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@ahmadia
Created January 17, 2016 23:02
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Some commands to scrape the MLU stats. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import requests"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"r = requests.get('https://mlustats.herokuapp.com/api/players')\n",
"d = r.json()\n",
"df = pd.DataFrame.from_dict(d[0])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Assists', 'Bands', 'Blocks', 'BreakPoints', 'Callahans', 'Catches',\n",
" 'CompletionPercentage', 'DPointsPlayed', 'DSE', 'DTE', 'DateAdded',\n",
" 'DateModified', 'Drops', 'FirstName', 'Fouls', 'G_A', 'Goals',\n",
" 'Injuries', 'JerseyNumber', 'LastName', 'OPointsPlayed', 'OSE',\n",
" 'PlayerID', 'PointsPlayed', 'Rank', 'TPOP', 'Team', 'TeamShort',\n",
" 'ThrowsAttempted', 'ThrowsCompleted', 'TimeoutsUsed', 'Turnovers'],\n",
" dtype='object')"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.keys()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"#### The DataFrame object df is a dictionary-like object with the above column names. Let's say we wanted to find inconsistent data. For example, who are the people for whom throws is less than completions plus turnovers?"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"completions = df['ThrowsCompleted']\n",
"attempted = df['ThrowsAttempted']\n",
"turns = df['Turnovers']\n",
"names = df['LastName']"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['Burton',\n",
" 'Hibbert',\n",
" 'Bjorklund',\n",
" 'Perston',\n",
" 'Wong',\n",
" 'Graham',\n",
" 'Sickles',\n",
" 'Chan',\n",
" 'Wodatch',\n",
" 'Glazer',\n",
" 'Mazur',\n",
" 'Woodside',\n",
" 'Klein',\n",
" 'Foster',\n",
" 'Lynch',\n",
" 'Trytiak',\n",
" 'Kuzmowycz',\n",
" 'Houser',\n",
" 'Wodatch',\n",
" 'Sliva',\n",
" 'Stearns',\n",
" 'Kittross-Schnell',\n",
" 'Doi',\n",
" 'Nitikman',\n",
" 'Hooker',\n",
" 'Hayes',\n",
" 'Savage',\n",
" 'Hennessy',\n",
" 'Boucher',\n",
" 'Johnson',\n",
" 'Cole',\n",
" 'Cranston',\n",
" 'Neeley',\n",
" 'Cable',\n",
" 'Adamson',\n",
" 'Zid',\n",
" 'Panna',\n",
" 'Kissmann',\n",
" 'McGuirk',\n",
" 'Goldstein',\n",
" 'Vu',\n",
" 'Tessarolo',\n",
" 'Montgomery-Butler',\n",
" 'Meinershagen',\n",
" 'Jeske',\n",
" 'Sheridan',\n",
" 'Gibson',\n",
" 'Entz',\n",
" 'Janinis',\n",
" 'Castine',\n",
" 'Alarcon',\n",
" 'Cantone',\n",
" 'Chu',\n",
" 'Mehta',\n",
" 'Phan',\n",
" 'Marshall',\n",
" 'Lindsey',\n",
" 'Bosco',\n",
" 'Taylor',\n",
" 'Kim',\n",
" 'Jensen',\n",
" 'Minderhout',\n",
" 'Brown',\n",
" 'Murray',\n",
" 'Melius',\n",
" 'Paparone',\n",
" 'Rupp',\n",
" 'Adams',\n",
" 'Hancock',\n",
" 'Quandt',\n",
" 'Mieser',\n",
" 'Purifico',\n",
" 'Tsai',\n",
" 'Doherty',\n",
" 'Hoy',\n",
" 'Roth',\n",
" 'McCarty',\n",
" 'Herman',\n",
" 'Bellinger',\n",
" 'Fiske',\n",
" 'Fleming',\n",
" 'Blackman',\n",
" 'Chan',\n",
" \"O\\\\'Connor\",\n",
" 'Marcus',\n",
" 'Cofer',\n",
" 'Pineda',\n",
" 'Casey',\n",
" 'Miner',\n",
" 'Bender',\n",
" 'Xu',\n",
" 'Laws',\n",
" 'Hard',\n",
" 'Dennin',\n",
" 'Richardson',\n",
" 'Mercadante',\n",
" 'Wilburn',\n",
" 'Weintraub',\n",
" 'Ouyang',\n",
" 'Schwartz',\n",
" 'Walter']"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[names[i] for i in range(len(names)) if attempted[i] < completions[i] + turns[i]]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"snames_ind = sorted(range(len(names)), key= lambda x: names[x])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"snames = sorted(list(names))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[(103, 'Adamson'),\n",
" (180, 'Baer'),\n",
" (176, 'Chan'),\n",
" (166, 'Johnson'),\n",
" (96, 'Neeley'),\n",
" (126, 'Phan'),\n",
" (221, 'Richardson'),\n",
" (224, 'Taylor'),\n",
" (38, 'Wodatch')]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[(snames_ind[i], names[snames_ind[i]]) for i in range(1,len(names)) if snames[i-1] == snames[i]]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def find(lyst, key):\n",
" for i,x in enumerate(lyst):\n",
" if x == key:\n",
" print(i, end = ' ')\n",
" print()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Assists', 'Bands', 'Blocks', 'BreakPoints', 'Callahans', 'Catches',\n",
" 'CompletionPercentage', 'DPointsPlayed', 'DSE', 'DTE', 'DateAdded',\n",
" 'DateModified', 'Drops', 'FirstName', 'Fouls', 'G_A', 'Goals',\n",
" 'Injuries', 'JerseyNumber', 'LastName', 'OPointsPlayed', 'OSE',\n",
" 'PlayerID', 'PointsPlayed', 'Rank', 'TPOP', 'Team', 'TeamShort',\n",
" 'ThrowsAttempted', 'ThrowsCompleted', 'TimeoutsUsed', 'Turnovers'],\n",
" dtype='object')"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.keys()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def printPlayerHeader():\n",
" print('%-10s %6s %8s %6s %7s %5s %6s %6s' % ('Name', 'Rank', 'PtsPlayed', 'Cmp\\%', 'Catches', 'Drops', \\\n",
" 'Goals', 'Assists'))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def printPlayerRecord(ind):\n",
" print('%-10s %6d %8d %6s %7d %5d %5d %5d' % \\\n",
" (df['LastName'][ind], df['Rank'][ind], df['PointsPlayed'][ind], df['CompletionPercentage'][ind], \\\n",
" df['Catches'][ind], df['Drops'][ind], df['Goals'][ind], df['Assists'][ind]))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Prial 1 174 84.6 180 3 24 14\n"
]
}
],
"source": [
"printPlayerRecord(12)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"rank_ind = sorted(range(len(df['Rank'])), key = lambda x: float(df['Rank'][x]))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[12, 4, 1, 5, 6, 56, 20, 3, 114, 16]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rank_ind[:10]"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Name Rank PtsPlayed Cmp\\% Catches Drops Goals Assists\n",
"Prial 1 174 84.6 180 3 24 14\n",
"Wong 2 156 88.6 145 7 28 15\n",
"Hibbert 3 284 91.7 283 3 21 35\n",
"Kolick 4 163 93.8 264 0 15 28\n",
"Graham 5 149 92.6 224 1 21 21\n",
"Boucher 6 70 89.9 95 4 8 10\n",
"Markette 7 144 92.6 228 0 7 24\n",
"Perston 8 174 88.0 127 4 37 13\n",
"Esser 9 161 85.9 75 2 6 3\n",
"Hirannet 10 174 94.7 353 2 16 18\n"
]
}
],
"source": [
"printPlayerHeader()\n",
"for i in rank_ind[:10]:\n",
" printPlayerRecord(i)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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YGhxEpNtFcLaDDVs+Y+aMOUO6y0cF/kHE4/Fg0PdsENygl3g8XyWK83g87Nq9\ni2Xbt1LudqBPSUTExyBMeqTbidxzDN/iakZFx3LVtBmMHTtWDQ4rPZZ/6VRWrfsEzZeHTt9x2JGa\nht26hfnzvhoEdjqdrF+yhLlJSewuP8rYvOBuN34A4sLCKbbVEBwRQmFhIePGjevRe7kYqMA/iAQH\nB2Pv2e552B2cTkpWVlbGyx+9z8nYCGKvySdrWFq7t8yaplF1+AjPbFzL+O1buffGRf2y56sy+KWn\np5M/K57Vmz4kI2sROt35rW2paZQUL2bKZBPZ2V9ldSksLCTM6cSg11PmKGPWqLjzXtsVOp0gVELS\nMA9btn8xpAO/asINIhkZGZSU63A6u7evU2urm5M1BtLT09m1Zze/fOcNWuZNJ/vORURnpF9gcExH\nQs4oRj14N8fHZPDkyy/2yc5VysVHCME99yzi0hk+Thz5O1Un9+Dztd2BapqPmqr9lBS9wpQJDTz0\n4O1n3V0eP3qUGKORhpYWohMEIeaet1dDTUbCwr2cKD80pLd7VC3+QSQsLIyRo+eyZ/86pk/p+qbr\nuwqqyRk3n+Jjxbyw6nNSv34bobExXX69EILU6VNoSIznD+++yU/v/vpFvShG6R1Go5EH7r+dObOP\nsHLVdrbv/AyJEaSbSRPSmf/ATEaPHn1el2J5cTHxoaG4vV6CzP61Vc1GE00tzQgRhtfrHbTpzv2l\nAv8gc8mMy/j3G6sZM8pFRHjnq3GbrE627pVcd/Nknv/kA5K/tqhbQf9M0RnpeK6dx98++Df/9ch3\nhuyHRuk5IQTZ2dlkZ2fzkNeLy+UiKCjogrl5PC4XRr0er8+H7OZ2nufS6QRepxdNY0iPWQ3ddz5I\npaWlMW32ffzz/XKszRfu8G9odPD6exXMufwbrN+9E9+0CYQnJvh1/YQxo6lMjmHF2jV+laMMDFJK\nmpubqa+vx2q1omlan13bYDAQGhraaUK24JCQtta+0Uirzb/6+TQNn0+H0RCkZvUog8vsOfkYDEZe\n/NerTMrRmDIxjmjLVwtd6urt7Nhby95Dei6/6jsMHzGSl9avYcQt3wzI9VPnzWXpi29wxdxL+3RD\nEyUwXC4Xe/fuY8OOgxSfqMThM4DeBJoHo3QxYlgiMyePJjd30oBIE56elcWJQ4cYk5qKo9RIfYOz\n3ZW6XeFwu6mrNTB29IwA13JwUYF/kJo+YxajRo9hx/bNvPzu5wSb6ggyCVxuicsTzuSpd/Dwo5cQ\nFRXFkhXBhBN+AAAgAElEQVTLMUwagz5AqW7NUZG40hI5ePAgkyap/VIHC03TWLd+E+8u2YjDnElo\n6nQiZ6QSF/xVWmWPy0F5fQUvr97H6x//iZuumML8efn9miY5c+RIdmsaOiEYGTGcgweOMmdOzwJ/\ni9tN+fEQbr5yboBrObiowD+IWSwW5l95NZddfiWNjY243W6CgoKwWCxn3cbuLy0hat70gF47OCuT\notISFfgHiebmZv766jvsrw0iaco3iQ+3tHueMchMdPIIopNH4Ha08PamxWzb+yLfeeD20yvI+9ro\n0aPxRERgtdvJTkjmk4JDTJ3qJTi4e+HL59M4Xu0mOX4SqaldnxxxMVJ9/BcBg8FAXFwcKSkpxMbG\nnhX0NU2jpLqK8MT4gF4zPCmBosqKgJap9A6r1crTz77CES2HzFlfw9xB0D+XyRxGxvTbqAyZwW+e\nfa1HKT8cDgfl5eUcPXqUkpISampquj2N0mAwcMVNN7G7qgqzycTIkJEsX1aD19e9/v6jlQ0UHQjn\nuoUqV49q8V/kPB4PbiTG4J7dGnckKDyMptaWgJapBJ7P5+P5l9+iNnwqKaNndfv1QggSR+ZSrdfz\nf3/9F088/q1O93aQUlJRUcH2des4tH410fgIFuADbD4N4pKYctXVTMrN7fIYwuw5c9i9eTOFx4+T\nN2wk647YWbakkvlXxRNk6nyQtuRkI4s/t/Pot35LVlZWl655MVOBX+mxod1mGhxWr1nPocYIMmfO\n9KuchMyJlNSf4LMlK7j5pms7PK+hoYH3X3kJe9FBpoYauDIzgRDTV9N+pZSctNrY/tbL/OnN15ly\n7Y3MW7iw0xa4Xq/nvkce4bmnn6awspLZI8ezvSSIf//rGOOnmBmVFdFu109jk4utO2vYscfLd7//\nB2bNnN3zH8JFRAX+i5zJZCJI6HDb7ZgCOEPD2WQlOjwiYOUpgdfS0sI7S7eQMvM7AenaSJ1wJYvX\n/Zm5sy85L9kftKVNfvN//4e52Jk6pv1NgoQQpEZFkBoVQavLzb8/eot/19Zw8z1f63R6pcVi4dEf\n/5h//PWvbDh0iEmJ6YzwJnN4ayk715UxbJSByGgdBqPA49IoP+Hm0CEnsUnj+cMfnzgrDcRQpwL/\nRU4IwYikZKorqogdeeGt7LqjuaKKKclq9e5AtmPnbjxRYwgKCe/85C4wmIIhPpdNW3Zww3ULzzrW\n2NjIW//3B64xeshJ7FrmzNAgE/eMzuSdjSv5zGzm+ltv6/QLymKx8Ojjj7Nx/XpWfPghhuZmooMi\nSQ6Po/Z4M5WFDqzOVlo1HbrISL7x6F1cuXChWmx4DhX4h4CJGcP59+HigAZ+9+FjjJra+TZ6Sv9Z\nvaUAy7DAbn4XPzyXlZv/xvXXLjgrSH/8xuvM8tnISe1eY8Cg13HrqAxe/mIJh8ZPICcnp9PX6PV6\n5ubnM3P2bA4fPkzJsWOUHjmCyW4nMSiIKSNGkDFiBDk5OWqdSQdU4B8Cpubm8c4Lz+K9Yi6GAGy6\n3lJbR3hdE6NHjw5A7ZTe4Ha7KatqJHVc9/LWdyY4LIoajx6r1Xp6r4aamhrqC/YwNSetR2WaDHpm\nW0LYvnJFlwL/lwwGA2PHjmXs2LE9uu5QpqZzDgERERFcNnospas2+F2WlJKTy1Zx/YzZQ3rJ+0BX\nW1sLwTHoeuN3FJJAdXX16Yc7Nm0kN1ig9yP3TU5CHDUFu6mrqwtEDZVOqMA/RNx41ULCDh6l/liJ\nX+Wc3L6bbIePOTP8myWi9C63240wBnYK72mGYNxuN9A2Xbhg1QryUvzLAWXQ65gcJNi5ZXMgaqh0\nQgX+IcJsNvPtRbdhfX8JjaVlPSqjquAghnXbePDm24Z0ZsPBQK/Xg+brlbKF9J2+27PZbAS5nUQE\n+9+FmBoWQl3pCb/LUTqnPr1DSGZmJj+86TZsb39K6aZtXc7E6PN4OLZsJcYVG3j8a/cTE9OztM5K\n34mOjkaz1/XOZiOOutM7sblcLoIDtAo22GjA2aIWBfYFvwO/EGKBEOKQEOKIEOJHHZyTL4TYLYTY\nL4RY4+81lZ4bOXIkT33jEbKKyjnyt39SsacA3xl78Z7J43BQunk7R59/lVmtGr945D9ISPDvll7p\nG2FhYVhCDThbmgJartftQuduOj2P32Aw4A3QSj6vpmEMwOQDpXN+zeoRQuiBPwNXACeB7UKIT6SU\nhWecEwU8D1wlpSwXQvRPpifltOjoaB69736OHDnCyu1b2bV4Fbq46LbN1o1GpMuFVlWLrrGZ2aPH\nMHfRHaSnp/d3tZVuumRSFstL9pM2NnDTbmtLD5I3NvN0V09ISAgtXolP0/wa3AWwOlyEWLqWR0jx\nj7/TOacBR6WUJQBCiLeBG4DCM865C3hfSlkOIKVUw/b9xOFwUFFRQVNTEz6fD4PBwNWz53LP9TfS\n2NhIXV0dXq8Xk8lE/PR44uPj+zUdr+KfuTOnsnTz22ijZwZkdo+UEmf5NuY9ePnp50JDQ0kcO55D\nlUcYm+RfIsA9LS5mTL3E32oqXeDvpzoFOHOksBw49zeXBRiFEKuBcOBZKeU//bzukLJr126WLduC\nTie4+uqZTJgwocuvbW1tZdeOHWxZvpymigqihCCctj4+L2ADmqQkcfhwZlx1FRMnTuw0CZcyOCQl\nJTF9TDw7Dm0gdeylfpdXdXQnY5N0jBw58qznp15xJduf28vYpJ6XXW1roSkyllGjRvlZS6Ur/A38\nXRk5MgK5wDwgBNgshNgipTxy5klPPvnk6f/n5+eTn5/vZ9UuDgUFBTzzzBIslnyk1Pjf//2EH//Y\n1OniKa/Xy+ovvmDdBx+Q5HYzJTqa+PT0dpfEa1JSUVfHlr/8hcUhISy4+26mz5gx5FPXdpeUklVf\nfEHRnj3k5OVx6WWX9fvP8K5brmP/b/5Kc+0IIuJ6noPebq1DK13F/Y/ff957Gj16NEvDLFQ1t5AY\nEdaj8rdW1JF3y71qtlgn1qxZw5o1a/wuR/gz6i+EmA48KaVccOrxTwBNSvm7M875EWCWUj556vFL\nwDIp5XtnnCN7ZfbBReDFF99g795E4uPbAn1l5X5mzGjm61+/rcPXVFdX8+aLLyKKi5mVnExoN1rw\nTXY766uqsOTmcvv99xMZGen3exgq9u3bx0e//z0To6PZ3djIbT/+MWPGjOnvalFUVMTv/v4JUZPu\nIjym+yt57c31VG97ne/ddRm5ue1vvLN75042/PVZHsxOOysbZ1fsrahmtSGKh376X4SGhnb+AuU0\nIQRSym63Lvz9et0BZAkhMoQQJuB24JNzzvkYmC2E0AshQmjrCjro53WHDLPZhMfjOP3Y47FjNnec\nf6S8vJy//upXDKuoYH5GRreCPkBUSAjXZGZiLijghd/8hoaGhh7Xfahpbm4mUghSLRYihaC5ubm/\nqwRAdnY2/+/Ba2nd+wYVh7d2eYqnlJLq43tp2PYKj95xaYdBH2ByXh5jFt3JP4pKaXa6uly3PSer\nWO4xcdf3HlNBvw/51eIHEEIsBJ4B9MDLUsrfCiEeBpBSvnjqnP8E7gc04O9Syj+dU4Zq8XegsrKS\nX/3qJVpbs5BSIyLiGD//+TeJjz9/IK2uro7nn3qKqUBGAObaH6ispDgmhkd/9jP1oewCm83Gn3/7\nW5xVVYSmpvLtxx8nLKxnXR+9ob6+ntfe+oh9J32EpE8nLn0MOv35vb1S06grP0zLia1kWRw8ePeN\nJCV13oEvpWTj2rVsfet1pofomJyc0G7rX0pJeVMz22oaKYtJ5u7vPtZummelcz1t8fsd+ANBBf4L\nq62tZffuvQghyM2d1O4CKk3T+Msf/kBccTHjuvAh7arNpaWY587lrq9/PWBlXsw8Hg+NjY1YLJYB\nmQpY0zSKiopYsW47uwvLEGGJaMHxSJ0JHV6w1yBbKhk7PIGr8qcyZsyYbudkqqioYNvatRzasJpR\neBhmNmE2GvBqGja3m312H+74ZKYuuIbJeXkEB3h3uKFEBf4hbv3atWx56SWuHj48oAOKXk3jo5IS\nbv7Rj1QWxIuM0+mksrKS2tpaPB4Per2euLg4kpKSurwl4oXY7Xb27t5NdWkJzpYWDEYj5sgoRk+Y\nyPAA/50OVSrwD2Eej4df/+AHXGk2ExXAXba+VNrQQGF0NN9/4gn1YVWUAaS/BneVAeDAgQNE2Gy9\nEvQB0iwWWkpKKC0t7ZXyFUXpWyrwXwR2rF1LVi8OvgohGKHXs2vr1l67hqIofUcF/kFOSsmJw4dJ\n6uX59kmRkZwoLOz8REVRBjwV+Ae5hoYG9E4nIb28t2hMWBg1ZWV4vd5evY6iKL1PBf5BrqWlhdA+\nGHA16HQYpcRut/f6tRRF6V0q8A9ymqbRV/NsdIDP1zu7OimK0ndU4B/kjEYjfdX54pUSUy93KSmK\n0vtUsvVBLjY2lmYp0aRE14tdPq0uF4awsIAs7BkKXC4XLae2EQwLC1OprpUBRQX+QS44OJjIhAQa\n7XZienFKZ43NRmpWVpcXcEkph9xir6qqKjZu3MLOnYVUVtYhRFsqAimdJCXFMmXKGGbNmq62r1T6\nnQr8F4FRubmULFvWq4H/REsL4yd1nJ3xS7W1tfz5n+9wsq6Rq6ZN4pYbrr3ovwCam5t566332bz5\nMDpdCtHROaSnRyFEW0+qlBqtrU0sXlzKJ59sYvbssdx++02Eh4f3c82VoUr18V8ELpk9m2KfD62X\n0l44PR4qjEZyp0zp9Ny3PllKxbCpJN79OJ8druDw4cO9UqeB4siRI/zsZ//Dtm0tpKdfRXr6RMLC\nok8HfQAhdISFRZOWNpH09AVs3tzET3/6O4qLi/ux5spQpgL/RSApKYnE8eM5XF3dK+UXVFUx4dJL\nu5SaucXpIjgyGr3JBMFhuFxdz80+GEgpKSoq4oUXXuWOOx7hmmseZts2H0LEoGmdf/HqdHrS0iah\n10/g6af/roK/0i9U4L9I3Hj33ez1eGgNcKCtbWmhxGxm4Q03dOn8m6+Yi3vDh5S+9xdy9C3k5OQE\ntD79SdM03nrrPX7967fZsUNPcbHEYrkFIbLZvr2c9eu34XQ6u1RWVFQioaG5/OlPr50eBFaUvqKy\nc15EVq5Ywe7XX+eqzEz0Adi71OX1sri0lGsee4zJkyd3+XU2m43m5mYSEhIwGC6eYaSlS5fz5pt7\nyMiYz549Sykv9xAZ+VWq6ubmKqKiWpkzZ3qXxzXKynYzc2Y0Dz74td6qtnIRU9k5FS6bN4+kyy9n\nZUkJXk3zqyynx8OyEyeYfMstTOrCoO6ZwsPDSUlJuaiCvsvl4tNPN5CaOheHw0pZWQkREWdveB8R\nkUhDg4/6+voul5uSMp4NGwqora0NdJUVpUMq8F9EdDodd9x7Lwnz5rG4pISG1tYelVNhtfJpWRkT\nb7uNq6+77oKtV03TaGpqor6+nubm5i7v5zrYHDp0CLvdQlBQGGVlBQiRghDn70xlMERz4sTJLper\n0xkQIoXNm7cFsrqKckEXT5NMAUCv13PHvfeyIyeHz159leH19YxJTCS0CyturQ4HBdXV1ERHc9tP\nftJh/7zVamXbrp3sKi6ipLoKzRyEMOiRbg8mr8aIpGSmZueQO2kyZrM50G+xX7RtnN62f25l5XHM\n5vZ/NkZjMK2ttm6VbbGksXPnQa6//hp/q6koXaIC/0VICMHUadPIys5m9fLlfLJyJfEuFwkmE/Hh\n4USazeh1Orw+H412O7U2GxVeL83h4Uy75Rbuufzydlfotra28sGyJWw4WohxwigsV0xleFIChjNW\npbpaW6mpqOZfBYd4c+0XLMydxlWXzRuQ+892R9vKWzc+nwebzUpERES75/l8XozG7n2sQkOjKC+v\nxuPxDPqfkzI4qMB/EYuKiuKm225j4fXXs3//fkqKith9+DD11dX4vF4MRiNxKSmkz5zJFaNGkZOT\n02HgOXToEH//7ENcE7MZ+diDGDq4gwgKDSUuazhxWcNxtbSweNladvz1z3zz5ttJTk7uzbfbq7Kz\ns9HpPsHhsAKmdrt5ANzuBtLSUrtVtk5nQEoDdrudyF7eV0FRQAX+ISE4OJgpU6YwpQsLsNqza89u\n/rpyKUl3XENqateDd1BYGNm3XEPVgUP89l+v8p+33cOwYcN6VIf+FhUVxcyZo1izpgBofxzD6WzB\nZHKSmJjYt5VTlG5SgV+5oOLiYv66cilp9y0iLDamR2Ukjh1NvTmY/333X/zigYeJiem8HLfbzb59\n+yjaswd7czNCCEItFsbm5ZGTk9MvM4buvHMRJSXPsXVrOSEhTozGtlw8muajtbUBqGHmzAndrpum\nedHpfF1aIKcogeD3PH4hxALgGUAPvCSl/F0H500FNgO3SSk/OOeYmsfvJ03TcLvdmEwmdAGYww/g\ndDp58oVnMdx4OTHDM/wur3TrLtL3n+B793+jwzparVbWr1nDzs8/J9ZuJ9NsJsRoRNKWIbTY7cYW\nGcklCxcya86cPs8Warfbue++RykqsqDTRZ+aR+0iOdnCqFEjzuv7dziaKSs7TGnpMVyutsVdwcFm\nhg0bTmrqKIKDw2luriUy8jhPPPGfffpelMGvp/P4/Qr8oq2j8zBwBXAS2A7cKaUsbOe8FYAdeFVK\n+f45x1Xg76Hy8nK2rF3F4e3r0WlepMHE2BmXMX3uZX5ngfx46WKW+6yMvHZ+QOoqpaTwtXd5eMJ0\npuSd3+1UUVHBa3/8I+kNDYxPSCAiOLjdchrsdvZUV2PNyODr3/se0dHRAalfV3300ad8+OFRLJYR\nSCkxm80En1NXl6uVgoINlJdXAWmEhGSg17fNcPL57DgcJ4BSUlNTiIoK4/bbJ3DttQv79H30FZfL\nhdVqRafTYbFY0OvbHx9Ruq+/Av8M4Akp5YJTj38MIKV8+pzzvg+4ganAZyrwB8bunTtZ+c/nmZ2o\nZ1J6AsEmA61ON7tKqtnSoOe6hx5jdA9TJrhcLn74f78j5TtfIzg8LGB1rj9+AuOSjfzi2989a31A\nTU0NL/7yl0zXNEbGxXWprH2VlRyyWPj2z37Wp5kua2trefzx35OWtgCd7vxuHbu9iY0bl+JwpBEW\nlt3uOQCa5qG5uRCfbwMfffQXRowY0dtV71P19fWs2/AF+wvXEBau4fVKNG8k0/IWMHPGbLVHQQD0\n18rdFKDsjMflp547s2IpwA3AX049pSJ8AFRUVLDyjRe4f2Is00emEGxqCy6hwSbmjE7j7tHhfPLS\nMzQ0NPSo/IKCAnzDUwMa9AGiM9KpwEtZ2Vd/Npqm8fpzz5Hn9XY56ANMSEoio66Ot156KaB17Exc\nXBxz506gvHzfecfcbgebNy/D6cwmImJMh0EfQKczIkQcMTEzef31D3A4HL1Z7T5VXl7O3175b4Is\n63jg0VTu/85IHvpeFjfdY6a8/i1eevX/Lqr3O9j4O0LWlSD+DPBjKaUUbU28dr+dnnzyydP/z8/P\nJz8/38+qXTwaGxtxuVxnzRbZsnYVs+J1xIS338edHB3O5PBGdmzZxJVXX9vtax48cZzQ7IyeVrlD\nQgiMWemUnCghPT0dgMOHD6MrLyenBzN+pqSk8K+9e6moqOjT6aK33noje/f+D42NlVgsSaefP358\nH83NMVgsmZ2W4XDYCApyMnPm1Rw/voV16zaQlzeZ1tbW090iYWGB/eLtC263mzfefpb5NwQxMvvs\nqa3xCeFcuyibVZ8f56NP3uTO2x/sp1oOTmvWrGHNmjV+l+Nv4D8JpJ3xOI22Vv+Z8oC3T93WxwIL\nhRAeKeUnZ550ZuBXzvbG2x9TVdPIr594DGjrKz+4dS0Lp8Zf8HWT02N5ffOaHgX+oopyIuaM7fzE\nHghJTqDoQDlzTz3etGIFOR3053dGJwSj9Xq2rFvHojvuCFwlOxEWFsb3vnc/v/3tiwBYLEn4fF6K\niw8TFpbf6esdDhtebzVz5uTR0mKjrlHy8/9+mitvmYA5IggktNQ7CTGGkztmGtOnzuzSbKiBoGB/\nAfEpTYzMzm73uBCCufOG8bf/20JT081ERUX1cQ0Hr3MbxU899VSPyvE38O8AsoQQGUAFcDtw55kn\nSCmHf/l/IcSrwKfnBn3lwm69acFZt8Verxd8XsxBF17lGW424WztWfKvOmsTIy2984E0W6Kotu4H\n2u5myvbsYW5q9xY9nWlsQgLvrl7NtYsW9elm8JmZmfzkJw/z7LOvUlpaiV5vxu2OIiSk41a6pmnY\nbNUEBzuZNm0cR0uO0uJpJDo1jJjIkYyfP4yR49ruhKSUWOttHNm7h02vrGLOpCu54rL5Az753b79\nGxk37cJ/O0ajnqyxOg4cOMCsWbP6qGbKl/zq45dSeoH/AD4HDgLvSCkLhRAPCyEeDkQFFUhMTCQz\n86uuA4PBgD7ITLP9wrn3G1ochEX1bMaLpmmIAE0LPZfuVLoIgIaGBixCYPDjWiEmE0Eez6l8On0r\nMzOTX//6R1x6aRxFRctxOIy4XK1oZ2RH1TQNl6sVq7WK5uZiMjLCyM0dy8GjBehjXGRNSSE2KQpT\ncDJlR776ohZCEBUbwdR5Y1n47ansb1zPCy//idYeJt/rKw5HM+ERnQ/chobrsDvUXgT9we+mg5Ry\nKbD0nOde7ODc+/29ntIWECbMvoJdBYvJz0nr8LxdZQ1MuPSeHl0j2GTC43QS1AuLijxOJ1GnZnS4\n3W4CkZ3GKARutzsAJXVfaGgo9913Nw0NNr74opXWVivNzSeRUgdIhJBERoaRmRlHWtoEvF4v2/dt\nIWVsNOFRX43RGAwmWm3tB0JzaDD5t+axY+UBXn7jRb71wKMDNq9PSEgkzdYmkpIvnH6itVnDEq32\nHe4PA/ueUenQJbPn8sqG5Yyos5IWe/4H7EhlPQfdETwybVqPyh+WkERDVQ1BIzofpOwuW1UNeQlt\nA6ImkwlvADZj954qqz9FR1tIS7OQmJiNpml4PB5AYDQaTi9Y0zSN3QU7ic+KOCvotx3zYQru+CMp\nhGDKvLGsbdzFF6tXsPDKq3vz7fTYxHGz2bW7gFE5Ha8jcbu9FB3QuOqR3hlHUi5M5eMfpGJjY1n0\nyH/y1lEny/afoLqpBbvLQ0WDjU/3nuDjCj13fOc/ezy/fVRyCs3llQGudRtPeTUZyW19+tHR0TT4\nfPj82DjG4fHgNBj6dC5/exITo3G7m4C27qygoCCCgs5eSV1WXoYMcREdf352T6+7mei4C69EFkIw\n/ZpxrN+7grq6usC+gQAZN24c9VXRHC5sfw9oKSVrV5wga/hslZSun6jAP4iNHDmSh3/+NMaZd/B2\nuYE/7WnigxozkfPu5ZGf/Yq0tI67gTozaex4XHsPB3xjFbfdjiwuY9SoUQBYLBZSJ03iqB9B7GB1\nNRPy8/t9QdCUKblIWYqmeds9LqXkxMnjJAw7f9zF5/MidCcZnZvR6XWCQ4IYlhfDlu2b/K1yrzAa\njdxzx3dZvVhj3cpimpu/2oe4ssLKR+8WUV8xkhuuu70fazm0qa6eQS4yMpJ5V17FvCuvCmi5KSkp\nZAaFUnvkGPHZgVtRWrGrgDmjxp6VY2fm/Pl8vmcPo3pQniYlhV4vD8yd2/nJvSwmJobc3EwOHjxG\nQsL5Uxmbm21oBg8h4edPXW2sKWPCjHjCIrs2ppI1KY31r2zhmgUX3iGtvyQnJ/Pwgz9nw6bVvP7C\nKoLMHrxeDaM+lml59zD9ppn93jU3lKnAr7RLCMEtl1/J/yz5gOiMtA7z73eHvbEJ76Y9XPnAI2c9\nP2rUKBYnJ3O4poZR8Rdem3Cu3RUVxI0fT0pKSucn94FrrrmcnTtfwelMIjj47K4nm82GOfL8AVmX\noxWv5xAzrprd5etEWMJwaq3YbLYON4XpbxaLheuuWcSCK6/DZrOh0+mIiIgIWBJBpefUb2CIsdvt\nFBUVsWb1Kj798F0++eBtVny+lIKCgvPSO2RlZTFvWBbHlq7yu8vH5/VS8tHn3DH7MmJjY886ptfr\nufe732W7TsexbnT57K+qojg6mrseesivugXS8OHDeeCBBZw8uRKH4+zppXZHCybz2W0tp91GXeUG\nFn1zHInpXU9XIYQgPNZMY2NjQOrdm4xGI9HR0URFRamgP0D4nZY5IJVQSdp6XVlZGVs3ruJowXpS\nLBpJUZKoMCNCgN3ppbIRSuskluQcps5ewLhx49DpdLhcLp597WVKM+MZPn9uj7oVfF4vRf/+jJn6\ncO695bYOP/wnT57kH3/8IxlNTYxPSCCsgz77JoeDPVVV1Kelcf9jjw3IFa1btmzlpZc+QdPSiIsb\njdkcyeEjh3GY64hPjcZpt9FUexy9oYybH5lETl73u9NWvLaDhZNvx9rcRPnJQ1RUHsXeakVKSXBw\nKImJmaQk5zAmZ9yg3v1M6Vi/ZOcMFBX4e4/L5WLFss8o2vkps0YbmZidQHBQR9kiJUUn6tl4oBlh\nmcQNt95HTEwMdrudv775Tw6H6si49opuJW6z1dRS9tFy5liSuHvRLZ2m5G1qamLdqlXsXrGCeIeD\n4SEhhJhMSClpcbs56nDQFB7OJQsXMvvSSwf05iUNDQ1s3ryNZcs209oaTGVVE1atitjkYELD7cxc\nOJzx00cSHtX991BX1cirP3+H7NQ4cqcGk5oeQlJyOOHhQW1f5nYP1VUtnCxvYf9ejcjwUVw658bT\ng+rKxUEFfuU8VquVf770DGnBx7nqktQOA/65pJRs21/J2kN6Ft37Q0aOHInX62XlurV8sHMzwdMn\nkpQ7/oKLu1obGqncvhf93sN8bd4CpuTmdetuweVysXfvXop276a1uRmdEIRERjJu2jTGjBkzYBYv\nSSkpLy9n0/aN7C3ajdPlxBwcwqRRucycOpOUlBQ8Hg+lpaUcOHCA1fs/Jv+OPBLT4zAYzv4S1DSN\nFqsdzadhMOoJjQg572fm8/nYva6Aw1s3kmqp58H7b8RkuvDvVdM0jhTVs2q5lWGp87l64SLMZnPA\nfx1FczwAACAASURBVBZK31OBXzlLa2srLz//NFOSq5g5sWcDn2VVVt5e38It9//0dMqImpoa1mzZ\nxNqD+5DJ8eiSYgmKjUZn0ONze3DV1KOdrCG40cb8SVOYNe2Si3auttfr5d8fvUtBxR7SpyaROT6D\nILMJZ6uT4wWllG6vIC/zEm669ib0ej0Oh4NfPvszrvnuVIJD2rqxHK1ODu46zr6DNVTU/P/27jw6\nqutO8Pj3VqlKpdK+r6UNhMQqBAiBjUEYYwPeEts4scniJR3Hk3QSZ5l0unsmnumTTieTPvHJuNuJ\nlzhJ22OcxHGMN2wwyIBACLCQkCwEQgvadwmttd75QwILtFWpJJWW+znHx1V6t977VfHe7y1368Hu\nbQQvL7BY0NvNmGICWL0iiiXpCQgh+HDvQXxEOWuzvPDu9yNj1Rqn47VYbBw+WEP1pTge+cr35u2/\ny0KiEr9yjZSSva+8RNjAcbZviHdrXRW1Hbx5Wst/+8FPr7tKHBgYoKamhrqGemrbWrHZbei9dCRE\nRBITHYPJZJr1g4m5Q0rJq39+lWp5gZvuzxpx9Q5gtVg59qeTLPVL54F7H0AIwZ/f3MuVsApWbEwh\n71AxOacasC9Ow3d5Gr7R4Wi9P2s9Ze3tp7euib5zJRhqLhFkayd9ZRd3fC6KS2drWZGwltBJzIN8\n8kQdZ0+F8bXHfjQrhn2WUlJVVUVp4Vn6OjsBiTEomNSVq0hOTp6VzVVnC5X4lWuKCgvJ3ffvfP3u\nRLRa91tRvH+8moGQ7Xx+98NTEN38UF5ezssfvsBtX988atK/ymqxcvC5Izx537cxmUy0tbXx01//\nD9qMZtpjlhC+fTM6v4nnDa4uvEjrW6+zY0kL27bo0HYbWJexftJJ8dDBajpbsnj4i497LLHa7XbO\nnD5N/nvvoGmoId2gJWDoxNdtsVDYb8cWFUfmjl1kZmWpKRtH4akZuJRZRkrJkQN/YWdmyJQkfYBt\nmXFcLDw4J5oOzpRjp46StME0btIH0Ol1xK+PIffUMWDweXv9FQOfBq8k8u7bnUr6/b0DXOnpYt0P\n7+HT0ExefL6VRFOqWwl7y1YT7Z3HKCwqnPQ63GE2m3ntxecpfeFZ7nZ082RaAjcnmVgZE8nKmEhu\nSjTxjbQEPkcvF19+jld+858MDAxMvGLFKSrxzzNVVVVo+mtImGBkRFfodVrS4yWnT56YsnXOZTab\njeJLRSxamehU+UXpSRSUnsFsNvPMf71K8IMPk5K5jYrCOsz9E48o2lDVgClBgHTgk7YYR9bn+NPb\ntW71rdBqNey4K5xDh/deN4T0TLDb7bz+0ov4n83jy0uTSQgJGvUkJoTAFBzInrRkQkvO8Nrzvx2c\ni0Jxm0r888yF0hJWmMSU376vXBTCxZK8KV3nXGWxWNDoNHjpnKvDMBgNWO0W9u3fT31MHFErV7Fk\n8RKSIpdS8UkTjZdbsVnto2/LbKWrpRm9l43+Dhsx4XEk376RgoEwTuXXufU94kyBGHybKS8vd2s9\nrjr28cdoz+Zx9+JENJqJ91ONRnDn4gR8is9w5KOPZiDC+U8l/nmmoaaM2IipH6UyMtSXjtY6j415\nP5vodDpsFht2++jJ+kZWixVzv5X3ikuJ3bYdGLyajY8zsTFjE959wVw8WUfluXrqq1poqW+nua6d\n2vImzh0tx+hlJtAQQlx0PN4Gb4RGQ8TObPYeasE6xgnDWelrvDlbNHODvdntdk7vf5fb4iKdSvpX\nCSG4zRTFmQ/eU1f9U0Al/nmipaWFyspKWhovEx489Z2atFoNwb6MGNZhIdLpdCwxpXL5/I3TS4+u\norganfBDs2oVuhvazxuNRpanrWBz1q2kRqcTIuPQ94Rg6Asl2nsRUUFRLF4UR1BQEGJYovQND6In\nKomS4tGHPnaWKT6I+voylz4jpaSzs5PGxkba29tdelRUVlZGSFc7kS50ArwqzM9IZN8VSktLXf6s\ncr35295uAenr6+Pp//MSvVY9kbYWdF5TP3kKgJcGp69y57tbMrfw55OvkrgsftzHag6Hg+r8Wno0\nBkJXpI9ZTqfTERYWNmIco7qGi2NOY2hcuZS8c4dYnTH54RhCQ33o7qliYGAAwwQT3lutVgoLC3n/\n/ZNUVHSh0RiR0kx4uJZdu7JYv37dhB3DivNOkOE/+eGz1wT4UJR7jJUrV056HYpK/POCEAKdlwaN\n1YZe743FasfHMPU9W6125nXbfFekpaUReTKO0/sLWLcjY9Tk73A4OPn2aaK9TNR4d2K8Iak7w2od\nwFs/esufgJgwLh4YQErpVJ2OlIOtaQYG+pEOiUarwWg0YvTV0NfXN27i7+3t5dln/0hJiY6goI3E\nxyde2+aVK/X87ncFvP9+Ht///iOEh4892FxPWyvBPuOfYMYT5GOgp312TkAzl6ijeJpd7ZxSeekS\neoOBFStWEBQUNKXb8PHx4V9+/CT9/f3sf+v/0dx+icBRxnx3h83moLNPzMoB0TxBq9XyyEOP8vJr\nv+PwH46xaEMipiWxaDQa7HY7l8/XUn6iCpM+ic23bOHM4SOTq3Afp+GO3t9Ik0NHX58VX9/Rh82W\nEjo7O6ipr6G1sxW7sOPlo0doBNLuwNJr4WxxH+9++B63b91OZOTI6RKtVivPPvtHLlyIISnplhHf\nIyAghoCAGBobi/nlL3/PP//zN8acDc3hsKNxo+GBViNw2Ge2FdJ8pBL/NLJarez9/Qt0l+ezNAi6\nbPD837zYct+jZN1085Ruy9/fH39/f6LjU6mrLyQlYWoTdENrN6ER87s3rqt8fHx44qvfoKSkhJxj\nhzn68l/pamrE2t5BkDGQ5KQlhK4y0tTUhJjk2EJeOj1Wqx0v3cjqOCEEQqcbs4K3q6uL4rISBsQA\nAbGBxCw2odMPjsh6lcMh8T5YTW1QA7985d9JjUzlgbvuv+7i5Ny5c5SUaEdN+sNFRa2gsrKRjz8+\nzl13jT4xkI9/AL0d9U5++5F6zVZ8pqHxwkKjjuJp9PFHBzDU5LHnlqRrLRg29Q7w4l9fIj4pmejo\n6CnfZtqyFbyZ9zpb1jp3+++sokudpK66c8rWN19otdrB+XU7u0kdMJORHEJCVhJGgzcWm526yx+z\n/2A7pT7R6LO3jXpFPR4/vxB6eprwMY5+4pA2G15e158UpJSUV1yiqqmKsJRwosJiGGtX6O604BMU\nyrodmdi32zl/opR/e/4XPHj7btaszkBKyXvv5REUlOXU/hQVtY4PPniNO+64ddSB9BatXkPJudOk\nRbr+2AugpLObxTvWTeqzymdUq55pIqXkkyMfsG159HXN1gJ9DWRGCgryp6dNfFxcHPqgxZRfnlzr\nG4mk60oXjY2N9PT0ADBgtlFcq2Vt5vqpDHXOk1JycP97HH7pF2zzbeap7GS2rkomOTqUqGA/4sMD\n2Zgay/e3pRDVVUtVQS6XLl5wqeNVgH8oV65YR11m6enHR5qve8wjpaT402JqempJyEwkMDxwzKQP\n0FjbS1D04NzMWq2W5ZtWsOaRTPZ+vJejx4/R3d1NRUUHwcHONRjw8QmitzeIy5cvj7o8PSODixpv\nesyuNwvus1gplV5krFOJ311uJ34hxA4hxHkhxEUhxI9GWb5HCFEohCgSQuQKIVa5u825or/nCsF+\nI5+1B/vq6e2anmaRQgi27tjN+6e6sLjYxttmt3G6sIDc84UUddTycfFpij4t5r0TNaxYv2vWTvHn\nKR8f+oiKg6/x+AYTi6NDxrwiDjAaSPDRsDRAS2dlCVWVFU5vIzwsgpZmicMx8mTR3dDGohjDddu9\nUH6BVnsb8SsTnOpgVlwwgGn5suv+FhwRzE2PbmZf/j7OfHIGIXxcunsUwmfM4RV8fHxYlr2NEzUN\nTq/vqpM19SzZtOW6+ZqVyXEr8QshtMCzwA5gGfCQEGLpDcUqgM1SylXAvwDPu7PNuUIIQVTCIi41\njBzf5lLrADFJIyfjniqpqanELb+DD/Iuu3R1WVFVRbveTvT65UQsTSJ6/XJOVNWTdxG277hr2uKd\ni2pra/nknVd5eK0Jo/fEz+83RfnSWVHJypgAGi+eo6ury6ntGI0++PtF09LSO2JZT0kZG5Z9djLu\naO/gcmsNcctMTnWOam/pp6nFl/ilCSOW+Qb4svYLWbx95F3M5h6X9iMpzXiPMXsawNYdOykOjOBs\nfaPT6yxubKbAL4xtd93t9GeUsbl7xb8eKJdSVkkprcBe4N7hBaSUJ6SUV/fyk0Ccm9ucM27ZeR/v\nlHZR1zo496rd7uDUhXou2ULJWLt2Wre96577qbOmcjC/xumDtqalgaCEaK6mjJqaK+Q0hmNMWIp+\nCiZbn0/yj+awMcoLPx/nfpdNyVE4is6iRWLy01J3ucrpbSUmpFJdYcVm/aw1y0BnD4bqi6xaHQUM\nVtKeu3COiNRItBMMHAeDj4QOv9vOkps3XytvGbDQUNlARdElKoouMdA7QOjaMK70VXPlinPDQ1gs\nPej1LcTFjX2Y+/v786WnfsAhrT8fV9ZgsY19Z2q12zlWVcsH0oc9T/1AzSEwRdyt3I0Faoa9rwWy\nxin/OPCem9ucM5YtW4b1S9/lT2+9hjh3GbNdErk4na/8/Z5pv101GAx85e++y6sv/wevHTjH3TfH\n4u87fscZIQTIwaabpwuaOFnrR+J9XyCovHtaY51rent7uZD/MTs3RDj9mYggX24NhJz8M8RkZVJV\nW40l1bkTalBgIOFhqVwqLyM1LRiJpOGDI3x5UxDeQ7Oqtba2YDc48A9xrsVLUX4b3fYk1mQsoezU\neU7tL6axsgOBHwgDUoIQFuzWLuoqL9Lb9TZZWV9Drx//7qaxsZCdO1dN2BksPDycx3/8z7z359f5\nVX4e6TpJekQI/t7eCKDbbKGwuY1CqyBu7Xoe/8IXp7wZ9ELmbuJ3+v5PCLEVeAwYtR3j008/fe11\ndnY22dnZboY2O6RnZLAyPZ2Ojg70ev2Y7Zung9Fo5LFvfI8jOYf4zbuvszbJwdq0iDHb+EcFRfDR\n4fOUW8KxJ69j7bdup+ytj/n8is0zFvNcUFpayhI/Gz5OPOIZ7v7VSZz9qIDO2FjC9H40t7QQF+vc\n7GiLklP4pKCVqqoO9M01pFlq2bRpxbXl1XXVBMY5lxgvfdpB7lEdS25exu//8U90tRvwDUgg3LQG\nIUa2EOrviqHy3BEaGjRs3HgXcXGxo1YYt7dXYjQWceutTzgVR2BgIA997et0PvAgZ/LyeOP4Ufoa\nB++OjQEBpN65m7/buJHg4GCn1rcQ5OTkkJOT4/Z63JqIRQixAXhaSrlj6P2PAYeU8uc3lFsF/BXY\nIaUcMRSgmohl+rW2tnIq7xhF+QcI9O4nOkgSZJQIBH0WaOgS1LdL6nqgOzCAiJUpDNS0sDllFbvv\n+TwajWoAdtXhQ4cQx/9I9opElz9b23qFn+ZX05y5Gb+s20lOXuT0Z81mC0def5nword45n+vIyJi\ncLwbu93BR7kHSd6UMu6zfSklZ/NaOX5Uhz5oESW5zfiHrMA3cPw+H71dPXR82kR/VzMtLcEkJ2eR\nlXXLtbkIzOZumpoKMRrP8cMf7iEhYWSdgTI9PDIDlxDCCygDtgH1QD7wkJSydFiZeOAQ8CUp5aht\nGFXinzk2m42mpibq6+vpvtKFdDgwGH2Jjo4mOjoaHx8fGhsbaWtrIzIycsTYMQoc2L8fwyevc8uy\nySW42tYr/HB/Ee1b7mP9nq/g5cTjHnNPD3UfvE9CezOLw/xoac/ljjuDSV4UQnd3N6cvnCFx3dhN\nLlub+jn8bhu9Mhmr3Z/io11EJK5Dq534pt9ht1OZW8Hm9TdRU1NKQcEJ/PyiWL48AyGseHu3sX17\nOtnZNxMSEuLSb6G4Z7KJ361HPVJKmxDiW8AHgBZ4SUpZKoR4Ymj5b4H/CQQDzw01CbNKKVWDcA/x\n8vIiNjaW2HEeMURFRREVFTWDUc0tPn5+9Fknf6ESFxbAl5dGceJKBzW/+Q+0q9IJXb4SY1jYdc0m\nHXY7PU2NtBedRXu+lC9mZbJ9zxfw8vKirGwzBz7cy0FHJXHxV7AE2LHZHNc6czkcko7WAZpqeykp\ntNDa7seSTXcRZBe881wBkYkb0DiR9AE0Wi1Cr0FKDSkp60hOXk1JyX4yMszcdddOYmNjx23Fo8w+\nas5dRXFRdXU1b//6J3xzU8KkekdLKXkut5qd3/wJ/v7+5Obnk1taRktfH9rQMPDyQprNONrbiAsO\nJnvlctavWzeifkhKyeXLl3nzzb2crP6IwDA9Or0DIQQWs8QQEEJgdBymZWnEpZrou9LH7378F3wC\n1mEwulbXVJVfwYblWfj6Dg75bbNZqK09yE9+8iRJSdMzGqwyMY9c8SvKQhQfH48mPJGq5g6SIl2v\neLzc0oUj2ERi4uAIl/fu2sW9u3bR19dHW1sbNpsNvV5PWFjYuFfSQggSEhLIzr6D3grJ2nsyMfeb\nkVKiN+jR3dAC5+yhIqyWaIJdTPoweAcxvJ7Hy0uP0biUv/1tP0899aTL61M8S9XYLXBWq5Xqy1Vu\nzd+60AghyLx1F3lVk5t8Pq+qg8xtd464WzAajZhMJpKSklx6fBIaGkp/ax9aLy1GfyO+Ab4jkr7F\nbKHg4AWCI12vl3DY7TgsjhHxhIXFU1RURXNzs8vrVDxLJf4FTqvVYvAef/IMZaT01avpCE7hxAXX\n5r09ebGeFv8kVmdkTFksUVFR9LX0YR+nI1RTVSNWsy+6Sfxb9/f0E+DrP6Jll0ajBcIpK3NtBi/F\n81TiX+A0Gg2RkZFTPjn7fKfX69nzxLc5ORDG0dLaCe+YpJTkltVxvDeYLz353SmtDNXpdCRHJ1JX\nPvZJqKWmFSkn14eku/UK4cGjt+4yGIIpK6ua1HoVz1GJX1EmKTAwkMef+kfO+yzhudxqTl2sx2y9\nfiJws9XG6fJ6fnO8mhL9Yh7/3j9NSw/Uzes2U51fOebylpoOdAbX57l12B30NvYSGz16KzCjMYia\nGufH3FFmB1W5qyhu8Pf352vf+SHV1dWcOnqYQydyCdFLvDUCs0PSbhEkrb6JHfdvvVaZOx2WLVuG\n10d/o6GygeikkfM82Cx2xCQ64bXWtBARGDHmXLoajRbrDSc7ZfZTiV9R3CSEIDExkcTER+m970E6\nOjowm83o9XqCg4Px83P9SttVXl5ePHTXQzz/1ouEPhmK3vv6TmF6Hx0Om2sJeqCnn966HtasWz1m\nGbvdSkCAGsBvrlGPehRlCvn6+hIXF8eiRYswmUwzkvSvSklJYeOiLPLfyMNuv76iNyopFKvF+cH2\nrGYr9SV1rFi0fNz6iJ6edpKSFsyAu/OGSvyKMo/cu+teFmkSObH3GJaBz2a5CosNQ4grTq3D3DdA\nzdlqUqIWT9iD22rtICUl0Z2QFQ9QiV9R5hGtVsue3XtYG5xBzn9+RO2FwVHTIxIi8QuyMdA7dvKX\nUtJa00JdQS3LTMtITEgcd1s2mwWNpo1ly5aNW06ZfdSQDYoyT1VWVvLa23vpMfRhyoynubaV3Dea\niEwc9sxeSiwDFrqaO+mu7ybYJ4jlqcvHrMwdrra2hJtuCuSxx740jd9CGY9HRuecKirxK8r0cDgc\nXLp0iWOnj1FS/ilHDn2C0K3Exy8EaZdYei3oNToiQyMwxZicni+iv/8KHR3H+dd//YEawdWDVOJX\nlAWgpaWFuro6vLy8WLRokVNX5ldJKTl9+jQ/+9nLhIVtwMfHH19fX5en1bTZLFRXH+HrX9/BLbds\ncvUrKFNIJX5Fmcd6enr4wxt/5XRdEyI+CWm1oKut5p71a9i1fbtLE+UcPZrL88/vIzr6JoxG1+aw\ntVoHqKk5zt13r+aBBz6venx7mEr8ijJP2Ww2/u2531KRkErczZvRaIcmR+/tpebtv/BAfDT37trp\n0jrz80/x0ktvIGUC0dGpI6ZcvJGUktbWy/T1fcru3beyc+ftKunPAirxK8os0tjYSHl5Of0DZgze\nekwmEwkJkxu//+zZs/wqv5DE3XtGfN7a30/Ti/+XX333Wy7P59zW1sYrr/yZs2er0GhiCQ6Oxdc3\n6NpJQEpJf/8VOjsbsFhqSEoK4tFHv0B8fLzL30GZHirxK7NKfX09paXn0Wo1LF++nPDwcE+HNCMu\nXrzIWweOUlzXCdHLEHoj0maG5gsk+MG9t95ERsZql04Az73yKueSVxCxbMWoy6vfeZNvpiWybt26\nScVcX19Pbm4eBQXnaWxsB/RDCcVCSIgfK1aksHnzBpKSktRV/iyjJmJRZgUpJW+++S779hUBKYAD\njeYYDz98M7fdttXT4U2r3BMneeGdXHxX7yRhXep1Y+NIeRtdDdU8s28/n6ut5/N373I6ifZbrOiM\nxrELGHywWq2TjjsmJobdu+9j924wm8309PQgpcRoNGIcb7vKnKUSvzKlysrKeOut85hMe/DyGuzq\nb7Fk8sorr5GWlkJc3Pzs3v/pp6U8/85xom97DIP/yNE3hRAExSTiF/Yobx78I8GBx9i65Ran1r0k\nJori6kqCE5NHLJNSIi9XErV2ajpReXt7q/lzFwDVc1eZUidOnMVgSL+W9AH0eiMazXLOnCn0YGTT\nR0rJ6+8eIijz3lGT/nBeem9iNz/Inw8cx2w2O7X+jZmZaIsL6GtrHbGs6Vwhi3SDg8QpirNU4lem\nVF+f5bqkf5VWa6C/3zLKJ+a+6upqqnsgKNa5SccNfoH0ByVRWFjkVPnQ0FCe3HU77a//gZrcI3Q3\n1NNRXUnV+/vwz8vhG198UD17V1yiEr8ypdauXUJ39/nrZqSS0oHZXMbKlUs8GNn0KSo5j8a0yqXk\n65uYzomi806XX5ORwb9+7RF22XsIOPQOUXmHeTQujP/1rScXTMW5MnXUM35lSq1Zs4a0tNOcP/8+\nISErkdJBe3sB69f7k5aW5unwpkVnTx9648jJT8bj7etPV02fS5+JiIjg/nvu5n6XPqUoI7md+IUQ\nO4BnAC3wopTy56OU+TWwE+gDHpFSFri7XWV20uv1fO97X+PEiZPk5ubj5aVl9+6VZGWtRzvU8Wi+\n8fbywmF2rVWN3WbFW6+bpogUZXxuJX4hhBZ4FrgNqANOCSH2SSlLh5XZBSyWUqYIIbKA54AN7mxX\nmd0MBgNbt25h69Ytng5lRiSZorEcqYBlzrej766vYEv8+GPdK8p0cfcZ/3qgXEpZJaW0AnuBe28o\ncw/wBwAp5UkgSAgR6eZ2FWXWSE9fhU9nJeZe52a4ctjtOKrPcHPW5DpcKYq73E38sUDNsPe1Q3+b\nqMz8bMytLEje3t7csWEVdac/xJke6PVFx1ibHEFERMQMRKcoI7n7jN/ZcRZubO4w4nNPP/30tdfZ\n2dlkZ2dPOihFmWl33nEbl2r+yLnjbxO/4c5rA6kNJ6WkviiXiNYCvvqtxz0QpTLX5eTkkJOT4/Z6\n3BqrRwixAXhaSrlj6P2PAcfwCl4hxG+AHCnl3qH354EtUsqmYWXUWD3KnGexWPivP73JsdJaNPFr\nCElegc5gxG4x015zAWvFKVZE+vD1Lz9IQECAp8NV5gGPDNImhPACyoBtQD2QDzw0SuXut6SUu4ZO\nFM9IKTfcsB6V+JV5o7m5mdyTp8krvkhfvxmDXs/KFBPZGzMxmUyqs5UyZTw2OqcQYiefNed8SUr5\nMyHEEwBSyt8OlXkW2AH0Ao9KKT+5YR0q8SuKorhIDcusKIqywEw28ashGxRFURYYlfgVRVEWGJX4\nFUVRFhiV+BVFURYYlfgVRVEWGJX4FUVRFhiV+BVFURYYlfgVRVEWGJX4FUVRFhiV+BVFURYYlfgV\nRVEWGJX4FUVRFhiV+BVFURYYlfgVRVEWGJX4FUVRFhiV+BVFURYYlfgVRVEWGJX4FUVRFhiV+BVF\nURYYlfgVRVEWGJX4FUVRFhiV+BVFURaYSSd+IUSIEOKAEOKCEOJDIUTQKGVMQojDQogSIUSxEOLb\n7oWrKIqiuMudK/5/AA5IKZcAHw29v5EVeEpKuRzYAHxTCLHUjW3OSjk5OZ4OwS0qfs9S8XvOXI7d\nHe4k/nuAPwy9/gPwuRsLSCkbpZRnh173AKVAjBvbnJXm+s6j4vcsFb/nzOXY3eFO4o+UUjYNvW4C\nIscrLIRIBDKAk25sU1EURXGT13gLhRAHgKhRFv3T8DdSSimEkOOsxw/4C/CdoSt/RVEUxUOElGPm\n6/E/KMR5IFtK2SiEiAYOSynTRimnA94B3pdSPjPGuiYXhKIoygInpRSufmbcK/4J7AO+Cvx86P9/\nu7GAEEIALwGfjpX0YXKBK4qiKJPjzhV/CPAnIB6oAh6UUnYKIWKAF6SUdwohNgFHgCLg6oZ+LKXc\n73bkiqIoyqRMOvEriqIoc5NHeu7O1c5fQogdQojzQoiLQogfjVHm10PLC4UQGTMd43gmil8IsWco\n7iIhRK4QYpUn4hyLM7//ULlMIYRNCHHfTMY3Hif3nWwhRMHQ/p4zwyGOy4l9J0wIsV8IcXYo/kc8\nEOaohBC/E0I0CSHOjVNmNh+348Y/qeNWSjnj/wG/AP770OsfAf82SpkoYPXQaz+gDFjqiXiHYtAC\n5UAioAPO3hgPsAt4b+h1FpDnqXgnGf9GIHDo9Y65Fv+wcocYbFBwv6fjduG3DwJKgLih92GejtvF\n+J8GfnY1dqAN8PJ07EPx3MJgU/JzYyyftcetk/G7fNx6aqyeudj5az1QLqWsklJagb3AvTeUufa9\npJQngSAhxLj9G2bQhPFLKU9IKbuG3p4E4mY4xvE48/sD/D2DTYdbZjK4CTgT+8PAG1LKWgApZesM\nxzgeZ+JvAAKGXgcAbVJK2wzGOCYp5VGgY5wis/m4nTD+yRy3nkr8c7HzVyxQM+x97dDfJiozW5Kn\nM/EP9zjw3rRG5JoJ4xdCxDKYkJ4b+tNsqcBy5rdPAUKGHm+eFkJ8ecaim5gz8b8ALBdC1AOFSj5E\nogAAAjdJREFUwHdmKLapMJuPW1c5ddy605xzXPOw85ezSeTGpqmzJfk4HYcQYivwGHDz9IXjMmfi\nfwb4h6F9SjDy38JTnIldB6wBtgFG4IQQIk9KeXFaI3OOM/H/I3BWSpkthFgEHBBCpEspu6c5tqky\nW49bp7ly3E5b4pdSbh9r2VBFRZT8rPNX8xjldMAbwCtSyhH9BGZYHWAa9t7E4JXBeGXihv42GzgT\nP0MVQy8AO6SU490ezzRn4l8L7B3M+YQBO4UQVinlvpkJcUzOxF4DtEop+4F+IcQRIB2YDYnfmfhv\nAn4KIKW8JISoBFKB0zMSoXtm83HrFFePW0896rna+Qvc7Pw1g04DKUKIRCGEHvgCg99juH3AVwCE\nEBuAzmGPtDxtwviFEPHAX4EvSSnLPRDjeCaMX0qZLKVMklImMXiX+OQsSPrg3L7zFrBJCKEVQhgZ\nrGT8dIbjHIsz8Z8HbgMYej6eClTMaJSTN5uP2wlN6rj1UC11CHAQuAB8CAQN/T0GeHfo9SbAwWAL\ngoKh/3Z4uHZ9J4Oti8oZ7IgG8ATwxLAyzw4tLwTWeDJeV+MHXmSwNcbV3zvf0zG7+vsPK/sycJ+n\nY3Zx3/kBgy17zgHf9nTMLu47YcDbQ/v9OeBhT8c8LPbXgHrAwuCd1WNz7LgdN/7JHLeqA5eiKMoC\no6ZeVBRFWWBU4lcURVlgVOJXFEVZYFTiVxRFWWBU4lcURVlgVOJXFEVZYFTiVxRFWWBU4lcURVlg\n/j+toIjA18eAUwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x90dbc88>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"\n",
"N = 50\n",
"x = np.random.rand(N)\n",
"y = np.random.rand(N)\n",
"colors = np.random.rand(N)\n",
"area = np.pi * (15 * np.random.rand(N))**2 # 0 to 15 point radiuses\n",
"\n",
"plt.scatter(x, y, s=area, c=colors, alpha=0.5)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"areas = [400*(x)+10 for x in [y**3 for y in list(1-df.Rank/252)]]"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"colors = np.random.rand(252)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x583e7f0>"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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/B8qALuBbD2/ovFzdvtPCu40tFL/+bWpnWckX8Hr55fkTfF1Kamrmn7I2PDzM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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x57e3fd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(df.Goals, df.Assists, s=areas, c = colors, alpha=.35)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.4"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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