQ5bfriQERERERymDK/REJs27k3210QEREREZTxIdId6b4X\nvyjzS0RERNKiYY8iIiIikssU/BIREZG0PPfWmmx3QUREREQkJk+GPRpjmoAbgcVAT2A98DBwvbV2\nRSf7zQEejXd8a+2BIJ0xphy4Fjgd6A2sAH5grW1N/QpEREQkVRu27Mp2F0REREREYko7+OUGvp4B\n+gJ/AX4BDAfOABYaY6Zaa9+OsfsKnKBZNIcCC4HHQ85VCNzrrv8jcA+wCPi+MWagtfaqdK9HRERE\nRERERETyhxeZXzfiBL6usNbe2r7SGLMUuAv4DnBctB2tte8BX41cb4ypAV4G1gCnhWw6DSfwdYu1\n9otu2+uBPwNXGGPutNa+4sE1iYiIiIiIiIhIHvAi+HUCsDo08AVgrb3bGHMTsCCFY34bJ6B2hrV2\ndcj6S4E9wM0h59lrjLkO+AdwPnB5CucTEREREZEc0tq6LGxZVeBE8p/ue/FLWhPeG2MKgK8Te+ji\nLqDYGBNM4pijgfOAJ621vwpZXwJMAV6w1m6K2O1ZYAcwK/Hei4iIiIiIiIhIvksr88taux/4brRt\nxpjhOHN/LbfW7knisDcDAeCLEetbgEJgeZR+7DPGfAAMS+I8IiIiIiKSo5TxIV7Yt7+N77W2Ul5e\nke2uUF5ezFlnnEJNTa9sdyVn6b4Xv3hS7TGSmxH2fZwg1h1J7DcUOBon6+v/RWyudx83xth9EzDU\nGFPgBuVERERERESkG2sYfhSvbdkCW7LdE1j7wctMGv8a06Yp+CWSaZ4Hv4wxAeB2YB7OcMRbO98j\nzGfdx29F2dY+dDJWPfX29aXA9iTOKSIiIiIiInmopLyGkvKabHcDgG0bV2a7CyLdlqfBL2NMEfBj\n4Gyc4YnHWWv3JrhvIXAG8JG19o9RmuxwH4tjHKIEaLPWKvAlIiIiIiIiIiJAmhPehzLGlAO/wwl8\nvQnMtdauSuIQ03GGNv46xvYN7mOssH0NsDWJ84mIiIiIiIiISJ7zJPPLGFMHPIhTjfE54Chr7dok\nD7PIfbwvxvZ3gd3AwCjnLwT6Aa8keU4REREREREREcljaQe/jDGlwAM4ga/HgGOttalkYE3FCW49\nHW2jtXavMeZpYIoxpjLiHFOAMuCpFM4rIiIiIiI5prV1WdiyqsCJ5D/d9+IXL4Y93gxMA/4BLEwx\n8AUwHnjNWrunkzY/x5nb66b2FcaYIPA1oA1nvjEREREREREREREgzcwvY0wTcKm7+AZwrTEmWtNv\nWGt3GWNuxJmU/qbQjcaYepw5uz6Oc8qfAucClxtjxuAOsQTGArdYa19N9VpERERERCR3KONDpPvR\nfS9+SXfY41QgiJN1dV6MNm3AMmAXcL27fFNEm3r3cVNnJ7PW7jfGHOXufypwGPA2TgDuRyn0X0RE\nRERERERE8lhawS9r7f0kMXTSWhu1rbX2zUSP4w6rvNL9JyIiIiIiIiIiEpMXc36JiIiIiIiIiIjk\npLSrPYqIiIiIiEh2XfOlL7Fzd2e1wzLnw/dXYEZmuxciIgcp+CUiIiIiItLF/evV5TRNPDPb3QBg\n8Lw52e6CiEgYBb9ERERERCTntLYuC1tWFbjOFRYGCZZUZLsbImnRfS9+0ZxfIiIiIiIiIiKSt5T5\nJSIiIiIiOUcZHyLdj+578Ysyv0REREREREREJG8p+CUiIiIiIiIiInlLwS8REREREREREclbCn6J\niIiIiIiIiEjeUvBLRERERERERETylqo9ioiIiIhIzmltXRa2rCpwIvlP9734RZlfIiIiIiIiIiKS\nt5T5JSIiIiIiOUcZHyLdj+578Ysyv0REREREREREJG8p+CUiIiIiIiIiInlLwS8REREREREREclb\nCn6JiIiIiIiIiEjeUvBLRERERERERETylqo9ioiIiHQRxpjewOvA9dba2+K0/SzwXeBca+2dUbYv\nBq4DRgE7gD8A11pr10RpOw34GjAJ2A88AnzRWrsivSsSia21dVnYsqrAieQ/3ffiF2V+iYiIiHQB\nxphK4DdAFdAWp20L8A23XYe2xpjTcYJdDUAr8ChwDvAPY0xNRNvZwGPASOAnwP3AMcAz7nlERERE\ncpoyv0RERERynBtk+g0wIcFd7gAqYhyrEvgBsByYYK3d6q7/C/DfONlgV7nrCoDbga3AZGvtx+76\nu4GHgG8Dp6R2VSKdU8aHSPej+178knbwyxjTBNwILAZ6AuuBh3HS8eOmwhtjSoGrgTOBfsBHON9E\n3mSt3RjR9i5gaYxD/Ye19toUL0NEREQkJxljPg98FSjFydCaF6f9ucARwJ+ARVGanA7UAte1B74A\nrLU/NcZcDZxjjLnaWtsGzAeGAd9uD3y5bR81xjwEHG+M6WGtXZ/WRYqIiIj4KK1hj27g6xngQuBV\n4FZ3+QzgWWPMkDj7B4EHcYJnHwK3AR8AlwF/McYUR+wyDljlto/891A61yIiIiKSoy4DVgCzgLs6\na2iMaQaWAT8j9t9Gs9zHv0bZ9jhQD4xOoO1jQCFwWGd9EhEREcm2dDO/bgT6AldYa29tX2mMWYrz\nx9l3gOM62f8yYDbwLWvtNSH7fw+4FOebyTvddUFgOPB7a+1X0+y3iIiISFdxIfCwtbbNGDM8TttW\nYCdwBc4cXtEMxpkH7J0o2951H4cBL7ttwRkiGavt0Dh9EhEREcmqdCe8PwFYHRr4ArDW3o3zB9WC\nOPt/FuebzC9HrP82TtBra8i6ETjBupfS6bCIiIhIV2KtfcgdgtgpY8xpOF86fi5y6ogI9cAua+2u\nKNs2uY81IW0Boh0vsq2IiIhITko588udAPXrwO4YTXYBxcaYoLV2T5T9RwL9gdustftCt1lr3wPO\njdhlrPuo4JeIiIhICGNMA/A9nAz5e+M0D+L8nRZN+/rSkLZtMdpHthURERHJSSkHv6y1+4HvRtvm\npuQPB5ZHC3y52ueSeNUYswgn+2s8zjeLv8SZMH97SPv24NdwY8zf3eXtwB+BL1trV6Z6LSIiIiJd\n3G1AMXBJAm13AL1ibCtxH7eFtA24x47XNmWNjVXpHqLL6s7XDp1f/0033RS2fMMNN/jdnYzy+rkv\nLAx4ejzxT3e+7+Ndu+578Uu6wx47cDPCvo/zh9IdnTTt7T4eCzyAUyXyhzgT2l8B/NkYExqcaw9+\nfQVn3okfAW/izGfxjDGmj0eXICIiItJlGGOOxpkn9ZrQiowhIj8RbwBK3flUI7UPYdwU0jZ0fWdt\nRURERHJSuhPehzHGBIDbcUpwP4tT/TGWCvfxaOACa+1/u8cowMn8OgXn28v27LLtOMGuE6y1r4ec\n80vAv7vtTvLsYkRERES6hpPdx1ZjTGuU7T81xvwUmGOtfQLn76npwADgrYi2A91H6z6+GbL+7Tht\nU7ZmzZZ0D9HltH/73x2vHRK7/ksuuSJsOV9+Vn499/v2xZ0aUHJEvryWk5Ho6173ff7JlWw3z4Jf\nbpbWj4GzcTKzjrPW7u1kl/3u43PtgS9whlMaY67CCX6dihv8staeGOM43wA+DRxtjCmPGCopIiIi\nku9+S/TKjdOAI4H7gRc4WJ3xSZzM+Tl0DH7NATaGfNH4ZMj6h6K03Qc8k1q3RURERDLDk+CXMaYc\nuBdYiPMN4eHW2lVxdmtPkX8ucoO19n1jzCZgULxzu2W/X8T59rIvB7+hFBEREcl71trfAb+LXG+M\n+Txu8Mta+/OQTffjZOdfbYy5z1q7wW1/HjAUp+p2u8eB94GLjDF3uEWJMMbMB44A7rPWrvPhskRE\nREQ8k3bwyxhTBzwITMEJZB1lrV2bwK7tQapoE6i29227e45SYAyw01r7cpS2Ze7jzkT7LSIiItId\nWWs3GGOuxplr9QVjzL1AH5ysewvcHNJ2vzHmEpzg2j+NMb8AKoGlwGrgqkz3X0RERCRZaU147wal\nHsAJfD2GM5dEIoEvcFLkdwOz3Xm+Qo87HGdOsJfcVX2Ap4H/idKHcmAisNpa+34KlyEiIiLSVbS5\n/9Jqa629HVgCrMGZY/Uw4Gc4f8ttjGj7J+Ao4HXgfGARTjBsRnsmmIiIiEguSzfz62ac+ST+ASy0\n1u5KdEdr7WZjzP8CZwHXuMfCrTz0LbfZT9y2y92hjeOMMWdYa3/htg0A3wQagJsQERERyWPW2juB\nOxNsextwWyfb7wHuSfBYjwCPJNJWREREJNekHPwyxjQBl7qLbwDXGmOiNf2GtXaXMeZGoM1aGxqk\n+gJO8OzfjTFzcDK95gPjgF9Zax8IaXsx8ChwlzHmJOA9YCYwCWc+ipsREREREZG80Nq6LGw5sgqc\niOQf3ffil3Qyv6YCQZx0+vNitGkDlgG7gOvd5QPBL2vtGmPMVHfbiTjBrBU480eEveqttU8bY6YA\nNwJzceabeAe4DrjFWrsnjWsREREREREREZE8lHLwy1p7P0nMGWatjdrWWrse+Lz7L94xXsWZjFVE\nRERERPKYMj5Euh/d9+KXtCa8FxERERERERERyWUKfomIiIiIiIiISN5S8EtERERERERERPKWgl8i\nIiIiIiIiIpK3FPwSEREREREREZG8lXK1RxEREREREb+0ti4LW1YVOJH8p/te/KLMLxERERERERER\nyVvK/BIRERERkZyjjA+R7kf3vfhFmV8iIiIiIiIiIpK3FPwSEREREREREZG8peCXiIiIiIiIiIjk\nLQW/REREREREREQkbyn4JSIiIiIiIiIieUvVHkVEREREJOe0ti4LW1YVOJH8p/te/KLMLxERERER\nERERyVvK/BIRERERkZyjjA+R7kf3vfhFmV8iIiIiIiIiIpK3FPwSEREREREREZG8peCXiIiIiIiI\niIjkLQW/REREREREREQkbyn4JSIiIiIiIiIieUvVHkVEREREJOe0ti4LW1YVOJH8p/te/OJJ8MsY\n0wTcCCwGegLrgYeB6621KxLYvxS4GjgT6Ad8BPwBuMlauzGibTlwLXA60BtYAfzAWtvqxbWIiIiI\niIiIiEj+SDv45Qa+ngH6An8BfgEMB84AFhpjplpr3+5k/yDwIDAbeAz4DXAocBkwwxhzmLV2t9u2\nELgXWAj8EbgHWAR83xgz0Fp7VbrXIyIiIiIi2aeMD5HuR/e9+MWLzK8bcQJfV1hrb21faYxZCtwF\nfAc4rpP9L8MJfH3LWntNyP7fAy7FyfC60119Gk7g6xZr7RfddtcDfwauMMbcaa19xYNrEhERERER\nERGRPODFhPcnAKtDA18A1tq7gXeABXH2/yzO0MUvR6z/Nk7Qa2vIukuBPcDNIefZC1wHBIDzU+i/\n5Kn66pJsd0FEREREREREsiytzC9jTAHwdWB3jCa7gGJjTNBauyfK/iOB/sBt1tp9oduste8B54a0\nLQGmAM9ZazdFHOpZYAcwK9Vrkfwza3wffvvEO9nuhoiIiIiIiIhkUVrBL2vtfuC70bYZY4bjzP21\nPFrgyzXafXzVGLMIJ/trPLAR+CXOhPnb3TYtQCGwPEo/9hljPgCGpXotkn8C2e6AiIiIiIiIiGSd\nF8MeO3Azwr6PE3+4o5Omvd3HY4EHcKpE/hBYBVwB/NkY0x6gq3cfw6o/htgElLvnFhERERERERER\n8WTC+zDGmABwOzAPZzjirZ00r3AfjwYusNb+t3uMApzMr1OAS3Cyy4Ju210xjtW+vhTYHqONiIiI\niIh0Aa2ty8KWVQVOJP/pvhe/eJol5WZp/QRn4vnlwHHuhPSx7Hcfn2sPfMGB4ZRXuYunuI873Mfi\nGMcqAdpChklKN1dZqQnvRURERERERLo7zzK/jDHlwL3AQuBN4HBr7ao4u7VPXP9c5AZr7fvGmE3A\nYHfVBvexJsaxagivDCkiIiIiIl2UMj5Euh/d9+IXT4Jfxpg64EHcaozAUdbatQns+qb7GCubq4iD\nQxjfxakqOTDK+QuBfsArifda8t327bGKkIqIiIiIiIhId5H2sEdjTCnOZPVTgMeAOQkGvgCewQlo\nzY6cqN6tFlkBvATgDp98GphojKmMOM4UoAx4KsXLEBERERERERGRPOTFnF83A9OAfwALrbUJDz20\n1m4G/hdoAa5pX2+MCQLfchd/ErLLz3Hm9ropou3XgDbgx6ldgoSaMqJntrsg0m2MaKnLdhdERERE\nRETyWlrDHo0xTcCl7uIbwLXGmGhNv2Gt3WWMuRFnUvqbQrZ9ASd49u/GmDk4mV7zgXHAr6y1D4S0\n/SlwLnC5MWYM7hBLYCxwi7X21XSuRxyHjW3mmddXZ7sbaQtkuwMiCWiqL+f19zbEbygiIiIiIiIp\nSXfOr6lAECfr6rwYbdqAZcAu4Hp3+UDwy1q7xhgz1d12IjATWIFT7TGszqm1dr8x5ih3/1OBw4C3\ncQJwP0rzWsRVXFSY7S6IdBulxbrfRERERERE/JRW8Mtaez9JDJ201kZta61dD3ze/RfvGFuByA+R\n1QAAIABJREFUK91/IiJdWkFAOYoiIiLRtLaGfQ+uKnAi3YDue/GLF3N+ieQmxRREREREREREur10\nhz2K5KyAol8iIiIiXZYyPkS6H9334hdlfomIiIiIiIiISN5S8CvHnTBrULa7ICIiIiIiIiLSZSn4\nleNMv9psd0EkJ1xw9EiG99f9ICIiIiIiIslR8EtEuoTCwgABVUYUERERERGRJCn4JSJdQltbtnvg\nD8XzRERERERE/KVqjyJdUFlJITt27ct2N8QTuRX96ttYyYdrtma7GyIUFQbYuy9Po94ikpDW1mVh\ny6oCJ5L/dN+LX5T5JXnt+JkDs90FX1x28jiG9q3JdjckD502b0i2uyDd1GFjm2lpqgKgR3UJ8yb2\nzXKPRERERCRfKPNLOsiXYViBABwxuR/3P7kiq/1oqCll7aadnh5zWL9arj1zEt+97yVeeHutp8eW\nzMq1262o0JselZcUsX3XXk+OJd3D0iOGEQDeXbWFPo0V/OWZD7LdJRHJMmV8iHQ/uu/FL8r8kg7y\naVLxspIiDp+c3eyBs440/ODyWVntQzYEi7x/eykvUbzeb17d/185ezI9a8s8OVZ3cOrc7p1x17Ou\njJJgIcXBQob1q6WiNJjtLomIiIhIHlHwS8JUlgUZ2FyV7W54KpADuTVlCtqkrY02Tpw9KOH2SzR8\nL6t69ShnwZR+2e5Gl3Fkd/9ZRZnaK4++hxERERGRLFPwK8dl+o//LywZT2FBfrwscuVzU670I13J\nZqb4UZ2xub6COeN7J9R2wZT+nLtouPedkLznR9ZiPH5n3M6f1Jeq8q6VTZWvFV5FREREJPPyI8oh\nnvje52fSv1d6WV9TR/byqDexJfsZUdkD6TtqSn/mTuyT1D5tPn1yHTmgR8JtZ47tnfNDTouD+fc2\nPHFYY1Lth0Qp3tC3scKr7iRtwtCGrJ3bL1NH9eK2z83MdjdERERERLIi/z515ZHzF4/I2DffS+YP\n7TJzrCya2pLtLsTUUFPaYV1XT14Y0qeGU+cNoSRYmNR+nr92kzhe6FxTZSVFXHjMSI87440BTVU0\n5uG8WO1DThOJO8+d2IfZ4zpm82VzfreCgtg917xzIiIiIiJdj/6Kz7BjZwzg939/N267s440TBvd\nxNsfbvK/UzgfwruKwk4+mIaJkfJ10uxB/PrxdzzskWNESx2fOtJw7R3/z/Njd0V+ZX4l4uLjR4Ut\nTx3VRElxIZu27ebnf7ZZ6lW48xePYOKwRl5+Z13ctkdO6cf/5Ujlu/FDGjqtMHrmgmE0JBHQO2uB\n4dk3VndY31RfwZsZev+L1Nk7THGwgO27MtaVbqOty39NICJ+aG1dFrasKnAi+U/3vfhFmV8ZNrC5\nOm6bmWObmTuhDwUZHK8XbdhRKrrCxxc/5zTr1aO8w3PcXUddJjtM0it9GioY0NTxPpswtJE54/3v\nU5+GxIbrzRjTTFlJESNa6uK2PW3e0HS75ZnhLXX824ljmDGmKX7jNN7DmnqUp7yvdE/JDrcVERER\nke5DmV8+a6wtZc3GnYCT9ZWI5vqDH54zFf/yKtCWie4eMqJXQtlz2VJeml+3VbIZGeMG1zNhWCM7\ndu31qT+556TZg/hwzTYKAs7r87v3vZTwvlXlxRQEAuzPkdm9E3krmDCskT49K/n7y6t86cNk06hM\noDRMGNrA82/Fzs7LRV78Djp/8QgG9a6mtrKY/3rgdQ96lZuMMb2B14HrrbW3RWyrAr4CnAj0A7YA\nTwI3WmtfjHKsxcB1wChgB/AH4Fpr7ZoobacBXwMmAfuBR4AvWmtXeHd1IuGU8SHS/ei+F78o88tn\nlxw/hlEDe3DY2GYWHNI/293JC4lm1mTagc9uORLEyJbLThnHrChzOOWz2soSLjp2FBccM4rayuKk\n9z9kRE8fepWahCsdevg692OI7NIjhqWxd9fN1zT9ajH9arPdjaSddaTpsC7ZV0VZSRGLprYwfXSz\nN53KQcaYSuA3QBURPyJjTDlOoOsLwCrgNuAhYDHwlDFmekT703GCXQ1AK/AocA7wD2NMTUTb2cBj\nwEjgJ8D9wDHAM8aY3J2IU0RERMSl4JeHon1cammq4srTxnPeohF5lxEUzXafsn0ijRlUn3DbyISC\njGeU+Pg5OiPzaqV4Cq8vO59DiolOY5cJveq8G26Y8mUFAowZmPg9HmV35k/qm/L+9TUlnRzb+yfr\n5DmDPTvWzHFdL/Bz1oJhCQ3/TcbnThrr6fFygRtkehyYEqPJ54CxwG3W2sOstVdba08HDgeCwA9D\njlUJ/ABYDkyw1l7jtr0AGIyTDdbetgC4HdgKTLbWXmmtPR8nqNYD+La3VyoiIiLiPQW/XLMnHPyg\nVF/dsWKfV/L5AzzA7j37st2FAwIHHjMTWYgZh+riT3pxklUe22XrsrP9404lHrl42oAD/y8rSe3n\n7aVE5ib0W9+elRwzfQANNaX0qI4djIpmdBqBM6DTueG8Dji39Kpi/sTUA3WRpozoFXV9pt4HUzF3\nYt+kg4rx7pPxQxvS6VLOMcZ8HngZGIOToRXNiTjDEb8SutJa+wRO0GyMMaY9Ono6UAv8p7V2a0jb\nnwIWOMcY0/6kzAeGAf9trf04pO2jOJllxxtjeqR3hSIiIiL+UvDLdd6xozhsTDNzJvThmATn5oqU\n7Q/duSBTI/7Syt7q4k+Un8HZdtUVB4funTp3iO/n81JpceaDR+kmA/VuqODSE0YzZ0Ifrjxtgjed\nSkNVeTDbXQDghFmD+NZnpvPtS2YkvE9lWZDTD0+vQECqAd9UnLlgGCUevWY/c/xoigq71q/10YNi\nx0zGDUkviJlnLgNWALOAu2K0+SHw5dBgVoj2GqWV7uMs9/GvUdo+DtQDoxNo+xhQCBwWq+MiIiIi\nuSDtcXjGmCbgRpz0957AeuBhnIlY406Caox5Eoj1yeYz1trbQ9reBSyN0fY/rLXXJtH1MD2qSzlv\n8QgA/vnG6lQPE18XD7zEk+eXlxoPEi5OmzeE3z7xDsXBQj5z/Oj4O6TpC0vG88QLHzOsXy0tTVW+\nny/Ul86axPrNO3nqlVW8uHzdwQ0JvrgG9/amcmkyQoO+qQbCJpmeTDK5M/dXLJnIH0rlHAsP7c/0\nMc3UVBRTWZZe8C6V53Dy8J6p/e7w8AdaVOgcrCCXxtF2YuGh/TlySuy5MAc1V7PgkH48+8ZqNmzZ\nFbbNqdqbO5nGGXAh8LC1ts0YMzxaAzdrqwNjTAMwE2fY4rvu6sE476rvRNmlvc0wnGyz9nG5yztp\nmzslaUVERESiSCv45Qa+ngH6An8BfgEMB84AFhpjplpr345zmLHAG8Cvomx7NmJ5HM4krj+K0vZv\nSXQ9p2WqwqMfMjIHVaIy/HP083mbMKyReRP7EAgEMpLZ0bexkjPSmjA89SzAIX1qoE8NL7wdvVpd\n+wf8WLry/ZML2to8zOBM8Lnw5HyB7BbDuOT40Zz3zVij0TJr+ugmfvHwW9nuRqdMv1pOiZNVGggE\nWDJ/KEvmD+3wsz1/8QhuS6KqaldnrX0ojd1vwcn4arXW7nHX1QO7rLW7orTf5D7WhLQF2JhAWxFP\ntbYuC1tWFTiR/Kf7XvySbubXjTiBryustbe2rzTGLMVJy/8OcFysnY0xA3AqFv3JWvvVzk5kjAni\nBNZ+H69turIdvsl0/OjKMyZ6dqzCXMw46DDhfddTUVpEsCj7c0HFM3KAt5NWh2of6hqv2EE2gl/5\nFXBrS2hYcawWgRj/B5g1rpknXlyZUq9KgoXsypE5BS86dhS3//7VbHejI/dJKS/NjWGrfhozWEMi\nE2GMuQ44GydD68shm4IcHAoZqX19aUjbthjtI9uKiIiI5KR0g18nAKtDA18A1tq7jTE3AQvi7N9e\njimRr29H4PTX9696cyp7KQPmTOrHmjVbPDnWmEH1vPF+tC+Hs6extixsuUdVCRWlRWzb6U9lSq9f\nPYdP6ktFjA+zyZ7ruMMG8ru/xR2NnJIRLXWcdaTx7Hix4knxMt/y9fZtri9n5brtKe27ZP5QfvVI\nZjKBOvvxp/PcfP6Usfz+7+/SXF/OqXOH8NA/P+DXj0cbseW/Q0f2ys3gVzdSkEDEORDI3/eDRBhj\nvopTtXEtsNhauylk8w4genUEaK8wsS2kbQAoTqBtyhobMzvEPpd052uHzq//hhtuyGBPMs/r574w\nTna85I7ufN/Hu3bd9+KXlMdPuaWvv46T/RXNLqDYzdiKJZngVzJtfZVOdksiGRVdNYukorSIuRNj\nV0nzVBIfaA4b00S9Wy2usbaUycN7csExo7zvkk8fstIdfhgq2msrWOTNMMqrTp9Ar7pyT44FXTND\nD/yrqhdMY7hrSbCAnnVl8Ru26+SHPy7NKnqJvL9Fa2P613HV6RM4c4HxfUL6jL4Fe/lC76Tjfv5e\nGTWgjub6cq5cMp7rPjXZvxNJQowxhcaY/8IJfH0CzLfWvh7RbANQGuNvtPYhjJtC2oau76ytiIiI\nSE5KOfPLWrsf+G60be5krMOB5SHzS0QzFudP/5nGmJ/gTK66AbgPuMFauzmiLcBwY8zf3eXtwB9x\nqhulNpYmis6CGEvmD+XDNdEKKXVfR0zuR0tTJcP711FanHYNhU6NaEk+8BgsKuSGc6fw7qrNDGqu\npqiwgLGD67nk+NG03v+K533sSrHL0w4fxpGT++bMXEVeyHbwOK1KpD6K9mOZMLSB598Kn1utjc7j\nMT1rOw+ixfvxf/6UcXzv1y+zb/9+zlkYdd7unDZxWGO2uxBbJ0+cnxlQVy7JfoXSaAIEcvZ+9Isx\npgS4FzgapzrkAmtttInq3wSmAwOAyLTQge6jDWnbvj5yHtfItinzKgO9K2n/9r87Xjt07+v369r3\n7ete73ldmV733U93vv5cyXbzfOZsNyPs+zifge6I03ys2+6rwD/d9mtwSnr/zRhTFdEW4Cs4FYd+\nhPMH2TnAM8YY31KOpozoyQ8un8WPrpzNgkP6+XWaA8LnzMn9UEptZTHTRzfTo9rfKT8WTW3homOd\njK1kf7VXlgUZPbA+bC6cycN70lDjXZ/bgy65/GfHrHG9w5aPmjYgOx1JQO6/8lN3+uHJF0Y7bX7q\nxdQCUSKCg/rUcPZRMYJPMSIlg/tUxz1X6J7RzjtmUD3f+sw0vnXxdGaO7d1hey6IdQ/36lHOMTMG\nxtgquSbbgfBMM8YEcAoPHQ28AsyIEfgCeNJ9nBNl2xxgY0i2WLy2+3CKH4mIiIjkLE+DX+4fXrcD\n83AqNd4ap+0G4HlgpLX2QmvtFcAk9xijCR9SuR0n2DXZWvspa+1V1tqZOGn9fYiRhZaKaN8Ul5UU\n+T7URjp38pzBVFdEm3Ikulz53JNLAczayhIuPm4Uk0wjX1g6iYY4WTySmGSyahZO7c/cCcnH6k3/\nWk6b13l1vAuPGZnw8a4+azLVFcWUl3TM1ty3P/oFnbtwRMLHj6b9qLWVJdSHBJ69yM7x+z777mUz\n+dr5U6hJ4j0ol+RjIKi0+ODv5DnjczOQmmH/hjMX61vAHGvtqk7a3g9sAa42xhxIqTbGnAcMBf4r\npO3jwPvARcaYlpC284EjgN9aa9d5dhUiIiIiPvBsjJoxpgj4MU5VoeXAcdbamDOKW2vbgGnR1htj\nvgB8ClgCXOmuPzHGob4BfBo42hhTbq1NbUZoDqbjVVWGT9heWhoMS9UrLUm8klZkil/1J/GHTDb3\nqj6w39qtnY0a9U46qYgVFSUZSWUMPUcigcjKytKMplgWB4tobKyiOBh+W9XUlKXVj872LU5ymGlj\nYxWLG6tYPGtI2LqGmlLWbtqZ0DkT7WdFRUmMlokdpyRikv+qqsSez7Ky4oyn1lZXH+zbpl2xKxL+\n4Tsxi992qv3YZy4exaPPf8SaDTs6tPnUohEsnjWEO/7wWodtlZWlFEaZM6yxsYpARIXW+vpKCmLM\nLzZuRNOB/++JEU0JfZ6qqsIzK8si3kvbVVeFTxdUWhK9XajI11d5uXfPe0NDFfU1paxz74lAAAb2\n79HpPqmeu66u3LN+V3fyXjNsYAO1Vandk52ZOroppf4HiwvTfl+88YJp3HH/y/SoLuW848dQF/F6\nKw4WsmOXP4VNco073PEr7uLLwOeMiVqA5IfW2k+stRuMMVcDPwReMMbci/NF4ik4Qxhvbt/BWrvf\nGHMJ8Dvgn8aYXwCVwFJgNXCVT5clIiIi4hlPMr+MMeU4fxSdjZOdNTfON46dstZuc4/TZIzp9Gt2\nN4j2Ik4p7r6pnjPUmCHhEzovmNISo6V3ytzsix7VJcwc33lWSHNDRYd1Iwf28OWDTa6aPdGTpzqt\ndIiJpqc3fcgBXzz7ECrKghQWBLjyjInZ7g7Qeebe6Qu8qyoZqaQ4lQxPb9Nqzj929IH/HzGlf0L7\nLJw+kIKC2P2IlVsVuUdbW+zMr2RNG9Mctnz87MFR2zVGTMY/sE+0ebUz65qzD3Ey40qLuOmCDt/T\nJOQr5x3qca9S5+Xvh59ct4DpY5uZMbY3F50wNv4OPhk1qJ7brpjDDZ+e2iHwBR3fJ9It1pBDok3N\nNwKod9efCFwf5d9XCKnwaK29HedLxjXAJcBhwM9wssbCvgW01v4JOAp4HTgfWITzd98Ma+17nl6d\nSIjW1mVh/0Qk/+m+F7+knfnlpss/CEwBngOOstau7XwvcOfzGgWss9ZGTrYKUAbsB/YYY0qBMcBO\na+3LMdoC7IyyLWGhk88tPWIYj7/wEaMH1dNcWxK2befOxLOxIie027ypY8YGwNfOn4J9fyMjB9Sx\nccPBiuEbNnasHn7M9BZ++8Q7rNl48HKLCwu48dxD+GjNNm755fNh7SvLgmzdsefAeb7y3x2n5khn\n4r1t23ZlZOK+0HOM7FfDpGGN/OvNNVnp16lzhzB+aAPP2dUH1g1srmLNmi1MH9WLF9462K/q0sK0\n+tHZvrt3J5fVEHqs0EkX68uD3PKZaezf30Z5aTCl/kbus23broT269ezkrWbdrBr937OXTT8wHF2\n7gy/ts2bdx7YdtioXqxZv40dO/dSVlLEw//68EC7HTt2p/XzvvHcQ3j7w02MHVxPRWmQJ176mJ//\nufO5nLds2XHgnBs3xE4+TbRf00Y0sm/PcHbs2svcCX3C9tu/b3/Ufdat3cKO0uhZqVu37mRflP3W\nrNlCW8SYzXXrtjJ9VC/sexuitm+3PsZ1DmmqCmt36Qmj+dtLKxk7pIGywkDUn0FDRZBh/Wp584ON\nNNaWcqhpiPuzinx9bd+e3PO+9Ihh3P3Qm1G3rVvr3BPLPjsDgIJA9H6HirZ9U4z3+1AbNm5nTXni\n2cSd2bxpR8x+evpeuHcvn17kDIFt27M3pWPv2b0vqf3OXDCM//mL83zNGtec0L6VEYHsYFF+TF1g\nrb0TuDNi3Quk8IWmtfYe4J4E2z4CPJLsOURERERyQVrBLzco9QBO4Osx4FhrbaKlEKcADwF/AMLG\nAhljmoFBwPPuMMg+wNM4qfzjItqWAxOB1dba91O/mnDzJ/Vl/iSPsotCRMunOHnOYHpUlzJtdFOU\nrR2VlwRZMm8o3/tNeBywuryY6paOiXJXnzGBv720kuH96+jTWJlKtzuXhblkigoLuPTEMXztzn+y\nYuXm+Dv4oKlHOYeO7MXTr31Cn8YKDndfLxOGNTBnQh9ef3c9RxzSj+ryrjFHkN+VOmMZ0qeGa5ZO\nZN/+NirLDgYBOkvKKysp4ozDhwHw4NPeJh30qiunV135wX4ksE9lmbfPcUEg0KE4QbsjDunPrx6J\n9n2BY9a4Zp54sWPx21jXMWNMM3959oMDyyXBAqaO7MUr76zn2TdWx9iLmKlkdREZRpNMTybFyZIM\nBAJ8Ycl4PlqzjZ51ZRl5Lc4c28yqddv5eN02Xo8S6APnecg3J80dwq//GlmwL/fNndCH8pIitu/a\nm7OFEkTEe5dcckW2uyAiGab7XvyS7rDHm3Hm7foHsDCJwBc41YNWA4uMMTPbV7rDHL+PE5j7AYBb\nrehFYIwx5oyQtgHgm0ADzrwVXdK8ibGHOUafxDm5IUl9GytZMn8o4z0a8jE7cmLhXC5v6LMLjxnJ\nty+ZzvVnH3Jg6GphQQGfOtLwjYumMc+r4ZkxjBrY+TxEXUVZSVFY4Csd2SgwMHpQ/Odh2qjEgtvx\nzB7Xm8mmMeb2MxcYgkWJv7UvntZC/16VBHAC8cGiQoJFhVx83Kik+zYhjfeYosICWpqqDtxH8SRT\nZCCa4mAhSxcM46rTJ1BWEpkRlH9Br3Ynzx/GgOb4VTtzTSAQYOqoJuZN7JvU61tEREREBNLI/DLG\nNAGXuotvANfGmFz1G9baXcaYG4E2a+1NANba3caYi4F7gYeNMfcA63EqBw0Hfumm9re7GHgUuMsY\ncxLwHjATpzrk44RMzuq3WJXJmuvLWbkuufn2xw9pyFrGTWfGDa7nxeUdizeNH9JAWQ72N1XpJnYE\nAgF6VHecayZTZo3rzS8fjp0F1JUN6VvDP145OHVgU315J60T96kjDT//v86HMYZKJMYSL0OoV10Z\nJ80elPA5O1NSXMglJ4zh0v98nB1RJtcvKnQyt558qWP2VzRV5cXccM4htJF+ptN5i9OrBineGdq3\nhrc+3NRhfWVZkM+dNp4rbn0iC706KBtJdXmYyCciIiIiCUrn69OpOJPMtwHnEXty1fYxMO3rDrDW\n3g/MwQlqHYNTtXEn8Flr7dKItk/jDJX8DTAbZ3LWCuA6YIG1NjNlETsRjFEhrTOp/jGeTtLDOQuH\nH/j/qEH1UducfvjQDuuOnNKPS04Y3bFxjn6gSORnm2rXYwVA03H45L788IrZSe1TkkDVy0yYO6Fj\n9mK6P6MZo5sZ2OzMSzZhaAODeyc2CXq8884Y08yxMwak1bdkNNeX8+8XHJrRIOmiqQeLdBQWBJic\nwLDDyMBXIMk3p551ZVTEmHPMD10hkFFUlEAnfcqcPWfhcHr1KKeqPMjlp46Lv0MnevU4GHheMm9I\nJy0Tl27mnoiIiIhIMlJO4XEDVwlHe6y1Udtaa/8OLEzwGK/ilOHOqljDqob2q+X91Z2P/MyFP/hn\njGli2849rNu0kzMXRR/a1LOunIHN1WHzaY0a2IOiFAJ8/or9A506slfMbbmoobo0xUqDiZk2yvuf\nx7EzBrBvf1tYsCVZsZ7BYFEB1545ia079lBd4d2cWsGiAo6fOYjf//1dz47Z6fkKCygs8OO+iR1Y\n6dWjnPMXj+Cl5euYPrqJ8tKOb/VtbW1pBa5rK8Pn9poXJQDqJy/fS5fMH8pP//QG4GS/lXYYBpma\nES11VJcH2bw989/NNNdX8I0Lp3pyrM8cN4r7n1xBQ00pszP8PIuIiIiIeCF/xq9lULTMkh7VJRwz\nfQCPhFSd81M6SQ+FBQUsPNQJVjTWlcVsV+7RB8BsKU8kCyVH0keqK4qZPd77D5UThzVSXFRAIBDg\nlLmDPT/+8TO9GcoXS1FhQYcgSzx+z/lVVBhg774ciGInkOE2Y0zzwRUev9ZLigs5a8Ewfvf3dxnU\nXO3L6zcZ6Vze1JFNfLRmG+9/soWjpw/wbKL7wgIngPvUq6t48On32bM3eqVOr/j1quzfq4rPnTzW\np6OLiIiIiPhPwS8PHDmlH8fOGJjgRM1JfjzJjdhMmJamqrDlvn5UkMxxXgRYSooL+cxxo9m3bz+D\n+tT4kvU1a1wzYwd7U+jALzn4Eu/U9NHNPPHix9nuRhRxfpI+pJ3OndiXuT4XdciEYFEBS+Z3HOrt\nhV49yjl+5iBGtNTxH794vmODrnYDiIhkUGvrsrBlVYETyX+678UvuTaGrWuI+AzZu6Ei4Qpl6Z4r\nq9y+TB7eyOA+TrWwES11jM5qxcHMfHIc0id8vikv5vz61sXTGDu4ngnDGqlJY1jfgkP6pd2XLi2X\n7pGsSu4HkeycXrkuF4aUd2Zov1rPih7E0pWe0Tx7+YmIiIhIjlPml8QW49NJYUEB1yydyKatu6mt\nLOnSH6IT7rkPl1hV7s08VqfOG8LQvjWUlxTx4DPv88o760O2etvx4qICdns8dMvzmIXXz1WcDhYV\ndt3XfzLGxCiOIYkpCARYPG0Az725NmwuxWzI9UChiEg7ZXyIdD+678UvyvxKhYefdVMNHFWUhc9n\n1bOTubtS1sknpMKCAnpUl1JQkO0P/voUVxAIMMn0ZMQA/zPwLjr2YIGE9uy/nJPGS+L8xSMSanfF\nqeMoCRZSUlzIZaekV0nPO8ndi20JRECOnOJkFVaVBzkjSgXYXNKFY/Ce6UrvhgrAiYiIiEgmKfMr\nFWn80e7VH/xD+9bQt7GSD9dspbykKK1qewnLww+XiX5gLikKjxPnXtXLzBg/tIELjh7JyvXbmB9v\nrqdMfbj16HW5ZN4Qpo1qSuj4owfVc+vnDgOgJJgrhSHi/MBTiA6dNm8ocyb0obIsSEUiBSRERERE\nRERykIJfHkhn8vNU9wwEAlx75kTeeG8DLU1VVKcxZ5TEN29SX15/byP729ooKixgxujm+DtFOGHW\nIH77xDsAHHVof6+7CMCI/nVhwx4bako9PX4gEGDa6CgBonSOme4BPAqyLZgS4zmJcfzcCXr5q1dd\neba7kLK+jRV8uGZbtruRMYneS90lSy6yiEiylWNFREREJH8o+JXrOvmQUlZSxIRhjZnrSzceplJd\nXsxVp4/nhbfXMtn0pLw0+Vtn8dQWaiuL2be/jcPGJB88S8TciX146tVP+HDNVg4d2YveDRW+nMdL\nOT/nV+Thkzx+5m6bbhLRSMKFx4zi53+xFBcV8Nq7G7Ldneg8fIEkeqjuMuTQ9KulvrqEdZt3UVgQ\nYMkCk+0uiYiIiEiWKPiVYSMG1IUtHxkr26QTGfvgkuX0gAuOHsmPH3gNgP69KqO2SfdnkXDWXgBM\n/zpM/7r4bWMoKAgwc2zvlPdPRGlxEdefM5kt2/dQW6lswGzoLlk1XUHfnpV86cxJAJxLp7V6AAAg\nAElEQVT3zUez3BsJlYn7pKAgwJfOmsy/7GqG9aulZxfOYhQRERGR9Cj4lWEVpUE+c/xoHnv+I0YN\n7JG7k4bngCkje7J+y05Wrd/u35xmCX4A60pDv4oKC6ir6j7De3ItiSVTwekxg+p55vXVB5aDRd0r\n6pZrz3vKknzaRg6oy90sthxUV1XC4ZP7ZbsbIpKi1tZlYcuqAieS/3Tfi18U/EpBuh+6Dhnek0OG\n90x5/4xllmR5bExhQQGLpw3Iah8qy4IsmtpCZZkm+/ZLcVFuFw+YPLwnP/8/e2A52WxNv27Xk+cM\n5s0PNrJx627mT+xLsKjzOchmj+vNPX99+8Byj+pStm3Z6VPvxC8XHzeaj9c685h98+7nstwbERER\nEZGuQcEvD/gZjIo2qXbWYlI5mFiS7s++s92Pnj6AE2cNSu8E0qlAILWhv2HH8KgvsVSWBfnsiWP4\n+8srGT+kgaYeyWUB+nW7NtSU8fULprJlxx4aEyhsMGdCb15/bwPvfLyJ048cTnlpUMEvj2Rybr2C\nQIBh/WpZvWF7h22Jvh+WlWT/V393mXdMRNKjjA+R7kf3vfgl+38Bd0H9e1byj5Dl+mpvK+qF6tuY\n/IeqYFEBe/buB5y+SmqqlO2VlnifbRce2p+xg+vTHqKZic/QE4c1MjHB4hKZnPOrrKQo4UBGaXER\nl586DoDGxio/u9UtXHzcKP7rgdcpLynkgqNHZvDMziu+rqrj751Agi++vj0raelVxXufbAn7fSEi\nIiIikq8U/ErB7PF9ePT5j1i9YQdD+9Zg+tf6dq5AIMCg3tW88/HmhPe5/JRx3PGHVykOFvJpLz+U\n5eA39X5lD5SVFDFznD8VGQVmjWvmlLlDfDl2DiYoSg4oCRaya88+z443ZUQvxg9poKAgQFGhP0N3\nF09r4Y9PvRd1WzDKcOGCROt3BAJcfcYEXnx7LS1NVXz5x0+n000RERERkZyn4FcKSooLufHcQ1i9\nYQe9GyoS/rY9VcnONzW8pY5lnz0s/RN3g7J1kZf4+VPGsW7zTkYN7EFpsW4PLxUXFbB3XxvNDeUc\nPzO3hpOOaEm9iqd0DZ89aQy33vMi+/a38alFIzw5ZnGUYeleOmn2YB597kN27AoN2h180xo9sAev\nrFgfdVs8ZSVFTB3VFLfdkD41CR8zGd3g14uIiIiI5BB9uk9RaXER/Xtp6FC6po1q4qlXV3l+3NED\ne6S0X2NtKWMH13vcm+6ptDg8MLB4WgtHTx8AJD48y0+XnTae79/7IrWVxZx1pPHtPJrbyB+Rc6/F\nm4tt1IAe3HzhVMoqShjYu4Y1a7b42b2MaIt4cSWa+RXPyXMGc99jy6ksC3L2Uf7dGyIiIiIimaLg\nl8d6N1QcqMTlleMOG8hLy9cBUFQYYHgeZamcPGcwq9ZvY92mnWzevift4x09vYXykiCzxvVOcI/w\nT4sKVHhn+ugm7v3rcnbt2UdRYQFzJ/b1Jeg1uHd12PKwfokNQz58SguzJ/Zl3bqtFBbkdsVJ6WjS\nsEb696zk/dVb6d1QwaEje8Xdp7G2LOvznfWoLmHFyoPLxXGqdHb2nrQ/YlvAo+jXoqktTB/dREmw\nMCcmxxcRERERSZf+qvXYp48ewS2/fIEdu/ZyqkdzGg1oquLMBcN4dcV65kzoE7UCpC8yEAmqqyrh\nK2cfAsB533w07eNNGNrIwObq+A1dOZCAlLdKi4u47lOTeP6ttYwdXJ/08N1EDetXy5QRPXnm9dVM\nGNrAmCQy94JFhb4HvvQa80dBQYAvnTWJleu201Rf7tu8W147efZgXnhrLfv2t9G7oSLpoiahr6cO\nmV8p9mnO+N489sLHYetqK9MrRCEi4oXW1mVhy6oCJ5L/dN+LXxT88tiApmr+4+Jp7N6zjx4eVYEM\nBALMm9iXeRP7enK8fDJlRC/eXXVw+FKylTfHDmngw9VbDyxXqMKjp/o0VtKn0d+Ko4FAgIuOHcWF\nx4wiEMiNIZWSGcXBQlqautbw8149yvnK2ZN55+PNTDKNab1eZ43rzRvvbzyw3CfF6r7HzRzEynXb\nWbNpB6fPH5Zyf5KhLFsRERERySQFv3xQWRaEfAiiZDiIMGpgD151J29eekRiH8DmTOjNC2+v5a0P\nN7J42gCqK4qTOueSIwzPvPYJazfuYNqoXtQkub/khkAgkLMZVvqQL5H696ryZM7IycN78uqK9bzx\n/gaOOKQ/1eWpvX/VVBTzxaUT0+6PiIjXlPEh0v3ovhe/KPglOeO8RSP401PvUVtVzOzxic3ZVVpc\nxDVpfGirqy7lB1fN5e1318WdMFtEJJcUFRZw/tEjs92NlORqsFpERERE8pOCX5Iz6qpKWLogM0Nu\nQpWXBmmuT27eHRERERERERHpGjwJfhljmoAbgcVAT2A98DBwvbV2RQL7PwnMiLH5M9ba20PalgPX\nAqcDvYEVwA+sta3pXIP4r7G2lDUbd2a7GyIiIiIiIiLSjaRdHssNfD0DXAi8CtzqLp8BPGuMSaTk\n4VjgDZwAWuS/Z0POVQjcC3wZeN091x7g+8aYW9K9FgkXOQywvDSxecwaaqJPOn/uwhFp96k7OOrQ\n/gf+P310UxZ7IiIiIiIiItL1eZH5dSPQF7jCWntr+0pjzFLgLuA7wHGxdjbGDACqgD9Za78a51yn\nAQuBW6y1X3T3vx74M3CFMeZOa+0rqV+KhDp6+gCeemUV23ftpX+vSgY2JzZB8yUnjObuh96kojTI\nkVP685xdw4DmKkz/Wi44eiQ/fuA1AE6YNcjP7ndZx0wfwL59bezZu4/jZ+pnJNIVDe5T7clx8rVe\nglfVkEVEREREEuFF8OsEYHVo4AvAWnu3MeYmYEGc/ce6jy8lcK5LcTK9bg45z15jzHXAP4DzgcsT\n7bh0rqaimK+eP4X3P9nK8JZaAgnOUDygqZovnzX5wPKIlroD/582uomedWXs3rOP4SHr5aCykiJO\nP3xotrshIkk6Z+Fwfv5nS1VFkHOU6Rrm6OkD+Jddzcp12wkWFXD8zIHZ7pKIdAGtrcvCllUFTiT/\n6b4Xv6QV/DLGFABfB3bHaLILKDbGBK21e2K0SSj4ZYwpAaYAz1lrN0VsfhbYAcxKqOOSsB7VpZ5/\nQz+4T42nxxMRyQWzxvVmyoieFBYUECxKe1aBvHHCzIEcdWh/FhzSj+ffWsOg3jU01JRlu1siIiIi\n0o2kFfyy1u4HvhttmzFmODAcWN5J4Auc4FcbMNMY8xNgGLABuA+4wVq72W3XAhQCy6P0Y58x5gN3\nXxGRHJKvA9ckmtLi1H+tzpvYh0ef+8jD3uSGY2Y4WV7BokJmju2d5d6ISFeijA+R7kf3vfjFl6+m\n3Yyw7wMB4I44zce67b4K/NNtvwa4DPibMaZ9oql693FjjONsAsrdc4uIZEWiw4NFIh07YyDD+9dS\nX13CpSeMBuC0eeE1Y0qKC7PRNRERERGRLs2LOb/CGGMCwO3APJzhiLfGabsBeB442lq7MmR9K3AR\nzoT6VwLtpQZ3xThc+/pSYHtaFyEiIpJh1RXFXH3GxLB1M0Y38eHqrXyweivHTB9AgYKrIiIiIiJJ\n8zT4ZYwpAn4MnI0zPPE4a+3eWO2ttW3AtGjrjTFfAD4FLMEJfu1wNxfHOFwJ0GatTTnw1diYWDXD\nfNSdrx269/V352sH769/6579YctFRYU5+zPO1X5lQle69suXTo7fKEmZvP6u9LMWERERkfzk2RBB\nY0w58DucwNebwFxr7apUj2et3eYep8kYU4yTIQYQa7b0GmBrqucTEREREREREZH840nmlzGmDngQ\ntxojcJS1dm0C+1UBo4B11tq3ojQpA/YDe4B3capKdqiPbowpBPoBr6R4CQCsWbMlnd27pPZv5Lvj\ntUP3vv7ufO3g3/Vv2BCefLp3776c+xl35+e+O187ZOf6c+VnrQw0ERHJtmBJJTd/50cUBX+W7a6w\nb99e/j97dx4mV1klfvzbIYEACQRIWAKyiOGw7yIiQgYdld1BFgEXNpF9FTSgQHAEcTAgQkPYF4ER\nlEEBcUAc9vWHIshyQDZnZItA2GkI6d8f91aobrqTTrqrq1L1/TxPPzf33lO33rerb56qU+d93003\nWp/DDzm03k2RBkW/k18RMRy4liLxdTOwTWb2tQJrA+BG4Bpg227XXQr4OPDncnjktIi4B9ggIkZ0\ne44NKBJld/WnL5IkSZIaQ3v7pC77rgKnud2iS68CS69S72YA8N47r/Pc8/fUuxkf4X2vWhmIYY8n\nUMzbdSew+WwkvgBuA14CtoiIz1YOlsMcT6dIzp1RFX8xxdxeE6tihwE/BDop5huTJEmSJEmSgH5W\nfkXEksD+5e5jwISI6Cn0xMzsiIjjKCalnwiQme9FxD7AlcAfIuIK4BXgX4GVgcsz86Kq61wA7A4c\nGhFrUA6xBNYE/iMzH+5PfyRpoHXWuwGSJM2lrPiQWo/3vWqlv8MeNwSGUXy+26OXmE5gEtABHFPu\nz6jcysyrI2I88H1g6/J6CRyQme3VF8rM6RHxpfLxOwIbA3+jSMCd1c++SFK/tdW7AZIkSZKkLvqV\n/MrMq5mNoZOZ2WNsZt4BbN7Ha7wJHF7+SFJDsdJLkiRJkhrLQMz5JUnqhZVgkiRJklRfJr8kqYas\nBJMkSZKk+jL5JUkDyEovSZIkSWos/Z3wXpIkSZIGXHv7pC77rgInNT/ve9WKlV+SJEmSJElqWlZ+\nSdIAGjKk68DH+efzv1lJkuaEFR9S6/G+V61Y+SVJA2ipxRZg9MLDZ+zv/LlxdWyNJEmSJMmSBEka\nQG1tbXxn53W47S/PseLSC7PCUgvVu0mSJEmS1NJMfknSAFt81Px8ZdMV690MSZIkSRIOe5QkSZIk\nSVITM/klSZIGzDJjFpzx7603Wr5+DZEkSZJKDnuUJEkDZu+tV+NXtzzJqBHz8sUNlq13cyTNxdrb\nJ3XZdxU4qfl536tWTH5JkqQBs8ziIzhkh7Xq3QxJkiRpBpNfkiRJkhqOFR9S6/G+V60455ckSZIk\nSZKalpVfkiRJc4mIGAs8ChyTmT/r4fw3gEOBccCrwBVl7Fs9xG4JfB9YDXgHuAaYkJlTeoj9NPBD\nYD1gOnAT8N3MfHqAuiZJklQzVn5JkiTNBSJiBHAVMBLo7OH8BODCcvc04C8UibAbImJYt9idKZJd\no4F24I/AbsCdEbFwt9hNgZuBVYHzgauBrYF7I2K5AemcJElSDVn5JUmS1ODKJNNVwDozOX88cCew\naWZ+UB6fCPwA2Bs4ozw2ovz3k8A6mflmefwG4DyKarAjymNDgMnAm8D6mflcefxS4EbgZGCHge+x\nJEnSwLHyS5IkqYFFxCHAQ8AaFBVaPdkbmAc4oZL4Kp0AvA7sVXVsZ2AUcEol8QWQmRcACewWEW3l\n4c8BKwHnVRJfZewfKZJfX46IRfvRPUmSpJqz8kuSJKmxHQw8DXwbCGCzHmI2oRgKeXP1wczsiIi7\ngS9ExMjMfKOMBfifHq5zC0UibXWKhNvMYm8GvgBsDPy2792R+qa9fVKXfVeBk5qf971qxcovSZKk\nxrY3sHZm3g209RKzIvBiZr7dw7lnyu1KVbGdwFN9jIViiGRvseN6aZMkSVJDsPJLkiSpgWXmjX0I\nW4yeE1QAr5XbhatiOzKzo4+xAFP7ECsNKCs+pNbjfa9a6XfyKyKWBI4DtgQWB14B/kCxrPZsLX8d\nEfMAdwAbZOZHqtIi4hJg114eflJmTpid55MkSWoSw4CekllUHR8+h7GdvcR3j5UkSWpI/Up+lYmv\ne4FlgBuAy4CVgV2AzSNiw8z822xc8hBgA3pYvru0FvACcFYP526fjeeRJElqJu8A8/Zybr5y+1ZV\n7BKzEdvWy7W7x0qSJDWk/lZ+HUeR+DosM0+tHIyIXYFLgJ8C2/blQhHxCeCHMzk/jCKx9tvMPL4f\nbZYkSWo2r9L78MPK8deqYleOiGGZ+X4fYivHp8wido6NGTOyv5eYa7Vy36G1+z/QfZ9nnt6mBJR6\nNt98Qwf9Hmzlex7sfz31d8L7fwNeqk58AWTmpRSTqH6hLxcpl9M+F/g/4PFewlahSNY9OMetlSRJ\nak6PA0tExHw9nFsB+AB4oiq2DVi+l1iArIqtPj6zWEmSpIY0x5VfETEE+BHwXi8hHcC8vXyr2N23\nKZbS/hfgZ73ErFluTX5JkiR1dRswnuL91IwJ8iNiOLAh8HBmvlUVu1sZ/wRdjQemZuajVbGV490n\n3h9PkVS7t7+NnzLljf5eYq5T+fa/FfsOrd3/WvX9gw96mzlG6llHx7RBuwdb+Z6H1u5/o1S7zXHy\nKzOnA6f1dC4iVqYYovjkrBJfEfEx4CfAuZl5S0T0FlpJfq0cEXeU+28D1wFHZ+bzs98LSZKkpnAZ\ncBRwXETckpmVLyePAkYCZ1fFXg2cChwZEb/KzFcBImIPYBxwclXsLcDfgW9HxNmZ+WwZ+zngX4Ff\nZebLNeyXWlh7+6Qu+64CJzU/73vVSn+HPX5EWRF2OkU5/dmzCAeYDLwOfGcWcZXk1w8olvI+i6IU\nfzfg3ohYek7aK0mSNLfLzKRIWn0a+HNEnBQR1wLfp1gU6Jyq2FeBI4EVgQci4uSIuJzifVsCJ1TF\nTgf2o5jf6/9FxM8i4jyKLx9fAo4YjP5JkiT1R38nvO+inLtrMrAZcB/Ft4ozi/8G8CXgK5n5+iwu\n/zZFsuvfqkrxiYijgH+nqEL7ypy3XpIkqeF10suq2Jk5ISL+lyJZdRDwPDAJmNi9Ej8zJ0dEJQm2\nH/AycCFFNf3UbrG/i4gvAccCewJvAL8BjqpUgkm1YMWH1Hq871UrA5b8ioihFN8qfpOiMmvbzJw2\nk/glgFOAqzLzv2Z1/czcrpdTJwJ7AVtFxAKZ+fZsN77UKGNR66GV+w6t3f9W7ju0dv/te+tq9f7P\nzTLzIuCimZxvB9r7eK0rgCv6GHsTcFNfYiVJkhrNgCS/ImIB4Epgc4rqrM9n5guzeNgZFMMuD+jP\nc2dmZ0T8hWLFomXofbXIWXFtYEmSNMci4l+AmzPTWaclSZIaSL+TXxGxCHA9sAHwJ+BLmfnPPjy0\nUsn1XE+T3EfEdODZzFyhXKloDeDdzHyoh2vNX27fnd32S5IkDZBfAR0RcSXwn5l5V70bJEmSpH4m\nv8qk1LUUia+bgW0y880+PnwiPc9ZsS+wBHAcUJlzYmngHuAhYK1ubVgAWBd4KTP/Pns9kCRJGjBL\nAp8HdgSui4jXgV9SJML+XNeWSZIktbD+Vn6dQLGq0J3A5pnZ0dcHZubEno5HxHbA4pl5fFXsk+XQ\nxrUiYpfMvKyMbQN+DIymSKZJkiTVRTmp/PXA9RExjCIRtjVwRzkR/WXABX5ZJ0mSNLjmOPkVEUsC\n+5e7jwETehq+CJyYmR0RcRzQ2VvSq5ue5t/aB/gjcElEfAV4FvgssB5wC1XLckuSJNVLRMxLsZr1\n9sAWwBTgGmAl4JGImJCZP69jE6W5Qnv7pC77rgInNT/ve9VKfyq/NgSGUQxd3KOXmE6KJbY7gGPK\n/Vklv3pcwjsz74mIDSiGQ/4LMAJ4Cvg+8B/dl/CWJEkaTBHxbxQJr60o3vv8CvhyZt5eFbM/xUrV\nJr8kSZIGyRwnvzLzaorVGvsa36fYzFxnJuceBnbo63NKkiQNoouA/wJ2Av6QmdN6iLkf+Omgtkqa\nS1nxIbUe73vVSr9Xe5QkSRJQLNizEDCqkviKiB2BWzPzBYDMvBu4u35NlCRJaj19rtySJEnSTH0a\n+BuwS9WxQ4BHI2Lj+jRJkiRJJr8kSZIGxiTg3zPz2MqBzNwI+AlwSt1aJUmS1OJMfkmSJA2McRST\n3Hd3JbD6ILdFkiRJJZNfkiRJAyMpJrvvbmuK4ZCSJEmqAye8lyRJGhhHAddExOcpVnVsA9YCPgt8\npZ4Nk+ZG7e2Tuuy7CpzU/LzvVStWfkmSJA2AzPw9sDbwALAqsCLwZ2DVzLyunm2TJElqZVZ+SZIk\nDZDMfBjwa2ppAFjxIbUe73vVSksnvyJiKHAg8C1geeB54ALgx5k5rY5N67OIGAs8ChyTmT/r4fw3\ngEMpJuF9FbiijH2rh9gtge8DqwHvANcAEzJzSg+xnwZ+CKwHTAduAr6bmU8PUNd6FRFLAscBWwKL\nA68Af6Do19PdYpux/4sBx1L0fyngaeBCYFJmftAttun6360dJ1N8yByfmbd2O9d0fY+IHwJH93L6\nl5m5c1VsM/Z/V+Dgsp2vAXcCR2dmdotrmr5HxPQ+hHX5+2+m/pfPOxr4EbAVMBp4jqJPx2XmO91i\n69r3iFgEOBz4JMV7rLbyVBvQmZmbzcGvQJIkSf3U6sMezwB+CkwBTgX+ARwPXF7PRvVVRIwArgJG\nAp09nJ9AkRQBOA34C8WHghsiYli32J0p3viPBtqBPwK7AXdGxMLdYjcFbqYY0nE+cDXFZL73RsRy\nA9K5XpSJr3uBvYGHKV63e4FdgPsi4hNVsc3Y/5HA7cABwEPAzymSACcB/9Uttun6360dGwCH0CJ/\n+6W1gA6K5G/3nyur2tl0/Y+IfwcuARai+L/7ZmBb4K6IWKEqrtn6PpGeX+8zy/MvAo9VtbOp+h8R\nCwF3UHxJ9SjF//nPAUcAN0bEPFWxjdD3i4G9gL8CtwG3lj+3lD+SJEmqg5at/IqIjSjeTF+ZmTtV\nHb8Q+EZEbNnI83OUb7ivAtaZyfnjKSojNq1UBEXEROAHFMmjM8pjI8p/Pwmsk5lvlsdvAM6j+Gb8\niPLYEGAy8CawfmY+Vx6/FLgROBnYYeB7PMNxwDLAYZl5alV/d6X4YPxTYNsm7v8EIICDMvP0qv5f\nCuwcEVtk5u+auP+V/s5L8SH0Iwn8Ju/7msDDmXl8bwHN2P8y0XkURQJi88zsKI//miLpdwywezP2\nPTMn9nQ8In5Dkfj9Wma+VB5ruv4D+1BUcZ2amTPGQUTEJcCu5c/FDdT3z5fPf++A/yYkSZI0x1q5\n8mv/ctv9g8UEig8Uew1uc/ouIg6hqPpZg+Lb6p7sDcwDnNBtKNwJwOt07d/OwCjglMqHAIDMvIBi\n2fbdIqIydONzwErAeZUPAWXsHyk+CHw5IhbtR/dm5d+Al6oTX+XzXwo8BXyhbGuz9n854O8UlQrV\nflluNyy3zdr/iqOBT1AMd+2uKfteVsAsCzw4i9Bm7P/+FEPN9q4kvsrn/jVwNvB4eagZ+/4RZbJ/\na+CczLyp6lQz9n/dcnt+t+PnlttPldtG6ftzFH+rkiRJaiCtnPzaBJiSmY9UH8zM54EnyvON6mCK\neZ42oah26skmFEm8m6sPlh8c7wbWKofQVWIB/qeH69wCLAas3ofYmyk+fGw8qw7MifIb+B9RVH/1\npAOYFxhGE/YfIDN3zczlM7P7h6uVy+2L5bYp+w8QEWsC36P4UPtwDyHN2vc1y+2skl/N2P/NgYcy\n82/dT2TmPpl5YrnbjH3vIiKGU/ztT6X4sqZaM/a/8n/a8t2OL1NuK/NzNUrfvwO0R8TmEfGJiFi2\n+qeHa0iSJGkQtOSwx4iYD1ia4g1xT54BVoqIxTLz5UFrWN/tDfwhMzsjYuVeYlYEXszMt3s490y5\nXQm4v4ztpKicmlnsQ2UsFMNFeosdN5O2z7Ey4XNaT+fK38PKwJOZ+V5ENF3/exIRiwPbU1QwPgv8\nojzVlP0v5/c5j6LS50TgP3oIa8q+82Hya/GIuBFYn6LtN1FM+l6pfmqq/pd/46Mp5m1amSLxU5k0\n/AbgyMysPH9T9b0X+wEfA47KzFe7nWvG/k8G9gBOiYhXgAeADSjmOZzKhxVhjdL3X5fbnqZN6KRI\nlknqo/b2SV32XQVOan7e96qVVq38qgxRmNrL+dfK7cK9nK+rzLwxMz8yyXc3i9H3/i0GdFQPJ5pF\nLL1cuy6/t7Ii7HSK1bTOLg83ff+jWPnvBYq+TwW+mJmVNjRr/79DMc/dXpn5fi8xzdr3SvLrO2Ub\nJgP3AF8B7omItcrzzdb/seV2GYr+Lksx5O0OisTv3VUVNc3W9y7K5O/BFMP4ug99hibsf1mdvTEw\nnGKxjzcphvtPAz6TmX+vamMj9P3jM/lZEUmSJNVFS1Z+UQyLg2KYXE8qx4cPQltqZRh979/sxnb2\nEj/ov7dyXpbJFJUg91GsBAat0f8ngR9TTIC/LXBbRHwpM/9ME/Y/IlaiGPJ6RmbeM5PQput7aRpF\npclumXlr5WBE7EJR8Xc+sB7N1/8Fy+0mwEXAHpXkf0QcQFENeiqwHc3X9+62oaj6+mlmvt7D+abr\nf5nY/AVFEvS3FFWf6wPjgbMjYqsy6d8Qfa9UIUbEahTVYzcCiwNP9+FLK0ndWPEhtR7ve9VKqya/\n3im38/Zyfr5y+9YgtKVW3qHv/XsHWGI2Ytt6ufag/t4iYihwDvBNikTQtpk5rTzd9P3PzAsr/46I\nLSk+GF5MsRBCU/W/THKeR1Hp1n2eo+6aqu8VmXlAL8cvi4hvAxuXCcJm639lfrtpwKHdEghnAIcC\nW0TE/DRf37v7Rrk9u5fzzdj/y4DVgB0z81eVg+XCL5Mofhc70SB9j4hFKFYgHV8+biWK5OzHy9V4\nn+3leSVJklRDrTrs8TWKb3J7G6axcHn+tV7Ozw1eZeb9gw/79yowPCKG9TG2+vjMYmsmIhYAfkOR\n+Hoc+JfMfKEqpKn7311mXkcxFGjVcr6zZuv//sBngH17mdOnrerfzdb3vvgTxe9gBZqv/5VrPpOZ\nXYaelYmwBymqc5al+fo+QznR/b8CD2bmE72ENVX/y6qvjYBbqhNfAOWKv48C20XECBqn76cBb1PM\nU/c2xXuJ3SjmZPx5L+2TJElSjbVk8isz36N4I7pCLyErUKwE2dv8IXODx4Elysn9u1sB+IBiVctK\nbBsfXU2rEgvF8u+V2OrjM4utifKb9T9SrAD3J2DjzPy/bmFN1/+ImCciPh8Rn2sK+gUAACAASURB\nVO8l5FmKfoymaOeSTdT/7cvt7yJieuUHOKg8/j/lseVovr5XXvt1I+KTvYTMX27fpfn6/xRF9Vdv\nVT2VBMbbNF/fq20KLAD8aiYxzfb/3tLl9tFezj9C8T5maRrntf8SMKH6/UO5cM53KF5DSZIk1UFL\nJr9KtwFLRUSXFaoiYizFyk29rQQ5t7iNYlWpTaoPltUDGwIPZ+ZbVbFQDNPobjwwNTMf7WPsB8C9\nc9jmWSrbfy3Fal83A+Mz8589hDZj/9uAa4BLy0n+u1uLIknwFEU7h9A8/b+AYr6v7j+Vub8uLPen\n0nx9hyLBcw/w++6vfTkkdCPgfeDPNFn/M/Ndivn8li2rGmcohz6vBbwM/INiQvSm6Xs3G5bb22cS\n02z/7z1fbqOX8+MoKqtepLH+7uf/aChjKO5RSZIk1UErJ78uLrcnlB8eKx8iTyyP9zanytziMoo3\n5cdFRHXFxFHASLr272rgDeDIsqoKgIjYg+LDxblVsbcAfwe+XVbZVGI/RzEk57/Kb7lr5QTg08Cd\nwOaZ+WYvcU3X/3I+s19TfIg6ovpcROxLMdn5dZk5hSbrf2ZelJnHd/+hKvlVHnuNJus7zEgAXQss\nAnyv2+nDgdWBy8pJ0Juu/1VtPq1MeFUcTlH1c3FmTgcupfn6XrEORaLnTzOJaarXvpw8/l5gfERs\nU30uIvakWAH1v8sqq0bp+2XAqRGxerk/IiI2K5//l7P3G5AkSdJAaevsbN3FhyLicoqJcu+lqCLa\niGJJ9Sszc6c6Nq3PImI3ilXeDsnM07qdOxH4LsWQkWspJg3egqJy4HOZ+X5V7LeBM4H/pZisd2lg\nB4phIp+uHsIREVtQzLdV+cAxAti13P9UrSb0jYglKYb2DaPoc/ehjhUnZmZHs/W/fO6xFFWJywD/\nDfyV4kPxZhQVXxtX5j5rxv53FxGnUgx9HN9tBcSm63tEfBy4iyL5+QeKua7WoxhK9TCwSWa+WsY2\nY/+vAr5MMdTt98AqFEOfE9ggM99o1r6Xz/8QsEJmjphFXFP1P4pVE2+lmF/rGophiGsCXwSeAz5T\nee5G6Hs57PIEinkKK0m4DygWZzk8MysL7qirzilT3qh3GwbdmDEjAWjFvkPf+t/ePqnLfrOsAler\n1/5L232NsRvsMaDXbBTXTvpyl/2tDru6Ti1pHu+98zpLvHsPPz3pxFkHD4C+/t173zefsu9ts4qr\ntVau/AL4OnAMxRxJB1MsR/4D4Gv1bNRs6ix/PiIzJwAHlOcPAlalWB1ry+oPAWXsZOCrwBRgP4ok\n4IUUSYXuk0z/jmJek0eBPSk+XPyGqg8hNbIhHy47vwfFa9f95weUK3A1Yf/JzOeAT1J8kFqT4u92\nReAU4JPVk/43Y/970OPffzP2PTOfAtYHLqKo9DqQYpL3k4GNKomvMrbp+k+RmKi8+9mf4u//DIq+\nz3gX0aR9B1iUPkwq32z9z8yHKZK8lwCfoljdczVgMrBe9XM3Qt8zsyMzD6d4vdYE1gUWzcz9THxJ\nkiTVT0tXfkmSJA2UiNhkZuerK1TVhZVfLaiV+2/l1+yz8mvgNWrlV7Nq5f43SuXX0FmHSJIkqQ9u\n7uX4exTDND8+eE2RJElShckvSZKkAZCZ3VdjHUqR8DqDYkEGSZIk1UGrz/klSZJUE5k5LTMfp5ir\n7Ph6t0eSJKlVmfySJEmqrcWBRerdCEmSpFblsEdJkqQBEBEXUKw2WT2p60jgX4Er69IoSZIkmfyS\nJEkaIG3dtp3Ay8BhwCV1aZE0F2tvn9Rlf7/9DqtTSyQNFu971YrJL0mSpAGQmbvVuw2SJEn6KJNf\nkiRJAyAijqWo9upJGx8OiezMTCfAl2bBig+p9Xjfq1ZMfkmSJA2MccD2FEMd/x/QAawNfAK4C3iP\nD5NgJr8kSZIGickvSZKkgfEucCmwT2a+DxARbcBPgEUzc896Nk6SJKlVDal3AyRJkprEV4GfVBJf\nAJnZCZwL7Fy3VkmSJLU4k1+SJEkD4x/A5j0c3x54cpDbIkmSpJLDHiVJkgbG94ArImJL4AGKLxk/\nCawDbF3PhkmSJLUyk1+SJEkDIDP/KyLWBXYHVgPeAv4H2DEzX6hr46S5UHv7pC77rgInNT/ve9WK\nyS9JkqQBkpkPAYdFxKLAa0BnZk6vc7MkSZJamskvSZKkARARQ4CjgEOARYCVgOMj4k3goMzsqGf7\npLmNFR9S6/G+V6044b0kSdLA+D7wNYphj+8CncB5wOeBk+vYLkmSpJZm8kuSJGlg7A58OzOvAaYD\nZOYfgW8CO9azYZIkSa3MYY+SJEkDY3HguR6OTwVGDHJbJNXYI488zL4HH8ZCiyw5W48bOrSoP5g2\nbWCnA5xnxFIDej1JaiYmvyRJkgbGTcAREfHtyoGIWAg4gWLVR0lNZNq09xm5zHqMic/XuymSpFlw\n2KMkSdLA2B9YB3gBmB/4LfB/wArAgXVslyRJUkuz8kuSJGlgvAJsAGwGrELxPusx4IbMHNjxTVIL\naG+f1GXfVeCk5ud9r1ox+SVJkjQwHgG+nJk3UQyBlCRJUgMw+SVJkjQwPgDmq3cjpGZhxYfUerzv\nVSsmvyRJkgbGdcCNEXEN8Azwbnm8DejMzOPr1TBJkqRWZvJLkiRpYKwB3A+MBZaqOt4GdAImvyRJ\nkurA5JckSdIciohbgW0yc2pmji+PLZCZb9e3ZZIkSaoYUu8GSJIkzcU2BubtduyFiPh4PRojSZKk\nj7LyS5IkaWC11fPJI2I08CNgK2A08BxwBXBcZr7TLfYbwKHAOODVMu6YzHyrh+tuCXwfWA14B7gG\nmJCZU2rXG0mSpP4z+SVJktQkImIh4A6KZNYfKeYg2xg4AvhMRGyamR+UsRMokmR/AU4D1qRIhG0Y\nEeMz8/2q6+4MXAo8CbQDywG7AZtGxPqZ+drg9FCtpL19Upd9V4GTmp/3vWrF5NeHOqdMeaPebVBp\nzJiRAPiaNBZfl8bja9KYfF0aT/ma1LUia5DsQ5H4OjUzZ3xiiIhLgF3Ln4sjYjmKCfjvBKoTYhOB\nHwB7A2eUx0aU/34SWCcz3yyP3wCcR1ENdsSg9E6SJGkOmPySJEnqn50iolL51Ebx/mq7iHipOigz\nLx6Etqxbbs/vdvxcisTXp4CLKZJb8wAnVBJfpROAg4G9KJNfwM7AKOD7lcQXQGZeEBFHArtFxHcz\nc/pAd0atzYoPqfV436tWTH5JkiTNub8D3d+pvwjs30PsYCS/Xiy3ywN/rTq+TLmtzM+1CdAJ3Fz9\n4MzsiIi7gS9ExMjMfKOMBfifHp7vFopE2urAg/1tvCRJUi2Y/JIkSZpDmbl8vdvQzWRgD+CUiHgF\neADYADgJmMqHFWErAi9m5ts9XOOZcrsSxZxhK1Ikyp6aSew4TH5JkqQGNaTeDZAkSdLAyMxHKCa4\nHw7cDrxJMfH9NOAzmfn3MnQximRYTypDOBeuiu3IzI4+xEqSJDUck1+SJElNIiKWBX4BjAV+C5xM\nMbRxWeDsiKgkqYYBPSWzqDo+fA5iJUmSGo7DHiVJkprHZcBqwI6Z+avKwYg4BJgEnA3sBLwDzNvL\nNeYrt2+V23eAJfoYO0cqK6S2olbuO8zd/V9kkQUZ0tYKi8iqWc0339BBvwfn5nt+ILR6/+vJ5Jck\nSVITKKu+NgJuqU58AWTmqRHxLYpVKEcAr9L7UMXK8cqQxleBlSNiWGa+P4tYacBMnDixy/6xxx5b\np5ZIGize96oVk1+SJEnNYely+2gv5x8BVi7jHgc2iYj5epjLawXgA+CJcv9xiqTa8lXHqmMBcs6b\nDVOmvNGfh8+VKt/+t2LfYc7632i/q1dffYvpnZ31boY0xzo6pg3afTWn/+c12n0/p1r5//xGqXYz\n+SVJktQcni+30cv5cRSrNr4I3AaMBzYBbqwERMRwYEPg4cysDGW8DditjO+e/BoPTM3M3hJu0hzb\nb7/D6t0ESYPM+1614oT3kiRJTSAznwHuBcZHxDbV5yJiT2BN4L8zcyrF3GAfAMdFRPXcX0cBIynm\nBqu4GngDODIiFqm65h4UCbVzB743kiRJA8fKL0mSpOaxJ3ArcFVEXEMxZHFN4IvAc8B+AJmZEXEy\n8F3gzxFxLcVE+VsAtwPnVC6Yma9GxJHAmcADEXElxdDJHSiGO54wSH2TJEmaI1Z+SZIkNYnMfBhY\nD7gE+BRwKEVSazKwXmY+WxU7ATiAYijkQcCqFCtCbtl9YvvMnAx8FZhCkUDbGLgQGF9WkkmSJDUs\nK78kSZKaSDn8cfc+xrYD7X2MvQK4Ys5bJkmSVB9WfkmSJEmSJKlpWfklSZIkqeG0t0/qsu8qcFLz\n875XrdQt+RURY4FHgWMy82d9fMyiwPHAVsCY8vE/KcvwJUmSJEmSpC7qkvyKiBHAVRRLaXf28TEL\nAjcCa1HMN/F3YHvgPyNiTGaeUaPmSpIkSRpkVnxIrcf7XrUy6HN+RcRywC3ABrP50IOBdYADM3OX\nzPwesDbwMHBSRIwZ2JZKkiRJkiRpbjeoya+IOAR4CFgD+ONsPnw/4AXgrMqBzHwT+BGwALDLADVT\nkiRJkiRJTWKwK78OBp4GNgEu6euDImJFYCxwW2Z2HyZ5c7ndZCAaKEmSJEmSpOYx2MmvvYG1M/Nu\noG02HrdiuX2y+4nMfAHoAFbqf/MkSZIkSZLUTAZ1wvvMvHEOH7pYuZ3ay/nXgYXn8NqSJEmSJElq\nUnVZ7XEODCu3Hb2c7wCGD1JbJEmSJNVYe/ukLvuuAic1P+971cqgr/Y4h94pt/P2cn4+4K1Baosk\nSZIkSZLmEnNL5der5ba3oY0LAc/390nGjBnZ30togPmaNCZfl8bja9KYfF0kac5Z8SG1Hu971crc\nUvn1eLldofuJiFiKovIr+/MEbW1tjB8/fo4eu/LKK3P66afP1mOuvPJKTjrppDl6vnq6//772Xvv\nvevdDEmSJEmSpD6ZKyq/MvPvEfF34LMR0ZaZnVWnx5fbu/r7PO+//wFTprwx24+bPPkCFl98idl6\n7Omnn8G6664/R89XT5dcchmPP/5EzdtdqZaY234/zc7XpfH4mjQmX5fGYxWeJElS65pbKr8ALgGW\nAQ6oHIiIkcDRwNvl+bpYddXVGT16TL2eXpIkSZIkSb1oyMqviDgO6MzMiVWHfwLsCPwsIjYFngK+\nAiwPHJiZLw92Oys++9lPsvvu32KPPfbmT3/6fxx88L6cemo7v/jFhTz00F9YcMEF+dKXtuLb396f\nIUOGsP32W/Piiy9w/fXXcv3113Llldew5JJL8sILL3Dmmadx33338N57Hay22poccMDBjBsXADz/\n/HPsuOO2HHDAIfzmN1fx0ksvcvjh32Pzzbfir399iPPOO4tHHvkrw4bNy/rrb8ABBxwyIyn3+uuv\ncdZZp3P77bfy5ptvMm7cSuy9936st94nu/TjwAMP47HHHuG2225h/vnn53Of+wL77nsg8847Lz/6\n0XH8/vfXzYg96qhj2XzzrXr8ndx6683853/+gr/97QmmTXufpZYay1e+shPbbbfDjJh//vOfnHXW\nz7nnnrvo6OhgpZWCffY5kNVXXwOAjo4OLrzwXG644XpeeOEFFl98Cbbeelt22eUbtLW1AXDAAXuz\nxBJL0NHRwT333MUaa6zFkUcezQ47bNPj70mSJEmSJLWWeia/OsufnhxTnpuR/MrMNyLis8AJwNbA\nl4BHgQmZeUWN2zpLlWRMxfHHf5/tttuRr399d+644zYuu+xixo5dmm233Y4TTzyZ73znYCJWYbfd\n9mSxxRZj6tSp7LvvHsw///wcdtiRDB8+nCuuuJz999+bc865iOWWW37Gtc8//2wOOeQIFlhgQVZd\ndTUef/wxDjxwb1ZbbQ1+8IPj+eCDDzjzzNM57LADuOCCy5g2bRoHHbQvr776CnvvvR+jR4/h2mt/\nw+GHH8ikSaez7rrrd7n2GmusyQ9/+GOeeeYpzjnnTF555Z9MnHgiu+22F6+9NpXMxzjxxJMZO3aZ\nHn8Xd955O0cffQQ77rgze+21Dx0d73LVVVdyyik/YeWVV2HVVVfn7bffZt9996Szczr77XcQo0eP\n4Ze/vJTDDjuA88//BaNHr8I+++zDAw/8hT32+Baf+MRK3H//fZx9djv/+Mf/ceSRR894vptuupEv\nfnELTjrpFKZPn97r70mSJEmSJLWeuiW/MvMi4KJezvU4HDMzXwL2qmW7Bso222zHN7+5JwDrrrs+\nt912M3feeRvbbrsd48YF8847L6NGjWLVVVcH4Je/vJQ33nids846nyWWWBKADTf8DLvuuj3nnnsW\nP/zhj2dce7PNvtCliulnPzuZUaMW4ZRTzmDYsGEAjB69OBMnHs3TTz/FI4/8lSeffIKzz76QVVZZ\nrbz2RhxwwN6ceeZpnHPOxTOuteiii3LSSacwZMgQNtxwI4YMmYef/3wSe+65D8suuxwLLzyKeeed\nd0a7e/LMM0+z+eZbceCBH67Usdpqa7Dllp/nz3++n1VXXZ3rr7+GF198nvPPv5RPfGIcAGuuuTa7\n774LDzzwJ15/fQp33XUXxx13Ap/73L8CsP76GzB8+HDOPfcsdtxxF5Zfvlj/YNiweTniiKMYOrT4\nc37++ed6/D1JkiRp7tHePqnLvqvASc3P+161MjfN+TVXWW21NbrsjxmzOO++29Fr/P3338e4cSsx\nevQYpk2bxrRp0wD41Kc+zX333dMldty4lbrsP/jgX9hww41mJL6K51+dK674DZ/4xDjuv/9eFl10\nMVZaaeUZ1542bRobbfRZHnvsUd58880Zj/vc577AkCEf/llsuum/APDAA3/qc9932eXrHHXUsbz9\n9ts89tij3HTTDVxyyYUAvP/++2WbH2Ds2KVnJL4A5ptvPi677NdstdW23HvvvQwdOpTNNvt8l2t/\n8YtblO25f8ax5Zdffkbia2a/J0mSJEmS1Hoacs6vZjB8+PAu+21tbXR2Tu8lupiT6x//+D/Gj9/w\nI+fa2tro6PgwcTb//PN/5LGjRi3S67Vfe+01Xnnl5V6v/fLL/2TEiBFAkaSrtsgii854jr6aOnUq\n//EfP+L2228F4GMfW44111wLgM7OzhltWmSRmbd51KhRHxlOWmnPG298mLCbf/4FerxG99+TJEmS\n5h5WfEitx/tetWLyq0GMHDmSddZZj/33P6TL8UqyqLqqq7sRI0YydeqrHzl+1123s9JKKzNy5EIs\ns8zHOO64E3q89pJLLjXj2GuvTe0S88orrwAfJp0KXRNS3U2ceDT/+79/52c/O5PVV1+ToUOH0tHx\nLtdcc3WXNr/wwvMfeexDD/2FhRZamIUXXpipU6fS2dnZJQH28sv/BGDUqFEzbYMkSZIkSRI47LFu\nulc0rb32ejz77DN87GMfI2LlGT833vh7rrvut12GIna31lprc++9d88YKgmQ+RhHHnkojz/+GGuv\nvS4vvfQio0aN6nLt+++/l8svv7jLkMFbb725y7VvvvkPtLW1zZgUv2hHb+sUFB566C9suulmrL32\nujOufddddwDMmJB+7bXX4bnn/sHTTz8143EdHe9y1FFHcN11v2GDDTZg2rRp/PGPN3a59g03XA8U\n84NJkiRJkiTNipVfg6hSaQUwcuRCPP54lhPAr8ZOO+3Kf//37zj44P3Yeeevs9BCC3HTTTdy7bVX\nc9BBh8/0urvtthff/vYeHHHEwWy//Vd59913OeecM1l11dX55Cc3ZNq0aVx11RUceuj+fP3ru7PE\nEkty3333cNllF7P99jsxzzzzzLhW5qMcd9zRbL75Vvztb49z/vlns80227HUUmNntPuVV17mrrvu\nYNy4YPTo0R9pzyqrrMYNN1xPxMqMHj2Ghx76C1de+Z8MHz4/77zzDgBbbLENv/rVL/ne9w5jzz33\nYaGFFuKKKy5n+vQP2G67HVljjZX41Kc+xUkn/YgpU15ixRXH8cADf+LSSy9i88236rL6ZfXvVZIk\nSZIkqZrJrxroXtVVOVZ9fOedv8Zpp03iO985iFNPbWeNNdbizDPPY/LkMzj55BN5770Oll12OSZM\nOIYttth6ps83blzw859PZvLk0zn22AksuOCCbLTRZ9l33wMZOnQoQ4cO5fTTz2Hy5NM588yf8+ab\nbzJ27Fj22edAdt75a12u9ZWv7MQrr7zM0UcfwahRi/DNb+7J17+++4zzW2yxNXfffQdHHfUd9tpr\nH3bd9Zsfac/RR0/klFNOYtKkn9DZOZ11112f0047k3POOZMHH3wAgAUWWIDTTz+H9vafccopRdxq\nq63Jz38+ecYwzMmTJ/PjH5/MFVdcztSprzJ27NLss88BfPWrH7a5++9VkiRJkiSpWptVM4W2trbO\njTbamKuv/l29m1I3n/3sJ/nmN/dkr732qXdTGDNmJABTprxR55aomq9L4/E1aUy+Lo2nfE38tqQx\ndbbivdLq/080Q/8ffPABjjrlChaPz886WP1y7aQvd9nf6rCre4lUX733zuss8e49/PSkEwfl+Zrh\nnu+PVu5/o7wHs/JLkiRJUsNpb5/UZd9V4KTm532vWnHCe0mSJEmSJDUtK780w2233VfvJkiSJEmA\nFR9SK/K+V61Y+SVJkiRJkqSmZfJLkiRJkiRJTcvklyRJkiRJkpqWyS9JkiRJkiQ1LZNfkiRJkiRJ\nalqu9ihJkiSp4bS3T+qy7ypwUvPzvletWPklSZIkSZKkpmXllyRJkqSGY8WH1Hq871UrVn5JkiRJ\nkiSpaZn8kiRJkiRJUtMy+SVJkiRJkqSmZfJLkiRJkiRJTcvklyRJkiRJkpqWqz1KkiRJajjt7ZO6\n7LsKnNT8vO9VK1Z+SZIkSZIkqWlZ+SVJkiSp4VjxIbUe73vVipVfkiRJkiRJalomvyRJkiRJktS0\nTH5JkiRJkiSpaZn8kiRJkiRJUtMy+SVJkiRJkqSm5WqPkiRJkhpOe/ukLvuuAic1P+971YqVX5Ik\nSZIkSWpaVn5JkiRJajhWfEitx/tetWLllyRJkiRJkprWoFd+RcRQ4EDgW8DywPPABcCPM3NaHx6/\nFvBD4LPAcOBx4PTMPKdWbZYkSZIkSdLcqR6VX2cAPwWmAKcC/wCOBy6f1QMjYh3gTuBLwHVAOzAC\nmBwRP65VgyVJkiRJkjR3GtTkV0RsRFHxdWVmbpqZR2XmJsDFwFciYstZXOJHwPzA9pn5tcw8HFiT\novrrOxGxfA2bL0mSJEmSpLnMYFd+7V9uJ3Y7PgHoBPaaxePXBV7JzN9WDmTmW8B/UvTlkwPUTkmS\nJEmSJDWBwZ7zaxNgSmY+Un0wM5+PiCfK8zPzIrBqRIzKzKlVx5cut1MGrqmSJEmS6qW9fVKXfVeB\nk5qf971qZdCSXxExH0WS6u5eQp4BVoqIxTLz5V5iJlFMjn9ZRBwIvATsAHwTuB+4ZUAbLUmSNBeK\niF2Bg4HVgNco5kw9OjOzW9w3gEOBccCrwBXAMWVlffdrbgl8v7zmO8A1wITM9MtHSZLU0Aaz8mvR\ncju1l/OvlduFgR6TX5l5UURMA84Hnqg6dQPw1czsHIiGSpIkza0i4t+BoyjmRD0DWIbiy8LNImK9\nzHy6jJtAMZ/qX4DTKOZRPRTYMCLGZ+b7VdfcGbgUeJJiwaHlgN2ATSNi/cysvI+TBowVH1Lr8b5X\nrQxm8mtYue3o5Xzl+PDeLhARn6d4c9YBXEaRSPsC8K/AD4EDB6SlkiRJc6GI2IAi8XUzsHlmdpTH\nfw1cCRwD7B4Ry1Gstn0nsGlmflDGTQR+AOxNkTgjIkaU/34SWCcz3yyP3wCcR1ENdsQgdVGSJGm2\nDWby651yO28v5+crtx8psweIiEWAq4D3gXUz82/l8WEU30TuHxGPZOaZc9rAYcPmYcyYkXP6cNWA\nr0dj8nVpPL4mjcnXRXWwPzAd2LuS+ALIzF9HxNnAs+WhvYF5gBMqia/SCRTDJfeiTH4BOwOjgO9X\nEl/lNS+IiCOB3SLiu5k5vVadkiRJ6o/BXO3xNYoVHRfu5fzC5fneyua3BkYAp1USXwBlSf4B5e5u\nA9JSSZKkudPmwEPV75UqMnOfzDyx3N2E4n3Xzd1iOijmZ10rIkZWxQL8Tw/PdwuwGLB6/5suSZJU\nG4NW+ZWZ70XEs8AKvYSsQLESZG9zglVWdHy0h2u/FBEvAx/rTxvff/8Dpkx5oz+X0ACpVEv4ejQW\nX5fG42vSmHxdGk8rVOFFxOLAaOCGiFiZooprs/L0DcCRmflMub8i8GJmvt3DpSoxK1EsKLQiRaLs\nqZnEjgMe7F8PJEmSamMwK78AbgOWiohx1QcjYizFm6beVoIEeK4S3v1EOSRyMeCFAWqnJEnS3GZs\nuV0GuAdYFjgXuAPYHrg7IpYtYxajb4sQVWI7qodRziRWkiSp4QzmnF8AFwNfB06IiB0zszMi2oBK\nCf7ZM3nstRTzgR0YEb+oWqloHmBSGXN5jdotSZLU6BYst5sAFwF7VFbCjogDKBYNOhXYjmIhor4u\nQjQ7sdKAaW+f1GXfVeCk5ud9r1oZ1ORXZt4UEb8EdgLuioibgY2AjYErM/N3ldiIOA7ozMyJ5WNf\njoh9gQuBByLiVxTfNm5GsTT3zRRv6CRJklpRZcL5acChlcRX6QzgUGCLiJifYiGivi5C9A6wRB9j\n50grDEvtTSv3HWav/432u1pkkQUZ0tZW72ZIc2y++YYO+n01u8/XaPd9fzVbf+Ymg135BUXl18MU\nk9MfTLHq0A+An3SLO4ZifomJlQOZ+YuI+DswgeJby/kplt3+PvAf5eT3kiRJragyBPGZ7nOoltX2\nDwLLUwyHfJWZL0JUfb1XgZUjYlgP77W6x0oD5thjj613EyQNMu971cqgJ78ycxrw7+XPzOJ6nI8s\nM28Fbq1B0yRJkuZmT1FUf/VW0TWs3L4NPA5sEhHz9TCX1wrAB8AT5f7jzNGpnAAAIABJREFUFJX6\ny1cdq44FyDlvdmsuDtHqC2M0Q/9fffUtpnd2zjpQalAdHdMG7R5shnu+P1q5/41S7TbYE95LkiSp\nBjLzXeA+YNmIWLH6XEQMBdYCXgb+AdxO8T5wk25xw4ENgYczszKU8bZyO76Hpx0PTM3Mj6zGLUmS\n1ChMfkmSJDWPyuJBp5UJr4rDgaWBizNzOnApRXXXcRFRXSl2FDCSrosQXQ28ARxZrrANQETsQbFa\n97kD3gtJkqQBVI85vyRJklQDmXlBRGwNfJligaDfA6sAm1MMTawsJJQRcTLwXeDPEXEtsBqwBUVV\n2DlV13w1Io4EziyveSVFIm2H8ponDFb/JEmS5oSVX5IkSc1lB6CyNvz+FKtinwFslJkzJhvJzAnA\nARQLDB0ErApMArbsPrF9Zk4GvgpMAfajWKn7QmB898n1JUmSGo2VX5IkSU0kMz8ATi1/ZhXbDrT3\n8bpXAFf0r3VS37W3T+qyv99+h/USKalZeN+rVqz8kiRJkiRJUtOy8kuSJElSw7HiQ2o93veqFSu/\nJEmSJEmS1LRMfkmSJEmSJKlpmfySJEmSJElS0zL5JUmSJEmSpKZl8kuSJEmSJElNy9UeJUmSJDWc\n9vZJXfZdBU5qft73qhUrvyRJkiRJktS0rPySJEmS1HCs+JBaj/e9asXKL0mSJEmSJDUtk1+SJEmS\nJElqWia/JEmSJEmS1LRMfkmSJEmSJKlpmfySJEmSJElS03K1R0mSJEkNp719Upd9V4GTmp/3vWrF\nyi9JkiRJkiQ1LSu/JEmSJDUcKz6k1uN9r1qx8kuSJEmSJElNy+SXJEmSJEmSmpbJL0mSJEmSJDUt\nk1+SJEmSJElqWia/JEmSJEmS1LRc7VGSJElSw2lvn9Rl31XgpObnfa9asfJLkiRJkiRJTcvKL0mS\nJEkNx4oPqfV436tWrPySJEmSJElS0zL5JUmSJEmSpKblsEdJkiRJklrMKy9P4aGHHhyU51p00QWL\n53zlrR7PL7jggnz84ysOSlvUmkx+SZIkSZLUQuYZNpwX3x/NhJ9ePijPN6StDYDpnZ09t+fNJ/nN\nr64YlLaoNQ168isihgIHAt8ClgeeBy4AfpyZ0/rw+OHAkcDXgI8B/wCuASZm5tQaNVuSJEmSpKYw\nz9B5WXKVL9S7GTO89fBL9W6Cmlw9Kr/OoEh83QZcDWwMHA+sBewwswdGxDDgemBT4GbgKuBTwMHA\nZyJi48x8r2YtlyRJkjQo2tsnddl3FTip+Xnfq1YGNfkVERtRJL6uzMydqo5fCHwjIrbMzOtmcomD\nKRJfP8nM71U9/ufA/sDOwEW1aLskSZIkSZLmPoNd+bV/uZ3Y7fgE4OvAXsDMkl8HAE8DR3c7fjIw\nAnhzANooSZIkqc6s+JBaj/e9amWwk1+bAFMy85Hqg5n5fEQ8UZ7vUUSsCiwL/CwzP+j2+GeB3WvQ\nXkmSJEmSJM3FBi35FRHzAUsDd/cS8gywUkQslpkv93B+9XL7cERsQVH9tTYwFbgcOCYz3x7YVkuS\nJEmSJGluNmQQn2vRctvbioyvlduFezk/ttxuA1wLvAKcCbwAHAb8vlxJUpIkSZIkSQIGd9jjsHLb\n0cv5yvHhvZxfsNxuBXwrM88DiIghFJVfOwD7Aaf1v6mSJEmSJElqBoOZ/Hqn3M7by/n5yu1bvZyf\nXm7/VEl8AWTm9Ig4giL5tSP9SH4NGzYPY8aMnNOHqwZ8PRqTr0vj8TVpTL4ukiRJUv0NZvLrNaCT\n3oc1Llyef62X85Xjf+p+IjP/HhGvAR/vbyMlSZIk1V97+6Qu+64CJzU/73vVyqAlvzLzvYh4Flih\nl5AVKFaC7G1OsMfLbW+VY0OBfk14//77HzBlyhv9uYQGSKVawtejsfi6NB5fk8bk69J4rMKTJElq\nXYM9QfxtwNcjYlxmPlE5GBFjgXHAb2fy2HuB94BNI2JIZlaGQRIRK1PMCfZgbZotSZIkaTBZ8SG1\nHu971cpgrvYIcHG5PSEi2gDK7Ynl8bN7e2Bmvg78ElgO+F7leEQMA35S7p4/0A2WJEmSJEnS3GtQ\nK78y86aI+CWwE3BXRNwMbAT8//buPV6Sqjr0+G+AGRCBCY9BBKMQhEUC4ouLiry85hoFbzI+gOsD\nwlUwNyJBjKIDgcwQBYM4oDcchaggRlFQg4poiFEQg4qJIhFwwQUhRnmMMAyvEWbg3D92NfQcus+z\nu7pPnd/385lPTVftrrN3r65+rF61a2/gosy8tNU2IpYCo5m5rG0X7wZeArw/IvanVHq9HHgu8PnM\nvKSOcUiSJEmSJGl2qLvyC+BQ4CRgK+AYYGvgRODNY9qdVP17XGauAF5MuaLjLsBRlKtEvgd4U197\nLUmSJEmSpFmn7jm/yMy1wPurf+O165iYy8x7gHdW/yRJkiRJkqSuBlH5JUmSJEmSJNWi9sovSZIk\nSZrIyMjydW57FTip+Tzu1S9WfkmSJEmSJKmxrPySJEmSNHSs+JDmHo979YuVX5IkSZIkSWosk1+S\nJEmSJElqLJNfkiRJkiRJaiyTX5IkSZIkSWosk1+SJEmSJElqLK/2KEmSJGnojIwsX+e2V4GTms/j\nXv1i5ZckSZIkSZIay8ovSZIkSUPHig9p7vG4V79Y+SVJkiRJkqTGsvJLkiSpwSLidOBdwP6Z+d0x\n2w4DjgV2AlYCFwInZeaDHfZzIPBXwK7AauBrwJLMXNHfEUiSJM2MlV+SJEkNFRF7Au8ERjtsWwKc\nV938KPBTSiLssoiYP6btGyjJrq2AEeDbwOHAVRGxsE/dlyRJ6gkrvyRJkhooIhYAn6LDj50R8Szg\nZOAqYL/MfLRavww4EXgbcFa1bpPq/zcDz8/MB6r1lwGfpFSDvaff45EkSZouK78kSZKa6QTg2cC3\nOmx7G7A+cEor8VU5BbgPOKJt3RuA3wHOaCW+ADLzXCCBwyPCz5SSJGloWfklSZLUMBGxO/A+4APA\n5sAfjmmyL+VUyMvbV2bmwxHxA+AVEbFpZt5ftQX4Toc/dQUlkbYbcG3PBiABIyPL17ntVeCk5vO4\nV7/4K50kSVKDRMT6lNMRbwROBeZ1aLYjcGdmPtRh263Vcue2tqPALeO03Wma3ZUkSeo7K78kSZKa\n5d3A84GXZuaaiOjUZkvKHF6drKqWC9vaPpyZD0+irdQzVnxIc4/HvfrFyi9JkqSGiIidgaXAWZn5\nw3Gazgc6JbNoW7/RNNpKkiQNHSu/JEmSGiAi5lFOd7wDWDJB89XAgi7bNqyWD7a1fdok207LokWb\nzuTus9pcHjvM7vFvvvlTWW9ep7OKJU3VBvPXm9WvB5M1F8Y4rEx+SZIkNcNRwEuBA7rM5dX+LX0l\n3U9VbK1f1dZ2l4iYn5lrJmgr9dXnLriQc879HPMXbDhx4z5b/dCDsNmug+6GJGkSTH5JkiQ1w+ur\n5aVd5vn6TrV+B8pk+PtGxIYd5vLaAXgUuKm6fSOwF7B927r2tgA5k46vWHH/TO4+K7V+/Z+LY4fp\nj//6n9/M6DYvY6NF2/ehV1Pjub5S76xd81ijXw/n8mv+sFS7mfySJElqhnOBb3dY/yrgRcB5lKsz\n3gtcCewP7Av8c6thRGwEvBi4LjNbpzJeCRxetR+b/NofuDczb+jFACRJkvrB5JckSVIDZOanO62P\niC2okl+Z+d1q3eeA44GlEXFFZj5SNT8e2BQ4p20XFwNnAsdFxBczc2W1j7cAOwGn92M80sjI8nVu\nexU4qfk87tUvJr8kSZLmmMzMiDgdeC/wk4i4BNgVOAD4HvD3bW1XRsRxwMeAayLiImA74CDK6Y6n\n1N1/SZKkqTD5JUmS1Gyj1b91ZOaSiPgl8HbgL4DbgeXAsrET22fm2RGxEjiuan835TTKEzLz3v52\nX3OVFR/S3ONxr34x+SVJktRgmXkscGyXbSPAyCT3cyFwYQ+7JkmSVIv1Bt0BSZIkSZIkqV9MfkmS\nJEmSJKmxTH5JkiRJkiSpsUx+SZIkSZIkqbGc8F6SJEnS0BkZWb7Oba8CJzWfx736xcovSZIkSZIk\nNVbtlV8RsQFwNHAksD1wO3Au8MHMXDvFfa0P/CuwZ2aayJMkSZIawooPae7xuFe/DCJhdBbwYWAF\ncCbwK+Bk4IJp7OudwJ7AaM96J0mSJEmSpMaotfIrIvaiVHxdlJmHtK0/DzgsIg7MzK9Pcl/PBv6m\nLx2VJEmSJElSI9Rd+XVUtVw2Zv0SSvXWEZPZSUTMAz4B/BdwY896J0mSJEmSpEapO/m1L7AiM69v\nX5mZtwM3Vdsn48+qtkcCv+1pDyVJkiRJktQYtSW/ImJDYDvg5i5NbgU2j4gtJ9jP7wKnAZ/IzCt6\n2klJkiRJkiQ1Sp1zfm1RLe/tsn1VtVwI3D3Ofs4G7gPe3aN+SZIkSRoyIyPL17ntVeCk5vO4V7/U\nmfyaXy0f7rK9tX6jbjuIiMOAVwKvy8z7etg3SZIkSZIkNVCdya/V1XJBl+0bVssHO22MiKcBZwBf\nzsx/7HHfAJg/f30WLdq0H7vWNBmP4WRcho8xGU7GRZKmz4oPae7xuFe/1Dnh/SrKFR0Xdtm+sNq+\nqsv2syj9fUfvuyZJkiRJkqQmqq3yKzMfiYjbgB26NNmBciXIbnOCvbZa/joinrQxIh4DbsvMbvuf\n0Jo1j7Jixf3Tvbt6qFUtYTyGi3EZPsZkOBmX4WMVniRJ0txV52mPAFcCh0bETpl5U2tlRGwL7AR8\ndZz7LqNUho3158DTgKV0n0xfkiRJkiRJc1Ddya/zgUOBUyLi4MwcjYh5wKnV9nO63TEzl3VaHxGv\nBbbOzJN73ltJkiRJkiTNanXO+UVm/gvwBeB1wPcj4oPAFZSE2EWZeWmrbUQsjYi/nuSu5/W8s5Ik\nSZIkSZr16q78gpLoug44HDgGuA04EThtTLuTKKc5dqz4ajNK59MhJUmSJM1SIyPL17ntVeCk5vO4\nV7/UnvzKzLXA+6t/47WbVFVaZj6/F/2SJEmSJElS8wyi8kuSJEmSxmXFhzT3eNyrX2qd80uSJEmS\nJEmqk8kvSZIkSZIkNZbJL0mSJEmSJDWWyS9JkiRJkiQ1lskvSZIkSZIkNZZXe5QkSZI0dEZGlq9z\n26vASc3nca9+sfJLkiRJkiRJjWXllyRJkqShY8WHNPd43KtfrPySJEmSJElSY5n8kiRJkiRJUmOZ\n/JIkSZIkSVJjmfySJEmSJElSY5n8kiRJkiRJUmN5tUdJkiRJQ2dkZPk6t70KnNR8HvfqFyu/JEmS\nJEmS1FhWfkmSJEkaOlZ8SHOPx736xcovSZIkSZIkNZbJL0mSJEmSJDWWyS9JkiRJkiQ1lskvSZIk\nSZIkNZbJL0mSJEmSJDWWV3uUJEmSNHRGRpavc9urwEnN53GvfrHyq4PFiw9g6603Y+utN2Px4gMG\n3R1JkiRJkiRNk5VfkiRJkoaOFR/S3ONxr36x8kuSJEmSJEmNZfJLkiRJkiRJjWXyS5IkSZIkSY1l\n8kuSJEmSJEmNZfJLkiRJkiRJjeXVHiVJkiQNnZGR5evc9ipwUvN53KtfrPySJEmSJElSY1n5JUmS\nJGnoWPEhzT0e9+oXK78kSZIkSZLUWLVXfkXEBsDRwJHA9sDtwLnABzNz7STu/0LgRGAfYBPgl8BF\nwN9k5kN96rYkSZIkSZJmoUFUfp0FfBhYAZwJ/Ao4GbhgojtGxMuAq4A/Ar4BfAS4G3gv8J2I2LBP\nfZYkSZIkSdIsVGvlV0TsRan4uigzD2lbfx5wWEQcmJlfH2cXI9Vyn8z8t7b7n13t9+3AGT3vuCRJ\n0iwREdsAS4EDga2Be4BvASdl5i/GtD0MOBbYCVgJXFi1e7DDfg8E/grYFVgNfA1Ykpkr+jYYSZKk\nHqi78uuoarlszPolwChwRLc7RsQfAAF8pT3xVTm5Wr6yF52UJEmajarE19XA24DrKFX2VwNvBH4U\nEc9ua7sEOK+6+VHgp5RE2GURMX/Mft9ASXZtRfkx8tvA4cBVEbGwfyOSJEmaubrn/NoXWJGZ17ev\nzMzbI+Kmans3q4DjgJ912PZItdykJ72UJEmanZYCzwDelZlntlZGxJuAz1CmnviTiHgW5cfDq4D9\nMvPRqt0yytyqb6NMVUFEbFL9/2bg+Zn5QLX+MuCTlGqw99QxOM0tIyPL17ntVeCk5vO4V7/UVvlV\nzce1HeWDUye3AptHxJadNmbmrzLz9Mz8ZofNr6mW1824o5IkSbPXa4C72hNfAJn5WeAW4BURMY+S\n3FofOKWV+KqcAtzHutX4bwB+Bzijlfiq9nkukMDhEeEVxCVJ0tCqs/Jri2p5b5ftq6rlQsok9pMS\nEU+j/HI5Cpwz7d5JkiTNYlUC6gM8URE/1sPAAmA+pdp+FLi8vUFmPhwRP6AkyTbNzPt5ojL/Ox32\neQUlkbYbcO1MxyC1s+JDmns87tUvdSa/WnNHPNxle2v9RpPdYTXHxNcpk7l+pMNcYJIkSXNCZj5G\nmbvrSSJiF2AX4ObMfCQidgTuzMyHOjS/tVruDPw7sCMlUXbLOG13wuSXJEkaUnWWqK+ulgu6bN+w\nWj7p6kKdRMQiymSrL6BMwPqXM+qdJElSA1UVYX8HzOOJKvktmVw1fqvtw5nZ6QfMsW0lSZKGTp2V\nX6sovxp2+3C0sNq+qsv2x1W/Vv4T8HvAV4CDq187Z2T+/PVZtGhT5s9f/0nrNBg+9sPJuAwfYzKc\njIsGrZrf62zgvwM/olz9EUpF/mSr8afSVpIkaejUlvyqSuxvA3bo0mQHypUgu/0KCUBEPI+S+FpE\nuTz3Eb1IfEmSJDVJRGwA/D3wp5QLDv1JZq6tNq9m8tX4q4GnTbLttMzlRPFcHjtMffxP3XjDiRtJ\nmnU2mL/enHg9nAtjHFZ1Vn4BXAkcGhE7ZeZNrZURsS1lroivjnfniHg2cBml/P7DmdnTy2qvWfMo\nK1bcz5o1jz5pnerVelHwsR8uxmX4GJPhZFyGz1z7sBkRGwMXAa8CbgT+MDPvaGuykvGr8eGJavyV\nwC4RMT8z10zQVpIkaejUnfw6HzgUOCUiDs7M0aoc/9Rqe9erNVbzVVwAbAWc2evElyRJUhNExObA\nN4A9gR8Dr8zM34xpdiOwT0Rs2GEurx2AR4Gb2truBWzftq69LUDOpM9zMVE815Pkkxn/yMjydW6/\n/e3v4sGHHgY27mfXJA3A2jWPsWLF/R2P+yaYy6/5w/IDZK3Jr8z8l4j4AnAI8P2IuJzyYWpv4KLM\nvLTVNiKWAqOZuaxatRh4IWVuiQer7WPdnpln928EkiRJwysiNgIuoSS+Lgf+ODMf6ND0SmB/YF/g\nn8fc/8XAdZn5YFvbw6v2Y5Nf+wP3ZuYNPRqCJElSz9Vd+QWl8us6yoeoY4DbgBOB08a0O4kyAX4r\n+bVPtVwAnNBl39dQJnWVJEmai04BXgJcBbyqyxUaAT4HHA8sjYgrMvORav3xwKasW41/MWWi/OMi\n4ouZuRIgIt5Cmbbi9N4PQ2pOxYekyfO4V7/UnvyqJlp9f/VvvHbrjbl9LHBsH7smSZI0a0XENsBR\n1c2fA0siolPTUzMzI+J04L3ATyLiEmBX4ADge5SJ8gHIzJURcRzwMeCaiLgI2A44iHK64yl9GpIk\nSVJPDKLyS5IkSb33YmA+pXL+LV3ajALLgYczc0lE/BJ4O/AXwO3VtmVjJ7bPzLMjYiVwXNX+bspV\nt0+Y6ErdkiRJg2byS5IkqQEy82JgvQkbrnufEWBkkm0vBC6cRtckSRrXb35zJx87u+v172o1f/76\nHPGWtw66G+oxk1+SJEmSJGlgttj19Xzr52smbliD31x/scmvBjL5JUmSJEmSBmbTrZ456C487sHb\nFg66C+oDk1+SJEmShs7IyPJ1bnsVOKn59tjsmnVu/9t9zxtQT9Q0U5oXQpIkSZIkSZpNrPySJEmS\nNHSs9JLmHiu91C9WfkmSJEmSJKmxTH5JkiRJkiSpsUx+SZIkSZIkqbGc80uSJElSVxd/9Sv89Nrr\nerrPp2w8H4DVD62Z0v3+/d+vZsHv/XFP+yJJaj6TX5IkSZK6+uI/fo2127y8tzt9cHp323iXHdho\nky162xdJUuOZ/JIkSZLU1bz15rHxwqfV/nf32OyadW57FTip+Tzu1S/O+SVJkiRJkqTGsvJLkiRJ\n0tCx4kOaezzu1S9WfkmSJEmSJKmxTH5JkiRJkiSpsUx+SZIkSZIkqbFMfkmSJEmSJKmxTH5JkiRJ\nkiSpsbza4yQtXnwAV131PQD22mtvLr740gH3SJIkSWquPTa7Zp3bXgVOaj6Pe/WLlV+SJEmSJElq\nLCu/JEmSJA0dKz6kucfjXv1i5ZckSZIkSZIay+SXJEmSJEmSGsvklyRJkiRJkhrL5JckSZIkSZIa\ny+SXJEmSJEmSGsurPUqSJEkaOntsds06t70KnNR8HvfqFyu/JEmSJEmS1FhWfkmSJEkaOlZ8SHOP\nx736xcovSZIkSZIkNZbJrxlYvPgAtt56M7beejMWLz5g0N2RJEmSJEnSGCa/JEmSJEmS1FgmvyRJ\nkiRJktRYtU94HxEbAEcDRwLbA7cD5wIfzMy1k7j/FsDJwKuBRcANwGmZeWG/+ixJkiRJkqTZaRBX\nezyLkvi6ErgY2JuSzHoucNB4d4yIpwL/XLW9EPhP4PXA5yNiUWae1cd+S5IkSarJHptds85trwIn\nNZ/Hvfql1tMeI2IvSuLroszcLzOPz8x9gfOB10XEgRPs4hjg+cDRmfnGzHwf8DzgOuBvI2JRP/s/\n7JyAf7B8/CVJkiRJGj51z/l1VLVcNmb9EmAUOGKC+78duAP4eGtFZj4AfADYGHhjb7o5/Ey0dFfH\nY+PjL0mS1F//dt/z1vknqfk87tUvdSe/9gVWZOb17Ssz83bgpmp7RxGxI7AtcGVmjo7ZfHnb/gfK\npMjsZvwkSZIkSWqW2pJfEbEhsB1wc5cmtwKbR8SWXbbvWC2fdP/MvAN4GNh5ht1snKYnc2Yyvsne\nt9d/Y7Lrhs3ixQcwb9485s2bN7R9bLJuz5uxMZktz6Vh76O6M36SJEmabeqs/NqiWt7bZfuqarmw\ny/ZWUqzb/e8b576a4/yy1h+zNZGn2atJCeWZaPr4JEmSpF6aNzo69gzC/oiIZ1Kqu76Sma/psP18\n4M3AbmNPi6y2HwacBxybmR/psP0/gY0yc+vp9G/evMtHN9tsIbvt9hx+9rP/4L77Si6uF+v6YVD9\nqWt88+eXC5GuWbN2yv1pyrp+mMnfmQ3jm4lh7+NMH+thGt9MniOz5Tk3mdewXh+PdRn0Yztd8+dv\nwOWXM2/Q/VBHoytW3D/oPtRu0aJNAZgNYz/0rUcyf6c3DLobmsMuWb54nduvftfFA+qJ5oJfX/0p\nvvnlf+jpPmfTa36vVWMf+GewOpNfi4A7gW9k5pOu6hgRXwAOAnbIzNs6bD8I+ALw3sz8UIftdwIP\nZeYO0+nfvHnU80BIkqSBGB0d/AcvdWTya8iZ/NKgmfxSnUx+9dawJL82qPFvraJc0bHbqYkLq+2r\numxf2dauk82A26fdO0mSJElDY4/Nrlnntld+k5rP4179UlvyKzMfiYjbgG6VWTtQrgTZbU6vG9va\nrSMing5sCOR0+7fffhOfYtdLUzk1aTaeYjLTcXQ6ZWg2Pg69NuhT2iZ7Oup09eM0sF4fU8N2+me/\nYzIX9CN+wxKX2XCadb2nlu/fl7FKkiRpuNVZ+QVwJXBoROyUmTe1VkbEtsBOwFe73TEz/7Oa12uf\niJiXme2nKe5fLb8/3Y5dfjmsWLF6unefssWL/4KrrvoeALvttjcXX3xp1/Xd2g6zmY7jibLQ9pg8\ne0yr+uI1LKbyvOmHznHpnZmMo65jarL7q+tY7ndM5oLJx2ryr0HDE5dOfa7ntXSyz/feH1Odx9yK\niaTZw4oPae7xuFe/1J38Oh84FDglIg7OzNGImAecWm0/Z4L7fwY4AXgH8H8BImLTat1D1fZZoduX\ngNmQ2JquJo9Nc1un5/Zk12nwjEt/zORxNSaSJGlQHlk7ypvfckRP97mgOivgkSmeFTD66KN8YNlJ\nbL/9tKY2V5tak1+Z+S/VxPaHAN+PiMuBvYC9gYsy8/FPuxGxFBjNzGVtuzgNOBj4SETsB9wCvA7Y\nHjg6M++uYxx180uAWpr+XJgNX5abHgMVxrk+s+G4lyRJc8f2e721b/teMMX2d+W/sGpVt5mhNBV1\nV35Bqfy6DjgcOAa4DTiRkthqdxJlAvzHk1+ZeX9E7AOcAvxP4JXADcCSzLyw7z2X1Ah+YZZmH49b\nSZIkTVftya/MXAu8v/o3Xrv1uqy/C+htDaI0y/mlsDsfG0nSbHTFFd/ht7/97aC7AcDdK+5gm50G\n3QtJkqZvEJVfmgNMOEiSJE3fyX97Bhttt9eguwHAvG32GXQXJEmaEZNfkiRJ0pB56qabs9WOew66\nGwO1x2bXrHPbq8BJzedxr37peGqhJEmSJEmS1ARWfkmSJEkaOlZ8SHOPx736xcovSZIkSZIkNZbJ\nL0mSJEmSJDWWyS9JkiRJkiQ1lskvSZIkSZIkNZbJL0mSJEmSJDWWV3uUJEnShCJiA+Bo4Ehge+B2\n4Fzgg5m5doBdU0Ptsdk169z2KnBS83ncq1+s/JIkSdJknAV8GFgBnAn8CjgZuGCQnZIkSZqIlV+S\nJEkaV0TsRan4uigzD2lbfx5wWEQcmJlfH1T/1ExWfEhzj8e9+sXKL0mSJE3kqGq5bMz6JcAocES9\n3ZEkSZo8k1+SJEmayL7Aisy8vn1lZt4O3FRtlyRJGkqe9ihJkqSuImJDYDvgB12a3ArsHBFbZubd\ntXVMkqQ5YPXq1TzwwAOD7gYACxYsYMGCBYPuxrSY/JIkSdJ4tqiW93bZvqpaLgRMfkmS1CMbLnwG\ny5Z/atDdAGD1A/fyugP35+ij3jHorkyLyS9JkiSNZ361fLjL9tb5oqt0AAATyUlEQVT6jWroS9+s\nWbOGn//8htr+3hZbPBWAe+55sOP2tWsfqa0vkqThtHCbYOE2MehuAHDfilt59NHO71mzgckvSZIk\njWd1tex2nsOG1XL2fiIG7rnnHt529LvZcvsX1vL31ps3D4DHRkc7bp+/9Qtq6ccw22Oza9a57VXg\npObzuFe/mPySJEnSeFZRrui4sMv2hdX2VV22T2jRok2ne9eeGR1dzYINHmX+I3fU+nfX77bhkTtY\nee313bbODXs/Z52bK6/9zIA6omHjc6HBPO6H1uqHHuDpL37NULxnT8e80S6/NkmSJEkAEXELsGFm\nbtdhWwILM3Ob+nsmSZI0sfUG3QFJkiQNvSuBp0fETu0rI2JbYCe6XwlSkiRp4Ex+SZIkaSLnV8tT\nImIeQLU8tVp/zkB6JUmSNAme9ihJkqQJRcQFwCHA1cDlwF7A3sBFmXnIALsmSZI0Liu/JEmSNBmH\nAicBWwHHAFsDJwJvHmSnJEmSJmLllyRJkiRJkhrLyi9JkiRJkiQ1lskvSZIkSZIkNZbJL0mSJEmS\nJDWWyS9JkiRJkiQ1lskvSZIkSZIkNZbJL0mSJEmSJDWWyS9JkiRJkiQ1lskvSZIkSZIkNdYGg+7A\nIEXEBsDRwJHA9sDtwLnABzNz7QC7NidExDbAUuBAYGvgHuBbwEmZ+YsxbQ8DjgV2AlYCF1btHqyz\nz3NNRJwOvAvYPzO/O2abMalRRLwJOAbYFVgFXAWckJk5pp1xqUlEbAV8AHg1sBXwa8rjvTQzV49p\na1z6ICK2BW6gPJYf6bB90o97RBwI/BXlGFsNfA1Ykpkr+jeC2W8q7+WT2Nf6wL8Ce2bmk36gjYjP\nAG/qcve/zcwlU/l7MzXTsUfElcBLu2z+88w8u63txsAS4A3AtsAvgLMyc2QmY5iJmsfftNhvBBwH\nvBn4XeBXlNecZZl575i2TYz9VMbfiNhHxP7Atyfaf/tr37DFvuaxD1XcoSfP++cCfwPsA2wE3Aj8\nXWb+fYe2jYh92/2nMva+xX7e6OjodO8760XE2ZTE15WUD1t7V/++lJkHDbJvTVcdQFcDzwAuA34K\n7EL5ErkSeHFm/r+q7RLKF8yfAt8AdgcOAL5PScqsqX0Ac0BE7ElJsMwDXtae/DIm9YqI9wPHU94o\nvko5bg4C7gde2HrTMS71iYjNgB9RkirfBv6d8v7xEspxs19mPlq1NS59EBGbUD547Qm8MzM/Omb7\npB/3iHgD8FngZuBLwLMox9gvgD0yc1XfBzQLTeW9fJL7+0vgQ8BoZq7fYfu1lETzxzvc/XuZOeGX\nq17pxdgjYhXlS//nO2y+JDN/XLVbn/La/yrg68B/UJ7LuwMfzsz39GJMU1Hn+Ku2jYl9RMyv7rcf\ncHm1rxdVt/8N2DszH6naNi72Uxl/1b4RsY+IZwF/2mXXL6LE+IrMfFnVfqhiX+fYq/sMTdyr/sz0\nef984HvAfMoPcXcCi4EdgNMy831tbRsT++r+kx571b5vsZ+zlV8RsRcl8XVRZh7Stv484LCIODAz\nvz6o/s0BSykH0Lsy88zWyqq65TPAh4E/qV4sT+bJXyaXAScCbwPOqrfrzRcRC4BP0eHUaGNSryoJ\neTzlA+KrMvPhav2XgIuAk4D/bVxq938oia8zM/NdrZVtv1a9CTjfuPRH9bh+GXj+ONsn9bhXSbSz\nKImv52fmA9X6y4BPUqrBav+COUssZRLv5ZPZUUQ8m/KrcLft8ykftr+amSfPoM+9spQZjD0itgc2\nBS6dxHgOoXwJ+lBmvre6/0nAN4F3RcSnM/Nn0x/KtCylpvE3LfaUKu79ePIX3v8LHEWp9Ph0tbpx\nsWcK429S7DPzNsr70joiYiElubGCEu+WYYv9Umoa+xDGHWb+vP8A8BRgcWZ+tbrvSZQfT98dER/P\nzFurto2JfWXSY+937OfynF9HVctlY9YvAUaBI+rtzpzzGuCu9gMIIDM/C9wCvCIi5lG+oKwPnNL6\n8lI5BbgP49QvJwDPplRVjGVM6nUU8BjwtlbiCyAzvwScQ6kGA+NStxdUy0+NWf+Javmiamlceiwi\n3kn5sPwcup9GMZXH/Q3A7wBntBJfAJl5LpDA4RExlz8vjWfC9/LJ7KR6v/8E8F888Zo21u9TfrS9\ndtq97a2Zjn33ajmZ8RwFrKE8f1t/Zy0lMTsPeOsk+9xLdY6/abF/B6Wq9IQx60+nJH0eaFvXxNhP\nZfxNi30np1MSC8dk5l1t64ct9nWOfdjiDjMf/wuAe1rJn+q+D1IqX9cD/ltb26bFfipj72vs5/KH\nuX2BFZl5ffvKzLwduKnarj6ovkR8gJJF7uRhYAGlNHJfSjLy8vYGVRLgB8BzI2LTfvV1LoqI3YH3\nUV5wr+vQxJjU61XAf3QqJ87M/5OZp1Y3jUu97qyW249Z/4xq2Zonyrj03jGUL077Un5x7GQqj3vr\n/f47HfZzBbAlsNvMutw8k30vr37FncifUeJwJPDbLm2mkizpqx6NfVLjiYgNKaf2XtPh9NsfUean\nq/Uza53jn0bbvprp2CPiD4BnUqoa2hPzZOZtmfm/qx+3Ghn7qYy/0pjYd9nnbsBbgCsz8/Nt64cq\n9nWOvTI0cYeejf9OYGFE/M6Y9dtVyxXV32pi7Cc19kpfYz8nT3usnlTbUT4Ad3IrsHNEbJmZd9fW\nsTkiMx8DPtppW0TsQil1vDkzH4mIHYE7M/OhDs1vrZY7U8omNUPVOeafpPzyfipl7pWxjElNImJr\nyjnvl1XHxinAf682XwYc11YibVzqdTblQ9sZEXEPcA3lw8rfAvfyREWYcem9twHfyszR6rjoZCqP\n+46URNkt47TdiSH5ED4spvBePu6cdhHxu8BpwCcy84qI6Na09YF4l4j41+r2Q5T5UE6ofrysRY/G\nvjvlebdPRHyK8nxcCXwR+OvMvK9q9yxKFePNHfrxaET8srpvbWoef6stNCP2rUT6dRFxAKX66XmU\n940LKJNHt163mhj7qYwfmhX7Tk6hVPK8d8z6oYp9zWOHIYo79Gz8yykX1vtcRBwN3EWZW/RPKZ9F\nrqjaNTH2kx079Dn2c7Xya4tqeW+X7a0s68Ia+qJKlVn+O8oL4TnV6i0xTnV6N2UOnSPGeREzJvXZ\ntlo+A/gh5dfST1Au0PF64AcR8cyqjXGpUVU1vDflijXfo5ym8W1gLfDSzPzPqqlx6bHM/OfMnOhq\nPVN53LcEHm4/rXictppAl/fy8ZxNORX13RO0a30gPpHypeDjlB9qDgeujojtutyvNlMc++5Vu5Mp\nk3yfQ/n1+xjge22ViVtWy/GezxsPw6m5fRp/qy00I/at9/U/Bi6hXDHtY8AdlKtrfzPK1eihmbGf\nyvihWbEfe7+dKBOGX5mZYwsyZkXs+zR2mAVxh6mNPzM/DRwKvJxyltkqymf6bwP/o+1zTeNiP4Wx\nQ59jPycrvyin00Ep0+uktX6jGvoiHp/v42xKVcuPgNY5xfMxTrWIiJ0pJa1nZeYPx2lqTOrz1Gq5\nL2UejLe03iAi4h2UX2LOBF6LcalVlXT8B8oH+a9S3pj3APYHzomIV1fl6sZlMKbyuBujHhnnvbxb\n+8OAVwKvG1Pp08lDlOPsNZl5Q9s+jgfeT3k9fN30ez8zUxl71XYl8BPg1a1fsqv1I5TTQJcCf8nU\nPrN2qnSsRR/HDw2KPU+8r78aODIzP1ntYz1K5dNBwNspY2pc7Jna+KFZsR/rHdXytA7bhj72fRw7\nDHncq75M9f3uDyn9fhj4HCW59Qrgf1Au9nJ01bRxsZ/C2KHPsZ+rya/V1XJBl+0bVssHa+jLnFf9\nwvP3lNLHm4E/qSb1gxIr49Rn1YvYJym/vC2ZoLkxqc9j1XItcOyYX0bOAo4FDoiIp2Bc6vY5YFfg\n4Mz8YmtllMnYl1N+BTsE4zIoU3ncVwNPm2RbdTHBe3mn9k8DzgC+nJn/ONH+M/O1XTadSrmAwasj\nYuMup7r21VTHXr2Wv6TT+oh4N3AY8L8oyZ/JfGYdHcS4W/o8/kbFnife13/cSvxAObUoIt5DSf4c\nRPmC17jYM7nxH0yV/GpY7Nvvuz7wRuBXmfn1Dk2GOvZ9HvtQxx2m9X63OeUK1WuAF7Tm8a3myfos\ncFREXJ+ZH6NhsZ/i2Pse+4GXyQ7IKso8A91OY1hYbR87yZx6LCI2Br5COYBuBF6WmXe0NVnJ+HEC\n49QLRwEvBf68y4vJvLb/G5P6tB7HWzNznfLn6svDtZRfiJ6JcalNVfW1F3BFe+ILIMuVcG4AXhsR\nm2BcBmUqj/tKYKMuk7Uao0mYxHt5J2dRPoe+Y4J246peC39KeS18xgTNe26aY+8qyxWwbgS2iYgF\nlOcnjP98fqDLtr6rYfzjtZ2NsW+9lvx47IbqdPlVlHkIoZmxn8z4f2+ivz1LY99uL8rpbV/qsn1o\nY1/D2LsadNxh2uP/n8AmwEez7QJW1RQzrffAw6tl02I/lbF31avYz8nKrywTqd8G7NClyQ6UK0F2\nO9dWPVBlgr9BmST6x8ArM/M3Y5rdSJkMdcMO87HsADxKOXdYM/P6anlpl8mGv1Ot34ESk32NSS1u\nofxK2u0LQOvLeqtE2LjUozXfwA1dtl9PmQB0O4zLoEzlveNGyofx7XlyLFqfE7JP/Zz1Jvle3knr\n191fd3rfiYjHgNsyc4eI2Ah4DvDbzPyPDvt6SrXsdqXIvpju2Kv5rHYF7s7MTsf/Uyiv/WsoF114\nhA6fWasKit8FfjbNIcxIHeNvWuwprzfQ/X19A544lelWGhZ7pjD+Bsa+3QHV8otdtt/KEMa+jrEP\na9xhRuPv+rkxM++KiLspMYXmxX7SY68j9nO18gvgSuDp1YR7j4uIbSlXdep2JUj1QPXkvoRyAF0O\n7N/lALqScsWLdS7pWt3/xcB11a+EmplzKfNrjP3XmvvrvOr2vZSYrIcx6bvM/C3lXPpnRrny6eOq\nsuPnAncDv6JMum5c6tG60ky3y9LtRKkevhOPl0GZynvHldVy/w772R+4t33eCT1hCu/lnSyj8/vO\nndX2pZTTIqF8eP4hZZ69sX3YGHgBcFc+caGJvpvh2PcErgJO77Dfp1MqX36SmaPV6SQ/BF5QVZOO\n3c9TgO9PaxAzUNf4aV7sr6Z8sd0vxkxYHeXKaU+lurJsQ2M/6fHTvNi3ezHlceg4x+4wxr6usTOE\nca/+9kzG/+vWbjrsd3NKJdwd0MjYT3rs1BD7uZz8Or9anlLNd9Sa9+jUav2kr1ihaTmFMt/DVcCr\nMrNb+ebnKL/QLx1T/n48sCnGqScy89OZefLYf7Qlv6p1qzAmdWs9nh+Nda+A9JeUN4nzs1yG+LMY\nl1pk5q2UD/D7R8Qft2+LiLdSrlTzT1X1sMfLYEzlcb8YuB84rvogBkBEvIWSyPxE/7s7a032vfxJ\nMnNZl/edOynzmZycma15f26mnO7wnIh4Y2sf1ee2DwJbUa4YV6dpj52ScL2LMmfjPq2V1XP17yjV\nL2e1tT+fMs/Lsra28ykTBY9S5l+pWy3jb1rss1zY4QvAs4D3tdZX8WxN/v2ptrs0KvZTGX/TYj/G\n84Drs/uV1WH4Yl/L2Ic07jCz8V9CmTv06Ih4vKKrquRaXt28oK19k2I/6bHXEft5o6MTXS28uSLi\nAsqExFdTsph7US5df1FmHjLArjVaRGwD3EY5ZetTwH91aXpqZj4cEacC76WUS15CKZU/gFLp8vIJ\n3jg0AxFxJvAXlAz/d9vWG5MaRcSXgcWU0+m+Cfw+8CrKqVh7Zub9VTvjUpOI2BX4LmXuha9RTuXY\nHfgjyq9cL83M26q2xqVPIuJwyvvIO1uJkrZtk37cI+LPKB+ofglcREksH0Q5DfIlToPwZNN4L19K\nSWot69Kutd9rgN0zc2xVyIsol0XfiJKwvA3YB3ghcAXwirqOpV6MPSIWU55rjwEXAvdQrny1C3BB\nZr6pre16lNebvYBvUZ1yQnnN+VBmvrenA5zAAMbftNgvAv4VeDYlntcCL6dUc38+M9u/9DUx9lMZ\nf6NiX+1nS2AFcGlmvnqcvzc0sR/A2Icm7lV/evG8fzPlTJoHKad8rqJcLXF3Sh7ij1pjalrspzj2\nvsZ+rie/NqD86nA45YPubcBngNP8MtI/1QeeL1My1/O6NBsFNq9+ISIi3k659PGOlFOOvgwsa33p\nV39ExBmU5NfL2pNf1TZjUpPq15GjKVc52RH4DeUN4aTMXDmmrXGpSURsD/w1JeG1FaVs++vA0sy8\nc0xb49IHEfGnlA9jx45NflXbJ/24R8TBwHHAH1BOJ/4n4ISxsVQx1ffyKHN4jWbm+hPs9yeU5NeT\n2lVJ56XAyygT6N5COT3iQzV/EerJ2CPipcBfUX5Rn0/5QeMTmTnS4W9uQqkCOJhymsj/oyRsP57r\nXgm47wY0/qbFfgvgJMrcd4uAX1Cuur18bDwbGvupjL9psd8Z+Dljkrxd/uZQxH5AYx+KuFd96dX4\n9wWWUE79fArlaomfpcOYGhj7qYy9b7Gf08kvSZIkSZIkNdtcnvNLkiRJkiRJDWfyS5IkSZIkSY1l\n8kuSJEmSJEmNZfJLkiRJkiRJjWXyS5IkSZIkSY1l8kuSJEmSJEmNZfJLkiRJkiRJjWXyS5IkSZIk\nSY1l8kuSJEmSJEmNZfJLkiRJkiRJjWXyS5IkSZIkSY1l8kuSJEmSJEmNZfJLkiRJkiRJjWXyS5Ik\nSZIkSY1l8kuSJEmSJEmNZfJLkiRJkiRJjWXyS5IkSZIkSY31/wFQErDaW3pL+gAAAABJRU5ErkJg\ngg==\n",
"text": [
"<matplotlib.figure.Figure at 0xa93f092c>"
]
}
],
"prompt_number": 33
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pymc.Matplot.plot(tau_att)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Plotting tau_att\n"
]
},
{
"metadata": {
"png": {
"height": 370,
"width": 597
}
},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAABKsAAALlCAYAAAAL/GWuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAWJQAAFiUBSVIk8AAAIABJREFUeJzs3Xd8VFX6x/HPJCQBDF06CopwUBTXAgIWFFHX7u6iP8vq\n2l0r6q66uKuiKOiq4FqiiCgWdAULK4iIgiCKSBcRPBB6J6QD6TO/P2YSMyGTOndK5vt+vXhN7r3n\n3vucmZsw88xzz3F5PB5EREREREREREQiQVy4AxARERERERERESmlZJWIiIiIiIiIiEQMJatERERE\nRERERCRiKFklIiIiIiIiIiIRQ8kqERERERERERGJGEpWiYiIiIiIiIhIxFCySkREREREREREIoaS\nVSIiIiIiIiIiEjGUrBIRERERERERkYihZJWIiIiIiIiIiEQMJatERERERERERCRiKFklIiIiIiIi\nIiIRQ8kqERERERERERGJGEpWiYiIiIiIiIhIxGgUipMYY54D7gfOtNZ+W279TcD4ALv9aK0dEIr4\nRERERELNGNMGeAy4EOgIbAQmAmOstSUV2l4H3Af0ADKBycCj1tr9lRz3QuBfQG8gD5gGDLfWplXS\ndgAwEjgJcAOzgYestRuD00sRERGR2nM8WWWM6QfcC3gq2Xy87/FpIL/Ctm1OxiUiIiISLsaYZsB3\ngAE+Az4CTgee8T1eUq7tcOAp4CfgRaAP3sRVf2PMmdbaonJtrwImAeuBFKArcD0wyBhzsrU2u1zb\nQcAsIB14E2gJXA2c5Wu72ZHOi4iIiFTD0WSVMSYR75ufQLcb9gHSrbUPOxmHiIiISIQZjjdRdY+1\n9uXSlcaYScBVxpgLrLUzjDFdgSeABcCg0oorY8zjwCPArcArvnXJvp/XAydYa/f51s8CJuCttnrA\nty4OGAfsA0621u4od/6vgOeAyx19BkREREQCcHrMqn8CRwFfB9h+HPCzwzGIiIiIRJquwBa81U/l\nfeh77O97vBWIB0ZVuDVwFJAD3Fxu3VV4q6PGliaqAKy1bwEWuN4Y4/KtPhvoCUwoTVT52s7Bm6y6\nzBjTuu7dExEREak7x5JVxpg+wD/wvpn6pZLtXYBWwEqnYhARERGJRNbaa6y13ay17gqbevked/se\nz8A7lMLcCvsXAAuB4323FJa2BfimklPOA9oAx9ag7Vy8CbLTqu2IiIiIiAMcuQ3QGBOPt9x8LTAa\neLaSZn18j4nGmKnAQKAx3jL3R6y1i52ITURERCTSGGPaAUOBx4HNwHu+Td2B3dbaA5Xstsn32BNY\n6mvrATZU0/ZnX1vw3jIYqG2PmsYvIiIiEkxOVVb9HTgBuLn8oJ8VlCar/gok4k1ufYW3LH2+MeZc\nh2ITERERiRjGmJHALuBlIAs4r9xA6G186ypT2qZFubYFvqqrmrQlwLErthUREREJqaAnq4wxPYER\nwCvW2h+raOrC+83dNdbaC6y1w621f8KbrIoH3jLGJAU7PhEREZEIsx7vzMifAm3xfml3gm9bAlBZ\n8oly6xvXsa0nQPuKbUVERERCKqi3AfoG7ZyA99vB4VW1tdaOxnuLYMX13/pmorkOKJ1SWURERKRB\nstZOLP3ZGHMh8BnwDt6JaPLwVqBXpvRLvf2+xzygfS3augIcu2JbERERkZAK9phVdwKnAhcEGFvB\nVcm6yizHm6zqVo9YPPXYV0RERKJDTd9bRAVr7efGmDnAYGNMdyCTwLfjla4vvW0vE+hljEmoZBiG\nytqWrk+rpm1txeR7MJfL/1L0eGLyaRARkdjg+PuvYCerhvoeZxhjKtv+jW/9EXinVm5urZ1fSbsm\nvsf8+gSTlpZbn92jVtu23kmBYrH/sdx3iO3+x3LfIbb7r77HZt/ht/5HG99ENGcBWGu/rqTJZrxv\nAg/FO1nNGcaYpErGojoCKAHW+ZbX4p2wplu5deXbAthybUvXp1bTttYa8jVZ09+7aH0OGvrflYbe\nP2j4fVT/ol9D72Os9M9pwU5WvQXMqWT9+cApwES841RlAd8BHY0x7a216RXal06VvCTI8YmIiIiE\nmwuYBuQYYzpaa90Vth8PuPHO6jcfOBM4A+9ENAAYYxoD/YFfrLWlt+vNB673ta+YrDoTyLLWrinX\ntnT9V5W0LQEW1bJfIiIiIkER1AHWrbVvW2ufqPgPKB1ofaJvXTbwke/8o8ofwxhzOXABMM9auzqY\n8YmIiIiEm7W2GPgY72DqD5TfZoy5HTgJ+Nxamwa8jzdxNMIYU358qYeBZsDr5dZNBXKBB40xrcod\n80agB/BGubbzgC3AbcaYruXang2cA3xayZeJIiIiIiER7Mqq2hgJXAjcYozpA3wPGLyJqh3ADWGM\nTURERMRJD+KtlhptjDkTWAWcAAzGW1F1G4C11hpjngMeApYbY6YDvfG+X/oOGF96QGttpjHmQeBV\nYIUxZgrQGbgc7y19o8q1dRtj7gD+BywxxrwPJAPXAHuokESrrZSUMX7Ld9xxf30OJ1Iruv5ERKJf\nUCurquChwmCb1toMvLcGvgR0Au7B+yZtPHCStXZTiGITERERCSlr7Q6gL973PX2AYUB3YCzQ11q7\nq1zb4cBdeN9L3QMcA4wBLqw4kLq1dhxwJd5B0+/AO7TCROBMa21WhbYzgN8Da4Cb8CbA/gecaq3d\nHNwei4iIiNScqwHPVOJpqAOaVaehD+hWlVjuO8R2/2O57xDb/VffY7PvUNb/BjUbYAPRoN+DBfq9\na9euud/ynj05IYspmBr635WG3j9o+H1U/6JfQ+9jjPTP8fdfoaqsEhERERERERERqZaSVSIiIiIi\nIiIiEjGUrBIRERERERERkYihZJWIiIiIiIiIiEQMJatERERERERERCRiNAp3ACIiIiLSsKSkjPFb\nvuOO+8MUicQiXX8iItFPlVUiIiIiIiIiIhIxVFklIiIiIkGlShYJJ11/IiLRT5VVIiIiIiIiIiIS\nMZSsEhERERERERGRiKFklYiIiIiIiIiIRAwlq0REREREREREJGIoWSUiIiIiIiIiIhFDswGKiIiI\nSFClpIzxW9bsbBJKuv5ERKKfKqtEolxGTj4bd+bg8XjCHYqIiIiIiIhIvamySiSKbd6Vy9OTllFQ\nVMLZJ3bh3mtOCndIIiIiqmSRsNL1JyIS/VRZJRLFJn7xKwVFJQDMXrYtzNGIiIiIiIiI1J+SVSJR\nbPPu3HCHICIiIiIiIhJUIUlWGWOeM8a4jTFnVLLtOmPMcmPMPmPMVmPM88aYQ0IRVyDTpk3l5Zdf\nCGcI1crNzWXkyEf56aflZetWrlzBAw8Mq3K/PXt288ADw9i1a6fTIYqIiIiIiIiI1JrjySpjTD/g\nXuCg0Z+NMcOBib7FF4GfgPuAWcaYBKdjC+TttyeQk5MdrtPXyLp1llmzvvAbVHvatKls2rSxyv2W\nLFnEwoULAJfDEYqIiIiIiIiI1J6jA6wbYxKBN6kkKWaM6Qo8ASwABllrS3zrHwceAW4FXnEyvoag\n7jPAaeY4EREREREREYk8Ts8G+E/gKOBrYEiFbbcC8cCo0kSVzyhgGHAzYUhWDR16Mbt37+KLL6bz\nxRfTmTJlGh06dGDFimW8886brFmzmvz8PA49tB3nn38hN9xwCy6Xi2XLljBs2O289NI4fve7E8uO\nd9ddt+JyuXjppXG1imPatKlMnfoxW7Zswu12c/jhXbnuuhs566whZecCuOeev/K7351Ix46dmDnz\ncwB69erF008/zamnnu13zBkzpjF69BMAXH75JZx//kU8/PBjDB16MYMGnUVq6jpWrVrJueeez0MP\n/YvU1HW8+ebrrFy5gn37cmnVqjWDBg3m9tvvJikpCYCioiImTnyDWbNmkpGxl86du3DVVddy/vkX\nlZ13/vy5TJw4gY0bN9CsWTKDB5/LbbfdSePGjWv/AomIiEjES0kZ47es2dkklHT9iYhEP8eSVcaY\nPsA/gKeAVhycrDoDb3nP3PIrrbUFxpiFwLnGmGbW2pCOID169HP8/e/DMOZorr/+Jtq0acO6dWsZ\nNux2hgw5lyeeGA14+PLLL3jrrfF07dqNs88+N+DxXC4XLlftbrn7+OPJvPji89x002306fM7srOz\nmTTpbR5//F8ce2wfevU6mvvvf4gxY57hb397iBNOOJlGjRqRlZWJtb/y2muv0qVLF0pK/I87cODp\n/OUvN/H22xMYNepZunfv4XfOq666lj//+XqaNj2EvXv3cuedN3Psscfzz3+OIDExkR9++J4PP5zE\noYceyp//fD0Ajz/+LxYu/J6//OVmevc+lgULvmPUqMdJSEhgyJDzmDVrJiNHPsK5557PbbfdyY4d\n23n99RQ2blzPCy+k1Op5EREREREREZGGz5FklTEmHpgArAVGA89W0qw7sNtae6CSbZt8jz2BpU7E\nGEiPHobExERatmzJMcccC8CGDamccsoAHnlkZFm7k07qx3fffcvy5cuqTFZ5PJ5aJ6t27tzB1Vdf\nx3XX3Vi2rkOHjtx887WsXPkTZ599Dl27dgOgW7cjy35u0aIliYmJ9OnTB4C0NP88X8uWLenUqbOv\nn73o0KFDueN34rbb7ixbXrRoIT179uLJJ5+hSZMmvj73ZfHiH1mxYhl//vP1bNiQyrx5cxg27G8M\nHXolACeeeDK7du1k+fKlDBlyHq+99hL9+w/kkUeeKDv2YYcdzr333sEPP3zHgAGn1eq5ERERkcin\nShYJJ11/IiLRz6nKqr8DJwCnWmuLjDGVtWkDrA+wf+no5i0ciK3WzjvvAs477wIKCgrYunUL27Zt\nYd26tZSUlFBUVBj08911172Ad8a/zZs3sX37VpYtWwLgyPkAevTo6bfcr19/+vXrT3FxMRs3bmD7\n9q2sX59KVlYGLVu2BLyzDwIMGjTYb98nn3wGgM2bN5GWtofrrruB4uLisu3HH38CTZs2ZfHiH5Ws\nEhERERERERE/QU9WGWN6AiOAV6y1P1bRNAEoCLCtdH1EDGpUUJDP2LHPMmvWFxQXF9OpU2d69z6O\nRo0a1WOA88C2b9/Gv/89imXLFpOQkEDXrt3Kbtlz4nwul6useqqU2+1m3LhX+OSTKeTn59GuXXuO\nPro3iYmNy2LIzvbmFFu1al3pcbOzswB4/vlneP75Zw46Z3r63mB3RURERERERESiXFCTVcYYF97b\n/3YBw6tpngckBtiW5HvcX5942rZtVqf94uJcNG6cULb/I4/8m2+//Yb//Oc/DBw4sGxg8AEDBpS1\na9XqEACaNUvyO29RUQHJyck1jsXtdnPttfeRlJTExx9/zNFHH01cXBypqal8+eUMmjdvQtu2zWjZ\nsikALVs2LTt248YJxMf/NvFiZeds1swbe5s2h5Rtr9hfgNdee43Jk99n5MiRnHPOOSQnJwMwdOhQ\nEhLiadu2GR07tvXtX0jbtq3K9l2/fj3Z2dl07doRgIceeoh+/fr5xeHxeGjRokWdX6PqOHXcaBHL\n/Y/lvkNs9199FxERERFpGOKqb1IrdwKnArcHGIuq/OBNmQS+za90fXaA7Y6Ki/N/WpYuXUr//v0Z\nPHhwWaJq1apVZGZmllUZlSZzdu3aVbZfdnY269cHutOxcpmZmWzatImhQ4fSu3fvsli+/fZbwJvM\nAoiPj6807uoqryr2LZClS5fSs2dP/vCHP5T1bffu3axdu7bsHCeddBIAc+bM8dv32WefZdSoURx5\n5JG0adOGrVu30rt377J/7du3Z+zYsaxZs6ZGsYiIiEjsKSgo4IMP3gt3GPUWqB9r1vzC4sULq93/\nqadGcPrpfUlNXVdt2xkzpnH66X2ZMuW/dYr1q69msnXr1jrtKyIiEkzBvg1wqO9xRoBxqr7xrT8C\n7+DrZxhjkqy1FW8HPAIoAar/X7kKFQcYr6mmTZNZuXIVs2bN5ZhjemPMMcyZ8xXjx0+ka9dupKau\n4733JtKsWXMyM7NJS8uldetOtGvXnhdffImSknhcLnjnnbdo3LgJhYXFtYglkQ4dOjFx4js0adKc\n5ORm/PjjAmbOnEFcXBx79mSQlpZLcbE3WTVjxpcUFbno0cOQkNCEvXv3Mm/ePI4++mhcriYHHd3j\nSQDgk08+o3//U+natRslJW7y84v8YjzqqF58//0Exo59id69j2P79q28++5EmjRpSnZ2LmlpubRp\n05mzzhrCM8/8m717szjqqJ788MN3zJs3j1GjniU9fT833/xXnn12NAUFJZx66mnk5u7j7bcnsHfv\nHu6996E6v0aBlFYXBPu40SYW+x/rr30s9199j82+gyrKGrq77rqVrVu3cNVVfw53KPVSWT8WLPiO\nf/zjfu6++3769u0ftHP16GG48cZb6d372Frvm5LyIh988C5Tp04NWjwiIiJ1Fexk1VvAnErWnw+c\nAkzEO9NfFjAfOBM4A/iqtKExpjHQH/jFWluv2wDr6qqr/syLL47h73+/hxdeSOGuu+6juLiYN954\nlfz8fHr37sMTTzzN3LmzmTt3Nh6Ph/j4eJ566t/85z/PM2LEP2nTpg1XXHE1mzdvZMuWzbU6/+jR\nz/Gf/zzHk0+OICEhgQEDBvL66xP5xz/+xsqVK/jTn/6PI4/szpAh5/Hxx5NZuHAB77zzIRdccDEL\nF37PnXfeybBhw7jssisPOvZJJ/Xl5JP7MW7cKyxduph///uFSmcrvPbaG8jOzmLKlP/y5pvj6dGj\nJ3fddS87d+5g3LhX2L9/H4ccksyjj45kwoRxTJ78AdnZWXTrdiRPPvlvTjttEAAXXXQZTZsm8/77\n7/DZZ5/StGkTjjvudzz22JN06NCxLi+PiIiIRLiUlDF+y3WZnS0zM6PWMypXZs6cr3n86bE0a9Gm\n3scCiG/krVIvKXZX2e6iy68DIHvPOnCXlC3nZO7h8kt/78g4pD169Dxo0pyayszMCHI04ROM609E\nRMIrqMkqa+3bla03xrTGl6yy1n7rW/c+8DAwwhgzz1pbOs3dw0Az4PVgxlYbQ4acx5Ah5/mte/TR\nkQe1O/743zFs2N/Klnv1OoZXX51Q7/MfdVQPXnpp3EHrJ058v+xnl8vFY4896bf9yCO78957U6r8\npr1JkyaMHfuK37opUz47qF1CQgL33fcg99334EHbrrji6rKfGzVqxG233cltt90ZsD+DBw9h8OAh\nAbeLiIiIOKWoqIjkrqfTrnu/6hvXxzz/iqR2J10PwL7Zo3EX55cte9bNx+0u8f7sQMJKRESkIQj6\nbIA1Za21xpjngIeA5caY6UBv4ALgO2B8uGJzwoED+9mwYUO17bp0OYyWLVuGICIRERERZ9SnkmXn\nzh1cccWlZcunn96X88+/iIcffgyADRtSmTTpbZYvX0ZmZgaJiYkceeRRXHnlNQwaNLhsv6eeGsHM\nmZ9zyy13HHSOtdMfIql5R7qecW+dYnQXF5C5YT77dv1M0YEMPO4SunXrxr59+0hPT8fj8VB0IION\nc36bDXnt9Ido3uUk8nJ28dHa7QC89NIYXnppDFOmTKNDhw5VnjM3N4exY//N3Lmz2bdvH127HsG1\n117PWWf99oXgjBnTGD36Ce6++36uuOIqADIy0hk37hVWrFhGWloazZs356ST+nLjjbfSuXMXAIYO\nvZjdu73jrl522WV06tSJDz/8X52em0igSioRkegXqmSVx/fPj7V2uDFmK3AHcA+wExgDPG6tLQpR\nbCHx669rGDbs9irbuFwuhg9/lPPPvyhEUYmIiIhElmbNmnPDDbcwefIHFBYWcu2115fd2rZ69Sru\nvvs2kpIaM2jQYFq2bMm2bVuZP38u//rXQzzzzFgGDjzN0fg87hK2LRxPftZWDmlnaNq2F+7ifPam\nfkurVq1ISEhg586dxCU0oU3PIWRu/A5PSRGtjzqLpOadKHa76HlYK1avXsUppwygd+/jyiazqcpj\njz1MUlISQ4b8ngMH9jNr1hc8+uhwRo9O5LTTzvBrW3r7ZEFBAX//+z1s2LCeQYMGM3hwZ7Zv38bX\nX3/JokULmTTpI5o3b84VV1zNF19MIzV1HVdeeSVHHnmkI8+diIhITYUkWWWtvQ+4L8C2FCAlFHGE\n04knnsz8+YvDHYaIiIhIREtOTubGG29lxoxp7N+/nxtuuKVs2xtvjMPtdvPaaxM4/PBuZevnzPma\nxx4bzldfzXQ8WZW782fys7bSusdgDjW/DRux5Ot3OOKII0hOTsblchGf0IQ2Pc8he+sS3ECbnucA\nsD83g2OOSfYlqwZy+eUHjzFamXbt2vPyy6+XzUw9cOBpPPzwA3z++f8OSlaVxbRkEevWreWGG27h\nxhtvLVv/wQfv8eqrLzJ79iz+8IehXHHFVaxbZ8uSVb169YrZiRtERCQyhO02QBERERGR2rjyymu4\n+OJL/RJVACeccCIAWVmZjsfQuEVn2h8/lOT2vf3WezweCgoKaNq0KXFxcUE/7xVXXFWWqAIYMOA0\nXC4XO3bsCLiPx+MdBD41dR2FhYUkJiYC8Mc/DmXIkHNp27Zd0OMUEREJBiWrRERERCQq9OvXH4D0\n9L2kpq5j+/ZtbNmyiZUrVwDgdlc9Q18wJCa3JTG5Le6SIvIyt1C0P43C/el06tSJJk2aAARlFsOK\nunQ5zG+5UaNGNG3alLy8AwH36dv3FDp16sz8+XO55JJzOfnkfvTvP5CBA09XokpERCKaklUiIiIi\nEhV27drFf/7zLN9/Px+Px0NcXByHHdaV4447nnXr1oZkdj2Px0NG6hwyN3yLuygfgPikZO+g6kVF\nZdVLwZaYmBQgnsD7JCU1Zty4ibzzzgS++WY28+Z9w7x53xAXF8cZZ5zFAw88TPPmzR2JV0REpD6U\nrBIRERGRoEpJGeO3HIzZ2TweDw8+OIzNmzdx3XU3cvrpZ3LEEUeSmJhIZmYG06dP9WtfWt1UMYHl\nLimsVxyZG+aRbmfRpE13Wh91JknNO9EoKZnpYy6jc+fOjiWr6qply5bcc8/fuOeev5Gauo5Fi35g\n5szPmTt3NnFxLh5/fHS4Qww6J64/EREJLSWrRERERCTiVLyVLjV1HRs3buCss4Zw881/9du2ceMG\nwD8x1aiR921uUZF/cqpof3q94srdvgJccXTu+xfiGvlXO9U0UeXAXYKVWrRoId9//y1XXHE1nTt3\n4aijenDUUT3405+u4KKLzmXlyp/KxRSioERERGpAySoRERERCapgVLLExzeiuLi4bDkpyZsIysjw\nTzbl5GSTkvIigF/7bt2OAGDtWgu0B7wDjmekzqlXXK64RuBxU1ywj8RyyarWrVuXJcjKJ35ccfF4\n3CV+x4iLiwcOTqQFW1raHj75ZAput4e//e2hsvXp6ekUFhbQocNRZevi472xFxY6G1MoqJJKRCT6\nKVklIiIiIhGnXbt2bN++lZEjH6Fv3/6cd94FHH10b376aTl33nkLxx7bh+zsLL77bh4dO3aiZctW\nZGdnle1/zjm/Z/z41/jhh++IT+5EWkEaB/aupaQon0ZNWtY5ruZdTiQ/aytbF6TQrGMfXHHxHEhf\nT+vWrcnLy6NJkybEx8eXtW/UuAVF+9PZufy/HNK2BwAtWnjP/+mnH5Odnc0VV1xFmzaH1jmmQIYM\nOY+PP/6QqVM/YsOGVHr3Ppb9+/f7bgGM46abfqtQa9fOO+D6M888w4ABA/i///tL0OMRERGpqeDP\nqysiIiIiUk+33343RxxxJN98M4dZs2bicrl4+unnOf/8i9i5cwdTpvyX1atXcfXV1/HKK29w7LHH\nsW3bVnbs2A5Aq1ateemlcRxxxJEU79tF9tbFJCa357CBdxCf0KTOcbXsNpB2x15KfGJTsrcsImf7\ncpKadWDz5s1kZmYCcMghh5S1b3v0BSQ2a8++nSvJ2b4cgCOP7M4f/3g5ubnZfPrpR2zevCng+Vwu\nVxW36LmqbJuUlMTYsa9w5ZV/JjMzg08+mcLcubM59tg+vPzyeE4+uV9Z2z/+8XL69j2FVatWMWnS\nJAoK8mv5zIiIiASPKxSzpoSJJy0tN9wxhEXbts0AiMX+x1rfb3za/1aGac9fCsRO/8uLtde+olju\nv/oem32Hsv5roJ3IE1Hvwb788gte/Ogn2nfvV33jepg+5jK/5Yvun1ppu93r5vPwjYM59dTTHY2n\nrhr635WG3j9o+H1U/6JfQ+9jjPTP8fdfqqwSEREREREREZGIoTGrRERERCRm7bWzaty2cYvOJHfo\n7WA0IiIiAkpWiYiIiEiQpaSM8VuO5NnZMtbNrnHb5oedpGRVFIim609ERCqnZJWIiIiIxKyeFz0T\n7hBERESkAiWrRERERCSoVMki4aTrT0Qk+mmAdRERERERERERiRhKVomIiIiIiIiISMRQskpERERE\nRERERCKGklUiIiIiIiIiIhIxHBtg3RjTBngMuBDoCGwEJgJjrLUl5drdBIwPcJgfrbUDnIpRRERE\nREREREQiiyPJKmNMM+A7wACfAR8BpwPP+B4vKdf8eN/j00B+hUNtcyI+EREREXFOSsoYv2XNziah\npOtPRCT6OVVZNRxvouoea+3LpSuNMZOAq4wxF1hrZ/hW9wHSrbUPOxSLiIiIiIiIiIhECaeSVV2B\nLUBKhfUfAlcB/YHSZNVxwE8OxSEiIiIiIaZKFgknXX8iItHPkWSVtfaaAJt6+R53AxhjugCtgJVO\nxCEiIiIiIiIiItHFsQHWyzPGtAOGAo8Dm4H3fJv6+B4TjTFTgYFAY2AB8Ii1dnEo4hMRERFn5BUU\nk5aVR6dDD6FRvCYhFhEREZHqOf6u0RgzEtgFvAxkAedZa7N9m0uTVX8FEoEJwFfA2cB8Y8y5Tscn\nIiIizsgrKOaxNxcx4q3FjPlwBR6PJ9whiYiIiEgUCMVXnOvxzvT3KdAWbxLqBN82F7AJuMZae4G1\ndri19k94k1XxwFvGmKQQxCgiIiJBNnvpNvZmeyf6/XVLFht25oQ5IhERERGJBo7fBmitnVj6szHm\nQuAz4B3gOGvtaGB0Jft865s58DpgEDCrLudu27ZZXXZrMGK5/7Hcd4jt/sdy3yG2+6++R56dmXl+\nyyWuuIgA8vadAAAgAElEQVSNVUREREQiR0jGrCplrf3cGDMbGGKM6W6tXV9F8+V4k1XdQhKciIiI\niARFSsoYv2XNziahpOtPRCT6BT1ZZYyJB84CsNZ+XUmTLb7HNsaYZKCFtfbbSto18T3m1zWWtLTc\nuu4a1Uq/tY7F/sdy38uLxf7H+msfy/1X3yO374UFxX7LOdl5QY1VVVoiIiIiDZMTlVUuYBqQY4zp\naK11V9h+PODGO1bVUqCjMaa9tTa9QrvTfI9LHIhRRERERByiShYJJ11/IiLRL+gDrFtri4GP8Q6m\n/kD5bcaY24GTgM+ttXuAj3wxjKrQ7nLgAmCetXZ1sGMUEREREREREZHI5NSYVQ8CZwCjjTFnAquA\nE4DBwAbgNl+7kcCFwC3GmD7A94DBm6jaAdzgUHwiIiIiIiIiIhKBgl5ZBWCt3QH0BcYDfYBhQHdg\nLNDXWrvL1y4DOAV4CegE3IM3qTUeOMlau8mJ+EREREREREREJDI5NhugtXY3v1VQVdUuE28ya5hT\nsYiIiIiIiIiISHRwpLJKRERERERERESkLhyrrBIRERGR2JSSMsZvWbOzSSjp+hMRiX6qrBIRERER\nERERkYihyioRERERCSpVskg46foTEYl+qqwSEREREREREZGIoWSViIiIOMMV7gBEREREJBopWSUi\nIiLO8IQ7ABERERGJRkpWiYiIiIiIiIhIxFCySkREREREREREIoZmAxQRERGRoEpJGeO3rNnZJJR0\n/YmIRD9VVomIiIiIiIiISMRQZZWIiIiIBJUqWSScdP2JiEQ/VVaJiIiIM1zhDkBEREREopGSVSIi\nIiIiIiIiEjGUrBIRERFneMIdgIiIiIhEIyWrREREREREREQkYihZJSIiIiIiIiIiEcOx2QCNMW2A\nx4ALgY7ARmAiMMZaW1Kh7XXAfUAPIBOYDDxqrd3vVHwiIiIi4oyUlDF+y5qdTUJJ15+ISPRzpLLK\nGNMM+A64C/gZeAnIBp4BPq3QdjjeJBbAi8BPeBNXs4wxCU7EJyIiIiIiIiIikcmpyqrhgAHusda+\nXLrSGDMJuMoYc4G1doYxpivwBLAAGFRacWWMeRx4BLgVeMWhGEVEJAxWb8rg1amraBQfxz1D+3BE\nx+bhDkmc4gp3ABIuqmSRcNL1JyIS/Zwas6orsAVIqbD+Q99jf9/jrUA8MKrCrYGjgBzgZofiExGJ\nGbszDrBw9S4O5BeHOxQAxk7+if35xWTvL+TVqavCHY6IiIiIiEQYRyqrrLXXBNjUy/e42/d4Bt6J\nredW2L/AGLMQONcY08xam+tEnCIiDV16dj6PvbWIwiI3h7VLZsQNfXG5wlvuUuL2lP28Nzs/jJGI\n4zzVNxERERERqcixAdbLM8a0A4YCjwObgfd8m7oDu621ByrZbZPvsSew1OkYRUQaoqnzN1BY5AZg\n6559bN2zj8PbNwtzVCIiIiIiIoE5dRtgGWPMSGAX8DKQBZxnrc32bW7jW1eZ0jYtnI1QRKTh2pOV\n57ecX1gSoKWIiIiIiEhkcDxZBawHnsY7C2BbYL4x5gTftgSgIMB+pesb1+Wklw+fzog3F5GZG+jw\nIiIiIiIiIiISaRy/DdBaO7H0Z2PMhcBnwDvAcUAekBhg1yTf4/66nDe/sIQte/bxxaKtDLvyhOp3\naIDato3dW31iue8Q2/2P5b7Dwf1PSIj3W27ZsmnEPUfBiifS+hVKkdr3xCT/txnNmzeJ2FgluFJS\nxvgta3Y2CSVdfyIi0S8UlVVlrLWfA3OAY4wx3YFMAt/mV7o+O8D2Gvl68Zb67C4iIiJ1FOax/EVE\nREQkSgW9ssoYEw+cBWCt/bqSJpsBF3AosBY4wxiTZK2teL/eEUAJsK6+MaWlxdZkgqXfWsdavyG2\n+15eLPY/1l/7QP0vKvIfoyor6wBpaYEKWsOjvq9ZLL/2kd73goJiv+WcnLygxhrNVVrGmA7ACOBC\noB2QAXwNPGqt3Viu3U3A+ACH+dFaO6DCcS8E/gX0xlvBPg0Ybq1NqySGAcBI4CTADcwGHip//rpS\nJYuEk64/EZHo50RllQvvG6NJxpjKjn883jdEG4D5vhjOKN/AGNMY6A/8Yq2t022AIiLhsCfzAHZL\nJm6PJ9yhiEiE8iWqFgG3Ar8AL/iWrwYWG2OOKtf8eN/j03iTW+X/+SWxjDFX4X0PdiiQgrea/Xpg\ngTGmRYW2g4C5wDHAm8BU4GJgkTGma/17KSIiIlJ3Qa+sstYWG2M+xvuG6wHgmdJtxpjb8X57N81a\nm2aMeR94GBhhjJlnrS30NX0YaAa8Huz4REScsnFnDqPfW0ZxiZtBv+vEX37fK9whiYSXcraBjAC6\nAPdba18oXWmMuQZ4F3geuNS3ug+Qbq19uKoDGmOSgVfwTmxzgrV2n2/9LGAC3mqrB3zr4oBxwD7g\nZGvtDt/6ScBXwHPA5cHoqIiIiEhdODVm1YPANmC0MeYLY8yzxpiv8b6J2gDcBmCttXjfEA0Alhtj\nnjHGTMf7huo7Ape9i4hEnAmfr6G4xA3AvBU7whyNiESwPwB7yieqAKy1k/C+Tzqv3OrjgJ9rcMyr\ngJbA2NJEle+YbwEWuN4YUzqK2NlAT2BCaaLK13YO3mTVZcaY1rXulYiIiEiQOJKs8r3x6Ys32dQH\nGAZ0B8YCfa21u8q1HQ7chff713vwlqOPAS601hY5EZ9ILCgoKmHxr3vYma47aUNlx1491xIcBYUl\nrNmcyb48/TfY0Piqmp7CW11VmQIgwRiTYIzpArQCVtbg0KVDKnxTybZ5QBvg2Bq0nQvEA6fV4Jwi\nIiIijgj6bYClrLW78VVQ1aBtCt6xFUQkSF6Y/BN2axYJjeIYcUNfOrY5JNwhiUgNuD0ennp3KdvS\n9tGqWRJP3nwKTZIc++/aWZoN8CDWWjfwYmXbjDG9gF7AemttkTGmj29TojFmKjAQaAwsAB6x1i4u\nt3t3vF/8bajk0Jt8jz3xVml19y2vr6Jtj5r0R0RERMQJUfruV0SqkrWvALs1C4CiYjcfzknl3suP\nr2YvEYkEqzdlsC3NexdXZm4Bc5Zt48IB3cIblDjOV3H1Mt4UX+mYnaXJqr8CM/GOPdUTuAQ40xhz\nibV2lq9NG6CgktmVAbJ9jy3KtQXIqkHbOklJGeO3rNnZJJR0/YmIRD8lq4Js1YZ03p1laZmcxF8v\nPZZWzZLCHZLEoMKiEr9l3QooEj0yc/1zDbsyDoQpEgkV31hS44DBwGK8swOCN3G1CfintfaDcu3P\nAGYDbxljjvBNUJOA9xbCypSub+x7TMBbhVVZ+4ptRUREREJOyaogGzP5JwDSsvKZMjeVWy/uHeaI\nREREJFIZYxrhHePzL3hvy7vUWlsMYK0dDYyuuI+19lvfzH3XAYPwDoqeB7QPcJrSb85Kv7nIw5sI\nS6xB2zpRJYuEk64/EZHop2SVgxb+spuLB3ar91hBqduyWblhLyebdhzevlmQopOGTLPFi0hE0B+j\nKhljmgJTgPOBtcCQ8pPQVGM53mTVEb7lTKCXMSahkglqSm/pyy7XtnR9WjVt66Rt28h5v9KiRZNw\nh+AnzgUtWzaNqOeoMpEeX3019P5Bw++j+hf9GnofG3r/nObIbIDym8feXFyvW7Cy9xXw9KRlTF+w\nmdHvLaOgwu1dTjmQX0T2vkB3E0i0cWmUYxGRiGKMaQXMwZuoWgacZq3dVqHN8caY0wMcojQDk+97\nXIu3WqpbJW1LE1q2XNvy66tqKyIiIhJyqqxyWHGJmw9mr+P+K35Xp/2/XLQVt8f71XRBUQmL1uzm\n9D6dghniQdZvz2bM5BXkF5Rw7XmGM0/o7Oj5REREYokxpjEwHegHzAUusdbuq6TpdKCjMaa9tTa9\nwrbTfI9LfI/zgeuBM4F1FdqeCWRZa9eUa1u6/qtK2pYAi2rSl0DS0nLrs3tQZWfnhTsEP24PZGUd\niKjnqLzSSoBIja++Gnr/oOH3Uf2Lfg29j7HSP6epsioEdqXXfXDc/AqVVMXF7vqGU63X/vcLeQUl\neIB3vtQXq1FJt96ISCRQUWcgo4ABwALg/ACJKoCP8L5XG1V+pTHmcuACYJ61drVv9VQgF3jQV7VV\n2vZGoAfwRrlDzAO2ALcZY7qWa3s2cA7waSXJMREREZGQUWWVHCQ9J99veU/mAdq1ahqmaCQoGtgH\nxszcAjweD62ba7IqEYkuxpgOwJ2+xV+B4caYypqOBkYCFwK3GGP6AN8DBm+iagdwQ2lja22mMeZB\n4FVghTFmCtAZuBzvLX2jyrV1G2PuAP4HLDHGvA8kA9cAe4AHgtZhERERkTpQskqq9eiERTx2Q996\nDxQvUlO/bMpg/LTVNE6M564/HkeXtsll25avTSNl6io8Hrj5oqPp37tDGCMVCS6Px8PWPYGKbKSB\n6A8k4K2BvTFAGw8wxlqbYYw5BRgBXAbcgzeZNB54zFq7u/xO1tpxxphM4EHgDiAdmAj801qbVaHt\nDGPM74HHgJvwVmX9D3jYWru5vp1MSRnjt6zZ2SSUdP2JiES/mE1WFRW7cbmgUbzuhKxOYbGbD75e\nx/3/V7dxt6Rqc1dsZ/KcVDq3PYR7/tSHZk0rm0m8dqL9LsAxH67A44Gc/fDm52t49Pq+Zdte+uTn\nsp9fn7ZaySppUD6ck8rXS7ZV37AauzMOUFBUohlkI5C1diq1GIbBWpsJDPP9q0n7ycDkGradDcyu\naSwiIiIioRKTyapFa3bzxvQ1NG3ciPsuP56uHZx9M++J9swB6Jt+h3g8Ht6Z6R0XbP32HKYv2MxV\nQ3qEOarwK/87s2lXwxyYUKQysxZvrfcxVq7fy4sf/Yzb42Homd25oH/ZkEQUFJWQ0CiOOFcDuzdY\nIo4qWSScdP2JiES/mCwreu1/v1Bc4iZnfyFvfL66+h1EHFIxkfn10vp/UIUGN0SV1EMDyJVLLb0w\nZWXZLLIfzV1ftv7dLy23Pz+P0e8tpaDC5B2O0QUoIiIiInUQk8mq8ran7Q93CFGnoKiET7/dwORv\nUjmQXxTucEREpBp7svL4Zvl2wFvF+e2KHWGOSEREREQksJhPVkntTV+wiWkLNjHzxy38d05quMOR\nSqiYQUqpyu43azZnMvmbVDbH4K2lezIO+C2v3JAemhPrAhQRERGROlCyKtJF4IBXn//w2yRB363c\nGcZIpKb0eVFiXUZOPs99sJyZP27h6feXURiq2+BERERERKTWlKwSEZEGb8bCzWUVhwWFJSxdmxbW\neEREREREJLCYnA0wqkTYjE0lbne4Q2jYglRI54nAijyRcKo4oHhxSWz9LdNfBAm1lJQxfsuanU1C\nSdefiEj0U7JKamzJr3sYP12zJwZVqHKREZb0FJHwquwvQmZuAXEuaJGcFPJ4RERERETKcyxZZYzp\nAIwALgTaARnA18Cj1tqN5drdBIwPcJgfrbUDnIpRas4DpExdFe4wRETEAQtX7+KNaWuIi4M7/nAc\nvzvq0OAcWCVdMUuVLBJOuv5ERKKfI8kqX6JqEdAFmAW8D/QCrgbON8b0t9aWTiN3vO/xaSC/wqG2\nORGfiIjEuBhLolR3Z/Drn3mrZt0l8OJHK3nzH4NDEJWIiIiISOWcqqwagTdRdb+19oXSlcaYa4B3\ngeeBS32r+wDp1tqHHYpFgkA3kTkkRB+Y9fpJrHPpt8BfyG5BDtF5RERERKRBcWo2wD8Ae8onqgCs\ntZOADcB55VYfB/zsUBwRIsa+wpeIoyswdum1l0rpwhARERGRCBb0yipjTBzwFFAYoEkBkGCMSQDa\nA62AlcGOQ0RERLw0x4KIiIiIRJOgJ6ustW7gxcq2GWN64R27ar21tsgY08e3KdEYMxUYCDQGFgCP\nWGsXBzu+qFPdQCMiNaDPqRLrPDFeSqT/SkREREQkmjg2G2BFvoqrl/F+bn7dt7o0WfVXYCYwAegJ\nXAKcaYy5xFo7K1QxOieIqQJ9PS4iIvWl/0rEYSkpY/yWNTubhJKuPxGR6OfUmFV+jDEuYBwwGFgM\nlI5l5QI2AddYay+w1g631v4JOBuIB94yxiSFIkapuwP5ReEOQXx27t0PqIpCfhPMnITb42Hh6l38\n8Msu3B4PuzMP8OGcdSxaszuIZ3GGBliPDPrTJCIiIiI14XhllTGmETAe+AuwHrjUWlsMYK0dDYyu\nuI+19ltjzCTgOmAQUK/qqrZtm9Vre33FxbvqfI7GTRL9lpOTk2p8rGD2Ky4u8Ae9u/8znxsvPpbL\nBnUP2vnqy+nXNFhK3P4f3TzUL/b8wmIAWrc+xG99o0ZxUfOcVKaq2Ctui6R+hiOWiudMSIj3W27Z\nsmmd45r89Vre/WINAPsKSpi7dBs7070J0m5dWtH7yDZ1Om6wnqeqjtO4cYLfcrNmjSPqWqlK48YJ\n9fp/rG3bZrTYe8BvXWJio1r9XtVVUpL/24zmzaPneZf6USWLhJOuPxGR6OdoZZUxpinwP7yJqrXA\nWdbaXTXcfbnvsZsDoUWNaKgF8Hhgwmerwh2GiDisNFEF8MEsW5aoAnjlo5/CEZLUUbj+b1GFm4iI\niIjUhGOVVcaYVsAXQD9gGfB7a+3eCm2OB5pba+dXcogmvsf8+saSlpZbr+315S5x1/kceRVusduX\nm1/tsUq/tQ5mv9zu6m/ecPp5rAkn+u6kyp7XYMSekbHfb7mkjtdgQVEJm3fl0qXtITStUJkSSlXF\nXrotEl/7UMYSqP9FRSV+y1lZB0hL86/YDIYDeYV17m99n6eavPb5Ff6W5tbgb2mkyM8vChhrTfqe\nlpZLdrZ/ZVVhYQk/rNjGJ/PW07Zlk0r3CYaCgmK/5ZycvKA+76rSEhEREWmYHElWGWMaA9PxJqrm\nApdYa/dV0nQ60NEY095am15h22m+xyVOxCjSEJWOVRWMcWHcHg+j313Klj37aNO8MU/efApJifHV\n7ygSBSJx7CR3iAebe/GjlezLK+LXLVkhPa+IiIiISHWcug1wFDAAWACcHyBRBfCRL4ZR5VcaYy4H\nLgDmWWtXOxSjSNh5IvIjs9eqDRls2eP91U3PyWfeiu1hjkikHqLg7rOf1u2tvlEdVZYH25enyTFE\nREREJDIFvbLKGNMBuNO3+Csw3BhTWdPRwEjgQuAWY0wf4HvA4E1U7QBuCHZ8Elp2SyZvz7QkN0ng\n1kuO4dAWB99uIr8J5+fpvIJi0nPy6XToIcS5XGTk+N+BuzPjQIA9RUJv4S+7ePtLS5vmjXnitoG0\nb9206h0iNy9cZs3mzIDb1m3LwuVycVTnFiGMSEREREQkPJy4DbA/kID3o8GNAdp4gDHW2gxjzCnA\nCOAy4B5gD97ZAx+z1kb+fOgxoq6f88ZO+YnCIjcA/52dyl1/PC54QdVDXkExGbkFdGzTlDhX5JRc\nhOvzdM7+Qp54ezEZOQWcbNpyxx8i43WS+ouCHE2dvD7NW3S7Y+9+Jny2ioev71er/SPnt7563/+8\ni+9/9s5Nct15hjNP6FzrY0TKn7mGej3KwVJSxvgta3Y2CSVdfyIi0S/oySpr7VRqcXuhtTYTGOb7\nJxWFeAyTYCtNVAEsW5sWxkh+k3ugkCcmLiE9J5+TTFvuVGKG6Qs2kZFTAMASm8be7LyD2kT5pSgO\nC+fl8cPPO2u9T7Rezu98aeuUrBIRERERiSaOzQYoDgnT1+MR8qV8UHyxcAvpvlvclto09mTl0a6S\n2bBiybpt2X7L+/KKGtaLLhLj13OkJJtj/GWIKapkkXDS9SciEv2cGmBdJGKt2+Y/81XO/sIwReKg\nSPlkKjGjPkmItKw8dqbvD1osUr1IuS1QRERERKQyqqwSiQEufTKNWdHwyj/8+kJK3B7+fG5PBp/Y\nJdzhxIS1W7OqbyQiIiIiEiaqrAqBoNa4hKliJhxntVsyefmTn/nix824VSkkDVDOgUI+nLOOz3/Y\nRHGJu9r2DVWJ2/v7/d6stWGOpOGq+Bc0v7AkLHHUlcfjYcOOnINmKRURERGRhkmVVZEuRitiStxu\nXvx4JXkFJSxbm8bh7ZvRu1vrcIcV8Ty+pF5tU3seJQPD4u0vfmX5ur0AJMTHcW6/w8MckZfb42HB\nz7vYl1fEWSd0JikxPtwhSQNU4naz8JfdJCbEc7JpW2UF6Ptfr2P20m0kJsQx/JqT6NqhWQgjFRER\nEZFQU2VVFEvdns3H89azcWeO4+cKdcps594D5BX89s3/f2evC3EEDUt1r9+WPftCEof4K01UAfx3\nTmoYI/E3b8UO3pyxhsnfpPLOl7+GOxyWrU3j9Wm/sDxCZhSVuiufFv9o7nomfL6GV6eu4usl26rc\nb/ZS7/bCIjdvzljjYIQiIiIiEglUWRWlcg8U8sykZZS4PcxavJUX7j6NJkm1fznzCopZvyObbh2a\nk9wkwYFIpSqRUNBUUFTT24EiIFgJiXe/tGU///DLbm65uHe1+zh1dWTvK+CVT37GA/z4y27G3H0a\nLQ5JdOhsEmxVJcq/XLS17OcPZq/jnL6H1eiYW5VcjwopKWP8ljU7m4SSrj8RkeinyqoQcKIqafbS\nbWXjvBQVu/n+5521Pobb7WHk20sY8+FPPPbmIgqibAyToGmIOZha9CnQWEmxeQOqRJq5K3aUXc4e\nYN7y7eEMR2qpIf55FRERERHnqbIqBOr1Zj1A6U1hkX+CoagOgzMvX7eXXRkHAMjMLWDeiu0Bx8wJ\n9QcOR8+nLIxI0Dn1a1VxPLW6/G3weDys2pAenICkXvTnN3aokkXCSdefiEj0U2VVFTJy8pm3Yjt7\nMg+EO5TfBBqAtg6f4CpOXZ6WFbmzLOkDTi0F4QlTRUTDoNcRZv64hax9heEOQ0REREREakiVVT4F\nRSXkFxTTIjkJ8N5a9/jExeQeKKJxYjzP3TGQpo0jaEynICQjvlqytfpGwTtd5IiFT++x0EeRGpoy\nd324Qwg//U0QERERkSiiyiq8FVT/Gr+Q+17+nilzvTNyLf51N7kHigDILyxh9jKNkxJKFW/9cVSD\nysQFUEUfA22KhadFREREREREIo+SVXinz07PKQDgi4VbKC5xsz+v2K9N7v7IvoUkGKkdT0R/9d5w\nUieffb+Rv6d8zztf2oiYDTACQpAGQNeR1ISuExERERGpCd0GCCxdm+a37HYH9+20EwmJUKduwv8B\nI/wRBMPuzANMnb8RgLnLt9One5ugHt/JZykSEmsiEl0aztcMIiIiIhJKSlZFG1/GQHmDIArhk2m3\n+A9qv3jNHkfOU98uuXDhCjSYv0S8bWn7WLM5kxN6HKpkgQCRXjkrDVFKyhi/Zc3OJqGk609EJPop\nWRXpIiRhEP4oghhB+DtTTmg+QFbV5Yh6OqTesvcX8sTEJRSXuPl8wSYObdkk3CEBUFzi5u2Zvx40\nC6k0bEqRiYiIiEhdKFlVicreXNfnDbcT+SYnEgyuKo6aV1AccFsgn/+wqe7BSFh58IR2kHsJmpk/\nbqa4xA1AzoEicnwTRTgtI6eATbty6NaheaXbf1y9m+9/3uXY+fMLa/83KtLEwm+ckuOxQ5UsEk66\n/kREop8GWEdvnktVdZtIYbG71sf7eN6G+oQj9aBEU+zKrUdyyuPxMG3Bpjrv/+wHK8oSZRXNcXhG\n1VkLNzt6/GhX1ZcRIiIiIiKRxrHKKmNMB2AEcCHQDsgAvgYetdZurND2OuA+oAeQCUz2tdvvVHy1\nFba3+TVMOjT05ESE3A0ZxQI/gYGuHI1Z5ay92XnhDuEga7dm8em3dU8y5xUUs3pTBn26HxrEqGpm\n9cYMBhzdrsbtI/JvpoMhRcqYVZERhYiIiIhEOkcqq3yJqkXArcAvwAu+5auBxcaYo8q1HQ5M9C2+\nCPyEN3E1yxiT4ER8oRbUz0SlCQTlEaRW9BEx0kz+Zr0jx63P35uZP26p9/ndtS/ClAZM/1WJiIiI\nSF04VVk1AugC3G+tfaF0pTHmGuBd4HngUmNMV+AJYAEwyFpb4mv3OPAI3mTXKw7FGJg+14edo0UP\nAY6dkZPPtAWbaNY0gYsGdCMxId7BIKoMJaRq+mEyEmJtSJb86sxMkJFKhXoiIiIiIlJTTo1Z9Qdg\nT/lEFYC1dhKwATjXGOPCm4yKB0aVJqp8RgE5wM0OxVdr+qDegPk+RL/zpWXeih1MX7CZ2cu2heTU\nJSWhurJqlynQ+DbRTH+tpBK6LEREREQkigS9ssoYEwc8BRQGaFIAJAIJwBl430LPLd/AWltgjFmI\nN6nVzFqbG+w4q+RC9y6EWUirMHwf4lauTy9bNeWb9Zx/SlfHT7042NU1+kAqUiMaky00KvuT9P3P\nO1m2Ni3ksUhopaSM8VvW7GwSSrr+RESiX9CTVdZaN96xpw5ijOkF9ALWW2sLjTHdgd3W2gOVNN/k\ne+wJLA12nFXSB/6wc/Q2QH1GbXD25RXxzIvfsmF7Nn8640jO7Xd4uEMKo7pf4IESODvT93NoiyZ1\nPm4kisgB1mPA3qw8psyt/3htOQcKad40MQgRiYiIiEgkcmw2wIp8FVcv4/0k9bpvdRsg0LvWbN9j\nC4dDq/yzXYR/jom127SC2dv123OCeLTIEmjGr6qLSA7eGCkzh5X6Zvl2BvbuQFJi5eOIffHjZuzm\nTAD+OyeVs07sQkIjp+5yjnTBf+3+Of5HDm+XXLOz1zMJtD1tH53b1uxcEl6e/2fvvsPjqM49jn9X\nxbJsyV0uuONybFyxjcGATQ3NoSc3AUIJEJIL3JB2k0AIoeRCcgMmcINDCYQaktB7B4MLGBeM+zHY\ncm+yLcuS1aW9f8xK1q52pa2a3dXv8zx+VjtzZuac2dn1zrvvOcfrZcHKnWzeVcYpk/vTu3unVrd5\nf0l8ulj/6q+f8rPvTKCgID8u+5P4UyaLuEnXn4hI6muTuznf+FQPAScDi3BmBwSnK2BViM0alndM\nbO3CkzShoRhuBCuraymrqIljZeJna1EZy9fvob4+uYIk7UPyn/On3rE89OqqoOu27i7jk2Xb/ZZV\n1fTcJRkAACAASURBVNQFLSvR27y7LKxysZ779dvTN5jcmmQLErdm1cZ9PPrGGt5bvIX7nl/eptlq\nVTV13PX0UrYVhXddioiIiEhqSXhmlTEmC3gEuBwni+pca22tb3UFzvhVweT4Hg/GWofWfnkN7PrS\nq1ceeXk5fss65mZH/QtuZoYn6m1zc/1PT15+RwoK8ukU0P2hU6ecZsdo+rxwewm/efAzyiqq+dEF\n4znr2KHNjtWxY/RtDDxeJGVWF+7ltr8voq7ey8lTBvLTiyZRUuV/w5uVlRlR3SIp261bp6DlE/GL\nfX5+67HXeBy3Wzf/DIesrIyQ+w0WwOzerTPF5bV+y3JjvD5itezrPUGPv2VvORkZ/u/hnj3z6NLZ\n/S5CkZ6veJzfnI7ZLa4Pdb0DdOgQ+38J+V1yg+4/O8zZNfN9n3ENOnXy/yzu3KlDyPpHcv4Cj9OS\n2rp6vtq8n8MKOtM14P+GeOrYymvXYO3WA4wc3J0+PQ69z1tqS0FBPl12RRbUCefcPPnXBY1/79hb\nTqe8juQF/N/UMce/TYHv1UiPGehHf/iA1+45N+LtRERERCS5JTSzyhjTCXgFJ1C1DjjJWruzSZFi\nQnfza1heEmJ9wnghaVKpQv1OHenYwPc+u5TS8mq8XvjrC8tjrlc8zfrHUup8GVUfLt4SvFCSvB6p\nqmm30crqWu59dik/vucjPl2xI+Q2C5b7Zyul0hA/6TYeUV29l4+WbGHusm2tty29mp4U7nh0Ib/8\ny1yu+9OH7DtQmbDjlBwMNS+Jv/99ejE/vucjSspCJSb7q6lNTKZhRbUyGEVEREQkMRKWWWWM6Q68\nBUwFlgJnWGv3BBRbB0w3xuRYawO/dQ8F6oCvYq1LUVErkwkG3Nzt2VNKWcBNQEVFTev7CaGu3hv1\ntpUV/jcvZWVVFBWVcjDgpubgwarGYzT8Ot30mIUBXWuC1aeiMvo2htpnOGV27StvVmZ/sf+y2tq6\nsPYfrO2t2b+/nKKi5hkNsZyLUEpLW7/Rjcdxi1s4f+8t3tIYFLzz8c+5/4bpzbbfv7+cJQEzFVbG\neH3EQ7Djl5VVNQuk7d1bRnVFeDf+iRTp+QpV/vk563nzs00AbNw6nDOObj6AfMO1X1nVcldf53oP\nnnVWXV0bdHkk3llQyNeb9nHK5AHk5R56X9WG2T2wtLTS7zyUl/t/Fh8srw55niI53wcCjhOovt7L\nq/MLmb9iB3sPOHUoKavm76+s4LIzRoV9nEjM/3J764V8yitreeqNVVz/nUlAy21ftmYnJQfCC2w1\nKCoqpeRgNRkeyA8xkLk3oNt20Z4yKnL9P0urAq7Hlrp6u/35IiIiIiLJIyHBKmNMR+B1nEDVHOAc\na22wPghzgROBGcB7AdsfA6yy1sbcDbA1qTROyFPvWN78dGNYA9lGKpmSl1LpNYlEMpzjFz/Z4Pe8\nOsXHdwp2paTb1dMQqAL490dfBw1WNXD7GltZuI+Vhfv4eut+fv7dI9vuwHFu+KqN+3h1/sZmyzfu\nTExAJZrxBPeWJC7L6/M1u3jktdVkZHi4/oJxeL1e1m7az3Hj+9G/V2cg8gxfEREREZFwJSqz6k5g\nGrAAODNI1lSDfwA3AbcaYz621jakQtwE5HNo1sA2lewz7e09UNX4S388pdsNfjLSOZZES5ZrbNXG\n4qi2i/rTN8KGt3acV+cVRluTqNQl2eQSD77iTGhQV+/l3n9/2bh83ood3Ptfx5GZEd4oAsnVKmlL\ns2fP8nuu2dmkLen6ExFJfXEPVhlj+gLX+Z6uBW40xgQrepe11hpj7gZ+BXxhjHkdGAOcBczDGZi9\nzaVrVk9Tn63a2XohFyV7wDBZtTacUVVNHVUaZyZp2c3FPPL6ajpkZXLd+WPpX5DXpscvDnMMJEkS\nLqQ2lVXUsG5LCaMHd2/zY4uIiIhI+5GIzKpjgGycH1SvDFHGC8wCqqy1NxpjtgDXAj8GdvjW3Wat\njbxfRBTSITASaXjt4ddWJ6Qe8ZKuAUO3r7T7nvuy2bLA2TBTTpIPpr59z0EeeX01dXX1XDXzCAb3\nDT3j2Z+fX94YTHzszTX89vKj2qqaAJSWJ3CcrzhdZq/MK2TNpiBZWxHuP7mvmviL59ukpXGnRBoo\nk0XcpOtPRCT1xT1YZa19mQhnGbTWzgZmx7sukpx27itnzaZiJgzrSY8uHcPaJpaAotfrZeGaXezY\nU87Jk/pHvZ9Ud6C8mrWb94dVNpWChW99vtntKrTosTfXsMk3ztGDr6zkrh9OC1m2adZb4Y7wx0aq\nq6+nrq6ezMyMdhGFWbclvOt4zcZ9LN+wN7qDhPjISabYbhJVJaRI6vjKvEKmju5Nv56dE1YfERER\nEUkNCZsNUCSYsooabvv7Iqpq6vhHhoe//vyEqPe1a185T7+3jswMD5eeZujZNXjg68v1e3n4VSeT\nbPXGfVEfryW1dfWUV9XSJcSsWQ1ciyN4oLa2PqZdJGsAq6Ss2m/WuWSzoclMnLuKK+K+/zWbivnL\niyvI8MBN358a9/2njIDLc9+BSu7+57IkvWpTXIKiZK/MK+SDJVuZdf1xZGVG9JuXiIiIiKQZfRsM\nIsl7FaW09xZtoco3+1xdvZe/vR68O2I4r8Hf31rLqsJ9LF+/l2c/+CpkuYdfXdX49/omgYN4Kauo\n4XePfc5P7p/HU+9av3X19V7WbyvhwMEEdq8KUyTXdcp3jW1H7+H7nv+SiqpaDlbWcsejC2PaVzp9\n9r29cHNMl0Go90CizlE077hFa3fz/IehP/vamjcOJ6esooYV0WbDiYiIiEjaULAKwrtLSKObODcd\nCBgT5/M1u8PbMMhr1LQr0NJ1RSE3rWxlQPFQGUMfL9sWVtXe/GwTO/aWA/DR0m1+ganZL6/kf55a\nws1/W8i+A5VtFgJK1iyoREmmrlnx9N6iLa2Wqa45lDHX2rWe1jxOsGTdlv3s3FdOdW1ynov3F2/h\n1w99ypNvr6Wu/tBrF+079ok3VrNlV/hdRuOh4e0WzXh34QS0nn53HX969gvWJCgTVkRERESSn4JV\nbaimtj5xGTbplBKRJJ5421JZXdtqueXr/bMAGgapLimragyilVXU8MLHG+JfyTB58CQ8gPXqvEKu\n+dMc/vTsF67POJgu74ZnP/iKiqrWr8F4SYqgXwx1+NeHX/OHZ5by278txLY2PluUF0ks56jkYDX/\neP8rdhdXMGfZdpZ9FZ8Mos9W7ojLfhIh8DSHE+AqLq1izaZi7n3uS+r1f5uIiIhIu6Qxq8IV403c\ngfJq7np6Kbv2lXPqlAFcfOrI+NTLp6ikkn0HKsMesNwtbXUvvLJwLwtW7GTcsJ4x7WdvSSX9C/L8\nlu07UEltXT29u3dqcdvygCDDhh0HGDWoW0z1SVYHyqt5eV4h4Iyh9PGybZw2dVCr25VV1LDE7mZo\nvy4M6hN6lrz2rLi0KqLysXV9S5yEdy31wru+TLS6em9M44N5vV6+3lYSr5o12rjDvxvy3OXbmWwK\n4n6cZLI/wuu3qdo6LzU19eR0yIxjjaStzJ49y++5ZmeTtqTrT0Qk9SlY1QbKKmp4bd5Gdu1zuoq9\nv3gr5x0/lE4d4zco9LzlO/h05U6uO38c3yiI8qY/TX7ALq+s4f7nV1BbV89nq3fFtK/AU7J8/V7+\n8uJyauu8XHq64aQjk3N2wWDJCKGCBfHIptlbUun3/LPVu1oNVnm9Xv74zFK27TlIZoaHO64+mr49\nWg4Ahuv5OV9z1cwjQq5fv62E/3txBfX1Xq47fyxmUPe4HDfVpclHQHhauO6/2hr/QJX74vfqrirc\nx4ufbKCsoqbVsoHjBFbXJGf3TBERERFJLgpWtYGa2no+WLrVb9nBytq4BqvAySa4/4XlfOPYoXHd\nb6pZsHwHtXXhz3wXScbHfc992XjL99Q7NumCVS2NBxOqG+DNj0Q2KLfX6+WVeYVs31vOD755BNlZ\nzXsTe4E9JRU8/e466uq9fO8bIymrqGHBqp2MG9qTiSN6sX3PQbbtOQg41+7zc9Zz/QXjIqpLKPNX\n7OT86YeHzDR84KUVjV1y739hOQ/8NPpZKSW+/v7mWsyg7vTulhvZhnFM3ApnrLBk0tY95d5auDl4\nPcLYtl2Pq9bOKJNF3KTrT0Qk9WnMqnC1q5QDdwVm6UQucS9WsD3HYwascFXX1LUYiLv5wQWUV9ZE\nlC0V2F0RQgS2fIsefGUVr87fyOK1u/nh3XNC7vcf733F8vV7WVW4j0deX83d/1zGR0u3cf8Ly9ld\nXE51rX879h2I/nUP1tyGQe+D2V92aOy4iqr0unleGEM2YTIMWQXw+Jtr3K5CCxJzlmLda3FpFas2\n7qOmtvnnQzLN7qkhqEREREQkHMqsCiHRX+2T59ahiSSp1K8e/JTTjx7otyycqr21cBOrNhbH92Yo\nmn35IkWxDqb/wZKtvDx3A4P65HPd+eNYtHYXT72zjm75HfjFd48M2mWusrqOK25/h8vPGNV8h3E6\nL4vWhjeD47Kv9zT+vSGgK9BrCzZy0pED4lOhECLJrktmSTHoeRtb29rg6PEQ9fshMdGWWPa6t6SC\nWx5dyMHKWnfHxQujEZEG9zftKmXkwPQc609EREREQlNmFe7EaJLyx+UkqVS918tbnwXvZtKS5z5a\nz+rCfRFPdx73WfJ8N2P3Pb886l3U1NbzzHvrOFhZy5pNxby3eAtPvG2p93rZd6CKp96xIbetqKrj\n2Q++8l8Y4UWe6EyMurokudgiVFtXz7uLtvDWZ5vScuyd1HxV5M0FGzlY6WRIBgv0efEmTUZTpNX4\n68sr0/K9JiIiIiItU2ZVCEnyvT79xClNpCqJxj0JNhV7VXVdTGOzBHbN+zBgzLM1m4pb3L6kLLas\nLjds3Fka/cZtlH702vyNvLZgIwD7Squ45BvxndUzrbXDDDGJXcnB6haD8yIiIiKSnpRZRZgDvsb5\nRsuD8yX81XmFYXeras8CYxF/e311mxw32qBlfYxpDIGXW7JkRbR3DYEqcLpptubnD8xPYG3iT/Ek\nR02LXUhT8ywlS3fSniEmPWjJ/JU7E1ATEREREUlmyqwKVwKCBX95YXnjtN453x7P+GG94n+QNlTv\n9eIheKZRvC1ZV5TwY4QrIQOsx/kU1tdHVsfK6uaDrkektcMlyY1zohWXVrldBYnC8vV7o9puW1EZ\ne0oqGXt4DzIzkuu3ILcC3sP7d+XrbSWNz3NzMt2piLS52bNn+T3X7GzSlnT9iYikvuT6Np1E2uJe\nen2TQacffjUxmUKfr9nFU++2TReKq//4EX969guNLxKHYF3zzKrY7jS37C6LKN768bLtzZalWnJX\nsmSSBIo0cCjJpXDHAX794Kfc//xyyitrGpd/va2EW/++iPueX84P/ncOW3aXRbTfJL1cIxLsys7K\nDGxZOrRURERERBKtXWRW1Xu9rN9WQvf8HHp1zY1qH+u2JnZ2qsAxigB27StnTpCgQbgKdxzgwVdW\nxVKtiK3dvJ95K3awvyw9MkoSkjUVhkRkp0UyO+Fnq3fF/fh+vNENbP/3N9eEf4gId19RVctr8zdS\nV+/l7OOGkJebHWHtwvPUu5bLzxiFh9QLALalX/51AeOH9aRzx8S8DrHYvb+C3fsreOfzLZw/43AA\nHn19NXVNApG3P76IW6+cSv9enRuXufV6J3rCBJFglMkibtL1JyKS+tpFZtWTb1vuenopNz+ykE1R\nDuIc6a/krWrl3sHr9XLPv5bFdIh/f/h1TNtHa9lXe3h9waag69rFLVMCAlzx2OVXW0taL5Tk5i7f\nEXR5PK6rV+YV8vbnm3lv8Raen5O4906wrLXkkbh3aKR73lNSyYdLt7Eqwtk921LTMcx2FVf4raur\n9/KvwFk5W6DApYiIiIjIIe0iWPXJl87NYXVtPY+/tbbV8l5v8BuHDdsPxDxwdrj2lFSyp6Qypn1U\n17Y0SHDipNpNV6xZB4nIggrcZTzO6b8/ii0AE9dWJmHU8t1FWxr//uTL4EGxQDUuvccSJ77v3h17\nD/L4W2t5b9GWqAOuG5p0l041u/dXtF6ondlaFOcffkREREQkLbWLboBNbdoVXWYVwO+fXMzx4/px\n5czRcaxRcPHofuZaV7yEBPQSF92Ipjua3/YJaG/z1rofAmypBsk6PlSibdtTxpC+XRJ6jFfmFSZ0\n/4n0wEsr2b7noNvVCMqNd1Sot8mKDXt55LXEzXAa62dcTMd2/6NLRERERFJQwoNVxpjDgDXALdba\n+wLWXQU8EmLThdbaaYmuX6TmrdjB5Wea2Gd6auULfKzf79/+dGPEM5HpniJOEhC50Q2fu+zmYl6d\nv9GVY3++Zrcrx42HtgxUbU+BjJ1Q3cnv/feXbVyTBNEHlYiIiIjESUKDVcaYPOBFIJ/gsZAJvsc/\nAIF93rYmsGqt8PJcC12mUuH7+APPu3fzkwKnJ6FWbtjLkSN6xXWf6XZO2yIRK14xQ6/Xy5+fW05V\nvGe5TMoR1lM3Ra4wCbsLNj2bb362iRc/2eBaXUREREREUknCglXGmME4gaojWyg2Hthrrb0pUfWI\nVm1dYu8i65Iw4jXni22cftTAmPfTYtOivBdO5m5mgTM5/uvDr3kpxpvS5Ls6IgvShtXtKJ6NTPD1\nEU6gqryy+Yye0r7tKq5g256D9O/VmefnrHetHuWVtZRV1iT8OMn4uSXumT17lt9zzc4mbUnXn4hI\n6kvIAOvGmJ8AK4BxwIctFB3nK9fu3PzIQrerENRT71q3qxCUW7G9B19Zxf6yKh59fTV3PrUkaJmS\nsupmy+I+uH2a3QXGuznBXoN4XTPh7GZrURm/fujT+BxQ2kTT+Ob6bSV8vGwbFVWRBxzXb295ls07\nn1pMdbyz8iJ09z+X8eTbyfnZLiIiIiISTKIyq24ACoEfAgY4ObCAMWYA0B1YnqA6JLW6+uSMPqze\nWOx2FZLKzn3l/OaRhVHdxMYiCRPvJIjX5m+kT49OfPHVHsoqEp+5IvHT8BbbsP0Adz61BC/w2apd\n/OqSSRHt57E31vA/Pzgm5PqKqroWxx2rrE7vjDx9lLVfymQRN+n6ExFJfYkKVl0DvG+t9RpjRoUo\nM9732MEY8zJwLNARWAD81lq7KEF1a1VrX66TuUtaMkjI7HgunvO4Baq83ui7qyXBNRfqNairr8cT\nYQVDlS4pq+LVBRsj2lcoTYNHVdV1PPmOZcvuUs49/vDIdtTC5fzFV3uirJ0kiyfeXtv4Etst+6mq\njiwLasfe8lbLtNSN9L3FLg7PKCIiIiKSpBLSDdBa+561trWIRUOw6kdAB+BR4D3gFGCuMea0RNQt\nHpT14q51W/bHdX9t+nqm8LUT6jy99ElhxPuqrq0PeiqeencdHy3dFvH+gnn0jTWNf3/85XY+XbWT\nrUUHeeCl5j2Pa+vi3G0zDJEG+CQxdhdX+D2vT8AHQn0LmbSxjm+X9PQfpoiIiIhEISHBqjB5gI3A\nJdbas6y1N1prL8QJVmUCfzfG5LhYP0mAWG/PvV4v9z6XJtO8RyCZwxpvfrYp4m1WFe4LunzpuqJY\nq+OnIfDw0tyWAwI/uvvjkOvCGiw+xZRV1LB+Wwl19W0fpEt2icji3Lgz+WYqTATFpUREREQkXhI2\nG2BrrLV3AXcFWf6JMeYZ4DLgBODdtq5br555La4vKMgnKzP+cb6CgvzGv2tSuK9hdnboyyo3t0OU\n+8ykoCAfr9cbcTed1nTv3snv3Eciku2ysjLIz+/Yarl3Fm/lX++v81vmCXI9RFvnaHXsmBXymN26\nd/J7npWV2eK+6r3QvVunFsvEQ++CfDweDxmtvJ2CZdM0tLUuAVlXBQX5MWXwxPLaV9V7+fF9cwGY\nZHpz2zXTGtdlZqbu506k8vJynPMY0ORevSI/t629Hhkh3g8dOrj2X3BC9OzZmZ5dc/2WZadZG0VE\nRESkbbiZWdWSL3yPQ9w4eFL8OJwUlYhOIjNRUv2X+3BikIGBqmQR11PfRi9kPA6TiJomYly3cN37\n7BeNfy+1u9m6uxRwuqoFdolLZ0+/tZbyyppm10giwnVuvt4iIiIiIqnItZ88jTETgC7W2rlBVjf8\nNFvZhlVqtGdPWSvrS8nMiH+cr6iotPHvfftaH7Q3Wa1cvzfkuoqK6qj2WVtTR1FRaYtjv0Rr//5y\nioqiy/hq+pq1prbOy4ED0V3SwW52Izl2PFRV1oQ85v5i/+u1toUBpcHJrCren/hrvGhPKRkeD9Fc\nNg1tTcR4Vjti7BYWz9d+87b95Hhgzab2NRNoaXk1Dz7/ZbOIZmuf/8G09npUVgafpKE6zWYC3Lv3\nIPUBbapJszZK+GbPnuX3XLOzSVvS9ScikvrczKx6HfjIGNMzyLrjfY+L27A+jb5oZdwc/UgevZpa\njZETjWAZWamcrVFbV89XcR4oP5XsSqJgdMNlFM3YY6nuoy/iM5h/axIRZE8V7bflIiIiIhILNweT\neB64AbgT+GHDQmPMt4GzgI+ttavdqNjf31rrxmH97Eyim9l4mrt8R9Tb7tlfwd4oM5OSgtcb1z5G\nhTvaNrMq3h2knpuzPq77C8pLUo5Of8tjn7tdBfFpFkxJwPWy7Os98d+pSJJTJou4SdefiEjqczNY\ndQcwE/iBMWY8MB8wOIGq7cD3Xayb6558x7pdhaSycWcpN/9tIdUJyMxK1QSltxe2dSZM+CcqRU9p\nUKl6fUh0/vcfS92ugoiIiIhIu9cW3QC9BLl3tdbuA44G/g84DPgxcCTwCDDZWruxDeqWtIpLq9yu\nQlKpq/cmJFDV5uIY+FhsW+6u6qZk6aL4wEsrYpp1T9Jf4OURTcZie+7m11SyvO9FREREJPUlPLPK\nWvsE8ESIdcU4XQFvSHQ9UkW919vuBjpOBm1yzsOZCjBF7Qvonrm16KBLNfH3xVd7WNHCgP8tmbNs\nGxOG9SIv180E1MT76IttjBzYze1quCj2AMvrn26MeR8iIiIiInKImwOsSxDPvLeOe/65LKZ97C5O\nz/GuEunZ979qmwNFHa9yP9D16apdIdf9o63OXxTs5ugGcn/ybcsdTyyiLs2zZhau3pVUA76nopfn\nFrpdhaSV3u8eEREREUmU9E4ZSEEfLY19dqrHXlsVh5q0L1uLIp+uvj0qq6gJujyZB773xnC7vL+s\nOikHx/Z6vXjimKk3f+XOJAiHSjqZs2wbc7/cQeGOA25XRURERERSkIJVaWj+l9vdrkJKeehVBffC\nta0dBvUqKmvdrkIzKwv3Me7wnm5XIy24OczS8ii7qCa74tIqnnxbk4S0d7Nnz/J7rtnZpC3p+hMR\nSX3qBphEypPwprg9aLPB7NNg8OGUbUIM9U7GJu/cq257krxWFqZnEE5ERERE2o4yq5LIu4s2u10F\niVAkY/3sKq7gwMHqqI6jLlrRK9weWzekT5YpU1FE4s8Y0xe4FZgJ9Ab2Ae8Dt1hrCwPKXgb8FBgB\nFAP/9pVrNpuFMWYmcDMwBqgAXgNutLb5FLLGmGnAHcBkoB74APhV4PGjoUwWcZOuPxGR1KfMqiTy\n6vyNbldBIlS0vyKi8i98vCFBNWkbO1Nw8P51W0tiivZt3p18XR+TMdtLpJEu0Fb5AlWfA9cAq4A/\n+55fDCwyxgxvUvZG4HHf0/uBL3ECV+8aY7ID9nsRTnCqFzAb+BC4AlhgjOkaUPYEYA5wBPAY8DJw\nNvC5MWZw3BorIiIiEgVlVonEoK3uyeI4lnZMNA6NpJuU7dqahHQuI3IrMAD4mbX2zw0LjTGXAE8B\n9wDn+oJGtwMLgBOstXW+crcBv8UJdj3gW5bn+3s9cKS1tsy3/F3gUZxsq//2LcsAHgLKgCnW2u2+\n5c8A7wF3A99OXPNFREREWqbMqijoC7mIpJMkiYWKtCfnA7ubBqoArLXPABuA04wxHpxgVCZwZ0Og\nyudO4ABwdZNlFwHdgHsbAlW+ff4dsMAVvn0CnAKMBB5tCFT5yn6IE6w6zxjTIy4tFREREYmCglUi\nItJu1evXB2ljvqym/8HJrgqmCugAZAMzcJJ45zQtYK2tAj4DJhhj8n2LZ/gePwqyz4+BnsDYMMrO\nwQmQHd9iQ0REREQSSN0ARVJATW2921UQEZE4sNbW44w91YwxZhQwClhvra02xgwDdllrgw0YuNH3\nOBJYAgzDCWwFGxyxadkVvrLgdBkMVXZES+0QERERSSQFq0Ri0FZJGdUKVolIkvNqZPWY+DKu/oLT\nM/dh3+KeBA8oAZT4Hrs2KVvly7oKpyzA/jDKRmX27Fl+zzU7m7QlXX8iIqlPwSoRSXtV1XWtF2rH\nXluwkW55HdyuhqQJhawi5xtL6iHgZGARzuyA4HQFDBZ8osnyjlGW9YYoH1hWREREpM0pWCUiIuwv\nq3a7CiLtkjEmC3gEuBwni+pca22tb3UFzvhVweT4Hg82KdsngrKeEPsOLBuV3/3ud7FsHlddu+a6\nXQU/GR7o1q0TBQX5rRd2UbLXryXhXH+p3L5wpXsb1b7Ul+5tTPf2JZoGWI+KfjcWh1eDM4uIOHwf\nh5pdMnzGmE7AKziBqnXASdbanU2KFBO6O17D8pImZTsaY7LDLNt0eUtlRURERNqcMquiULijlJED\nu7ldDUkC81fubL2QiEg7ohB+eIwx3YG3gKnAUuAMa+2egGLrgOnGmJwgY1ENBeqAr5qUPRYY0mRZ\n07IAtknZhuVft1I2KkVFpbFsHlclJRVuV8FPvRf27y9PqnPUVEMmQLLWL1bp3j5I/zaqfakv3dvY\nXtqXaMqsisLmXel50UnkFq/d7XYVpB2qr/eyaO1uXX+SVBSkCp8xpiPwOk6gag5wYpBAFcBcIBOY\nEWT7Y4BV1tqDTcoCnBhkPycC+621a8IsWwd83mpDRERERBJEwSoRkRTzyrxC/vrySma/vNLtqohI\ndO4EpgELgDOttWUhyv0DJ3B0qzGm6fhSNwH5HJo1EOBloBT4pS9rCwBjzJXACOBvTcp+DGwGGufT\nTAAAIABJREFUfmiMGdyk7CnAN4CXrLV7o2ybiIiISMzUDVBEJMVU1Wh2Q5FUZYzpC1zne7oWuNEY\nE6zoXdZaa4y5G/gV8IUx5nVgDHAWMA9nYHYArLXFxphfAn8FlhljngP6A9/G6dJ3Z5Oy9caYa3HG\ny1psjPkHkAdcAuwG/juOTRYRERGJWMKDVcaYw4A1wC3W2vuCrL8M+CnOr37FwL99ZWOahUZERETa\njroBhu0YIBvnlF0ZoowXmAVUWWtvNMZsAa4Ffgzs8K27zVpb03Qja+1Dxphi4Je+8nuBx4HfWGv3\nB5R90xhzBvA74CqcrKxXgJustZtibeTs2bP8nl977c9i3aVI2HT9iYikvoQGq4wxecCLOKnqzb7H\nGmNuBP4H+BK4HxiPE7g6xhhzYuCXMBEREZFUZq19mQiHYbDWzgZmh1n23zg//IVT9gPgg0jqIiIi\nItIWEhas8o2B8CJwZAvrb8cZr+EEa22db/ltwG+Ba4AHElU/ERERiZ8PFm/lolNHuF0NSRLKZBE3\n6foTEUl9CRlg3RjzE2AFMA74MESxa3BmuLmzIVDlcydwALg6EXUTERGR+Htv8Ra3qyAiIiIiaSJR\nswHeABTiTLX8VIgyM3C6Bs5putBaWwV8BkwwxuQnqH4iIiIiIiIiIpKEEhWsugaYaK39DPCEKDMM\n2GWtLQ+ybqPvcWQC6iYiIiIiIiIiIkkqIWNWWWvfC6NYT2B9iHUlvseu8alRfGnGIxERERERERGR\nxEhUZlU4soGqEOsalndso7rExcFKTV4oIiLtm9ern3REREREJDYJmw0wDBVAhxDrcnyPB9uoLhHJ\ny8uhoKD5cFqfzd3gQm1ERESSQ0FBPvn5KfU7kyTI7Nmz/J5rdjZpS7r+RERSn5uZVcWE7ubXsLwk\nxPqkVFevX5NFRERERERERGLhZmbVOmC6MSbHNwNgU0OBOuCrtq9W68pKqygqKm22/GBZpQu1ERER\nSQ5FRaWUlur/QlEmi7hL15+ISOpzM7NqLpAJzGi60BjTETgGWGWtTcpugCsL97ldBRERkaSzt0SB\nKhERERGJnZvBqn/gZE/daoxpOnbVTUA+8LArtQrDig173a6CiIhI0rnlsYUUl4aaO0VEREREJDyu\ndQO01lpjzN3Ar4AvjDGvA2OAs4B5wCNu1U1EREQiV1FVx6vzN7pdDRERERFJcW2RWeX1/WvGWnsj\ncL1v/Y+BI4BZwExrbU0b1E1ERERERERERJJIwjOrrLVPAE+0sH42MDvR9RARERERERERkeTn5myA\nIiIiIpKGZs+e5fdcs7NJW9L1JyKS+twcYF1ERERERERERMSPMqtEREREJK6UySJu0vUnIpL6lFkl\nIiIiIiIiIiJJQ8EqERERERERERFJGgpWiYiIiIiIiIhI0lCwKkr19d7mCz2etq+IiIiIiIiIiEga\nUbAqStv3HHS7CiIiIiIiIiIiaUezAYqIiIhIXM2ePcvvuWZnk7ak609EJPUpsypa6vEnIiIiIiIi\nIhJ3yqyKox171TVQRERERJks4iZdfyIiqU+ZVVEKllj18bLtbV4PEREREREREZF0omCViIiIiIiI\niIgkDQWrouXRoFUiIiIiIiIiIvGmYJWIiIiIiIiIiCQNDbAeJeVViYiIiDQ3d+5crrr2v+nao6/b\nVQGgrq6WzkNOcrsaIiIiEgEFq6KkXoAiIiIizdXV1XHJt870W7b4wESXaiPt0ezZs/yea3ZAEZHU\nkxTBKmPMHcBvQqz+l7X2orasj4iIiIhIInTo1JM77p5Nxr0Pu10VAEr2bufB++9l3LjxbldFRESk\nUVIEq4AJQBVwV5B1K9u4LiIiIiISgzeWlNJnxHS3q5GUuvc/gu79j3C7Go0y7PvU19e5XY24UiaV\niEjqS5Zg1XhglbX2drcrIiIiIiIiIiIi7nF9NkBjTBdgELDc7bpEwqNBq0RERERERERE4s71YBVO\nVhWkWLBKRERERERERETiLxm6ATYEq3obY94DpgBe4APgN9bada7VrAXKqxIRERERERERib9kyqz6\nBbAfeAhYCFwILDTGTHCrYiIiIiIiIiIi0raSIbOqFtgIXGGt/aRhoTHmYuBp4DFgsjtVa4FSq0RE\nRESCmjk5H1jW+HzxgYnuVUbandmzZ/k91+yAIiKpx/VglbX2+hDL/2GMuQaYYYwZmWzdARWrEhER\nERERERGJP9eDVa34ApgBDAGSKljVo0ceBb06u10NERERkaTzxpJS+oyY7nY1pJ1SJpWISOpzNVhl\njMkEJgCZ1tpFQYrk+h4r265W4fEotUpEREREREREJO7czqzKxhlM/YAxpsBaW9+wwhjjAY4Famg6\n6EGS2LvvIJn19a0XFBERERERERGRsLk6G6C1thJ4HegO/Dpg9c+BscA/rLUH2rpurVFilYiIiIiI\niIhI/LmdWQVOUOpY4PfGmBOB5Tiz/50ArALU6VxEREREREREpJ1wNbMKwFq7AZgCPIGTSfVfwCDg\nbuBYa22xi9UTEREREREREZE2lAyZVVhrtwDfd7sekbCb91PQLbf1giIiIiLtzMzJ+TQdcnTxgYnu\nVUbandmzZ/k91+yAIiKpx/XMqlT12Jtr/J5v3lXqUk1ERERERERERNJHUmRWpYO/vb7a7SqIiIiI\nJIU3lpTSZ8R0t6sh7ZQyqUREUp8yq2JQ7/WyZXcZ5ZW1bC066HZ1RERERERERERSnjKrYvDXl1ay\nZF0R3fNz3K6KiIiIiIiIiEhaUGZVDJasKwKguLTK5ZqIiIiIiIiIiKQHBatERERERERERCRpKFgl\nIiIiIiIiIiJJQ2NWiYiIiEhczZycDyxrfL74wET3KiPtzuzZs/yea3ZAEZHUo8wqERERERERERFJ\nGsqsEhEREZG4emNJKX1GTHe7GtJOKZNKRCT1KbNKRERERERERESShoJVIiIi7dwpkwe4XQURERER\nkUYKVomIiLRzJ0w4zO0qiIiIiIg0UrBKJEmcd/xQrpo52u1qiEg7lNcp2+0qiIiIiIg0UrBKJEmc\nfdwQjh3b1+1qiITljz+a5nYVJE6+f9YouuXluF0NEREREZFGmg3QBd88djAXzBjGlX/40O2qSJLI\n6ZCJx+MB4LixfZm/cqfLNTrkgZ/O4Lp7P3G7GmlpWP8urN92wO1qRKWgW67bVZAYXHP2EWRkeMjN\nyWLc4T3dro6koZmT84Fljc8XH5joXmWk3Zk9e5bfc80OKCKSepRZ1Yb6dM/llxcdyfnTDwegQ7ZO\nvzg8blegBbk5imknQp8enTj3uKFuV0NacdcPj3G7CgmRkeFh6ug+ClSJiIiISFJqt3ehv/juRF6d\nV8i6rSVtdsy7fujfbaZDVibVNfVtdvx0c8yYPny2apffskvPHM1Tb61xqUbihkF98ti8q8ztaoTt\nxCP7c8qk/vTqmsuW3alT73j43/+cxi//+qnb1YhIn+6d4ravVM6ki1WGx0O91wvA0H75ZGdlsm7L\nfpdrJYn0xpJS+oyY7nY1pJ1SJpWISOpLitQeY0yWMeanxpjVxphyY8x6Y8zNxpiEBNNuv3IqRwzp\nwX9ffGQidh/U5JEFzZbV1CV/oCo7K4MHfjqDEyYGnynquycPb+MaHdI5p/mAwDNTNFNlUO+8mLY/\nceJhTB3dO6ptPQlO6xrcJz+h++/c0b2BoQf1ifx1O3JEL/oX5JHTIZOeXTvGtT6jh/QIu+yAgvDr\n3i2vQzTVaSY/twPXXzAupn3Eqy5u+O4pI/jhOWPcrkbYQn3uR+qUSQO4/4bjueiUEfz8OxP57eVH\n8etLJsVl3yIiIiKSnpIiWAU8ANwDFAF/BrYBtwPPxusA3z9rFDdfNoXHfn0yA3yBgcyM1pt/xZmj\nmDi8V8zHP3lS/2bLTj9qYMz7bU2oG7tw4xNer9MN7PIzRnHMmD7N1h8/vuWbmePH9QvzSJEZ2DuP\n82cc3mx5ogMvkfjJt8czc9pgAM48ZhBTTPOAZYNLTjONf3ujONZRo/vwo3PHMn1i8+usQff84AMo\nX3veOO64+ugW9z96cPdW69AlxGxi3z5pGNeeN7bV7UPp2CHT7/n4YT0p6HYoyPOdk4dz0akjGp+f\ne3zbBCwnDOvJLy+K7IY7LzebMUMPBZRCvSbRGjaga1jlTp0ygNuvmsp/tvK6jB3ag99dcRT3XHcc\nZ0wd1Lj80tNNC1s1l9Mhk++cPJycDplMChK4D9eJEw/jtiuntlru2LF96dI58qDW6VMT95ncKSeL\nwX3yOSrMoPJPvj0hbse+9LSR/Py7/uMFjRjQrdXtLjvdcMQQ570f7P+wcF1y2kg6dczmG0cN9Lv+\np49v+f+HK88azVUzR/P7q49m/DB1VxQRERFpT1zvBmiMORb4AfCctfY7TZY/DlxmjJlprX0j1uMc\nN64fGUEiGQXdOlK0vzLoNtPG9GHGBCdj5dpZMQ4wHeTYp04ZiN28HxukK8QJEw9jx97ymLpJTBze\ni5nHDubxt9bSMTuTc44fynMffc3BylqunDma6po6/u+FFS3uo3PHQ5fIlWeNZseecjbtKgVg2pi+\nZGe1HB26cuZoLjzhcF5dsJGPlm6Lui1NXTVzNEeO6EWnjlnkd8qmtLwGgHOOG0JGRuKiVdedP5YH\nXloZdvnxw3oxflgvLjxhGACbdpay2BYFLTswxsyqnGwnoPOziycxd5n/eR7Wvwv9enbmklNHcv8L\ny1mzqdhv/WRfEO1vvzqJsvIa1m4u5sFXVgGHglTXnT+W6/8812+7o0b1ZtHa3Y3Pb7p0Mr9+6LNm\ndRvUJ5+83JaznzzAf543lodeXUVd/aFwXW5OFj/45hHc/8Jyv7K/vGgS81fsYFj/rgzqk8/A3nmM\nGNyD2rp6hvXJY9Ha3Wzfc7DFY4YyZmgPVhXua7Xc2MN7RhwcvWrm6KCfQ7EYPbg7azYVc8a0IcyY\nOIDX5xW2us05vgzE1mrys+8cCnB868RhDOmXT4fsTCb4AgfTxvTl01WhJwPIzPBwzTljOGqUf4Dm\nnuuO44/PLGX3/gq+eexgzKDuvPnpJjrlZLFkXfD3CMBlZ4xqpcbw7ROHceYxTpA40kksvnPyCN75\nfEtE24RjiinglMkDyMp0fiA557ghvDp/Y7NyHuDyM0cxY0J8MprACYieMLE/eOCMqYNYUbiXEyf2\nDytQ6vF4+MV3D2Ugd+qYzesLDtW7e34Opx01kH99+HVUdbvwhGF4Aa/Xy+lTB3HLo5/7re/dPZeR\nA52g2sWnjmD5+r1RHUdEREREUo/rwSrgOt/jbQHLbwQuBa4GIg5Wde6YxcHKWsAZyDzUDeK1543j\n2Q++ahYUuv2qqfTv1RmAjh3icJq8zfNl8nKz+dUlk1i+fg9/fu7QzXhmhofLzxjFH59Z6lf+vy4c\n12pwKbC8x+PhjqsOZc00HUy3vt7Lt04cxuZdpXy+ZnewXXDVN0c3/p2VmcHNl09mzhfbKa+s4dQp\nA8nOymT6+H7MXb6DvNxsyipqmu2ja14Ol55mIg5WhbqhO65Jttat35/Ky3M3kJuTxelTB4WVLRfo\nN5dO5qMvtrHANwPfKZMH8MGSrc3KTQjIsPvBN4+gorqWp99dF9ZxBvXJ45JvjGTlhr18GeebrqH9\nnK52DTfDTf3m0imNf08d3btZsKpBhsdDl84dOGpUb+q9Xor2V3LSkU42RacgXe2u8N1Ub95dynHj\n+tGlUwfuuPpoHnx5Jdt8gaIrzhwVMlD1o3PHMGJAN3YXl9OxQxaD++ZT0C2X2x5f1FjmyrNGMbiv\nfzfCaWP70rNrR85pkkHl8Xg46oi+ABQVlfL9s5z3T21d5Hlq1543lhUb9vLYG2uornW66o47vCcr\nNjR/zSINPHXIzmy9UATMwG784rsT8Xg8FBTk4/V6mTSygKUtBHyAxtekpeBuYDfBhgG5m/rWicNY\nv62E3fsrOOe4IXywZCsHK2vJy83m1u8fRVZmRtAMp+75Odxx9dFU1dQ11mWMrwtjaXk1+w5UUdAt\nl+v/HNmPBEcM6c7pTTLAAv315yfwxqeb+GjpVsYe3pORA7vx1DsWOPQeikR2VgZHH9GHect3hCxz\n34+PJ7+T/zkIvG6mjOrNd08eTl29N66zLF7yjZFMGlnQ+Dr/x8nD+Q9Cd93u06MTu/aVN24b6Oxj\nh5Cbk0ltnZfTjhrYGCSPNljVpXMHrjzr0P8xf/jhMdz08ELqvV56dMlheJNMwd5Bxg47Zkwfjh4b\nv8CeiIiIiCSPZAhWzQCKrLWrmy601u4wxnzlWx+xZ39/Fs++vYbS8hpOnTIgZLnBffP59SWTWLh6\nFw+/ugovcNkZJqLxXMLR0uhU43wZGg3xrIt9XZoG9833y7pq6SbmyBG9+OKrPY3PLzrN4GnlRjoj\nw8NZvgyEo0cX8X8vOoGwSSMLOKxXJwYU5DF2qH/Xi8yMDE6Z7H8+rzhzFKdOGUi3vA48+/5XfLba\nGfT8tAi7OX7rxGE8P2c9Ho8TRJxsCoIGq5rqnp/D95vc7GRnZTBpVG+Wrg0efAumd/dcLj51JN3z\nc+jYIZPTjhpEfb2Xj744FFw7eVJ/sjIzuOFb43njs00M6et058nKzGDyyAKqaur45wdfs+xr5zUI\n1k3K4/FwyuQBnDJ5AGs3FfOnf36B10vUY00F7jscx47txxNv21b3dYwv8NNU00yq3JxMcnOyGDO0\nh1+3nv69OrfapbBBTnYm3fNz/DI8BvfN5+bLpvDWwk0M6p3HxBG9yMzI4MITDuf9JVs5YnCPxkyw\nlgw7rCu3XTmVfQeqKNxxgBc/2RBWnb514jByc7KYOroPQ/t14Z8ffEV2VgbfPWUEP/vL/OZt6JDJ\nwN55jQOlX36GYfHa3dTVe/neaYbisiru+aczdXv3/BzMwNa7XoFzHdfUtj6m3fQJ/fxee4/Hw3Xn\nj+WqP34Ucpv+BZ0b/x4QJKNvWP8u1Nd7ueLM0c3WBeqen8MffnRo4ojp4w9j9aZ9jBnSgx5dWh6P\nKzsrg+ys5sHV/E4dmgV3wD/LM5STjhzgF4AbP6xnYzbO988cRU52JhfMOJwLfF2Ia2rrKS6tomh/\nBWcfOwSAAQWd2VrkBFsbgvsnTeofNNh+7/XHU+/1snlXachB/oO2JSCAO/ywLq2er2C+eeyQxkyn\ny84w7NhTznuLncywU32fNZG48XuT+HDJVnp3z2XamOafAdlZGZx59OCI6xmu3t07ceP3JrFuy36m\nju7TajD4mrPHUFCQ2DHxRERERMQdrgarjDE5QH+ged8hx0ZgpDGmp7U2olQUj8fDN6aEHyw5+og+\nDOqTR129Ny6BqsDshi5BblgaeDwefvYfE3llfiEDCvIaM4fOmjaYhat3UXKwmlGDugWtV25OFjOn\nDeaMowfxwpz1vLVwM0eOLOCCE4dTeqAi7PoeObKAmy6dTOnBaiYM7xVRdzqPx9PYje0/Th5OVqZz\nE3r2cUPC3gfAWccMZtSg7nTIzmhs6/kzDuelJoGGhhvKltx0xVTenb+Bfj0788mX2/2CTg0aZqYa\n3r9r481kQ3c9gO+dNpJTpwzg/SVbyc7M4BxfWyYM79Usw6prnhNsuXLmaN5dtJmunXM4oZVuPKMG\nd+fmy6awp6SSI0f4729Y/66NWV5NPfDTGfzsgflUVdf5Lc+M4LUKFhwI13+cNJzqmjpq671Bsy7i\n5fDDunDd+f6DcM+cNoSZ04ZEtJ9+PTvTr2dncjo0z2bq17MTxaVVVFbXMXF4L/7zvDFkZ/mXK+iW\ny39dOL7xeaiAxQ3fGs+cZdsY2Dufo0b1drpc+RzWqzO3XDGFdZv3M2VU75i6qTbNeumen4MZ1K1Z\nphMED1x+89jBvL5gEx07ZHL1zCMal/cOEgBvmokXqZ5dOzK9lXHsIjFz2mDe+HQTWZkev4HZv3vK\nCP75wVetbv+Ds4/gg8Vb6dGlI8eNCx58uSBg7Ltrzh7DE++sJTszg0tOc67zi08dwfDDupKZ6WHh\n6l1s3FnKuccPpZMvgHbLFUdRV+dl74FKbno41H9nhxw/rh8vfrKBiqpaOnbIZHqU3f7Omz6U8SN7\nO0HTHrlUVtc2Xu9nHRM6wyyULp06cN705mMBxqK1cdECDevflWH9g4+9NrhPfmM3dEl+MyfnA8sa\nny8+MDF0YZE4mz17lt9zzQ4oIpJ63M6sakjJCDUwU4nvsSuQ8MEq+vXsHHLdqVMG8P7i5l3DQpk5\nbTCFOw5QXFrF6MHdWx2TKDBDBWjsVrVzbzlDfF1ULj/D8OQ7ltwOWfzsOxM5/LAujeW/fdJwvn3S\n8MZfmiP9Sj88xA1CJLrl5XDlzOAZGWZgN79MsW+fOIzn5qwHaBzEvml7wBljpWmwqmHA8pbkZGdy\njC8r4NK+hktPN9z++CI27nTOyA3fGk+Xzh3YuruMKaOCZzV5PB769ezMpaeFP5B0Xm42F8wY1npB\nn6H9ujC0X5dmy48f15ePl21rlqmRm5PFHVdN5cuv9/LMe+F1PYynnl07ckMMgz6PGdKdVRsPdUHM\nzWmbj59hh3VhxoR+fPKl01Vr7OE9uOw0Q0aGhz0llQzv3zWmIFKPLh1bfN2H9O3CkL7NX+cG500f\nystz/ceZOrxfl2Zj2d1+5VGUVdTSLa9Dq5l0F8w4vDGb7Lzjh3LO8UM5+oi+5HXMagyupoILZhzO\npJEFdM7N9gusnXbUQCaN6MXna3fzvO8zBPyzxsCZKfKcCAfcH9A7r1nALjMjg2ljnc+UYAHCDI+H\njCwPfXt04oozR/H4W2uB0JMO5HTI5JYrprBywz7GDu0R9nuh6b6/d9pIMjwepvl+3CgqKqVjh6xm\nwTe3BY5XFosrZ45m1r+WcbCyJuT/MyIiIiKSHtwOVjV8k68Ksb5heXznd4/CRaeMYOqoPtz59JJW\ny2ZlehjSN5/br5rKzn3lDO4TfTeFvNxsv3E7TpjYn0kjC8jKzGizm/14uewMw33PL6e0vIarZ45m\n7OE9qKyuo6yyJmTGVHZWBo/9+uSYj/1fF47nw6VbOaxXZ8YP64nH4wkaKEoG2VmZ3HzZFJ582zJv\nhRNgaZhJsFfXXE6YeFiLwarRQ3qwZqMzQHiwWfgabngzPJ6Isx5iceXMI7jx4U+prqmnT/dcv+s6\nkTweD1ecOTpot7ZIul4FzkrYIYYstabOOW4oR4/uw5PvWNZsKiYnO5PvnTaS3wYMNp2dlUn3/PDG\nvDpr2mB6d8/F66Vx9rmGMfhSSUvv017dcjntqIGs2VTMqsJ9nD39cPr2aD6uUVubNqYvO/YeZPue\n8hazS/t070SfyS3Xt2lwavLIAmZMOIyBvfOo93oZdljbvH/C0S2vA/vLqhN+nIG98/jTtcfi9caW\nJSpt440lpfQZMd3takg7pUwqEZHU53a0o6GfWqg+cg0pAFFN6xXvsSx69+4CrQSrenXtyJ9+PINe\nviyAIQN7tFg+GuFO/J5sY3kUFOTz6M3+XXGuuTC8MXyiOVbg85GH9wpROjn9+KJJ9OqxlvLKWi4+\n3dCzq3NNeQMG67/49FF+7f315Ufx2twN9C/ozClHDWqWhXPhqYZjJvQnM8ND3xayCeOtoCCf+352\nIms3FjN1TN+gA2/H6ziJcNEZo3n38y3U1XvJzcnkrOnD6BingHFBQT53jejNyvV76NuzM32CBF3C\nbVdDuZm9Iw/GThndJ+k+N1rzh+uT72b4+u9Mist+LjhlJF275FJcWsnM44bSqWN2yNfHzdft15dP\n5dZHPqUyoIvyPTfMSLnrSURERESSg9vBqhLAi9PNL5iuvvUlIda3JL7zw/u8ds+5iditSDO5OVlc\ndU7zzCePx9PiddijS0cubzIuUTD94zyBQLgG9M5nQO/UvHntnt+Rl/90TsL2n5nhYcKIQ6Hotvqs\n0Wda8vJ4PJzawuyGyWLM4T157q5vul2NuDPGnATMsdZGPq2oiIiIiMTE1Tx6a201sAkINajIUJyZ\nAkONaSUiIiKSCM8D24wx9xljprVaWkRERETiJhkGfZgL9DPGjGi60BhzGDCC0DMFioiIiCRKX+Aq\noAvwhjFmozHmj8aYI12ul4iIiEjaS4Zg1ZO+xzuNMR4A3+NdvuUPu1IrERERabestTXW2restd8H\n+gD/CeQD840x1hjzO2NM8vfTFBEREUlBbo9ZhbX2A2PMv4DvAJ8aY+YAxwLHA89Za990s34iIiLS\nfhljOgBnAN8CzgKKgNeAkcBqY8yN1tr/c7GKSWnm5HxgWePzxQcmulcZaXdmz57l91yzA4qIpB7X\ng1U+lwKrgCuAG3DGsfot8L8u1klERETaKWPM+TgBqm8CVThjWJ1nrZ3XpMx1OJngClaJiIiIxFFS\nBKustbXA733/RERERNz2BPASTub3+77vKoGWAPe0aa1SxBtLSukzYrrb1ZB2SplUIiKpLymCVSIi\nIiJJpg/O4OrdGgJVxpj/AD6x1u4EsNZ+hiaCEREREYm7ZBhgXURERCTZTAO+Bi5usuwnwBpjzPHu\nVElERESkfVCwSkRERKS5WcDvrbW/a1hgrT0WZzzNe12rlYiIiEg7oGCViIiISHMjcAZVD/QcMLaN\n6yIiIiLSrihYJSIiItKcxRlcPdDZON0DRURERCRBNMC6iIiISHM3Aa8ZY07FmfXPA0wApgMXulmx\nVDBzcj6wrPH54gMT3auMtDuzZ8/ye67ZAUVEUo8yq0REREQCWGvfBibiRFyOAIYBXwBHWGvfcLNu\nIiIiIulOmVUiIiIiQVhrVwFKyYjCG0tK6TNiutvVkHZKmVQiIqkv7YJVxpgs4L+AHwBDgB3A34E/\nWGtrXaxaWIwxhwFrgFustfcFWX8Z8FOcgV+LgX/7yh4MUnYmcDMwBqgAXgNutNYWBSk7DbgDmAzU\nAx8Av7LWFsapaS0yxvQFbgVmAr2BfcD7OG0rDCibVufAGNMT+B1O2/sBhcDjwCxrbV1sKnObAAAg\nAElEQVRA2bRqe5B63I1zY3iitfaTgHVp13ZjzB3Ab0Ks/pe19qImZdOu/b7jXwLc4KtrCbAA+I21\n1gaUS4v2G2Pqwyjmd/2nS9ubHLcX8D/AN4FewHacNt1qra0IKOta240x3YGfA0fhfF/y+FZ5AK+1\n9uQoT4GIiIiItCIduwE+ANwDFAF/BrYBtwPPulmpcBhj8oAXgXzAG2T9jThBDID7gS9xvsS/a4zJ\nDih7Ec4X9V7AbOBD4ApggTGma0DZE4A5ON0cHgNexhlA9nNjzOC4NK4FvkDV58A1wCqc1+1z4GJg\nkTFmeJOyaXUOjDH5wDzgemAF8H84N+x/BF4KKJtWbQ9kjJkK/IR2dO3jjH9ThROoDfz3XJN6pmX7\njTG/B54CuuB8ds8BzgU+NcYMbVIundp/G8Ff77/61u8C1japYzq1HWNMF2A+zg9Ka3A+77cD/w28\nZ4zJbFLW7bY/CVwNrATmAp/4/n3s+yciIiIiCZJWmVXGmGNxvgA/Z639TpPljwOXGWNmJus4E74v\nyC8CR7aw/nacrIMTGjJujDG3Ab/FCfQ84FuW5/t7PXCktbbMt/xd4FGcX57/27csA3gIKAOmWGu3\n+5Y/A7wH3A18O/4t9nMrMAD4mbX2zw0LfRkXT+EEH89N03NwI2CAH1tr/9Kk7c8AFxljzrLWvpmm\nbW9kjOmAc9PYLICe5m0fD6yy1t4eqkC6tt8XnLwJJ2hwprW2yrf8BZxA3S3A99Ot/dba24ItN8a8\nghOo/Z61drdvWVq13edHOFlSf7bWNvbTMcY8BVzi+/dkkrT9VN+xP0/ImRARERGRkNIts+o632Pg\nzcCNODcBV7dtdcJjjPkJTlbNOJxfg4O5BsgE7gzoGnYncAD/tl0EdAPubfjSDmCt/TvOVNxXGGMa\nujOcAowEHm340u4r+yHOF/fzjDE9YmheOM4HdjcNVPnq8AywATjNV990PAeDgc04mQBN/cv3eIzv\nMR3b3tRvgOE4XT8DpWXbfRkmg4DlrRRNy/bjfF7XA9c0BKp8x38BeBhY51uUru1v5AvMnw08Yq39\noMmqdGz7JN/jYwHL/+Z7PNr3mAxt345zjYqIiIhIG0u3YNUMoMhau7rpQmvtDuAr3/pkdAPOOEUz\ncDKJgpmBE3Cb03Sh7ybvM2CCr0tZQ1mAj4Ls52OgJzA2jLJzcG4Wjm+tAdHy/cr9PzjZVcFUAR2A\nbNLwHFhrL7HWDrHWBt4QjfI97vI9pl3bGxhjxgO/xrkJXRWkSLq2fbzvsbVgVbq2/0xghbX268AV\n1tofWWvv8j1N1/YDYIzpiHPt78f5YaWpdGx7w2fakIDlA3yPDeNLJUPbfwHMNsacaYwZbowZ1PRf\n0NaJiIiISFykTTdAY0wO0B/nS2wwG4GRxpie1tq9bVax8FwDvG+t9RpjRoUoMwzYZa0tD7Juo+9x\nJLDEV9aLk5XUUtkVvrLgdJ8IVXZEC3WPiS9Ic3+wdb5zMQpYb62tNsak5TloyhjTG/gWTnbgJuBp\n36q0bLtvfJpHcbJo7gL+FKRYWradQ8Gq3saY94ApOHX/AGeA8YbMorRrv+8674Uz9tAonGBNw2DV\n7wK/tNY21CHt2h/gWmAgcJO1tjhgXTq2/SHgSuBeY8w+YBkwFWecvv0cyrhKhra/4HsMNnyAFyew\nJSHMnJyP8/I6Fh+Y6F5lpN2ZPXuW33PNDigiknrSKbOqIW1/f4j1Jb7HriHWu8Za+561ttmg0gF6\nEn7begJVTbvWtFKWEPt27Zz5Mq7+gjPr0sO+xWl9DowzM9xOnHbvB0631jYcP13b/guccdquttbW\nhCiTrm1vCFb9wleHh4CFwIXAQmPMBN/6dGz/Yb7HAThtHoTTDWw+TrD2syaZK+nYfqAxWHsDTre2\nwK7AkIZt92U+Hw90xJlcogyn+3stcJy1dnOTOrrd9sNb+Des2dYiIiIiEjdpk1mF000MnG5jwTQs\n79gGdUmEbMJvW6RlvSHKu3LOfGOLPISTabEIZ7YoSP9zsB74A86A6+cCc40xZ1hrvyAN226MGYnT\n/fMBa+3CFoqmXdt9anEyOa6w1n7SsNAYczFORt1jwGTSs/2dfY8zgCeAKxsC9saY63GyLf8MXEB6\ntr/BOThZVfdYaw8EWZ92bfcFIZ/GCVi+ipNVOQU4EXjYGPNNX5De9bY3ZPcZY8bgZGa9B/QGCsP4\ngSkixpjDcGZHvMVae1/AuquAR0JsutBaOy2g/EycQeXHABU4syTeaK0tCtzYGDMNuAPns6YeJ7Pz\nV9bawthaBG8sKaXPiOmx7kYkKsqkEhFJfekUrKrwPXYIsT7H93iwDeqSCBWE37YKoE8EZT0h9t3m\n58wYk4XzpfxynODNudbaWt/qtD4H1trHG/723Wy8ijN1+jjSrO2+gOSjOJlkgeP0BEqrtjew/8/e\nvcfXVZWJ//8EWijQlGsJFxUQw0JACKRehiFQAUUIOvy84MAoX7wrgiAIclEuVUEZLIoSB2XkoqBS\ndVCICA5YwAF1GowygA8ocpVLhbaJXGqh+f2x9wlJmtOmOac5+5x83q9XXpu99tr7POvsACfPefZa\nEUeXab8ypfQRYK88odeI4y/N0fYC8MkRf/hfCHwSOCiltB6NOf6SI/LtN8scb8SxX0mWRDk0In5Y\naswXGplL9l68mwKMPaW0MdnKlLPzc3YgS6K+Ml+p9cEyr7la8tUMfww0kyXSRipVWX4ReH7EsUdG\nXOsw4Aqy/392kS3icSSwT0pp1pBqXVJK+5A9dvsUWXJ8I+Bw4I1536qMT5IkaTwa6THAJWQf8so9\nurBhfnxJmeNFt4iVjw1eGtsiYFpKaeoY+w5tX1nfNSqltD7wE7JE1b3AGyPi8SFdGv49KImIbrJH\nY3bK5+pqtLF/HPhn4GNl5qRpGvLPjTb2sbiD7D3YjsYcf+m6D0TEsMex8sTVH8gqYF5BY46/NLH6\nm4A/RMR9Zbo11Njzqqo9gZuHJqoA8tVg7wHenidvijD2C4BnyeZXe5bsM8SRZPMJfq1MbKslpbQN\n2STwr1tJt12BpyLi1IiYM+JncFXF/H27kCxRtXtEnBwRhwEfInts8TND+q5FVsH8d2BWRJwQER8A\nOsmmVTivGuOTJEkar4ZJVkXEP8g+QG5Xpst2ZCsFlpsDo+juBVryieRH2g54kWzFw1LfJlZcbanU\nF7LlvEt9h7avrO8ak3+DfRPZCmF3AHtFxCMjujXUe5BSWjultH9Kaf8yXR4kG8NmZDFu0ShjJ5uX\nCOBnKaXlpR/gE3n7L/O2bWi8sZfu/R4ppdeW6bJevn2eBhw/2UTYyylfOVNKOjxLY44fYB9gfeCH\nK+nTUP/NI1sEBbKk1GjuJvtcsjXFuO9vIXt8bvBzQ75Ay6fI7l9F8mqyO8mqZ29aSdfX5P1W5TCy\n6qjzI+LvpcaIuIRsTEfmVa0A+5FViv1nRPx1SN+byB53PCSltAmSJEk10jDJqtytwJYppWGrGOVz\nQbRSfqXAenAr2cpDew9tzL+dfwNwV0Q8M6QvZI8ujDQbWBwR94yx74vAb8cZ85jkY7iW7Jvl+cDs\niPjbKF0b7T1oIptL5Ir8W+6RdiP7g/5+shjXonHGfgnZfFUjf0pzV12a7y+m8cYOWTLmN8DPR977\n/I/JPYFlwO9owPFHxPNk89G9Iq8cHJQ/Crwb2aNJj5JNwt1Q48+9Id/+aiV9Gu2/eY/l21TmeCtZ\n9dITFOf3fr1R+s0k+/ezUscCfyEb43dG65BSehmwMVm14aqU3qtfjnLsZrLJ5XcZQ9/5ZL93e43h\nNSVJktaIRktWXZ5vzy59e5hvz8nby80LUg+uJPsQfWZKaWg1wqlk81wMHdvVQD9wUl6xBEBK6f1k\nfwxcPKTvzcBDwEfyKpZS3/3IHlH5r/yb5DXpbOCfgNuAA4d+IzxCQ70H+VxcPyL7w+fEocdSSh8j\nm/C2O58Ut9HGftkoj7PMYUiyKm9bQoONHQaTNdeS/RF68ojDJ5D9QXllPul2w40/V4r7gjxBVXIC\nWWXN5RGxnGz+nUYc/+5kiZk7VtKnoe59PmH5b4HZKaW3DT2WTyK+K3B9XslUhLFfCXwlpVRK8ExP\nKe2bv/YPVv8dWMGHgbaI+DXDH30eqrRq6DoppatTSk+mlPpSSj8fpTJze7LfqftHuc4D+XaHIX0h\ne2SwXN/WUY5JkiRNiKaBgaouaFNzKaXvkU3O+luybwf3JPt2cF5EvLuGoY1JSulIsolOj4uIC0Yc\nOwf4NNkjFNeSTVJ7ENk38/tFxLIhfT8CfAN4mGyC2K2Bd5E9NvFPQx9rSCkdRDZXVOkPhOnAv+X7\nr1+Tk6ymlLYge9xtKtm4Rz76V3JORCxttPcgr/r7NfAy4Hrg/8j+iN2X7A+OvUrzdjXa2EeTUvoK\n2aOAs0eskNdwY08pvRK4nSxZ+d9klRPtZI8X3QXsHRGL8r4NN/789X8MHEL2+NfPgVeTPQocwOsi\noj/v13DjTyndCWwXEdNX0a+hxp6ylfVuIZsf6hqyx/J2BQ4A/gr8c+m1az32/BHEs8nm2CslzF4k\nWwTkhIgoLexSjfflSEb5f39K6eQ8Bsj+Hfk9WcLpbXksb4uIG/K+Abw8ItYf5fofI5vP6oMR8e2U\n0vVkybktIuLJEX0PBLqBz0fE6eMZz/z58wduvvnmYW0L+trGc6m6cu3cQ4btH3z81TWKZPU8Gf/N\nF457F7vttjsAM2c2A7BwYX8tw6pIV9fcYftDVwdshPGtSqOP0fHVv0Yf4yQZX7kv2qqm0SqrAN4L\nnE42z8+xZMtMfxZ4Ty2DWg0DjL4aEBFxCnB0fvwTwE5kqyd1Dv3Qnve9CPhXYCFwFFnC7lKyJMDI\nCY1/RjY3xz3AB8j+GPgJQ/5oWIPewEtLib+f7N6N/Pks+SpNjfYe5HOFvJbsj59dyX5ntwfOB14b\nQyaYb7SxlzHq738jjj0i7gdmAZeRVVIdQzah+HnAnqVEVd634cafexdQ+gvi42T/DlxINv7B/7s3\n6Pg3YQyTmDfa2CPiLrKk7HeA15Ot/Lgz2WTf7UNfu9Zjj4ilEXEC2b3aFdgD2CQijqpmomoVmsgq\nnf4tIg6KiFMi4h1kc06tDVwypPJsKrC0zHVK7dOG9B0o039kX0mSpAnXcJVVkiRJlUop7b2y40Or\nP6vwWkdSpqp6JedcChwBHBARv0gp3QVsGxEbjNK3VFn1voi4LKXUTVbF2BLZo+ZD+5Yqq86KiLPG\nM5758+cPnHz+tcx8Vcd4Tq9b9VpZ9bd7b+TrZ/4/2tvbax2KJKl+rPHKqimr7iJJkjTpzC/T/g+y\nRxZfOXGhjOp3ZMmq0iqGi4AdU0pTR1aekT12CS9V8y0a0r5wFX0lSZImnMkqSZKkESJi5EqdU8gS\nVBeSTfy/xqWUdgNmRMStoxwurVT4fL69l2yezm3J5uoaqpTQiiF9S+1/WkXfcVlu4X7dWD4wwKJF\nzwzOrTJJ5lpp2PFB44/R8dW/Rh/jZBnfmtaIc1ZJkiRVVUS8EBH3ks2zNWeCXvZa4JcppU1HObZX\nvl2Qb0sJrdmj9J0NLI6Ie8bY90WyhWokSZJqwmSVJEnS2G0ObDxBr/VDss9qZw9tTCm9i2xi+Jsj\n4u68+WqgHzgppbTxkL7vB1qBi4dc4mbgIeAjKaVthvTdj2yVwP+KiKeqPxxJkqSx8TFASZKkEVJK\nl5CtmDd0AtFmsmTOvAkK43NAJ/ChlNKuwP8AiSxR9VfgfaWOEbEopXQS8A2gN6U0D9iabNXNYEjC\nKyKWp5SOIlsFcUFK6UpgOvBvwJPAiRMwNkmSpLJMVkmSJK2oacR2AHgKOB74TpVfayD/GSYink4p\nvR44EzgE+ARZMulbwBkR8cSI/hellBYBJwFH5fFeCpwWEYtH9P1ZSuktwBnAB8iqsn4CnBoRD1Y6\noM72ZqB3cH9BX1ull5TGrKtr7rD9o446vkaRSJLGy2SVJEnSCBFx5AS+1mXAZWWOLQKOzX/Gcq2r\ngKvG2PdG4MYxhilJkjRhTFZJkiSNkFI6g1GqnXJNvPSI4EBETNSE63Wju6efltaOWoehScpKKkmq\nfyarJEmSVtQKvJPsUboFwFKgDXgVcDvwD15KWpmskiRJqiKTVZIkSSt6HrgC+GhELANIKTUB5wKb\nRMQHahmcJElSI1ur1gFIkiQV0L8C55YSVQARMQBcDBxWs6gkSZImAZNVkiRJK3oUOHCU9ncCf57g\nWCRJkiYVHwOUJEla0cnAVSmlTqCX7Au+1wK7A2+tZWCSJEmNzmSVJEnSCBHxXymlPYD3ATsDzwC/\nBA6NiMdrGlwd6GxvJsvxZRb0tdUuGE06XV1zh+27OqAk1R+TVZIkSaOIiDuB41NKmwBLgIGIWF7j\nsCRJkhqeySpJkqQRUkprAacCxwEbAzsAc1JKfwc+ERFLaxlf0XX39NPS2lHrMDRJWUklSfXPCdYl\nSZJW9BngPWSPAT4PDAD/CewPnFfDuCRJkhqeySpJkqQVvQ/4SERcAywHiIibgP8HHFrLwCRJkhqd\nySpJkqQVbQ78dZT2xcD0CY5FkiRpUjFZJUmStKIbgRNTSk2lhpTSDOBsslUBJUmStIaYrJIkSVrR\nx4HdgceB9YCfAo8A2wHH1DAuSZKkhudqgJIkSSt6GngdsC/warLPTH8EboiI5bUMrB50tjcDvYP7\nC/raaheMJp2urrnD9l0dUJLqj8kqSZKkFd0NHBIRN5I9EihJkqQJYrJKkiRpRS8C69Y6iHrV3dNP\nS2tHrcPQJGUllSTVP5NVkiRJK+oGfpFSugZ4AHg+b28CBiJiTq0CkyRJanQmqyRJklb0GqAH2ArY\nckh7EzAAmKySJElaQ0xWSZIkASmlW4C3RcTiiJidt60fEc/WNjJJkqTJZa1aByBJklQQewHrjGh7\nPKX0yloEI0mSNFmZrJIkSSqvqdYBSJIkTTY+BihJkqSq6mxvBnoH9xf0tdUuGE06XV1zh+27OqAk\n1Z9GTlYNLFzYX+sYNMTMmc0AeF+Kw3tSTN6X4vGeFFN+X6x8kiRJajCNnKySJElaXe9OKS3J/7mJ\n7LPS21NKTw7tFBGXT3hkdaS7p5+W1o5ah6FJykoqSap/JqskSZIyDwEj/8p9Avj4KH1NVkmSJK0h\nJqskSZKAiNi21jFIkiTJ1QAlSZIkSZJUICarJEmSJEmSVBgmqyRJkiRJklQYzlklSZKkqupsbwZ6\nB/cX9LXVLhhNOl1dc4ftuzqgJNUfK6skSZIkSZJUGFZWSZIkqaq6e/ppae2odRiapKykkqT6Z2WV\nJEmSJEmSCsNklSRJkiRJkgrDZJUkSZIkSZIKw2SVJEmSJEmSCsNklSRJkiRJkgrD1QAlSZJUVZ3t\nzUDv4P6CvrbaBaNJp6tr7rB9VweUpPpT1WRVSmkr4B7g9Ij46hjP2QSYAxwMzMzPPzcirqpmbJIk\nSZIkSSq+qiWrUkrTgR8DzcDAGM/ZAPgFsBtwFfAQ8E7g+ymlmRFxYbXikyRJ0sTo7umnpbWj1mFo\nkrKSSpLqX1XmrEopbQPcDLxuNU89FtgdOCYiDo+Ik4E24C7gSymlmdWIT5IkSZIkSfWh4mRVSuk4\n4E7gNcBNq3n6UcDjwH+UGiLi78AXgPWBwyuNT5IkSZIkSfWjGpVVxwJ/AfYGvjPWk1JK2wNbAbdG\nxMjHBufn272rEJ8kSZIkSZLqRDWSVR8G2iLi10DTapy3fb7988gDEfE4sBTYofLwJEmSJEmSVC8q\nnmA9In4xzlM3zbeLyxzvAzYc57UlSZIkSZJUh6q2GuA4TM23S8scXwpMm6BYJEmSVCWd7c1A7+D+\ngr622gWjSaera+6wfVcHlKT6U5XVAMfpuXy7Tpnj6wLPTFAskiRJkiRJKoBaVlYtyrflHvWbATxW\nyQvMnNlcyelaQ7wvxeM9KSbvS/F4T6Sx6e7pp6W1o9ZhaJKykkqS6l8tK6vuzbfbjTyQUtqSrLIq\nxnvxpqYmZs+ePa5z582bx5e+9KXxvvSE6Ovr46STTmLBggWDbT09PXz4wx+uYVSSJEmSJEmVqVll\nVUQ8lFJ6COhIKTVFxMCQw7Pz7e2VvMayZS+ycGH/ap/39a9fyB57zBrXuRPljjt6+OlPf8qb3tQ5\nGOd3vnMl9957X2HjLlUkFDW+ych7Ukzel+LxnhSTlW6SJEmNqZaVVQDfAV4GHF1qSCk1A6cBz+bH\ntRIDAwOr7iRJkiRJklQnJqyyKqV0JjAQEWcNaT4XOBT4akppH+B+4B3AtsAxEfHURMVX8s53vpUn\nnnic6667luuuu5Z5865hiy22oLf3Di6//Nvcc8/dPP/8c2y22eYceGAn73vfh2hqauKOOxZw7LEf\n42tfu4i2tj0Gr3f00R+mqamJr33totWK45prrubqq3/EQw89wPLly3nFK7bhiCPezxvfuP/gawF8\n4hMfpa1tD7bccit+/vNuADo6Xsupp57BgQcePOq1b7llPt///nf505/u44UXlrHlllvxjne8m7e/\n/V2Dff72t7/xH//xNX7zm9tZunQpO+yQ+OhHj2GXXV4DwNKlS/ne977DDTdcx+OPP87mm7fw1rf+\nC4cffgRNTU2DY29paWHp0qX85je3097ezpw5c9hvv/04+ujj+MlPfsyTTz7BCSecXDZWSZIkSZI0\nuVQ7WTWQ/4zm9PzYYLIqIvpTSh3A2cBbgbcA9wCnRMRVVY5tTM455zw+9aljSenVHHnkB9h00025\n7757OfbYj7H//m9mzpxzgAGuv/46LrnkW2yzzbbst9+by16vqalpMHkzVj/60VVccMGX+cAHPsKu\nu7axZMkSrrjiMs466zPsssuu7Ljjqzn++E8zd+6XOOGET7P77rOYMmUKixcvIuKPnHPOeWy11ctG\nvfZtt/2K0047kUMPPYwPfvCjLF36PD/+8TzOP/9cdtzx1ey00y48++yzfOxjH2BgYDlHHfUJNtts\nJj/4wRUcf/zRfPvb32XrrV/Gpz/9Se6++y7e//4P8apX7UBPz//yzW928eijj3DSSacNvt6NN/6C\nAw44iC996XxmzJg22P7tb3+T4447kfXX34Cddtp5td4fSZIkSZLUuKqarIqIy4DLyhwb9ZHDiHgS\n+GA146hEa2tinXXWYaONNmKnnXYB4P77/8TrX/9PfPaznxvs197+On71q1v43e/uWGmyamBgYLWT\nVY899lcOP/wIjjji/YNtW2yxJR/84Hv5wx9+z377vYltttkWgG23feXgP2+44Uass846g3GP5oEH\n/sKBBx7MMce8tErKzju/hs7O/fnd73rYaadduO66a3jiicf49rev4FWvagVg113beN/7Dqe39w4e\nfvghenr+lzPPPJv99nsTALNmvY5p06Zx8cX/waGHHs6222bz5k+dug4nnngqU6ZMYebMZh555BEA\n9t33zVZTSZLUoDrbm4Hewf0FfW21C0aTTlfX3GH7rg4oSfWnZhOs15MDDjiIAw44iKVLl/Lwww/x\nyCMPcd999/Liiy+ybNk/qv56Rx99HAD9/f08+OADPProw9xxR7bqX6Wvd/jh7wXg2Wef5aGHHuTR\nRx/mj3+8J7/2MgD+8Idettpq68FEFcC6667LlVf+CICurguYMmUK++67/7BrH3DAQVx88X/Q29sz\nmKzadtttmTJlxV+z1tYdKhqHJEmSJElqTCarxmDp0uc5//x/54YbruOFF15gq622ZuedX8OUKVPW\nyATnjz76COeeezZ33PG/TJ06lW222Zbtt88SR5W+3uLFi/n3f/8Cv/rVLQC8/OXbsOuuuw279pIl\nS9h4443LXqO/v48ZMzZcoWJs4403yY//fbBtvfXWH/Ua66233vgHIUmSCq27p5+W1o5ah6FJykoq\nSap/JqvG4Ctf+TLz59/EnDlf5LWvfR3rrpvNvXTwwW8a7FNK3CxfvnzYuc899xwbbLDBmF9r+fLl\nnHjisayzzrpcfPF3aG3dgbXWWou//OV+rr/+Z2O4wsofOTzrrNN4+OGH+OpXv8Euu+zKlClTWLr0\nea655urBPtOnN/P444+tcO6dd/6eGTM2pLl5BkuWLF7hEcennvobABtttNHYBitJkiRJkjTCqPNI\nTXYjK4buvLOX9vZZ7LXX3oOJqj/+8Z7BhA0wmJB64onHB8/r6+vjgQfuX63XXrJkMQ8//BAHH/w2\nUtqRtdbKbtGvf30b8FL109prr73CuVnflVde3Xnn79lnn31pa9tj8PG822//H+ClRFtb2+789a+P\n8pe/vBT70qXPc+qpJ9Ld/RN2372dF198kZtu+sWwa99ww3VANr+VJEmSJEnSeFhZNYrm5hnce2/k\nE47vzE477cJNN/2Cq6/+Edtssy1/+tN9fPe7l9LcPIPnnnsWgO23b2XzzVu49NKL2WCD6TQ1weWX\nX8J6662/Wo/ubbzxJmyxxVb88IdXMXPm5kyf3sxvfnMbP//5z1hrrbUGX2/69GYAbrvtVjbYYANa\nWxPNzTN4+umnuP32/6G1NbHZZputcP1Xv3pnbrjhOlLakc02m8mdd/6eefO+z7Rp6/Hcc88BcNBB\nb+OHP/wBJ598PB/4wEeZMWMGV131PZYvf5G3v/1QtthiS/bYYxZf+tIXWLjwSbbfvpXe3ju44orL\nOPDAgwcnfIfKH1uUJEmSJEmTi5VVozjssPfw9NNP8alPfYJ77w2OPvqT7L33G7n44m9w4onH8qtf\n3cKcOV/kgAMO4q67/o+BgQHWXnttvvCFc9lkk00588zTuOCCubz5zQcye/a+q70a4DnnnMfMmTP5\n/OfP5LOfPZmnnvob3/zmpWy33fb84Q/ZyjqvfOX27L//AfzoR1fxuc+dDsBBB72VLbfcilNP/RTX\nX9896rVPO+0sdtppZ+bOPZdTTjmBP/7xbi644BvMmvXawWuvv/76fP3r32Lnnd9Cs0wAACAASURB\nVF/D+eefy5lnnkpTUxNf+9pFbLHFlgCce+75/Mu/vJ2rrvoen/70J7n55pv46EeP5tRTzxh8raam\nptUeuyRJkiRJmtyaGrXypampaWDPPffi6qvHMs+TJsLMmVk12MKF/TWORCXek2LyvhSP96SY8vvi\ntyIFM3/+/IGT5l476SZYv3buIcP2Dz7+6jI9i+XJ+G++cNy72G233YHG/+9do48PGn+Mjq/+NfoY\nJ8n41vjnLx8DnCDPPvsM99+/6vmrXvaylztBuSRJqmud7c1A7+D+gj7ns9TE6eqaO2zf1QElqf6Y\nrJogf/zjPRx77MdW2qepqYlTTjmdAw88eIKikiRJkiRJKhaTVRNkjz1mceut/1vrMCRJkta47p7+\nSfcYoIrDSipJqn9OsC5JkiRJkqTCMFklSZIkSZKkwjBZJUmSJEmSpMIwWSVJkiRJkqTCMFklSZIk\nSZKkwnA1QEmSJFVVZ3sz0Du4v6CvrXbBaNLp6po7bN/VASWp/lhZJUmSJEmSpMKwskqSJElV1d3T\nT0trR63D0CRlJZUk1T8rqyRJkiRJklQYJqskSZIkSZJUGCarJEmSJEmSVBgmqyRJkiRJklQYJqsk\nSZIkSZJUGK4GKEmSpKrqbG8Gegf3F/S11S4YTTpdXXOH7bs6oCTVHyurJEmSJEmSVBhWVkmSJKmq\nunv6aWntqHUYmqSspJKk+mdllSRJkiRJkgrDZJUkSZIkSZIKw2SVJEmSJEmSCsNklSRJkiRJkgrD\nZJUkSZIkSZIKw9UAJUmSVFWd7c1A7+D+gr622gWjSaera+6wfVcHlKT6Y2WVJEmSJEmSCsPKKkmS\nJFVVd08/La0dtQ5Dk5SVVJJU/6yskiRJkiRJUmFUXFmVUpoCHAN8CNgWeAy4BPhiRLwwhvN3Az4H\ndADTgHuBr0fEtyqNTZIkSZIkSfWlGpVVFwJfBhYCXwEeBeYA31vViSml3YHbgLcA3UAXMB24KKX0\nxSrEJkmSJEmSpDpSUbIqpbQnWUXVvIjYJyJOjYi9gcuBd6SUOldxiS8A6wHvjIj3RMQJwK5k1VWf\nSiltW0l8kiRJkiRJqi+VVlZ9PN+eNaL9FGAA+OAqzt8DeDoiflpqiIhngO/nsb22wvgkSZIkSZJU\nRyqds2pvYGFE3D20MSIeSyndlx9fmSeAnVJKG0XE4iHtW+fbhRXGJ0mSpAnW2d4M9A7uL+hrq10w\nmnS6uuYO23d1QEmqP+NOVqWU1iVLKv26TJcHgB1SSptGxFNl+swlm4z9ypTSMcCTwLuA/wf0ADeP\nNz5JkqR6kFLaCrgHOD0ivjrK8SOATwKtwCLgqrzvM6P07QQ+A+wMPAdcA5wSESt8AZhS+ieyRW7a\ngeXAjcCnI+IvVRqaJEnSuFRSWbVJvl1c5viSfLshMGqyKiIuSym9AHwbuG/IoRuAf42IgQrikyRJ\nKrSU0nTgx0Az2RQKI4+fQjbH5++BC8jm9vwk8IaU0uyIWDak72HAFcCfyRat2QY4EtgnpTQrIpYM\n6bsP2eetp8g+h20EHA68Me/7YCXj6u7pp6W1o5JLSONmJZUk1b9KklVT8+3SMsdL7dPKXSCltD/Z\nB6+lwJVkia83A28i+6bvmArikyRJKqyU0jZkiardV3J8DtnKyftExIt5+1nAZ4EPk63KXEp6XUiW\nqNo9Iv6et98A/CdZtdWJedtawEXA34FZEfHXvP0K4BfAeWSV7pIkSTVRSbLquXy7Tpnj6+bbFUrU\nAVJKG5N9QFsG7BERf8rbp5J9K/jxlNLdEfGN8QY4derazJzZPN7TtYZ4T4rHe1JM3pfi8Z6oWlJK\nx5EloqYBNwH7jtLtw8DawNmlRFXubOBYsoVsLszbDiOrjvpMKVEFEBGXpJROAo5MKZ2UV63vB+wA\nnFdKVOV9b0op/QI4JKW0SUQ8XaXhSpIkrZZKVgNcQlauvmGZ4xvmx5eUOf5WYDpwQSlRBZCXsx+d\n7x5ZQXySJElFdSzwF7LFaL5Tps/eZJ+l5g9tjIilZHOG7pZSah7SF+CXo1znZmBTYJcx9J1PliDb\na1UDkCRJWlPGXVkVEf9IKT0IbFemy3ZkKwWWm9OqtOLfPaNc+8mU0lPAy8cbH8CyZS+ycGF/JZdQ\nFZUqErwnxeE9KSbvS/F4T4qpzivdPgz8d0QMpJR2LNNne+CJiHh2lGMP5NsdyBal2Z4ssXX/Kvre\nmfeF7JHBcn1bVxK7JEnSGlVJZRXArcCWKaVhH2jyVW1aKb9SIECp7DyNPJA/Irgp8HiF8UmSJBVO\nRPxiDAvJbMrYFrIp9V2aV12NpS9lrj2yryRJ0oSrZM4qgMuB9wJnp5QOzb8dbALOyY9/cyXnXks2\nn9UxKaXvlpZJTimtDczN+3yvwvgkSZLq1VTGvpDN6vYdKNN/lQvkjEVnezPQO7i/oK+tkstJq6Wr\na+6wfVcHlKT6U1GyKiJuTCn9AHg3cHtKaT6wJ9k8B/Mi4melvimlM4GBiDgrP/eplNLHgEuB3pTS\nD8m+zduXbFnm+cBXKolPkiSpjj3H2BeyeQ5oWY2+TWWuvdIFctR41mpqYuONN1jhsdo6f8x2mNHG\n0kjjK6fRx+j46l+jj7HRx7emVVpZBVll1V1kk6EfCzxItpzyuSP6nU72Ld5ZpYaI+G5K6SHgFODt\nwHpk8yd8Bvj3fLJ1SZKkyWgRK1/IBl56bG8RsGNKaeoon59G61tqX7iKvuNy3R39zHxVRyWXkMbt\njDPOqHUIkqQKVZysiogXgM/nPyvrN+r8WBFxC3BLpXFIkiQ1mHuBjpTSuqPMRbUd8CJw35C+ewLb\nDmkb2hcghvQttf9pFX3HZfmqZuNSYSwfGGDRomcGF5Bo9AUlGn180PhjdHz1r9HHOFnGt6ZVOsG6\nJEmS1oxbgbWBvYc2ppSmAW8A7oqIZ4b0BZg9ynVmA4sj4p4x9n0R+O04Y5YkSaqYySpJkqRiupIs\ncXRmSmno/FKnAs0MX8jmaqAfOClfVRmAlNL7yVZovnhI35uBh4CPpJS2GdJ3P+BNwH9FxFNVHosk\nSdKYVWPOKkmSJFVZRERK6Tzg08DvUkrXAjsDBwG/Ar41pO+ilNJJwDfIFq6ZB2wNvIvskb6zh/Rd\nnlI6CvgJsCCldCUwHfg34EngxIkYnyRJUjlWVkmSJNXWQP6zgog4BTg6P/4JYCdgLtA5ciL1iLgI\n+FeySdOPIlud+VJgdkQsHtH3Z8BbgHuAD5AlwH4C/HNEPFitgUmSJI2HlVWSJEk1FBGXAZet5HgX\n0DXGa10FXDXGvjcCN46l7+rqbG8Gegf3F/S1rYmXkUbV1TV32P5RRx1fo0gkSeNlZZUkSZIkSZIK\nw8oqSZIkVVV3Tz8trR21DkOTlJVUklT/rKySJEmSJElSYZiskiRJkiRJUmGYrJIkSZIkSVJhmKyS\nJEmSJElSYZiskiRJkiRJUmG4GqAkSZKqqrO9Gegd3F/Q11a7YDTpdHXNHbbv6oCSVH+srJIkSZIk\nSVJhWFklSZKkquru6aeltaPWYWiSspJKkuqflVWSJEmSJEkqDJNVkiRJkiRJKgyTVZIkSZIkSSoM\nk1WSJEmSJEkqDJNVkiRJkiRJKgxXA5QkSVJVdbY3A72D+wv62moXjCadrq65w/ZdHVCS6o+VVZIk\nSZIkSSoMK6skSZJUVd09/bS0dtQ6DE1SVlJJUv2zskqSJEmSJEmFYbJKkiRJkiRJhWGySpIkSZIk\nSYVhskqSJEmSJEmFYbJKkiRJkiRJheFqgJIkSaqqzvZmoHdwf0FfW+2C0coNwM233MyDDz4IQPOM\n9QDo73uuZiHtu+9+TJ/ePO7zu7rmDtt3dUBJqj8mqyRJkqRJqnnr3bjhrkfgrj/XOhQAFj/4W3be\neeeKklWSpPpnskqSJElV1d3TT0trR63D0Bis1zyT9Zpn1jqMlzz714ovYSWVJNU/56ySJEmSJElS\nYZiskiRJkiRJUmGYrJIkSZIkSVJhmKySJEmSJElSYVQ8wXpKaQpwDPAhYFvgMeAS4IsR8cIYzp8G\nnAS8B3g58ChwDXBWRCyuND5JkiRJkiTVj2qsBnghWaLqVuBqYC9gDrAb8K6VnZhSmgpcB+wDzAd+\nDLweOBb455TSXhHxjyrEKEmSpAnS2d4M9A7uL+hrq10wmnS6uuYO23d1QEmqPxUlq1JKe5IlquZF\nxLuHtF8KHJFS6oyI7pVc4liyRNW5EXHykPO/BnwcOAy4rJIYJUmSJEmSVD8qraz6eL49a0T7KcB7\ngQ8CK0tWHQ38BThtRPt5wHTg7xXGJ0mSpAnW3dNPS2tHrcPQJGUllSTVv0qTVXsDCyPi7qGNEfFY\nSum+/PioUko7Aa8AvhoRL444/0HgfRXGJkmSJEmSpDoz7mRVSmldYGvg12W6PADskFLaNCKeGuX4\nLvn2rpTSQWTVVW3AYuB7wOkR8ex445MkSZIkSVL9WauCczfJt+VW7FuSbzcsc3yrfPs24FrgaeAb\nwOPA8cDP85UGJUmSJEmSNElUkgyamm+Xljleap9W5vgG+fZg4EMR8Z8AKaW1yCqr3gUcBVxQQYyS\nJEmSJEmqI5Ukq57Lt+uUOb5uvn2mzPHl+faOUqIKICKWp5ROJEtWHUoFyaqpU9dm5szm8Z6uNcR7\nUjzek2LyvhSP90SSJEla8ypJVi0BBij/mN+G+fElZY6X2u8YeSAiHkopLQFeWUF8kiRJqoHO9mag\nd3B/QV9b7YLRpNPVNXfYvqsDSlL9GXeyKiL+kVJ6ENiuTJftyFYKLDen1b35tlxl1hSgognWly17\nkYUL+yu5hKqoVJHgPSkO70kxeV+Kx3tSTFa6SZIkNaZKJzC/FXhvSqk1Iu4rNaaUtgJagZ+u5Nzf\nAv8A9kkprRURpccCSSntSDan1R8qjE+SJEkTrLunn5bWjlqHoUnKSipJqn+VrAYIcHm+PTul1ASQ\nb8/J279Z7sSI6AN+AGwDnFxqTylNBc7Nd79dYXySJEmSJEmqIxVVVkXEjSmlHwDvBm5PKc0H9gT2\nAuZFxM9KfVNKZwIDEXHWkEt8Cvgn4PMppdlklVT7AbsB34+IayuJT5IkSZIkSfWl0soqgPcCpwOb\nAccCmwOfBd4zot/p+c+giFgIvIFsxb8dgY+TrSJ4IvBvVYhNkiRJkiRJdaTSOauIiBeAz+c/K+s3\namIsIp4Gjst/JEmSJEmSNIlVo7JKkiRJkiRJqoqKK6skSZKkoTrbm4Hewf0FfW21C0aTTlfX3GH7\nrg4oSfXHyipJkiRJkiQVhpVVkiRJqqrunn5aWjtqHYYmKSupJKn+WVklSZIkSZKkwjBZJUmSJEmS\npMIwWSVJkiRJkqTCMFklSZIkSZKkwjBZJUmSJEmSpMJwNUBJkiRVVWd7M9A7uL+gr612wWjS6eqa\nO2zf1QElqf5YWSVJkiRJkqTCsLJKkiRJVdXd009La0etw9AkZSWVJNU/K6skSZIkSZJUGCarJEmS\nJEmSVBgmqyRJkiRJklQYJqskSZIkSZJUGCarJEmSJEmSVBiuBihJkqSq6mxvBnoH9xf0tdUuGE06\nXV1zh+27OqAk1R8rqyRJkiRJklQYVlZJkiSpqrp7+mlp7ah1GJqkrKSSpPpnZZUkSZIkSZIKw2SV\nJEmSJEmSCsNklSRJkiRJkgrDZJUkSZIkSZIKw2SVJEmSJEmSCsPVACVJklRVne3NQO/g/oK+ttoF\no0mnq2vusH1XB5Sk+mNllSRJkiRJkgrDyipJkiRVVXdPPy2tHbUOQ5OUlVSSVP+srJIkSZIkSVJh\nmKySJEmSJElSYZiskiRJkiRJUmGYrJIkSZIkSVJhmKySJEmSJElSYbgaoCRJkqqqs70Z6B3cX9DX\nVrtgNOl0dc0dtu/qgJJUf6yskiRJkiRJUmFUXFmVUpoCHAN8CNgWeAy4BPhiRLywmtdaG/gf4HUR\nYSJNkiSpDnX39NPS2lHrMDRJWUklSfWvGgmhC4EvAwuBrwCPAnOA743jWscBrwMGqhCXJEmSJEmS\n6kxFlVUppT3JKqrmRcS7h7RfChyRUuqMiO4xXutVwOcqiUeSJEmSJEn1rdLKqo/n27NGtJ9CVh31\nwbFcJKXUBFwMPALcW2FMkiRJkiRJqlOVJqv2BhZGxN1DGyPiMeC+/PhYfCTv+yHg+QpjkiRJkiRJ\nUp0ad7IqpbQusDXw5zJdHgA2TiltuorrvBw4F7g4Im4ebzySJEmSJEmqf5XMWbVJvl1c5viSfLsh\n8NRKrnMR0Ad8qoJYJEmSVBCd7c1A7+D+gr622gWjSaera+6wfVcHlKT6U0myamq+XVrmeKl9WrkL\npJSOAN4CvCMi+iqIRZIkSZIkSQ2gkmTVc/l2nTLH1823z4x2MKXUApwP/Dgi/quCOMqaOnVtZs5s\nXhOXVgW8J8XjPSkm70vxeE+ksenu6aeltaPWYWiSspJKkupfJROsLyFb8W/DMsc3zI8vKXP8wvz1\nj64gBkmSJEmSJDWQcVdWRcQ/UkoPAtuV6bId2UqB5ea0enu+/WtKaYWDKaXlwIMRUe76q7Rs2Yss\nXNg/3tNVZaWKBO9JcXhPisn7Ujzek2Ky0k2SJKkxVfIYIMCtwHtTSq0RcV+pMaW0FdAK/HQl555F\nVnk10seAFuBMyk/eLkmSJEmSpAZUabLqcuC9wNkppUMjYiCl1ASckx//ZrkTI+Ks0dpTSm8HNo+I\nORXGJkmSJEmSpDpTyZxVRMSNwA+AdwC3p5S+CNxMlsCaFxE/K/VNKZ2ZUjpjjJduqiQuSZIkSZIk\n1adKK6sgS0zdBRwJHAs8CHwWOHdEv9PJHvsbtaJqiAFGfzxQkiRJdaCzvRnoHdxf0NdWu2A06XR1\nzR227+qAklR/Kk5WRcQLwOfzn5X1G1MVV0TsXmlMkiRJjSSl9DngtDKHfxARhw3pewTwSbL5QxcB\nVwGnR8Qzo1y3E/gMsDPwHHANcEpELKzuCCRJksauGpVVkiRJWrN2A5by0rygQ/1f6R9SSqcAXwB+\nD1wA7EqWuHpDSml2RCwb0vcw4Argz0AXsA1Zpfw+KaVZEbFkvMF29/TT0tox3tOlilhJJUn1z2SV\nJElS8e0K3LWyBWhSStsAc4DbgH0i4sW8/SyyKRo+DFyYt03P//nPwO4R8fe8/QbgP8mqrU5cY6OR\nJElaiYomWJckSdKalVKaAbwC+MMqun4YWBs4u5Soyp0N9AEfHNJ2GLARcH4pUQUQEZcAARyZUvJz\noiRJqgk/hEiSJBXbrvl2VcmqvckWqZk/tDEilgK/BnZLKTUP6Qvwy1GuczOwKbDLeIKVJEmqlI8B\nSpIkFVspWbV5SukXwCyypNSNwGkRcW9+fHvgiYh4dpRrPJBvdwB68r4DwP0r6dvKqhNkkiRJVWdl\nlSRJUrGVklWfAhYDFwG/Ad4B/CaltFt+fNP8+GhKk6VvOKTv0rzqalV9JUmSJpSVVZIkScX2Alm1\n05ERcUupMaV0OPBd4NtAOzCVbMXA0ZTap+Xb1em72jrbm4Hewf0FfW3jvZS02rq65g7bd3VASao/\nJqskSZIKLCKOLtN+ZUrpI8BeKaUdgOeAdcpcZt18+0y+fQ5oGWNfacKs1dTEJptMZ+bM5lV3HqPR\nrlXN6xdVo4/R8dW/Rh9jo49vTTNZJUmSVL/uADqA7YBFlH90r9ReesRvEbBjSmlqRCxbRd/Vdt0d\n/cx8Vcd4T5cqcsYZZ9Q6BElShUxWSZIkFVRKaW1gN2DtiPjfUbqsl2+fB+4F9k4prTvKXFTbAS8C\n9+X79wJ7AtsOaRvaFyDGG/fygfGeqclu+cAATz/9dxYu7F8j1y9VOqyp6xdBo4/R8dW/Rh/jZBnf\nmuYE65IkScU1lWwy9Z+nlIZ9bkspNZElnJYBvwNuJftst/eIftOANwB3RUTp0b5b8+3sUV5zNrA4\nIu6pzhAkSZJWj8kqSZKkgoqI54FrgY2Bk0ccPgHYBbgyIvqAK8mqp85MKQ2du+pUoBn45pC2q4F+\n4KSU0salxpTS+4FW4OIqD0WSJGnMfAxQkiSp2E4gq6D6fEppNvAHstX/9gHuAo4HiIhIKZ0HfBr4\nXUrpWmBn4CDgV8C3SheMiEUppZOAbwC9KaV5wNbAu8ge/zt7YoYmSZK0IiurJEmSCiwi7gdmAZeR\nVVIdA7wCOA/YMyIWDel7CnA0MAB8AtgJmAt0jpxIPSIuAv4VWAgcBewFXArMjojFa3ZUkiRJ5VlZ\nJUmSVHAR8TDwvjH27QK6xtj3KuCqCkIbVWd7M9A7uL+gr63aLyGV1dU1d9j+UUcdX6NIJEnjZWWV\nJEmSJEmSCsPKKkmSJFVVd08/La0dtQ5Dk5SVVJJU/6yskiRJkiRJUmGYrJIkSZIkSVJhmKySJEmS\nJElSYZiskiRJkiRJUmGYrJIkSZIkSVJhuBqgJEmSqqqzvRnoHdxf0NdWu2A06XR1zR227+qAklR/\nrKySJEmSJElSYVhZJUmSpKrq7umnpbWj1mFokrKSSpLqn5VVkiRJkiRJKgyTVZIkSZIkSSoMk1WS\nJEmSJEkqDJNVkiRJkiRJKgyTVZIkSZIkSSoMVwOUJElSVXW2NwO9g/sL+tpqF4wmna6uucP2XR1Q\nkuqPlVWSJEmSJEkqDCurJEmSVFXdPf20tHbUOgzVqVtvvYU//elP4z5/++1fPWz/+uuvG/znDTdc\nD4AlS54b8/X22eeNTJs2bdzxSJJWn8kqSZIkSYWwwVZ7MO+2R4C/1ToUABb/5TZ+3LY706ZtUetQ\nJGlSqUqyKqU0BTgG+BCwLfAYcAnwxYh4YQzntwOfBTqA6cDDwDzgcxHxbDVilCRJklRsG2y0JRts\ntGWtwxg08PeHah2CJE1K1Zqz6kLgy8BC4CvAo8Ac4HurOjGl9EbgNuAA4Drgq8BTwKeBX6aU1q1S\njJIkSZIkSSq4iiurUkp7klVUzYuIdw9pvxQ4IqXUGRHdK7lEV77tiIgFQ86/KL/uUcD5lcYpSZIk\nSZKk4qtGZdXH8+1ZI9pPAQaAD5Y7MaW0E5CAnwxNVOXm5Nu3VCFGSZIkSZIk1YFqzFm1N7AwIu4e\n2hgRj6WU7suPl7MEOAn4v1GO/SPfTq9CjJIkSZogne3NQO/g/oK+ttoFo0ln1ozeYfv+/klS/ako\nWZXPJ7U18OsyXR4AdkgpbRoRT408GBGPAueVOff/y7d3VRKjJEmSJEmS6kellVWb5NvFZY4vybcb\nkk2aPiYppRayxwAHgG+OOzpJkiRNuO6eflpaO2odhiYpK6kkqf5VOmfV1Hy7tMzxUvu0sV4wpbQh\n0A1sDlwwylxWkiRJkiRJalCVJquey7frlDm+br59ZiwXSynNBG4C9gCuAU6oKDpJkiRJkiTVlUof\nA1xC9qjehmWOb5gfX1Lm+KCU0vbA9cArgZ8Ah0bE8kqCmzp1bWbObK7kEloDvCfF4z0pJu9L8XhP\nJEmSpDWvosqqiPgH8CCwXZku25GtFFhuTisAUkptwG1kiapLgXdExLJKYpMkSZIkSVL9qbSyCuBW\n4L0ppdaIuK/UmFLaCmgFfrqyk1NKrwJuADYFvhwRJ1YhJgCWLXuRhQv7q3U5VahUkeA9KQ7vSTF5\nX4rHe1JMVrpJkiQ1pmokqy4H3gucnVI6NCIGUkpNwDn58bKr+aWU1gK+B2wGfKWaiSpJkiTVRmd7\nM9A7uO/qbJpIs2b0Dtv390+S6k/FyaqIuDGl9APg3cDtKaX5wJ7AXsC8iPhZqW9K6UxgICLOypsO\nAdrJVg18Jj8+0mMRcVGlcUqSJEmSJKn4qlFZBVll1V3AkcCxZPNYfRY4d0S/08kmXC8lqzry7TrA\naWWu3QuYrJIkSaoT3T39tLR2rLqjtAZYSSVJ9a8qyaqIeAH4fP6zsn5rjdj/JPDJasQgSZIkSZKk\n+lfRaoCSJEmSJElSNZmskiRJkiRJUmGYrJIkSZIkSVJhmKySJEmSJElSYVRrNUBJkiQJgM72ZrIF\nnTOuzqaJNGtG77B9f/8kqf5YWSVJkiRJkqTCsLJKkiRJVdXd009La0etw9AkZSWVJNU/K6skSZIk\nSZJUGJMmWXXIIQex+eYz2HzzGRxyyEFl2yRJkiRJklQ7kyZZJUmSJEmSpOIzWSVJkiRJkqTCMFkl\nSZIkSZKkwnA1QEmSJFVVZ3sz0Du47+psmkizZvQO2/f3T5Lqj5VVkiRJkiRJKgwrqyRJklRV3T39\ntLR21DoMTVJWUklS/bOySpIkSZIkSYVhskqSJEmSJEmFYbJKkiRJkiRJhWGySpIkSZIkSYVhskqS\nJEmSJEmF4WqAkiRJqqrO9magd3Df1dk0kWbN6B227++fJNUfK6tGOOSQg9h88xlsvvkMDjnkoFqH\nI0mSJEmSNKlYWSVJkqSq6u7pp6W1o9ZhaJKykkqS6p+VVeNkBZYkSZIkSVL1mawaAxNTkiRJkiRJ\nE8NkVRWZ1JIkSZIkSaqMyao1zASWJEmSJEnS2JmsqgETWJIkSZIkSaNzNUBJkiRVVWd7M9A7uO/q\nbJpIs2b0Dtv390+S6o+VVQVhtZUkSZIkSZKVVYV2yCEHcdttvwJgzz334uqrf1bjiGrD90GSpPrS\n3dNPS2tHrcPQJGUllSTVPyurVJesRJtYvt++B5IkSZI0UUxWNQD/iJYkSZIkSY3CZJXGrNpJsXpI\nstVDjLXieyNJkiRJWhNMVqnqKkli1CohVg+Jl3qIUZIkSZKkSlWcrEopTUkpfTKldHdK6dmU0p9T\nSp9JKY1p8vaU0iYppa+nlB5IKT2TUlqQUjq00rg0dv9/e3cfbldVH3j8ewmBUEkQwaiBB2Ew/GxB\nGCZoNfLW6UwtpM9IR2lKealVdKygCKg1WigwCnbqC2KxIyNCQaA2amkFbEFtChYEZSajgs8PBwpM\nFTSDJEQDSODOH2ufeHI45+a+nLd9zvfzPPfZuXvtc7LW/t1zztq/s/ZasibilwAAFphJREFU/Ugu\nHXPM0UxMTDAxMWGiZcyYZJMkSZIkzUQ3RlZdDHwEWAdcCPwAOA+4ZlsPjIjnADcBbwVuBT4BPBf4\nq4g4pQt10xgZpVFUddWPc2v8JGn4rVi2kEMWrd3yI/VT89+ef3+SVE/TGv3USUQsB94MrM7MlU37\nLwdOiogVmXn9FE9xGnAwcEpm/kX12A8AtwF/GhF/nZnr5lJH/cIxxxzNrbd+HYDlyw/l2mtvGHCN\nRke7czuX893tWA1b7AdVn2E7D/0wjm2WJKlbNj89yWlnvocdFywYdFUAmDcBn/zERSwYkvpIUq/M\nKVkFNEY/nduyfxVwInAyMFWy6m3Aw8B/b+zIzJ9GxAeBq4HfAz4+xzpqFrzA7WyUE0nDVJeZqGtM\nBpXQ7HZyVZ15XjWurr9zIy9Yetigq6Ex9a3H/m3XnmvPg1/ftefqhofuvIrNmzcPuhqS1HNzvQ3w\ncGBdZt7dvDMzHwK+X5W3FRH7AkuAWzJzsqV4TdPzq4m3utWbt8nNTB3a0q6O092nzjxfkiRJ0via\ndbIqInYE9gDu7XDI/cCuEbFbh/J9q+2zHp+ZDwNPAvvNtn6SpjZsk96bnKivucbOJG49mISVJElS\nv8xlZNXzqu36DuUbqu0uHcobSaxOj39sisdKwgvFqYzjKMQ6JH26XcdBxa8X/28/Vmbt9mN7Yaar\nzEqSJGn0TExOtt6BNz0RsRdl9NTfZuZvtym/AjgBOKD1NsGq/CTgcuD0zHzWvFQR8SCwIDMXz6Z+\nExNrJhct2oUDDngZAN/97nd47LGSP2vsd5/nxnPjeRi2cwMwf36ZTvCppzbX9vx30u3n7PZx3a7z\nXM/NXOrY7cfOtX3dfs7GcZOTR5qxGjJr1qyZfM9Hrxu7Oauu++gxW/3+W2dcO6CaaJQ9dOdVrL7s\n4+y8884dj3n+8xcCsG7dxn5Vq69sX/2NehvHpH0973/NJVn1fOBHwJczc0Wb8s8BxwL7ZOYDbcqP\nBT4H/FFm/lmb8h8BmzJzn9nUb2KC2TVMkiTVxuRk7ztLmhmTVYXJKvWCySrbNwpGvY1j0r6e97/m\nshrgBmCSzrfq7VKVb+hQ/mjTce0sAh6ade0kSZI0ECuWLQTWbvm9m6uzSdtyyKK1W/3u358k1c+s\nk1WZ+fOIeADoNPJpH8pKgZ3mpLqn6bitRMSLgB2BnG39jjjiF7fQaDi03tqkwTMmw8m4DB9jMjdz\nvV2wk0ZcJEmSNFrm2su7BTgxIpZm5vcbOyNiCbAU+LtOD8zMB6t5qQ6LiInMbL5t78hqe9tsK7Zm\nDaxb9/hsH64e+MVwSOMyLIzJcDIuw8eYzNVLWn7vznlsxEXD5/o7N47dbYAaHo6kkqT6m8tqgABX\nVNvzI2ICoNpeUO2/ZBuPvxLYEzi1sSMiFgLvBzZV5ZIkSZIkSRoTcxpZlZlfrSZSXwncFhFrgOXA\nocDqzLyhcWxEnANMZua5TU/x34DfAT4eEUcA9wGvA/YG3p6Zj8ylfpIkSZIkSaqXuY6sAjgROBvY\nHTgNWAycBZzQctzZ1c8WmbkROAz4TLV9G/AT4LjM/GQX6iZJkiRJkqQamfPMpJm5GfhA9TPVcW0T\nY5n5Y+DkudZDkiRJkiRJ9deNkVWSJEmSJElSV7jmsyRJkrpqxbKFwNotv7s6m/rpkEVrt/rdvz9J\nqh9HVkmSJEmSJGloOLJKkiRJXXX9nRt5wdLDBl0NjSlHUklS/TmySpIkSZIkSUPDZJUkSZIkSZKG\nhskqSZIkSZIkDQ3nrJIkSZKkGpj3nMUc/6a3MTHReczBvO1L2dObn+l5fdave5A1X/1az/8fSePH\nZJUkSZIk1cDil/7HQVdhK4/ffumgqyBpRJmskiRJGlMRsT3wduDNwN7AQ8BlwIcyc/Nsn3fFsoXA\n2i2/uzqb+umQRWu3+t2/P0mqH+eskiRJGl8XAx8B1gEXAj8AzgOuGWSlJEnSeHNklSRJ0hiKiOWU\nEVWrM3Nl0/7LgZMiYkVmXj+b577+zo28YOlh3amoNEOOpJKk+jNZJUmSNJ5OqbbntuxfBZwInAzM\nKlklaTxMzvslVp70xr7+n/PnzwPgqaeeflbZds88xTWfvbKv9ZHUGyarJEmSxtPhwLrMvLt5Z2Y+\nFBHfr8olqaM9DzluYP/3Dm32Pfyty/peD0m9YbJKkiRpzETEjsAewDc6HHI/sF9E7JaZj/StYpI0\nB0888QR//Cetg0UHZ9fnLuLM008fdDWkWjJZJUmSNH6eV23XdyjfUG13AUxWSaqFJctO4N7NPx90\nNbZYf/PnOdNclTQrJqskSZLGz/xq+2SH8sb+BX2oiyR1xQ47LRx0Fbaycfv5bNq0aUaP2bRpXrWd\n2eOmY2Jigp122qnrzyv1gskqSZKk8fN4tW037QvAjtX2Z7N58hXLFgJrt/zu6mzqp0MWrd3qd//+\nNCgTO7+Y1/3+qTN6zHbbTQDwzDOTXa/Pkxv+la/deGPXn1fqBZNVkiRJ42cDMEm5za+dXaryDR3K\nO9phh2fnvx799vitzjWObR4ah75sq1+NhQZle2D7+ds8rEWVpJrX5coAd679BosXL+r+E6snfvOo\nFXz5husGXY2BmZic7H7GVpIkScMtIu4DdszMPdqUJbBLZr6w/zWTJEnjbrtBV0CSJEkDcQvwoohY\n2rwzIpYAS+m8UqAkSVJPmaySJEkaT1dU2/MjYgKg2l5Q7b9kILWSJEljz9sAJUmSxlREXAOsBO4A\n1gDLgUOB1Zm5coBVkyRJY8yRVZIkSePrROBsYHfgNGAxcBZwwiArJUmSxpsjqyRJkiRJkjQ0HFkl\nSZIkSZKkoWGySpIkSZIkSUPDZJUkSZIkSZKGhskqSZIkSZIkDQ2TVZIkSZIkSRoaJqskSZIkSZI0\nNExWSZIkSZIkaWiYrJIkSZIkSdLQ2H7QFei2iNgeeDvwZmBv4CHgMuBDmbl5gFUbCxHxQuAcYAWw\nGPgJ8BXg7Mz8l5ZjTwJOB5YCjwJ/XR33s37WedxExIeBM4AjM/PmljJj0kcRcTxwGrA/sAG4FXh/\nZmbLccalDyJid+CDwG8BuwM/pJzrczLz8ZZjjUmPRMQS4HuU8/nxNuXTPvcRsQL4Y8pr7HHgS8Cq\nzFzXuxaMr1Hvg0XEfwXe36H4c5l5XD/rM1fdfK0Nq6naGBFvAv5Hh4fenpmv6nX9Zmsc+tvTbWNd\n4xgRuwF/Qmnfi4B/AS4HPpqZT7ccW7sYTrd9dY1fO6N+jdWpfb2M4SiOrLoY+AiwDrgQ+AFwHnDN\nICs1DqoPlTuAtwB3Uc7/HcDvAd+MiJc0HbuK8oYFcBHwvykv4BsjYn4fqz1WIuIVwDuByTZlxqSP\nIuIDwJXAIsr71hrgtcBtEbFP03HGpQ8iYhHwz5SL7O9R3r9+CLwbuCki5jUda0x6JCJ2Br4ILGSO\n71MRcRwlObU78Enga8AbgFsjYpfetGDsjXof7CDgScoFdOvP6sFUaXa6+VobVttqIyWeAB/i2fHs\ndOE1cOPQ355JG6lhHCNiIfB14FTgO8AnKF9a/inwNy3H1i6GM2kfNYxfO6N+jTVV++hhDEdqZFVE\nLKdcaKzOzJVN+y8HToqIFZl5/aDqNwbOAfYEzsjMCxs7q9EjV1I6sK+NiBdTOq+3Akc0susRcS5w\nFuWD6eL+Vn30RcQOwGdok6Q2Jv1VveG/j5KgOiozn6z2f4FywXM28AfGpa/eSvm268LMPKOxMyKu\nBI6vfq4wJr1TndsvAgdPUT6tc19dpF4M3AscnJk/rfbfCFxKGW317l62Z9yMSR/sQOCuzDxv0BWZ\ni26+1obVttpYORB4JDPf159adc05jH5/+xym0cZqdx3juAoI4B2Z+eeNnRFxFXBcRBydmTfUOIbT\nal+1u47x28qoX2NN1b5Kz2I4aiOrTqm257bsX0XJAp7c3+qMnd8Gftz8oQKQmVcB9wG/ERETlBfl\nPOD8lmGu5wOPYZx65f3ASyhDqFsZk/46BXgGeEsjUQWQmV8ALgHuqXYZl/75d9X2My37P11tf7Xa\nGpMeiIh3Ur59fRllBFQ7Mzn3xwHPBT7WSFQBZOZlQAJviIhR6wMN2kj3warRl3sB3x50XeaiB6+1\noTPNNlKVf6cvlequcehvb6uNr2naXcc4vhh4kDLqt9nnqu0rq21dYzjd9kE949dq1K+xpmof9DCG\no9ZROxxYl5l3N+/MzIeA71fl6oGq0/9Byjch7TwJ7ADMp8RhkjKqZIvqov0bwEHV8FF1SUQcCLyX\n8sZ4V5tDjEl/HQV8JzP/T2tBZr41My+ofjUu/fOjart3y/49q21jjiNj0hunUeazOJzyrXk7Mzn3\njc/7f2zzPP8E7AYcMLcqq8Wo98EOrLa1TlbR/dfaMNpmGyNiT2BXahbPcehvT7ON8yNifl3jmJnH\nZ+bemflMS9FLq22jT1LLGE63fXWNX7NRv8baVvt6HcORuQ0wInYE9qAEvZ37gf0iYrfMfKRvFRsT\n1ZvRRe3KIuKllDenezPz5xGxL/CjzNzU5vD7q+1+wJ29qOu4qebauZQyWucC4M/aHGZM+iQiFlPm\n0Lmxem2cD/z7qvhG4D2ZeX/1u3Hpn08BbwQ+FhE/AdYCr6DMr7CeX4y4Mia98RbgK5k5Wb0u2pnJ\nud+X0jm8b4pjl1LjDvIwGZM+WCNZtTgibgIOofyNfZWyMMY9HR85XLr9WhtG02ljI547RMS1wHJg\nAeVWnbMy85t9qOeMjUN/ewZtfKq6kIaaxbFV1Td8PWVk6gPAZ6uiWsaw1RTtq3X8Rv0aa5rt62kM\nR2lk1fOq7foO5RuqrZOq9lH17cifAxOU25ugfKNtnPrnXZQ5G07OzKc6HGNM+mdJtd0TuJ1yW8mn\nKZN7vx74RkTsVR1jXPqkGg1yKOUD9uvATym3j2wGXp2ZD1aHGpMeyMybMrPdpJ3NZnLudwOebL7N\ndopjNXfj0AdrdMjfRWnnpyjv4a8Dbo+Igzo9cJj04LU2dKbZxkY830oZiXQpcBPw68AtEfEbPaxi\n141Df7tDG2sfxyirjD5Madt64DWZ2YhP7WO4jfbVPX6jfo01nfb1NIajlKxqzKTfrmPavH9BH+oi\noLpf/lOUUSPfpKzkASVWxqkPImI/yjDqizPz9ikONSb985xqezhl8teXZ+a7MnMF8A7K8sy+Vvqs\nShB+lpJM/Dvgw5Qh23sBlzStHmdMBmcm59449dc49ME2U74F/w+ZeWxmvjczjwJOoFxotM53V2fj\n8PqZoMTz+Mw8OjNXZebrKBdY84DLqhGDQ28c+ttTtHEU4ngvZRW1vwGeT7nAbywMMAoxnKp9tY3f\nqF9jzaB9PY3hyNwGCDxebXfoUN44ST/rQ13GXkRsT1mq8vcpb1KvzczNVfHjGKeeqz7YL6V8m7Fq\nG4cbk/5p3L+/GTi95dvfiylL2R4dETthXPrpamB/4Hcy8/ONndVEvR+lfIu7EmMySDM5948DL5jm\nsZq7ke+DZeapHfZfHRFvAQ6PiP1qdDvgVEb+fa6aG/KCNvtvrlYsOwk4gnJ7/tAah/72VG0chThm\n5uWNf0fECsoXZldQJq2ufQynal9d4zfq11gzaV+vYzhKI6s2UOYO6DSMbpeqfEOHcnVJRPwS8LeU\nD5V7gF/LzIebDnmUqeMExqkbTgFeDfxhh/ukJ5r+bUz6p3Ee78/MrYYFV4mrb1O+hdkL49IX1aiq\n5cA/NSeqAKqViL4H/OeI2BljMkgzOfePAgsiYv40jtXcjXsf7H9V270HWYkuGvf3uVrEcxz629No\n41RqEcdmmXk9ZQqCX6nmHKt9DJtV7fsqsH/VvqkMc/xG/RprJu2bypxjODIjq6qJBB8A9ulwyD6U\nVWo63TOqLoiIXYEvUyYm/p/Ab2bm/2s57B7gsIjYsc18IvsAT1NWDtLcvL7a3hAR7cr/sdq/DyUm\nhxuTvriPMrqq07csjYvrTRiXftmj2n6vQ/ndlAld98CYDNJMPjvuoSQg9+bZ8Wj0E7JH9Rw7o94H\nqyaZPQiY12Gy2J2q7RP9q1VPjXw/rZpjbFFm3tKmeOjjOQ797em0sY5xrN5Pfg0gM7/S5pAHKMmA\n3alhn2Ma7dsyB2j1JeAumXlzm+OGMn6VUb/Gmkn7nksPX4OjNLIK4BbgRRGxtHlnRCyhrPrTaZUa\ndUFELACuo3yorAGObPPBCSVO82hZxrp6/CuBuzJzqIZD1tRllHuNW38a9x1fXv2+nhKT7TAmPZeZ\nT1DmW9ir9Vulaqj7QcAjwA8oE30bl957qNq2/USmfH5MUpZa9rUyODP57Gh0mo5s8zxHAuszs1Ny\nUrMzyn2w+ZTPzr+vJnneorpdYjnwFGUV0VEwDv206ygXXLu1KTu02n6rj/WZtnHob8+gjXWM4wTw\nJeCq1veTykGULzXvo559jum2735K/L5Ws/jB6F9jzaR9PX0Njlqy6opqe37VeWh0Ihr3UV7S9lHq\nlvOBV1GWqjwqM3/a4birKVnkcyKieXTJ+4CFGKeuyMy/zMzzWn9oeqOp9m3AmPRb43xeVCWoGs6k\njN65olq6+SqMS89l5v3AHcCREfGfmssi4k2UlU7+oRoV4mtlcGZy7q8FNgLvqb6dByAi3khJnHy6\n99UdOyPbB6u+ZLgO2BV4b0vxmcABwNWZ+Vi/69Yj4/A+93nKddD5zTsj4ljgaMpt4XcPomLTMA79\n7em2sXZxrObb+gJlsvF3N5dFxB8Cy4DrM3MdNYzhDNr3Y2oYPxj9a6wZtq+nMZyYnNzWyq71EhHX\nUCbBvYOSiV9OyeqtzsyVA6zaSIuIF1KGrc6nrIjzrx0OvSAzn4yIC4A/otx2cx1lYuOjKSNJfj07\nL4+pOYqICymrzh3ZPOzWmPRXRHwROIZyi9nfA78MHEW5NekVmbmxOs649EFE7A/cTJk/4EuUYdsH\nAq8Bfgi8OjMfqI41Jj0UEW+gfI68MzMvaimb9rmPiP8C/AXwf4HVlETwsZTh9q+q6y1pw2yU+2AR\n8W+A2ygXYF+hzC+4jDJx7F3A4Zn56OBqOHPdeq0Ns05tjIjnUUb7vYRyAfbPlNG1R1NG2x5afZEx\nVMahvz2TNlJWWK5jHJdQ6r0n8A/Ad4GDKasd3kep98PVsXWM4bTaV9fXYSejfo3Vrn29juGojawC\nOBE4m3Kf72mUZeDPoiwtrN55JeVDZRJ4IyUGrT9nUa16kJmrgFOr498B/Aplxa0VdXnB1thk9bMV\nY9J3xwJnVP8+hZIYuRhY3khUgXHpl8y8i3LheSXwq5RVGfenLJW9rJGoqo41Jr3V9j0KZnbuM/NT\nwO8C64C3UZIml1M6WSaqemNk+2CZeR9wCPCXlJFUb6cshPFhyvt2rRJVla681oZcpz7PTyjv9Z8A\nllDaeDBl1bllQ3yBPA797Wm3sa5xzMwfAi+n1PNAyvvlvsDHgJdn0yTydYzhdNtX1/hNYdSvsZ7V\nvl7HcORGVkmSJEmSJKm+RnFklSRJkiRJkmrKZJUkSZIkSZKGhskqSZIkSZIkDQ2TVZIkSZIkSRoa\nJqskSZIkSZI0NExWSZIkSZIkaWiYrJIkSZIkSdLQMFklSZIkSZKkoWGySpIkSZIkSUPDZJUkSZIk\nSZKGhskqSZIkSZIkDQ2TVZIkSZIkSRoaJqskSZIkSZI0NExWSZIkSZIkaWiYrJIkSZIkSdLQMFkl\nSZIkSZKkoWGySpIkSZIkSUPj/wPx6lROfZCfwAAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0xa927c3ac>"
]
}
],
"prompt_number": 34
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pymc.Matplot.plot(tau_def)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Plotting tau_def\n"
]
},
{
"metadata": {
"png": {
"height": 370,
"width": 597
}
},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAABKsAAALlCAYAAAAL/GWuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text": [
"<matplotlib.figure.Figure at 0xa782856c>"
]
}
],
"prompt_number": 35
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"observed_season = DATA_DIR + 'table_2014.csv'\n",
"df_observed = pd.read_csv(observed_season)\n",
"df_observed.loc[df_observed.QR.isnull(), 'QR'] = ''"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 39
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def simulate_season():\n",
" \"\"\"\n",
" Simulate a season once, using one random draw from the mcmc chain. \n",
" \"\"\"\n",
" num_samples = atts.trace().shape[0]\n",
" draw = np.random.randint(0, num_samples)\n",
" atts_draw = pd.DataFrame({'att': atts.trace()[draw, :],})\n",
" defs_draw = pd.DataFrame({'def': defs.trace()[draw, :],})\n",
" home_draw = home.trace()[draw]\n",
" intercept_draw = intercept.trace()[draw]\n",
" season = df.copy()\n",
" season = pd.merge(season, atts_draw, left_on='i_home', right_index=True)\n",
" season = pd.merge(season, defs_draw, left_on='i_home', right_index=True)\n",
" season = season.rename(columns = {'att': 'att_home', 'def': 'def_home'})\n",
" season = pd.merge(season, atts_draw, left_on='i_away', right_index=True)\n",
" season = pd.merge(season, defs_draw, left_on='i_away', right_index=True)\n",
" season = season.rename(columns = {'att': 'att_away', 'def': 'def_away'})\n",
" season['home'] = home_draw\n",
" season['intercept'] = intercept_draw\n",
" season['home_theta'] = season.apply(lambda x: math.exp(x['intercept'] + \n",
" x['home'] + \n",
" x['att_home'] + \n",
" x['def_away']), axis=1)\n",
" season['away_theta'] = season.apply(lambda x: math.exp(x['intercept'] + \n",
" x['att_away'] + \n",
" x['def_home']), axis=1)\n",
" season['home_goals'] = season.apply(lambda x: np.random.poisson(x['home_theta']), axis=1)\n",
" season['away_goals'] = season.apply(lambda x: np.random.poisson(x['away_theta']), axis=1)\n",
" season['home_outcome'] = season.apply(lambda x: 'win' if x['home_goals'] > x['away_goals'] else \n",
" 'loss' if x['home_goals'] < x['away_goals'] else 'draw', axis=1)\n",
" season['away_outcome'] = season.apply(lambda x: 'win' if x['home_goals'] < x['away_goals'] else \n",
" 'loss' if x['home_goals'] > x['away_goals'] else 'draw', axis=1)\n",
" season = season.join(pd.get_dummies(season.home_outcome, prefix='home'))\n",
" season = season.join(pd.get_dummies(season.away_outcome, prefix='away'))\n",
" return season\n",
"\n",
"\n",
"def create_season_table(season):\n",
" \"\"\"\n",
" Using a season dataframe output by simulate_season(), create a summary dataframe with wins, losses, goals for, etc.\n",
" \n",
" \"\"\"\n",
" g = season.groupby('i_home') \n",
" home = pd.DataFrame({'home_goals': g.home_goals.sum(),\n",
" 'home_goals_against': g.away_goals.sum(),\n",
" 'home_wins': g.home_win.sum(),\n",
" 'home_losses': g.home_loss.sum()\n",
" })\n",
" g = season.groupby('i_away') \n",
" away = pd.DataFrame({'away_goals': g.away_goals.sum(),\n",
" 'away_goals_against': g.home_goals.sum(),\n",
" 'away_wins': g.away_win.sum(),\n",
" 'away_losses': g.away_loss.sum()\n",
" })\n",
" df = home.join(away)\n",
" df['wins'] = df.home_wins + df.away_wins\n",
" df['losses'] = df.home_losses + df.away_losses\n",
" df['points'] = df.wins * 2\n",
" df['gf'] = df.home_goals + df.away_goals\n",
" df['ga'] = df.home_goals_against + df.away_goals_against\n",
" df['gd'] = df.gf - df.ga\n",
" df = pd.merge(teams, df, left_on='i', right_index=True)\n",
" df = df.sort_index(by='points', ascending=False)\n",
" df = df.reset_index()\n",
" df['position'] = df.index + 1\n",
" df['champion'] = (df.position == 1).astype(int)\n",
" df['relegated'] = (df.position > 5).astype(int)\n",
" return df \n",
" \n",
"def simulate_seasons(n=100):\n",
" dfs = []\n",
" for i in range(n):\n",
" s = simulate_season()\n",
" t = create_season_table(s)\n",
" t['iteration'] = i\n",
" dfs.append(t)\n",
" return pd.concat(dfs, ignore_index=True)\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 57
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"simuls = simulate_seasons(1000)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 58
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ax = simuls.points[simuls.team == 'Ireland'].hist(figsize=(7,5))\n",
"median = simuls.points[simuls.team == 'Ireland'].median()\n",
"ax.set_title('Ireland: 2013-14 points, 1000 simulations')\n",
"ax.plot([median, median], ax.get_ylim())\n",
"plt.annotate('Median: %s' % median, xy=(median + 1, ax.get_ylim()[1]-10))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 60,
"text": [
"<matplotlib.text.Annotation at 0xa93f00ec>"
]
},
{
"metadata": {
"png": {
"height": 323,
"width": 432
}
},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAA2EAAAKHCAYAAAAIZkAUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAWJQAAFiUBSVIk8AAAIABJREFUeJzs3Xu8ZXP9+PHXmQvjMrklMmEk3nLNJZdojFCh+7ebr5JK\nVFRfyWWEUJHvT5S+pohI8f2mG1HfIndfUS4h8Sa5J00al9GYxji/Pz5rm23P3ue296y5eD0fj/NY\n56z1WWt9ztlr77Pe6/P5vD99/f39SJIkSZLqMWpBV0CSJEmSXkwMwiRJkiSpRgZhkiRJklQjgzBJ\nkiRJqpFBmCRJkiTVyCBMkiRJkmpkECZJkiRJNTIIkyRJkqQaGYRJkiRJUo0MwiRJkiSpRgZhkiRJ\nklQjgzBJkiRJqpFBmCRJkiTVaMyCroCkwUXEFcAk4OjMPHo+n+so4Ejgx5n5nvl5rpGIiLcAPwOu\nzMwdenTMNwKfArYClgf+AfwfcEJm/qbDPksBhwK7A2sCjwOXA1/KzD8M8by7ARcCn8rMUwYotzPw\nWWBrYBxwH/Aj4P9l5pNDOdcQ6nJCdY4NM/OPw9jvW8A+1HBtjkRETAT+XP24bGb+swfHXBu4PzOf\n7fZYPajLKOA3wCszc+VByu4J7A9sBMwCbgZOzMwLB9hnA+ALwA7AeOB+4L+B4zNzZod9un5vjERE\nTAYuAx4b7G9Rt/nxuRoR62XmnS3rnqu+3Sgzb+/FeSTNH7aESYuW/sX0XCPRk/pFxJeAXwK7AaOB\n24AlgXcCV0fEx9vss2S1zxHAK4BbgDnAe4HfRsROQzjvmsBp1Y8df5eI+ATwK+BNwEzgDmAN4PPA\nbdVxuhIRuwCfGageHfbbgRKAMdx9F4Cu6xcRYyLii8AfgCW6r1JPfAl4LYP8fhHxFeAsYDPgLuBR\nYHvggog4vMM+mwPXA+8G/kV5b6xJCSaujYhl2+zT9XujC/0ty4VRL67DVSPiB8DZA5xjYf4bSMIg\nTNKLWBV8HAbMBj6emStl5ubASylP/0cB34iITVt2/TLweuC3wMTM3JJyw/llSkvVuRExfoDzrgP8\nGnj5IPXbDPg68Bywb2aulpmbVef6JbA6nW/EhqRqjfsxJQAdzn5LA9/u5tw1eQh4NfDqHrSCvYIS\n/C7wACwi+qrWlUOHUPYtwMHAY8BWmblJZgbwDkqL2NER8bqWfcYBFwBLA0dl5oTMfC3wSuBGYBPg\nq21O19V7o0u/BdajtBgvzt4MdGpNe3X1dXd91ZE0EgZhkl7MPlst/yszG61SZOaczPwipcvfaEpX\nRQAiYkXg45Sn+3tm5t+qfZ7LzCOAiylBXKOF6AUi4v3A74C1h1C/j1G6jZ+dmc8HPJk5Hdiz+nG7\niHjVEI7VWo8lI+IYStfOcdXqvmEc4suUG/K2XdIWFpn5bBZ39eBwC0XrQkSsCvyU0iI1FIdVy0Mz\n88bGysz8GXA05XWf0rLPnsBqwP9l5jFN+/wF+DfKg4sPV3Vp1Kur90a3MnNmZt6VmX8evPQireN1\n2LjWM3N2nRWSNHwGYZJelCJiNLAt5YbmRx2KXVQtm1vC3kVpHbi+w4396dXyfW3O+SvgXMrYmtMo\n484GcidwPvDd1g2Z+Xfgb5Qb6FcMcpzWeqxG6dZ4OKWb2bC6FEbENsCngWspLXIvFn0dvq9NNX7x\nLuBtwCPMGzy1ln8VpWXoX5Rrr9UZ1XLniFiuaX0jyG937T0AXEJ5QPCupk0jfm9oWBbItSept0zM\nIS3CIuI+yvigVwHHAm+l3Gz9LDM/1FTu7cAngS2AZYCHKQkhvpKZfx3G+daijB3akTI2ZByli9Nv\ngJMz84qW8mdRbubeA/yJ8uR+EiUIuQf4PvDVzPxXm3NtROlqtT2wImUczglA2y5lEbEX8B2AzBzq\nA6Z3Ubr0dRrA3hjz0vxZuVW1vLbDPtdVy00jYqmW5AVbAg8An8nMC6qEKx1l5tcp3RHnERFrACtT\nuireM9Bx2ngZMJHyO+ybmbdHxGkD7/L8eZek3Lj/C9ibMiZp2JqSzawFvA44iNKVbDpwNXBcZt7S\nYd/NgAOByZS/wRPV73JyZl7aUnYibRJzNJ3/tZTrcUr1/ThKgHo68K3M7K/Kn8XcwKQPeCoioHS5\ne6Aq807gE5T32bKU98Z1wNTMvGSYf6JO1qe8h88GDqB0CxxI43q9tV0ijcycFhH3Ul6HrYFfVQ8o\nNqcE5QNd57tSuh5ObTnXSN4bbUXEyyifA2+hfObMoryePwNOqlqFG2Un0yYxR9Pn5AqUrnwHUJKT\nzASuAA7JzHsi4pWU63lnyjVxJyX5zTktdWoc762Z+fM2df475TNrh8y8cgi/45A/V5vODbBFlYjj\n/sxcq9reSMwxT4KdiHgXsC/lOl8G+CslmD4+M+9uKbsX5fP0FEpr6VGUwH8VysOfC4FjWv9/RMQS\nlOQvu1Ou1VHAX4BLKZ/1OdjfQ3qxsCVMWjycQ+kidCfl5vg+eH7cyGmUrks7U246bqXcuH4auKUa\nfD+o6gn87dV+EyhjDu6mZBN8B3Bp1dWunTdQxmvsRvmH/Ahl3MKXgfPanOstVfndKUkybqMEmv8D\nfK7DOYY1KL/qcvjLzPz2ABkG314tm29mGl3/7u2wz18o3bFGUbrrNTsAiMy8YCh17CQitqC8pn3A\nuZn54DAP8TfgbZm53QgyqH2BEix9uTUz2wgdRLl+16a8zktQkjhcFxHvaC0cEZ/khdfGTZQb87cC\nl0TE8R3O0+m62JNy474t5X0zndLyeQpwUlO5BG5o+vla4Brgmapeh1LG1u1Eya55c1Xu7ZTAZr8O\n5x+u64FNM3Ov5gBkAINdr1AyHsLcLrKvoPxtB9rvgZZ9hnKugd4b86gCsN8B/0F5cPAHymu0PqUV\n9/qIWL7Nrp1e6+MomR3XoLyey1IexFwdEZMo19I7KOMIHwc2Br4XEXt0OMdAnzVDSo4xgs/V3zJ3\nvNcMyjX42zbnbj7HqIj4PqXFf2fgSeD3wHLARyj/B97ZoYqrUf4un6D8b8mqnh8HftPcehoRfZTP\npRMof7t7q9/tpZQHNjdGxJaD/U2kFwuDMGnxsAmwXZVUYgLlnyCUm/69KS1fO2bmK6qB8qtSnl6v\nDPykXZazZlXrx5mUJ7QnAqtk5uaZuQHlye2vKQHBER0O0cjwt3qVFKDx5BfgbRHx2qZzvRT4HuUm\n8ARg1czcipLE4puU1ot2fkoJDl490O8yVBHxZsoNSz8lSGhoPGF/rN1+mfkcpWUGYKWWbWdl5qwu\n6nRqRDxAuenalNIi9bHhHicz/5KZFw1ecp7zb0YJmm6j3ND2wicpgfiE6nVeDfgG5fX/bkSs0nT+\nycB/VT9OAV6WmVtn5iuAD1GCsYMi4iPDOP+nKH/HVaqkJ6sz9/2zX2PMU2Yex9xkCP3AGzNzUmb+\nrRoLdTTlIcekzHxV9btMoCTyADi2St3elcz8TWbeOoxdBrxeK/+oli9t2WfWAK1VrfsMeq6B3hsd\nHER5Pc4DXl595mwMrEtpWX8VpdVlqD5OSVAyoXqtNwGepnweXkZpqVs9MzelBKI/rfb7bJtjdd0l\ncCSfq5n5XkqvB4A7q2twsO6dhwP/Tgks35KZE6vrc1XKg4ZxwDlRpiNo9U7K32jz6rremNJy/c+q\njs3j+3YFdqEEamtl5oaZuQXlfXA+pavqsUgCDMKkxcVPMvN6eD4RwVNVdrPDKDeMH8jMyxuFM/Of\nmbk/5an66pSnoQPZnNJ95SHgoOZB39Xg+8b8UOt22P8x4D2ZOa1pv28wt5tYczazfSlPaK/KzIMz\nc05VflZm7sfcLk0vkJlPVgPSu07AEKWf2feqH6+qEhg0NG6kB+pKNZNy89T1TXeLnSg3NI0n3RtR\nUya4iBhD1d0T2LvxuvTAbcAemTkDIDNnZ+ZnKF0Sx1NaCBoaN6OnZubx1U091X7fY26mwGOizJ81\nFLdn5scy8+nqOM9RAqdnKP8jX9tUttON97rA2LJ7XtNUp+eq4O2nlFaIoQQevTbU67W57Ej2Gc5+\nQ31vbFQtz20OBjPzPuAQSvbGaW326+SSzDyh0cW06hrXaJV+BnhfZj5WbXuWEhg116PXRvq5OuQA\nMCKWofQe6Kd0Pf5F0zmeycwDKX+DcbR/iNZPSbLy+6b9rqO0KMLcLqgw9+/0v1kSuDTKz6A8ELyY\n8n6XhEGYtLhoN6HwtpRxCY8OMC7hB9Vyl4EOnpnXZubywDqNG5gWjRukUVXw1+qKDi1AjfEBzSmr\nd62W36O90zus74kqALuUcsP8CNDaFWkowUfjs7XX2fR2pNy8vgb4IWWM2S8jYrsen6edKZQuRidn\n5u96eNypHQK6RjbItwBUrbWTKH/TqW3KQ0l28i9Kq+lmQzz/PIlFqpvheyk3u0NJp34v5bp4TUQc\nV43xaT7ev2XmRzPzoSHWqZdGcr2O9Brv9XvjT9Xy+IjYpWo5AiAzf5qZ78zMU4dwnIb/bbOu0a3y\nhsx8omXb36rl6F60YrbqwefqULye0u1yWmb+sEOZk6vlm6suhc0e6/B+bzzseknTusbr9dGI+GjV\nQgxAZt6fmW/OzAOGWX9psWViDmnx8EibdetXy/ERcU2b7VCCNIAYykkyc1bVJW0zyliQtSlPP5uf\n1LZ7uPNwh0M2bjKa56hah3KD1mms0nC6Yg1L1S3y55QuVn8H3tT8RLfydLUc6KZoScrv0NP07VUL\nAJSnye+rbkrfRhlbt30vz9Ws6qZ0OCXYaDuxbxc6BXR/qJaNMUevpFwn/+o0ji0zZ0bEnZRgcR1e\nOIark+Fcm21l5qMRcTLlaf8hwCERcTelC+7PgctywaUMH+r1CnN/55HsM5z9hvre+Colk2JQ/o4z\nI+JKSuB8QWbeP9DObbR7rRtJgdq1qDW/ZvMtI2EXn6tD0TjGQJ+bjfGL4yldFJv/n7R+/jW0e39c\nQOldsRXlIcppEfFbyvvgwmyaHkGSQZi0uHimzbrGE8plgG0G2LefFz7NbKsauH4iL2xheI4ySPz7\nwAcH2H2e7Ictmm9wGgPtn25XkDKuoeciYldK69JSlKxhO3e42W+Md1mxzbZG6vvG7zCcrlIj8RVK\nELZNRIzOzDkDBNxnZOaZwz1B9ft8h9Ldbt8BxgiN9Ca1U3KJp6rlUlUdGi1Sna6LhhnVcqgTAg/n\n2uwoMw+MiBuB/Sg3oetUX/sDf4+IwzJzvrbidjDg9VppdJNsXK+NfcZFxJIdWrFb9xn0XMN9b2Tm\nvRHxGkrg/y7Kw5E3V19fi4hfAPu0eVDSyUDXzgKZA67Lz9WhaLwPnhqgzIym78fzwiBsyO+PzJwd\nETtQMpd+iBJMblV9HRkRt1M+Qzplz5ReVAzCpMVX44bjosx8WzcHiogNKf35lwCuonQVvBW4IzNn\nRMQ6dH+z0PAPygD/TjfRPe8WVGU/O4vyVPceStKFThne7qRke5zYYfsEylPrOcwd8zbSeq1MST5w\nXYfuSo3uP6MpN8V/owyab1f24hFWY3Xmjou6uErL3s4XIuILwJWZucMwjr90h/WNBwNPVsFl40Zx\nmUGO18jWNmPAUvNBZp4LnFu9bm+gJHZ5CyWz32kR8beW8YV1uKNaThygzJrVsnE9PUz5/Fiakrq+\nXRbM1n1gPrw3MvNh4BNVVszXUrrk7kLpbr0rJVX6kDK8zgedAvRO1/QL1PS52ngfDPSgrXl+uK7e\nN5n5DKVl/stV/XcE3gi8CdiAkik0hhE4S4stgzBp8dXos79epwIRsSZl/Mw9zUkz2vg05Ubh15Qu\neq03+at3U9EWd1JuWjel/XxD67dZN2LV3Dnfpdwc/h54czUovpNG97lOrYuNRBm3Zpv5z4ZRr0aL\nXB/l5rNdV54J1XIWVStEDn2OtKGaSZlUulNLwaspAeAD1ddwu4tuSPuup435rxrTA9xDaSEYGxEb\nZeY8A/yrJARR1fVPrdvnl2q8znrAzCymUcZb/qCaN+kiSlKVD1Dmt6pTo0vmJu1atao08BMpgdHv\nADKzPyJuoHRx3Zr2QVjjOr++aV1P3xsRMQFYLzMvrT5zflt9HVe1uFxKGYe3frbMiTWfPVstl2zd\nUKXMH8fQWtbq+FxtvHYbR0Rfh4c5jSD2n90ERxGxAuXz4K7M/HuWucfuBr4VZYL4GyjdHd9B53Gd\n0ouGiTmkxddVlKear4qIHTuUOZ0S6JzQYXvDxGp5a4d/4h9t+r7bhzuNtNAf7ZDh7sNdHv951Vin\ncyifhdcDkwcJwKA8eZ8DbBcR7bJB7l0tv99N3apuf42EK53S0H+qWl7Sw2yFrfV4NDNfnyUV9jxf\nQCPpy3eqdZ8Z6Hht7NW6okoO0LimflrVYwZlYt0+ypQH7exDuf4eo33Q2q3nmr5vbgX5GGUupa+1\n7lAFG40uorX/z63GTd1MCQw+0KZI43r9RUtiip9Uy4+2lG88vNmJEvw3z/PXs/dG1XXxJsrcb1u1\nKXINc8ds1f13bXShbfeA663DOM7Eajncz9XGdTiUrrLXUOYFW5m5Uyy0asxhd2mH7UN1enW+ea6Z\nKrhrBITee0r4RpAWW9VNa2Oy2XOaA7GIWCoiTqJ0FXmWNjePrYerlu+PiOcnZ42IFarj7N5UdqRZ\nvBpOp7SovAY4IyKWrs41JiKOo3RtmUdEvCQi1osB+su1cRrlafZfgLdm50mbn5eZ/wC+Rfn8/GFE\nrFGdf3REfJFyc/p35mb360ZjLq6PRcRnGpnLImKJiDiKMrXAM3Sen21R8KaIOLZKgd9oVfoGpbvZ\nw5S/dcMXKTeg+0bEIdWNemNS8g9S/l79wJHzKSht7qq1ZtP3P6IEBG+KiM816lXVbUPKtAvQlJ2v\neg3Xq7563sW2RWNupq9GxPMJXCLibcCRlL9p6yTXZ1JaYreNiBOa/tarUSalHgOcnZmPNnbo5Xuj\nev0aAd7pVde2Rr2XqH6nsZSJputsBYPSMgzwqebPm4jYibmfuUMJkEb6udq4Dl/eeN90PEGZeuGr\n1Y+nVmNfG+cZFxEnUgLHWZSJ2LtxbrX8fETs3LwhIt5DydQ4h5KoQ3rRszuitHg7hvK09j2UJ8oP\nUG6C1qGMuWrMHXNz50MAZeD4HpSJdO+IiLsoN27rUj5HvkgJCCZUX4O1JnWUmU9X/7B/QRnc/c7q\nfGtRur2dT+nO0updzJ3HatAHTBGxNXO7TfUD5w8Qv/0lyySpDZ+njL3aFLg7Iv5A+b1fRhnI/u4q\nCO5KZv48Io6kvI4nAUdExL2UcWLLUSZM/UAOb/Legcy3DHAD+ANlfq+PVb/bupTxK3+n5e+YmVdG\nxGeAr1MCroMj4k+UblurUl7Hr2Xmt5gPMvMfEfEQZSLfayLiz8CHMvP2iNiPEtT/J3BYte0llNcK\nSpfE5sQo61NaegB2YG6L4vyo948j4juU9+jlEXEHJYB5FeVv9vnWZAnVmKQ9Ka1bnwU+WP3uG1C6\n0N0E/Eeb0/XyvXEYZYzZBsAfq+vjKUqmzOUoXWU/nE3zxdXkJMrn4SrAbRHxR8prPZESoD1Nh4dF\nLUb6uXob5XVbDfhTRDycmds2Hbf1ffxlSjfB9wMXRcSDwKOU/w3LVvXdOzNvGeLv31Z1nZ1LmRj6\nVxHxMCWQX43S7b0fOKzqpii96NkSJi0a+mk/xqDTeqA8Tc7M91H++V5CSWqwEeXm/SfApDYZ8+Y5\nXpWk4jWUsVP3U7JerVgdY8vM/AKlW2M/1bxOQ6lfp+3VvDSbA6dSuv5sSPln/knKjVmnY7WtfweN\nm5bGzcw2A3xt0VK/JylPdb9ISdu+PuWm6QLgdZl51RDrMNjfh8z8EqUF4edV2Y0oGSJPB16TmecP\n8VxDrc9I9ukms9wRlO6Fj1Je58coLWGbZjUBebPMPIUytuh/KDfhm1Bac38E7Jhl8tle1LvT9ndT\nxj4tSbnpXquq1+mURBE/p7QqbER5j1xJucF9W0uw0Hy9dvP3G9K+mbk35Yb+t8AalGv+WmD3zPxK\nh31+Tbn2f1idZwPKxML/j9J1d55MmT18b5CZTwHbUbKA/pFyI78+5Ro5DdgoXzgHYqe/xUB/42H/\n7asunq+lfB5Oo4xDbLQk7Ui5LluP27PP1SqI2ZsyTnIVYGKVDKbtubJMGv7vlHT/v6b8H2h8pp5C\nea/9gBca7O/SafuHKN0br63OszElKPwp5f3Z2uIqvWj19fcvkKyskqQXsYi4gjL58jsWQMbAhULV\nDfFpyg33UOY0kyQtJrrqjhgRQ+kCMLn5yVfVveEASneo6ZQ+30dW/ZZbj78bZX6QDShPli4EpgyS\nxU2SpEXBjpSxZLVlcpQkLRy6HRN2NO2bpFdhbveS51PbRsQUSt/kW4CTKc3UBwBbR8TkzJzdVHZ3\nStayeyipTNekZNHaPiK2aMniJEnSIiMiVgfOAE7IzPkyAbkkaeHVVRCWmUe3Wx8RF1CCsw800j1X\nKW2PofQT3r6RuSoijqaMCdiH0jeZiFi2+v4eSl/lGdX6iyn/tA4HDuqm7pIkLSiZ+WBEvLdlTJOk\nhUiVhfbI6scjMvPLA5Q9Gdi/+nFiZj7Qw3rsTRkH+eHM/G617gpKl+7lh5LZt04RMZlyb78lMJrS\n+PKfmfnTgfZrOcY2lLGlm1MS1lwKHFKNpVws9DwxR0TsQUl3+u3MbJ5zYh/KC3FsS+rgYylzWOzd\ntG53YHngpJbMWGdSUrru1WH+IEnSoqHbhBSLPAMwaZHyrk4bqulDGtvn5+da87HPBI6iJIVZaFTT\nIFwKbEVJoPRtSgKjH0dEpzkeW4+xPWVeyPUpWY/Pp8QWv60adRYLPU1RX83vciwlc9eUls2TKBfP\nFc0rM3NWRFwHvDEixlfZkCZVmy9vc5orKQHdhkCv0jJLkmqUmTss6DpI0hD9Fdg0ItassmO22oaS\ncXQGJSvkfNdoEVsInUDJWLt9Zt4IEBFfobSGHR8R383Mf3bauWpkOZXyt9yimuibiDiHkuX5BDpP\nPL5I6XVr0icp87X8Z2ZOb9m2NvBohz/8fdVy3aay/cCfByi7TpttkiRJUi81pgJ5Z4ft7waeAK5m\nwcy3uFCoGmPWA25rBGAAmfkIZe7PZSnJ9gayIyUeOKMRgFXHuIwShL0jIlbsdd0XhJ61hEXEaOAz\nlK6FU9sUWYkyxqudRpKN5ZrKzsrMdk2srWUlSZKk+eUyynyb7wK+1mb7v1Hmwmt7bxoRm1HGlr0e\nWIoytOZbmXlqm7JvpfQm25iSRfx0SqK71nJX0DImrMqpcEBVn1dSJmV/kDJP29GNhpCImEhp6Dga\nuJmSa2FDymToF1AykT/WdK6zgD2BvTLz7Ha/I0BmPhMRTwITImJMZj7btHkCpYFlsAznA/WGu4Iy\nEfp2wCI/tUkvW8LeRmkF+3aHAYJj6dxvtbF+3AjKSpIkSfPLbMpN/+si4mXNGyJiS8r97w9p0woW\nEbtQktJNpgQ4J1Puv78ZEae2lP1IVWZt4HuU4O8gSpKLdp4fIxYRYyiTcR8FPExJcPcdStB3EGVS\n8FZvpUwO/jDw9Wq5d1WHZj+tjvv7DvVodiIlS/rpETEhIpavsqPvCFyQmfcNsv/a1bJdw01j38Wi\nN1wvg7A9q+VpHbbPBJbosG3Javn0CMpKkiRJ80s/8GPKffPbW7Y1uiJe0rpTRCxNCX6mAxtn5kcy\n81DgNcCPgI9VQRoRsTwlgHkI2DwzP5GZH6K0nr1kCHV8NyUb4Zczc7fMnJKZ+wGvprSkvb3qLths\nM+B9mfnOzJxS7X87JdiMRqHMvCAzj8nMQXMxZOYxwCGUuOBB4B+U6am+R0m8N5iVqmW7qTsWq95w\nPQnCqhd1Z+DWzLy7Q7HpdP6jNdY/0VR2XESMHULZkej3yy+//PLLL7/8AvqPuuzE/vf+4BP97/3B\nJ/qPuuzEBV4fvxaOr/333/8LfX19fVOnTr3gtttu+9kyyyzT9/rXv/605jKrr776Qe94xzuWz8xZ\nO+6449v7+vr6Lr/88vuB/hNPPPHpvr6+lQ899NBVq4Qe/UB/Zj538cUXv6evr69vl112+QXQf8IJ\nJ0zv6+tb7uCDD149Mx9sKnvTnnvuuWxfX1/f8ccf/93G+i233HL7UaNGjbrxxhufAPp/9atf/fex\nxx7bd/311x/RXL/MfGrSpEmr9vX1jb3qqqtmAv2XXnrpvX19fX1rrrlmX2b+qKns7D333HPDvr6+\nvjPOOOPOkfzNzj///P6lllrqP1daaaW+973vfX177LFH34QJE/rGjh2754EHHvjMYPtvtdVWbxw1\nalTfDTfc8GjrtrPOOusXfX19fR/96EePGEndRvg13/RqTNj2wNKUqL6Tu4DXR8SSbcZ6rQXMAe5u\nKvs6YGLTuuayUPrTjti0aU91s7sWcSuvPB7wOnix8zoQeB0IZs+e84LvvRZe3BqfCU8/XW5Xn3xy\nJk88MYutt96Wq666nPvue4RlllmWzDt56KGH2H//zzJt2lP8619lCNRjjz3N2LFP8bvf3QzADTfc\nzKOPPjbPeUaNGsVtt93OtGlPcdNNpZFpjTVeNc/1t/bar36+Ho1tjXP9/e9PMXNmP8sssxKTJr2R\nGTNm8fvf/4YHH7yfhx9+iMw7uOmmG+jr62PatCcZNWpp/vGP0plstdVeMc+5Ro8uHc6mTXti2O+D\nBx64jylTpvDyl6/Gt751JiussAIAH/rQvnzuc5/mxBNPZNVV12CbbbbteIxRo8bQ39/PI49M55ln\nXrht2rTS/tLfP7qW92jjOphfehWEbV0trxmgzNWU/rCTaGqyrVrRtgZuz8ynm8ruVZVvDcImA49n\n5h1d1lmSJEkaksmT38Bll13C//3fNbzxjW/miisuZZlllmGrrbZpW37GjBIoXHrpxW239/X1MWNG\nSaPw1FOl7NJLLz1PuZe8ZPDeiP39/Zx99nf4n/855/nzrrjiimy44casuupq3H//vfS3tOuMHdtp\n5A+MpBHo0ksvob+/n7322vv5AAzK7/SpTx3APvvsxf/+70UDBmHjx5ff9emnZ7zgGI11AMsuu+yw\n67Yw6lU+Q6X+AAAgAElEQVQQtinl1bppgDLnAocBR0XElZn5r2r9YcB4XjiW7HxK9pmDI+JHjXT3\n1YDFdShzBEiSJEm12HrrbVlyySW56qrLnw/Ctt329YwZ0/52eqmllgLg61//JptttsWAxx4/vrS6\nzJgxY55tzzwzc9C6/fd/f4/TT/8Wm222BXvs8SHWWWddVlihZHI/8MBPc//99w56jG5Nm/Y3ANZc\nc+I82yZOfCUAf/vbPIkeX2D11dcA4JFHHuYVr1j9BdseeeQvVZk1u63qQqFXiTnWBmZ2yIoIQGYm\nJXjaBrg5Io6PiIsoaTGvocyo3Sg7HTi4Ou7vI+KEiPhvSqCWlAmhJUmSpFostdRSbLnlNlx//bXc\nccftPPTQg+yww04dy7/qVWX62zvv/OM822bMmME3vnESF1/8vwCst976ANx667wJCP/4x9sHrdsl\nl/yS0aNHc9xxX2XLLbd+PgDr7+/ngQfuo6+vj/k8xImXvnRlAB54YN75rB966AEAVlxxpXm2Ndtk\nk00BuOmmG+fZdvPNNzJq1CjWX3+wqcYWDb0KwlZkCIkyqswr+1Ougk8D61MyweyWmbNbyp5KmZNh\nGmUS6O2As4DJmdkuY4okSZI030ye/AaeeeYZvv71r7L00kuz1Vav61h20qQdWGaZZTjnnO/y4IMP\nvGDbKad8jfPOO5eHH34IgG222Zbll1+BH/3oBy8IYh544H4uuOAng9ZriSWWZM6cOUyf/o8XrD/r\nrNP5618fAeDZZ59tt2vP7LDDTvT19XH22d/hiSfm3qrPmjWLqVNPBmCnnd404DFe85rNWGWVVfnZ\nz37yfL0Bbrjht/zud9czadIOLLfc8vPnF6hZT7ojZuaEYZSdSvvJnNuVPQ84b6T1kiRJknql0f3w\n9ttvY+ed38zYse0SeRfLLrsshxxyOEcffTgf+cgeTJo0mZVWeik333wTd975R1796g3YffcPAqWV\n7ZBDPs/hhx/CPvt8iMmTdwTgiisuY/nll+epp+btbNbfNMjrTW/alT/+8Q988pMfZYcddmLMmLHc\ndNMNPPjgA2yyyabccsvNPP744/N08RuKq666grvvTiZN2oF11lm3Y7m11nolH/vYJzjttKl84APv\nZfvt38CYMaO57rprefjhh9h55zfzhjfMbTm8++7kqquuYN11g9e/fjJQkpUceOAhHHrogey99wfZ\naac3M3PmP7nkkl+ywgorst9+nxl2/RdWvRoTJkmSJC02+vr6qm58cy2zzLJsscWWXH/9b54PlJr2\nmKf8DjvsxMorr8L3v38m1113Lc888wwvf/kE9tprb3bf/YOMGzd36q7tttuer3/9m3znO9/msst+\nzbhx49htt7fxmtdsyuc/f/CAdXvXu95Df38/55//Iy688HyWXXY8W265NV/84le4554/ccstN3P9\n9dey4YYbDft3vuaaK/nlL3/OaqtNGDAIA/jgBz/MmmuuxXnnncuvfvUL+vufY401JvLZzx7CO9/5\n7heUvfvuuzjrrNPZZZe3PB+EAWyzzXZ89avf4Mwzv83Pf34BSy+9NNtttz377rsfq6768gHPvyjp\n629NlfLi0G/62Rc3U1ILvA5UeB3oazd9i7sf/zMA6yz/Sv5js48v4BppQfIzQfD8ddA3WLmR6tWY\nMEmSJEnSEBiESZIkSVKNDMIkSZIkqUYGYZIkSZJUI4MwSZIkSaqRQZgkSZIk1cggTJIkSZJqZBAm\nSZIkSTUyCJMkSZKkGo1Z0BWQJEmLpo/usy+Pz5i1oKvRtRV3W54lXr4EALffcSf/dsJe8/V8yy+7\nJGecdup8PYekhZtBmCRJGpHHZ8xiuY33XNDV6NroZa4Hplffr8JyG79tvp7v8VvPnq/Hl7Twszui\nJEmSJNXIIEySJEmSamQQJkmSJEk1MgiTJEmSpBoZhEmSJElSjQzCJEmSJKlGBmGSJEmSVCODMEmS\nJEmqkUGYJEmSJNXIIEySJEmSamQQJkmSJEk1MgiTJEmSpBoZhEmSJElSjQzCJEmSJKlGBmGSJEmS\nVCODMEmSJEmqkUGYJEmSJNXIIEySJEmSamQQJkmSJEk1MgiTJEmSpBoZhEmSJElSjQzCJEmSJKlG\nBmGSJEmSVCODMEmSJEmqkUGYJEmSJNXIIEySJEmSamQQJkmSJEk1MgiTJEmSpBoZhEmSJElSjQzC\nJEmSJKlGBmGSJEmSVCODMEmSJEmqkUGYJEmSJNXIIEySJEmSamQQJkmSJEk1MgiTJEmSpBoZhEmS\nJElSjQzCJEmSJKlGBmGSJEmSVCODMEmSJEmqkUGYJEmSJNXIIEySJEmSamQQJkmSJEk1MgiTJEmS\npBoZhEmSJElSjQzCJEmSJKlGBmGSJEmSVCODMEmSJEmqkUGYJEmSJNXIIEySJEmSamQQJkmSJEk1\nGtOrA0XEHsBngA2AJ4Brgc9nZraU2xM4AFgHmA6cBxyZmU+3OeZuwOHVMWcCFwJTMnNar+otSZIk\nSXXqSUtYRHwJ+B7wEuAU4Arg7cBvImKtpnJTgLOqH08GbqEEZBdHxNiWY+5OCbpeCkwFLgP2Aq6N\niOV6UW9JkiRJqlvXLWERsSVwGCXw2iUzZ1Xrfwz8EDgS+HBErAkcQ2kh2z4z51TljgaOAPahBHBE\nxLLV9/cAm2bmjGr9xcAZlNaxg7qtuyRJkiTVrRctYfsBzwH7NAIwgMz8MXAacFe1ah9gNHBsIwCr\nHAs8CezdtG53YHngpEYAVh3zTCCBvSLC8WySJEmSFjm9CGR2AW7LzD+1bsjMj2fmcdWPk4B+SotZ\nc5lZwHXAJhExvqkswOVtznclsBKwYfdVlyRJkqR6ddUdMSJeRhmzdXFErEdp1XpDtfli4ODMvK/6\neW3g0cz8Z5tDNcqsC9xYle0H/jxA2XWAW7upvyRJkiTVrduWsNWq5SuA64E1gNOB/wPeDVwXEWtU\nZVYCHu9wnCeq5XJNZWc1d28coKwkSZIkLTK6TcyxTLWcBHwX+Ehm9gNExP6UDIhfA94FjAXaBVU0\nrR9XLYdTdkRWXnn84IW02PM6EHgdqPA6GL4xY0cv6CosksaMHe31tgjwNdL81G1L2HPV8lnggEYA\nVjkFuBfYNSKWoszztUSH4yxZLRtzhQ2nrCRJkiQtMrptCWt0DbwvM1/Q1TAz+yPiVmAipZvidDp3\nIWysbxxvOrBeRIzNzNmDlB2RadOe6mZ3LeIaT7e8Dl7cvA4EXgfdeHb2nMELaR7Pzp7j9bYQ8zNB\nMP9bQrttCfszpTWsU6tVYwLmf1JS1a8aEUu2KbcWMAe4u/r5LqCPEsC1KwslVb0kSZIkLVK6CsIy\n8xngd8AaEbF287aIGANsAjwGPAxcU51vUku5ccDWwO2Z2ehieHW1nNzmtJOBxzPzjm7qLkmSJEkL\nQi/mCTutWp5cBV4NBwITgLMz8zngHEpr11ER0dxydhgwvuk4AOcDTwEHR8QKjZUR8RFKavrTe1Bv\nSZIkSapdt2PCyMwzI+KtwDuA30fEL4FXUyZxTuDoqlxGxAnAIcDNEXERsAGwK6WV7NtNx5weEQcD\n36yO+UNKQPee6pjHdltvSZIkSVoQetESBiU4+mz1/X7AxpTsiK/LzOdHNWbmFGB/ykTMnwbWB04E\ndmtNwJGZpwLvB6YBnwS2A84CJrcmAZEkSZKkRUXXLWEAmTmHMh/Y14ZQdiowdYjHPQ84r7vaSZIk\nSdLCo1ctYZIkSZKkITAIkyRJkqQaGYRJkiRJUo0MwiRJkiSpRgZhkiRJklQjgzBJkiRJqpFBmCRJ\nkiTVyCBMkiRJkmpkECZJkiRJNTIIkyRJkqQaGYRJkiRJUo0MwiRJkiSpRgZhkiRJklQjgzBJkiRJ\nqpFBmCRJkiTVyCBMkiRJkmpkECZJkiRJNTIIkyRJkqQaGYRJkiRJUo0MwiRJkiSpRgZhkiRJklQj\ngzBJkiRJqpFBmCRJkiTVyCBMkiRJkmpkECZJkiRJNTIIkyRJkqQaGYRJkiRJUo0MwiRJkiSpRgZh\nkiRJklQjgzBJkiRJqpFBmCRJkiTVyCBMkiRJkmpkECZJkiRJNTIIkyRJkqQaGYRJkiRJUo0MwiRJ\nkiSpRgZhkiRJklQjgzBJkiRJqpFBmCRJkiTVyCBMkiRJkmpkECZJkiRJNTIIkyRJkqQaGYRJkiRJ\nUo0MwiRJkiSpRgZhkiRJklQjgzBJkiRJqpFBmCRJkiTVyCBMkiRJkmpkECZJkiRJNTIIkyRJkqQa\nGYRJkiRJUo0MwiRJkiSpRgZhkiRJklQjgzBJkiRJqpFBmCRJkiTVyCBMkiRJkmpkECZJkiRJNTII\nkyRJkqQaGYRJkiRJUo0MwiRJkiSpRgZhkiRJklQjgzBJkiRJqpFBmCRJkiTVaEwvDhIRXwQ+32Hz\nDzJz96ayewIHAOsA04HzgCMz8+k2x90NOBzYAJgJXAhMycxpvai3JEmSJNWtJ0EYsAkwCziuzbY/\nNL6JiCnAl4FbgJOBjSkB2dYRMTkzZzeV3R04B7gHmAqsCewFbB8RW2TmEz2quyRJkiTVpldB2MbA\n7Zl5TKcCEbEmcAxwLbB9Zs6p1h8NHAHsA5xSrVu2+v4eYNPMnFGtvxg4g9I6dlCP6i5JkiRJtel6\nTFhEvARYA7h1kKL7AKOBYxsBWOVY4Elg76Z1uwPLAyc1AjCAzDwTSGCviHA8myRJkqRFTi8CmY2r\n5WBB2CSgH7iieWVmzgKuAzaJiPFNZQEub3OcK4GVgA1HUllJkiRJWpB60R2xEYS9LCIuAbagBFuX\nAp/PzLuq7WsDj2bmP9sc475quS5wY1W2H/jzAGXXYfDAT5IkSZIWKr1sCfsc8DhwKnA98G/A9RGx\nSbV9pWp7O40kG8s1lZ1VtZINVlaSJEmSFhm9aAl7ltI6tVdmXtVYGRH/Dnwf+A6wOTCWkkGxncb6\ncdVyOGUlSZIkaZHRdRCWmft3WH9uROwLbBcR61Lm+Vqiw2GWrJaNucJmAqsMseyIrLzy+MELabHn\ndSDwOlDhdTB8Y8aOXtBVWCSNGTva620R4Guk+Wl+Zxi8CegD1qJMzNypC2FjfaOr4XRgXESMHUJZ\nSZIkSVpkdNUSFhGjKRM1j87M37UpslS1fAa4C5gUEUu2Geu1FjAHuLv6+S7gdcDEpnXNZaGkqh+x\nadOe6mZ3LeIaT7e8Dl7cvA4EXgfdeHb2nMELaR7Pzp7j9bYQ8zNBMP9bQrttCRtLScLxy9Z5uyKi\njxJIzQZuBq6uzjeppdw4YGvKZM+NLoZXV8vJbc45GXg8M+/osu6SJEmSVLuugrDMfAa4CFgBOLRl\n84GUubzOzcwngXMprV1HRUTz2LDDgPHAaU3rzgeeAg6OiBUaKyPiI5TU9Kd3U29JkiRJWlB6kR3x\nQEqL15ciYjJl7q7Nge2B24HPAmRmRsQJwCHAzRFxEbABsCtwDfDtxgEzc3pEHAx8E/h9RPwQmAC8\nh9IN8dge1FuSJEmSatd1Yo7M/DNlgubvUlq+PgWsAZwAvC4zpzeVnQLsT5mI+dPA+sCJwG6ZObvl\nuKcC7wemAZ8EtgPOAiZnZqf5xiRJkiRpodaLljAy80Hgw0MsOxWYOsSy5wHndVE1SZIkSVqozO8U\n9ZIkSZKkJgZhkiRJklQjgzBJkiRJqpFBmCRJkiTVyCBMkiRJkmpkECZJkiRJNTIIkyRJkqQaGYRJ\nkiRJUo0MwiRJkiSpRgZhkiRJklQjgzBJkiRJqpFBmCRJkiTVyCBMkiRJkmpkECZJkiRJNTIIkyRJ\nkqQaGYRJkiRJUo0MwiRJkiSpRgZhkiRJklQjgzBJkiRJqpFBmCRJkiTVyCBMkiRJkmpkECZJkiRJ\nNTIIkyRJkqQaGYRJkiRJUo0MwiRJkiSpRgZhkiRJklQjgzBJkiRJqpFBmCRJkiTVyCBMkiRJkmpk\nECZJkiRJNTIIkyRJkqQaGYRJkiRJUo0MwiRJkiSpRgZhkiRJklQjgzBJkiRJqpFBmCRJkiTVyCBM\nkiRJkmpkECZJkiRJNTIIkyRJkqQaGYRJkiRJUo0MwiRJkiSpRgZhkiRJklQjgzBJkiRJqpFBmCRJ\nkiTVyCBMkiRJkmpkECZJkiRJNTIIkyRJkqQaGYRJkiRJUo0MwiRJkiSpRgZhkiRJklQjgzBJkiRJ\nqpFBmCRJkiTVyCBMkiRJkmpkECZJkiRJNTIIkyRJkqQaGYRJkiRJUo0MwiRJkiSpRgZhkiRJklQj\ngzBJkiRJqpFBmCRJkiTVyCBMkiRJkmpkECZJkiRJNTIIkyRJkqQaGYRJkiRJUo3G9PqAEXEC8Flg\ncmZe1bJtT+AAYB1gOnAecGRmPt3mOLsBhwMbADOBC4EpmTmt13WWJEmSpLr0tCUsIrYE/gPob7Nt\nCnBW9ePJwC2UgOziiBjbUnZ3StD1UmAqcBmwF3BtRCzXyzpLkiRJUp161hIWEUsA36FNYBcRawLH\nANcC22fmnGr90cARwD7AKdW6Zavv7wE2zcwZ1fqLgTMorWMH9arekiRJklSnXraEfR54FfDrNtv2\nAUYDxzYCsMqxwJPA3k3rdgeWB05qBGAAmXkmkMBeEeFYNkmSJEmLpJ4EMxGxMXAoJai6vU2RSZQu\nilc0r8zMWcB1wCYRMb6pLMDlbY5zJbASsGH3tZYkSZKk+nUdhEXEaEo3wbuA44C+NsXWBh7NzH+2\n2XZftVy3qWw/8OcByq4zwupKkiRJ0gLVi5awzwGbAntn5uwOZVYCHu+w7YlquVxT2VlVK9lgZSVJ\nkiRpkdJVEBYR6wJHAadk5vUDFB0LtAuqaFo/bgRlJUmSJGmRMuLsiBHRR+mG+FdgyiDFZwJLdNi2\nZLV8uqnsKkMsO2Irrzx+8EJa7HkdCLwOVHgdDN+YsaMXdBUWSWPGjvZ6WwT4Gml+6qYlbD9gW+AT\nHcZ6NY8Nm07nLoSN9U80lR3XOndYh7KSJEmStEjpZp6wd1fLX0REu+2XV+vXoiTtmBQRS7YZ67UW\nMAe4u/r5LuB1wMSmdc1loaSq78q0aU91ewgtwhpPt7wOXty8DgReB914dvacwQtpHs/OnuP1thDz\nM0Ew/1tCuwnCzgQua7N+F2Ar4CxKNsPHgauByZT085c0CkbEOGBr4PbMbHQxvBrYqyrfGoRNBh7P\nzDu6qLckSZIkLTAjDsIy87vt1kfEilRBWGZeVa07FzgMOCoirszMf1XFDwPGA6c1HeJ84GvAwRHx\no8ycXh3jI5TU9CeMtM6SJEmStKB10xI2ZJmZEXECcAhwc0RcBGwA7ApcA3y7qez0iDgY+Cbw+4j4\nITABeA+lG+KxddRZkiRJkuaHXswT1qq/+nqBzJwC7F9t+zSwPnAisFvr/GKZeSrwfmAa8ElgO0r3\nxsmZ2Wm+MUmSJEla6PW8JSwzDwAO6LBtKjB1iMc5Dzivh1WTJEmSpAVufrSESZIkSZI6MAiTJEmS\npBoZhEmSJElSjQzCJEmSJKlGBmGSJEmSVCODMEmSJEmqkUGYJEmSJNXIIEySJEmSamQQJkmSJEk1\nMgiTJEmSpBoZhEmSJElSjQzCJEmSJKlGBmGSJEmSVCODMEmSJEmqkUGYJEmSJNXIIEySJEmSamQQ\nJkmSJEk1MgiTJEmSpBoZhEmSJElSjQzCJEmSJKlGBmGSJEmSVCODMEmSJEmqkUGYJEmSJNXIIEyS\nJEmSamQQJkmSJEk1MgiTJEmSpBoZhEmSJElSjQzCJEmSJKlGBmGSJEmSVCODMEmSJEmqkUGYJEmS\nJNXIIEySJEmSamQQJkmSJEk1MgiTJEmSpBoZhEmSJElSjQzCJEmSJKlGBmGSJEmSVCODMEmSJEmq\nkUGYJEmSJNXIIEySJEmSamQQJkmSJEk1MgiTJEmSpBoZhEmSJElSjQzCJEmSJKlGBmGSJEmSVCOD\nMEmSJEmqkUGYJEmSJNXIIEySJEmSamQQJkmSJEk1MgiTJEmSpBoZhEmSJElSjQzCJEmSJKlGBmGS\nJEmSVCODMEmSJEmqkUGYJEmSJNXIIEySJEmSamQQJkmSJEk1MgiTJEmSpBoZhEmSJElSjQzCJEmS\nJKlGBmGSJEmSVCODMEmSJEmqkUGYJEmSJNXIIEySJEmSajSmFweJiJWALwC7AS8H7gXOAk7MzDkt\nZfcEDgDWAaYD5wFHZubTbY67G3A4sAEwE7gQmJKZ03pRb0mSJEmqW9ctYRExHrgG2B+4DfgG8ARw\nPPDTlrJTKMEZwMnALZSA7OKIGNtSdndK0PVSYCpwGbAXcG1ELNdtvSVJkiRpQehFS9gUIIBPZ+Z/\nNVZGxDnA7hGxa2b+IiLWBI4BrgW2b7SQRcTRwBHAPsAp1bplq+/vATbNzBnV+ouBMyitYwf1oO6S\nJEmSVKtejAlbE3iA0lrV7AfVcutquQ8wGji2pYviscCTwN5N63YHlgdOagRgAJl5JpDAXhHheDZJ\nkiRJi5yuA5nM3CMzJ2bmcy2b1quWj1bLSUA/cEXL/rOA64BNqq6NjbIAl7c55ZXASsCGXVZdkiRJ\nkmrXk8QczSLiZcC7gaOB+4HvV5vWBh7NzH+22e2+arkucGNVth/48wBl1wFu7UmlJUmSJKkmPe3S\nFxFfBP4K/BfwOPCmzHyi2rxSta6dRpnlmsrOqlrJBisrSZIkSYuMXreE3QN8hZKo4+3A1RHx5sy8\nGRgLtAuqaFo/rloOp+yIrLzy+MELabHndSDwOlDhdTB8Y8aOXtBVWCSNGTva620R4Guk+amnQVhm\nntX4vprj62fA2cBGlHm+luiw65LVsjFX2ExglSGWlSRJkqRFRs/HhDVk5s8j4jLgDRGxNmVi5k5d\nCBvrG10NpwPrRcTYzJw9SNkRmTbtqW521yKu8XTL6+DFzetA4HXQjWdnzxm8kObx7Ow5Xm8LMT8T\nBPO/JbSrMWERMToidoqInToUuR/oo0y4fBewakQs2abcWsAc4O7q57uq/SZ2KAslVb0kSZIkLVK6\nTczRB1wInNNh3q5NgOcoWQ6vrs43qblARIyjzCV2e2Y2uhheXS0ntznmZODxzLyjy7pLkiRJUu26\nCsIy81ngx8DKwEHN2yLiE8DmwM8zcxpwLqW166iIaB4bdhgwHjitad35wFPAwRGxQtMxP0JJTX96\nN/WWJEmSpAWlF2PCDqa0bh0XEZOBPwCbAm+gtIDtC5CZGREnAIcAN0fERcAGwK7ANcC3GwfMzOkR\ncTDwTeD3EfFDYALwHko3xGN7UG9JkiRJql3X84Rl5l+A11KCqI2Bz1AmWz4JeG1m/rWp7BRgf8pE\nzJ8G1gdOBHZrTcCRmacC7wemAZ8EtgPOAiZnZqf5xiRJkiRpodaT7IiZ+ShVi9cQyk4Fpg6x7HnA\neV1UTZIkSZIWKl23hEmSJEmShs4gTJIkSZJqZBAmSZIkSTUyCJMkSZKkGhmESZIkSVKNDMIkSZIk\nqUYGYZIkSZJUI4MwSZIkSaqRQZgkSZIk1cggTJIkSZJqZBAmSZIkSTUyCJMkSZKkGhmESZIkSf+/\nvbuP1qyq7wP+ncwM0OgEFUZTtYol8LNBoVSXoajjxJW0VRJNTVzWmFKqlvj+LnYwsoAkY9r6QlyB\nFNSIkuAqJi5TrU3RKEZDUJOoWCQbo5J0pUpQZxAQJ8ww/eOcW58M9/Jy75197svns9asw91n32d+\niznPvc/37L3Pho6EMAAAgI6EMAAAgI6EMAAAgI6EMAAAgI6EMAAAgI6EMAAAgI6EMAAAgI6EMAAA\ngI6EMAAAgI6EMAAAgI6EMAAAgI6EMAAAgI6EMAAAgI6EMAAAgI6EMAAAgI6EMAAAgI6EMAAAgI6E\nMAAAgI6EMAAAgI6EMAAAgI6EMAAAgI6EMAAAgI6EMAAAgI6EMAAAgI6EMAAAgI6EMAAAgI6EMAAA\ngI6EMAAAgI6EMAAAgI6EMAAAgI6EMAAAgI6EMAAAgI6EMAAAgI6EMAAAgI6EMAAAgI6EMAAAgI6E\nMAAAgI6EMAAAgI6EMAAAgI6EMAAAgI6EMAAAgI6EMAAAgI6EMAAAgI6EMAAAgI6EMAAAgI6EMAAA\ngI6EMAAAgI6EMAAAgI6EMAAAgI6EMAAAgI42LceLVNUPJzk7ySlJHpjk20k+muSs1trXDuh7apJX\nJjkmya4kl439bp3ndU9J8ktJjktyW5IPJtnRWrtxOeoGAADobckjYWMA+0yS05Nck+S88eufT/LZ\nqvqRmb47klw8fvm2JF/IEMgur6rNB7zuszOEriOTXJDkY0lOS3JlVR2+1LoBAACmsBwjYWcneWiS\nV7XWzptrrKrnJLkkyZuTPL2qHp7k3CRXJnlSa23f2O+cJG/IEOLOH9vuO/73V5Kc2Fq7ZWy/PMk7\nM4yOvXYZagcAAOhqOdaE/eskfzsbwJKktfY7Sb6a5F9U1YYMIWtjkp1zAWy0M8l3kjx/pu3ZSe6X\n5K1zAWx8zXclaUlOqyrr2QAAgFVnSUFmDEK/mmE0bD57khySZHOSbUn2J7litkNrbU+Sq5KcUFVb\nxuZt4/Hj87zmJ5IckeRRSygdAABgEkuajthauyPD2q47qapHJnlkkq+01v6uqo5OckNr7bvzdL9+\nPB6b5M+SHJ0hsH31Lvoek+TqRRcPAAAwgYMypW8cIfuNJBuSXDQ2H5Fk9wLfctN4PHym755xlOzu\n+gIAAKwayx7CxvVfFyZ5cpLPZnhaYjJMSZwvVGWm/bBF9AUAAFg1lmWfsDlVtSnJ25P8uwxPNnx6\na23vePq2DOvD5nPoeLx1pu+D7mHfRdm6dcvdd2LNcx2QuA4YuA7uvU2bN05dwqq0afNG19sq4N+I\ng2nZRsKq6geT/H6GAHZdkh9vrX1jpsuuLDyFcK79ppm+hx24d9gCfQEAAFaNZRkJq6r7J/mfSR6X\n5M+T/KvW2jcP6HZdkidW1aHzrPV6RJJ9Sb480/fkJEfNtM32TYZH1S/ajTfevJRvZ5Wbu7vlOljf\nXOz4FLQAABKvSURBVAckroOl2Hv7vrvvxJ3svX2f620F8zOB5OCPhC55JKyqDkvyoQwB7Iok2+cJ\nYEnyyQz7hG2bbRy//6Qk17TWbp3pmyTb53md7Ul2t9auXWrtAAAAvS3HdMSdSf55kiuTPGV2c+UD\nXJphtOvsqppdG3Zmki35/lMUk+QDSW5OcsY4ypYkqarnZng0/TuWoW4AAIDuljQdsap+OMmLxy//\nIsmOqpqv6xtba62q3pTkdUk+V1UfSnJckqcm+VSGB3okSVpru6rqjCS/meTzVfW+JA9J8swM0xB3\nLqVuAACAqSx1TdhJGR4nvz/Jcxfosz/JWzLs+7Wjqv5PkhcleVmSr4/nzmmt3T77Ta21C6tqV5Iz\nxv7fSnJxkte31hbabwwAAGBFW1IIa619IPdySmNr7YIkF9zDvpcluWwRpQEAAKxIy75ZMwAAAAsT\nwgAAADpaln3CAACAlel5p/9idt9y4Da93JVNmzfmYx8+eKuihDAAAFjDdt+yJ4cff+rUZTDDdEQA\nAICOhDAAAICOhDAAAICOhDAAAICOhDAAAICOhDAAAICOhDAAAICOhDAAAICOhDAAAICOhDAAAICO\nhDAAAICOhDAAAICOhDAAAICOhDAAAICOhDAAAICOhDAAAICOhDAAAICOhDAAAICOhDAAAICOhDAA\nAICOhDAAAICOhDAAAICOhDAAAICOhDAAAICOhDAAAICOhDAAAICOhDAAAICOhDAAAICOhDAAAICO\nhDAAAICOhDAAAICOhDAAAICOhDAAAICOhDAAAICOhDAAAICOhDAAAICOhDAAAICOhDAAAICOhDAA\nAICOhDAAAICOhDAAAICOhDAAAICOhDAAAICOhDAAAICONk1dAMB8nnf6L2b3LXsO6t+xafPGJMne\n2/cd1L+np/vd99C886ILpy4DALgLQhiwIu2+ZU8OP/7UqctYdXZf/Z6pSwAA7obpiAAAAB0JYQAA\nAB0JYQAAAB0JYQAAAB0JYQAAAB0JYQAAAB0JYQAAAB0JYQAAAB0JYQAAAB0JYQAAAB0JYQAAAB0J\nYQAAAB0JYQAAAB0JYQAAAB1tWs4Xq6oHJ7k2yVmttV+f5/ypSV6Z5Jgku5JcNva9dZ6+pyT5pSTH\nJbktyQeT7Git3bicNQMAAPS0bCNhVXXfJO9PsiXJ/nnO70hy8fjl25J8IUMgu7yqNh/Q99kZQteR\nSS5I8rEkpyW5sqoOX66aAQAAeluWkbCqeniGAHbiXZw/N8mVSZ7UWts3tp+T5A1JTk9y/th23/G/\nv5LkxNbaLWP75UnemWF07LXLUTcAAEBvSx4Jq6pXJPlikkdnGLGaz+lJNibZORfARjuTfCfJ82fa\nnp3kfkneOhfAkqS19q4kLclpVWUtGwAAsCotR5h5eZKvJdmW5JIF+mzLMEXxitnG1tqeJFclOaGq\ntsz0TZKPz/M6n0hyRJJHLa1kAACAaSxHCDs9yT9trV2VZMMCfY5OckNr7bvznLt+PB4703d/kq/e\nRd9jFlUpAADAxJa8Jqy19pF70O2IDGu85nPTeDx8pu+ecZTs7voCAACsKr3WVm1OMl+oykz7YYvo\nCwAAsKos6z5hd+G2JIcscO7Q8XjrTN8H3cO+i7Z165a778Sa5zpYuTZt3jh1CavSps0bXdeL5P/b\nved9ujjep6vDWvo38l5deXqNhO3KwlMI59pvmul72IF7hy3QFwAAYFXpNRJ2XZInVtWh86z1ekSS\nfUm+PNP35CRHzbTN9k2GR9UvyY033rzUl2AVm7u75TpYufbevu/uO3Ene2/f57q+l/w8WDzv08Xx\nPl3Z1uLPBO/VlafXSNgnM+wTtm22saoOS3JSkmtaa7fO9E2S7fO8zvYku1tr1x6cMgEAAA6uXiHs\n0gyjXWdX1ezasDOTbEly0UzbB5LcnOSMqrr/XGNVPTfDo+nfcfDLBQAAODi6TEdsrbWqelOS1yX5\nXFV9KMlxSZ6a5FNJ3j7Td1dVnZHkN5N8vqrel+QhSZ6ZYRrizh41AwAAHAzLPRK2f/xzJ621HUle\nMp5/WZIfTfKWJKe01m4/oO+FSf5NkhuTvCjJE5JcnGR7a233MtcMAADQzbKOhLXW3p3k3Xdx/oIk\nF9zD17osyWXLVBoAAMCK0GtNGAAAABHCAAAAuhLCAAAAOhLCAAAAOhLCAAAAOhLCAAAAOhLCAAAA\nOhLCAAAAOhLCAAAAOhLCAAAAOhLCAAAAOhLCAAAAOhLCAAAAOhLCAAAAOhLCAAAAOhLCAAAAOhLC\nAAAAOhLCAAAAOhLCAAAAOhLCAAAAOhLCAAAAOhLCAAAAOhLCAAAAOhLCAAAAOhLCAAAAOhLCAAAA\nOhLCAAAAOhLCAAAAOhLCAAAAOhLCAAAAOhLCAAAAOhLCAAAAOhLCAAAAOhLCAAAAOhLCAAAAOhLC\nAAAAOhLCAAAAOhLCAAAAOhLCAAAAOhLCAAAAOhLCAAAAOhLCAAAAOhLCAAAAOhLCAAAAOhLCAAAA\nOhLCAAAAOhLCAAAAOhLCAAAAOhLCAAAAOhLCAAAAOhLCAAAAOhLCAAAAOhLCAAAAOhLCAAAAOhLC\nAAAAOhLCAAAAOhLCAAAAOhLCAAAAOhLCAAAAOhLCAAAAOhLCAAAAOhLCAAAAOhLCAAAAOhLCAAAA\nOto0dQF3pao2JXlpkv+Q5KgkX0/yriS/1lrbO2FpAAAAi7LSR8LOT/LmJDcmOS/J3yQ5N8l7pywK\nAABgsVbsSFhVnZxhBOx9rbVnzbRfnOTUqjqltfY/pqoPAABgMVbySNiLx+M5B7TvSLI/yfP7lgMA\nALB0KzmEbUtyY2vtS7ONrbWvJ/nyeB4AAGBVWZHTEavq0CQPSXLVAl2uT3JsVR3RWvvWvX39G264\nId/85i1LqHB92rp1azZs2DB1GQAAsKqtyBCW5AHjcfcC528aj4cnudch7OmnnpE79t2xmLrWra9/\n5U/zp1ddJYQBAMASrdQQtnk87lng/Fz7YYt58a2PesZivm1d27vn5qlLAACANWGlhrDbxuMhC5w/\ndDzeupgX33X1JYv5tnXtu7v+Olu3bskP/MBKXkZ4723dumXqEljAps0bpy5hVdq0eaPrepH8f7v3\nvE8Xx/t0dVhL/0beqyvPhv37909dw51U1SEZgtiftNaeMM/5P0jyk0mOaK0tNGURAABgxVmRwxqt\ntb9L8ldJHrFAl0dkeHKiAAYAAKwqKzKEjT6Z5B9W1TGzjVX14CTHZOEnJwIAAKxYKzmEvWc87qyq\nDUkyHt84tl80SVUAAABLsCLXhM2pqvcmeVaSzyS5IsnJSZ6Q5H2ttWdNWBoAAMCirOSRsCT5t0nO\nSnJkkpcneWCSNyT5hSmLAgAAWKwVPRIGAACw1qz0kTAAAIA1RQgDAADoSAgDAADoSAgDAADoSAgD\nAADoSAgDAADoSAgDAADoaNPUBUylqt6U5FVJtrfW/mjqeuijqn45yesXOP3fWmvP7lkP06mq52TY\nBP64JDcluTLJ61trbdLC6KKq7rgH3fx+WCeq6sgkv5rkp5IcmeT/JrksydmttdumrI1+qmprknOS\nPD3J/ZN8Ocnbk1zQWrsnPzNYharqwUmuTXJWa+3X5zl/apJXJjkmya4MPxvOaq3dupS/d12GsKp6\nXJJXJLFT9fpzQpI9Sd44z7n/3bkWJlJVv5LkzCTXJTk/yUOTPDPJk6vqMa21r01ZH12ck/l/Bzwo\nyQuT3JDkL7pWxCSq6oeS/HGGD1gfS/JnSZ6Q5LVJHl9VT2qt7ZuwRDqoqgcmuSrJUUk+k+S9GT4z\nvC3JT1TVMwSxtaeq7pvk/Um2ZJ7fCVW1I8MNmi9kuBaOzxDITqqq7a212xf7d6+7EFZVhyT5rZiK\nuV4dn+Sa1tq5UxfCNMabMGcmuSLJU1pre8b230vyviRnJfn3kxVIF621c+Zrr6rfz/CL+Bdaa3/b\ntyom8oIMAey81tqr5hqr6pIkzxn/vGei2ujnP2cIYOe31l4611hVb8hw0+YFSS6YpjQOhqp6eIYA\nduJdnD83w0yZ/38zpqrOSfKGJKdnuJG7KOsxiLw+yY8k+ejUhdDXeLfzYUmunroWJvXiJHckOX0u\ngCVJa+33klyUYXSMdWicovrTSd7eWvvDqeuhm382Hn/rgPZ3jMcf61gLE6iqTUl+Nsm3k5xxwOmd\nSb6R5DW96+LgqapXJPlikkdnGAGfz+lJNibZecBo+M4k30ny/KXUsK5CWFUdn+Q/Zvifd83E5dDf\n8eNRCFvfnpLki621vzzwRGvtBa21+aaqssZV1WEZfjfsTrJj4nLo64bxeNQB7Q8djzf2K4WJbE1y\nnwy/G/7eGsDxw/e1SY4a1w6xNrw8ydeSbEtyyQJ9tmWYGXHFbON4A/eqJCdU1ZbFFrBupiNW1cYk\n78xwl/uNSf7LtBUxgbkQ9sCq+kiSx2Z4c/1hhgcyGAFZ48Y5/0cmubyqHpnhQ/eTx9OXJzmjtXb9\nROUxrRcl+UdJzmyt7Zq6GLq6MMlzk7y1qr6d5PNJHpfkP2UI5QeOkLH2zM2KOGSB8z80Ho/K8NAW\nVr/Tk3y0tbZ//Dwwn6OT3NBa++48564fj8dmWEd6r62nkbDXZJjz+fylLKJjVZsLYa/J8Iv1wiSf\nzjAF4dNVdcJUhdHN3F3Mh2b4t39YhilHf5zk55JcVVUPm6g2JjLepHt5hukl1nysM621L2V4EMdh\nST6V5JYM05P2Jnl8a+2vJyyPDlpr384wKnJiVR01e278+tHjl4f3rYyDpbX2kdba3T2g74gMnxfn\nc9N4XPQ1sS5Gwqrq2CRnZ1hs+emJy2E6ezPcuTht9rHTVfXzSX47w93Ox0xTGp3cZzxuS/LuJM+d\n+yFcVS/J8OSj85I8Y5rymMjTMoyCvbm19p2pi6Gv8cbLb2e4SfPfM8yYeWyS7Ukuqqqfaq3dtPAr\nsEa8OclvJPlgVb0wyeeSPCrJf01ye4ZRsg3TlccENuf7o6QHmms/bLEvvuZHwqpqQ4ZpiN+Ief7r\nWmvtJa21f3zgvj+ttUuT/FGGO2DHTlMdncw9XnhvklcecBfs/Ax3Qp86rg9i/Th1PF40aRVM5dIM\n+wU+q7X2M621M1prT86wl+jj47pYF1prF2S4CfejGT4T3JzkT5L8Zb5/Dcw3LY2167YsPEX10PG4\n6L3C1nwIy/AktMcneeECczrd1SAZ7ngld16Yzdoydzf7+tba35tiMAayqzP8wDUlcZ0YA/dPJrm6\ntfblqeuhr3EU7OQkn2it/e7sudbaeRkeyPCzVXWf+b6ftWXcouDRGfaSfXWSk1prz0zygLHLN6aq\njUnsysLTDefaFz1Kvh6mI/7cePxwVc13/uNj+1Hmfa9d45qPE5JsbK19dp4u/2A8fq9fVUzgqxlG\nwxa6s7V5PLrbuX48KckPJvndu+vImvSQ8XjtAue/lOSfjP08vGkdGNcIfumA5sdmmH52p6fqsqZd\nl+SJVXXo7JY2o0ck2Zdk0Tfv1kMIe1fmf/7/UzLs/XFxhnVC5nuvbZszPIjhO1W1dXbX+3HK6skZ\n5nx/fqL66KC19r2q+mySH6uqo1trX5k7N+4Tc0KSbyb5m6lqpLuTxuOnJq2CqXx9PM57lzbDJs53\nJLFx9xpXVb+T4abMw2f3hBqXKRyX5MOttb1T1cckPplhbei2JB+ZaxxnUJyU5JrWmumIC2mtvbu1\ndu6BfzJ8IE+Si8c2IWwNa619L8mHktw/w15xs16dYfHtpRblrwtzc/vfNgavOa/OcLf7PffgiUms\nHSdm2Kriz6cuhP7GLSk+k2R7VT1t9lxVPS/DU3X/14HTl1mTrsvwcJbnzDVU1aEZHtaxP8mvTVQX\n07k0w2jX2VU1O4PmzCRbssT1outhJAzmvDrDiNevVNX2DOt/HpPhztc1GRZhs8a11t5VVT+d5GeS\nfL6q/iDDdKOnJGlJzpmyPro7OsltbsCsa8/L8CCG91fVBzN8GD8+yb/MsCfUiyasjX7emuS0JO+o\nqp/IsIn30zKMhr6xtWa0fJ1prbWqelOS1yX5XFV9KMOo6FMzzJ54+1Jef82PhN2F/eMf1onW2lcz\nzOt+d4aRr5dmeADDm5KcbIPWdeWZ+X7ofnGGD1znZ7gObp6sKqbwgJiOvq611q7JcEPukgzLFF6Z\n4YPWhUke01r7qwnLo5PxRszJSd6b5MczbOb7rQxPzXz9lLVx0C2YCVprO5K8ZDz/sgxPz3xLklOW\nuu/whv375RAAAIBe1vNIGAAAQHdCGAAAQEdCGAAAQEdCGAAAQEdCGAAAQEdCGAAAQEdCGAAAQEdC\nGAAAQEdCGAAAQEdCGAAAQEdCGAAAQEdCGAAAQEdCGAAAQEdCGAAAQEdCGAAAQEdCGAAAQEdCGAAA\nQEf/D/urqudHPlrUAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0xa92fe98c>"
]
}
],
"prompt_number": 60
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"\n",
"ax = simuls.gf[simuls.team == 'Ireland'].hist(figsize=(7,5))\n",
"median = simuls.gf[simuls.team == 'Ireland'].median()\n",
"ax.set_title('Ireland: 2013-14 points for, 1000 simulations')\n",
"ax.plot([median, median], ax.get_ylim())\n",
"plt.annotate('Median: %s' % median, xy=(median + 1, ax.get_ylim()[1]-10))\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 61,
"text": [
"<matplotlib.text.Annotation at 0xa933546c>"
]
},
{
"metadata": {
"png": {
"height": 323,
"width": 435
}
},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAA2cAAAKHCAYAAAAFeDBTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nANCB/wcX2FK1ebfC2wAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0xa9335acc>"
]
}
],
"prompt_number": 61
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"\n",
"g = simuls.groupby('team')\n",
"df_champs = pd.DataFrame({'percent_champs': g.champion.mean()})\n",
"df_champs = df_champs.sort_index(by='percent_champs')\n",
"df_champs = df_champs[df_champs.percent_champs > .05]\n",
"df_champs = df_champs.reset_index()\n",
"\n",
"fig, ax = plt.subplots(figsize=(8,6))\n",
"ax.barh(df_champs.index.values, df_champs.percent_champs.values)\n",
"\n",
"for i, row in df_champs.iterrows():\n",
" label = \"{0:.1f}%\".format(100 * row['percent_champs'])\n",
" ax.annotate(label, xy=(row['percent_champs'], i), xytext = (3, 10), textcoords = 'offset points')\n",
"ax.set_ylabel('Team')\n",
"ax.set_title('% of Simulated Seasons In Which Team Finished Winners')\n",
"_= ax.set_yticks(df_champs.index + .5)\n",
"_= ax.set_yticklabels(df_champs['team'].values)\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {
"png": {
"height": 379,
"width": 524
}
},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAABBkAAAL3CAYAAADP4Z06AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text": [
"<matplotlib.figure.Figure at 0xa9245a4c>"
]
}
],
"prompt_number": 64
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment