{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a huge t-wolves fan, I've been curious all year by what we can infer from Karl-Anthony Towns' great rookie season. To answer this question, I've create a simple linear regression model that uses rookie year performance to predict career performance. \n",
    "\n",
    "Many have attempted to predict NBA players' success via regression style approaches. Notable models I know of include [Layne Vashro's model](http://laynevashro.com/basketball/predsFAQ.html) which uses combine and college performance to predict career performance. Layne Vashro's model is a quasi-poisson GLM. I tried a similar approach, but had the most success when using ws/48 and OLS. I will discuss this a little more at the end of the post.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#import some libraries and tell ipython we want inline figures rather than interactive figures. \n",
    "import matplotlib.pyplot as plt, pandas as pd, numpy as np, matplotlib as mpl\n",
    "\n",
    "from __future__ import print_function\n",
    "\n",
    "%matplotlib inline\n",
    "pd.options.display.mpl_style = 'default' #load matplotlib for plotting\n",
    "plt.style.use('ggplot') #im addicted to ggplot. so pretty.\n",
    "mpl.rcParams['font.family'] = ['Bitstream Vera Sans']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I collected all the data for this project from basketball-reference.com. I posted the functions for collecting the data on my [github](https://github.com/dvatterott/nba_project). The data is also posted there. Beware, the data collection scripts take awhile to run.  \n",
    "\n",
    "This data includes per 36 stats and advanced statistics such as usage percentage. I simply took all the per 36 and advanced statistics from a player's page on basketball-reference.com."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.read_pickle('nba_bballref_career_stats_2016_Mar_15.pkl') #here's the career data. \n",
    "rookie_df = pd.read_pickle('nba_bballref_rookie_stats_2016_Mar_15.pkl') #here's the rookie year data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The variable I am trying to predict is average [WS/48](http://www.basketball-reference.com/about/ws.html) over a player's career. There's no perfect box-score statistic when it comes to quantifying a player's peformance, but ws/48 seems relatively solid. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hQ+R0OuXz+bR+/XrNmTOn2xPlPp9PTz31lCZPnqxXX31VGzZsUHNzs1577bWQ\nvnYgUJxYB3qQmZmpU6dO6S9/+YuuueYaSfKXwbvvvqukpCSlpKScM+7snkN9fb2kroeyTp8+rYMH\nD+q5556TdKY0JGnBggV66KGHNHz4cLlcLt12222y2+0aOnSo8vLytHnz5m7PnwDhRokAPYiJidHI\nkSO1bds23Xnnnf7p11xzjd5++23/pbr79u3TkSNHlJOTo2HDhunIkSPauXOn8vLyJJ35cqB77rlH\nkhQfH6+XX37Z/1wNDQ1atmyZVqxYoYSEBNntdqWmpupPf/qTZsyYodOnT2v79u264oorQvfCgT6g\nRIBejBkzRgcOHNDo0aO7THvnnXf8h7Li4+NVU1OjN954Q62trUpISFBubq5mzpypkydP6siRI8rM\nzPSP/+LJ9NbWVv+0s4e3fvzjH2vTpk0qLy+X1WrVV77yFc2dOzcErxboO77ZEAiiqqoq7dixQ0uW\nLAl3FCAoOLEOBNHQoUN1++23hzsGEDTsiQAATGNPBABgGiUCADCNEgEAmEaJAABMo0QAAKZRIgAA\n0/4PEIpbKZs73RcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f39aeb9fa10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Games = df['G']>50 #only using players who played in more than 50 games.\n",
    "Year = df['Year']>1980 #only using players after 1980 when they started keeping many important records such as games started\n",
    "\n",
    "Y = df[Games & Year]['WS/48'] #predicted variable\n",
    "\n",
    "plt.hist(Y);\n",
    "plt.ylabel('Bin Count')\n",
    "plt.xlabel('WS/48');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The predicted variable looks pretty gaussian, so I can use ordinary least squares. This will be nice because while ols is not flexible, it's highly interpretable. At the end of the post I'll mention some more complex models that I will try. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rook_games = rookie_df['Career Games']>50\n",
    "rook_year = rookie_df['Year']>1980\n",
    "\n",
    "#remove rookies from before 1980 and who have played less than 50 games. I also remove some features that seem irrelevant or unfair\n",
    "rookie_df_games = rookie_df[rook_games & rook_year] #only players with more than 50 games. \n",
    "rookie_df_drop = rookie_df_games.drop(['Year','Career Games','Name'],1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above, I remove some predictors from the rookie data. Lets run the regression!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  WS/48   R-squared:                       0.476\n",
      "Model:                            OLS   Adj. R-squared:                  0.461\n",
      "Method:                 Least Squares   F-statistic:                     31.72\n",
      "Date:                Sun, 20 Mar 2016   Prob (F-statistic):          2.56e-194\n",
      "Time:                        19:32:47   Log-Likelihood:                 3303.9\n",
      "No. Observations:                1690   AIC:                            -6512.\n",
      "Df Residuals:                    1642   BIC:                            -6251.\n",
      "Df Model:                          47                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.2509      0.078      3.223      0.001         0.098     0.404\n",
      "x1            -0.0031      0.001     -6.114      0.000        -0.004    -0.002\n",
      "x2            -0.0004   9.06e-05     -4.449      0.000        -0.001    -0.000\n",
      "x3            -0.0003   8.12e-05     -3.525      0.000        -0.000    -0.000\n",
      "x4          1.522e-05   4.73e-06      3.218      0.001      5.94e-06  2.45e-05\n",
      "x5             0.0030      0.031      0.096      0.923        -0.057     0.063\n",
      "x6             0.0109      0.019      0.585      0.559        -0.026     0.047\n",
      "x7            -0.0312      0.094     -0.331      0.741        -0.216     0.154\n",
      "x8             0.0161      0.027      0.594      0.553        -0.037     0.069\n",
      "x9            -0.0054      0.018     -0.292      0.770        -0.041     0.031\n",
      "x10            0.0012      0.007      0.169      0.866        -0.013     0.015\n",
      "x11            0.0136      0.023      0.592      0.554        -0.031     0.059\n",
      "x12           -0.0099      0.018     -0.538      0.591        -0.046     0.026\n",
      "x13            0.0076      0.054      0.141      0.888        -0.098     0.113\n",
      "x14            0.0094      0.012      0.783      0.433        -0.014     0.033\n",
      "x15            0.0029      0.002      1.361      0.174        -0.001     0.007\n",
      "x16            0.0078      0.009      0.861      0.390        -0.010     0.026\n",
      "x17           -0.0107      0.019     -0.573      0.567        -0.047     0.026\n",
      "x18           -0.0062      0.018     -0.342      0.732        -0.042     0.029\n",
      "x19            0.0095      0.017      0.552      0.581        -0.024     0.043\n",
      "x20            0.0111      0.004      2.853      0.004         0.003     0.019\n",
      "x21            0.0109      0.018      0.617      0.537        -0.024     0.046\n",
      "x22           -0.0139      0.006     -2.165      0.030        -0.026    -0.001\n",
      "x23            0.0024      0.005      0.475      0.635        -0.008     0.012\n",
      "x24            0.0022      0.001      1.644      0.100        -0.000     0.005\n",
      "x25           -0.0125      0.012     -1.027      0.305        -0.036     0.011\n",
      "x26           -0.0006      0.000     -1.782      0.075        -0.001  5.74e-05\n",
      "x27           -0.0011      0.001     -1.749      0.080        -0.002     0.000\n",
      "x28            0.0012      0.003      0.487      0.626        -0.004     0.006\n",
      "x29            0.1824      0.089      2.059      0.040         0.009     0.356\n",
      "x30           -0.0288      0.025     -1.153      0.249        -0.078     0.020\n",
      "x31           -0.0128      0.011     -1.206      0.228        -0.034     0.008\n",
      "x32           -0.0046      0.008     -0.603      0.547        -0.020     0.010\n",
      "x33           -0.0071      0.005     -1.460      0.145        -0.017     0.002\n",
      "x34            0.0131      0.012      1.124      0.261        -0.010     0.036\n",
      "x35           -0.0023      0.001     -2.580      0.010        -0.004    -0.001\n",
      "x36           -0.0077      0.013     -0.605      0.545        -0.033     0.017\n",
      "x37            0.0069      0.004      1.916      0.055        -0.000     0.014\n",
      "x38           -0.0015      0.001     -2.568      0.010        -0.003    -0.000\n",
      "x39           -0.0002      0.002     -0.110      0.912        -0.005     0.004\n",
      "x40           -0.0109      0.017     -0.632      0.528        -0.045     0.023\n",
      "x41           -0.0142      0.017     -0.821      0.412        -0.048     0.020\n",
      "x42            0.0217      0.017      1.257      0.209        -0.012     0.056\n",
      "x43            0.0123      0.102      0.121      0.904        -0.188     0.213\n",
      "x44            0.0441      0.018      2.503      0.012         0.010     0.079\n",
      "x45            0.0406      0.018      2.308      0.021         0.006     0.075\n",
      "x46           -0.0410      0.018     -2.338      0.020        -0.075    -0.007\n",
      "x47            0.0035      0.003      1.304      0.192        -0.002     0.009\n",
      "==============================================================================\n",
      "Omnibus:                       42.820   Durbin-Watson:                   1.966\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               54.973\n",
      "Skew:                           0.300   Prob(JB):                     1.16e-12\n",
      "Kurtosis:                       3.649   Cond. No.                     1.88e+05\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.88e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm \n",
    "\n",
    "X_rookie = rookie_df_drop.as_matrix() #take data out of dataframe\n",
    "X_rookie = sm.add_constant(X_rookie)  # Adds a constant term to the predictor\n",
    "\n",
    "estAll = sm.OLS(Y,X_rookie) #create ordinary least squares model\n",
    "estAll = estAll.fit() #fit the model\n",
    "print(estAll.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's a lot to look at in the regression output (especially with this many features). For an explanation of all the different parts of the regression take a look at this [post](http://connor-johnson.com/2014/02/18/linear-regression-with-python/). Below is a quick plot of predicted ws/48 against actual ws/48."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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VtCebqyLGX8uku+8Vlk8fQigcgaCbaW19VFfXQIjnfnGt/vqWD1C02z/s0L69pH/4J7aM\nG+y2vb3eY50J5vdU4L8jVlnrbgGPPFKLStUx2TxcgWbhbutriLRogaAbcS56K6xlPGE3ssJaRuNF\nIy8Y6tz2e+qkgVsiPLVQXUV5m+ff+eYf0Nklr5+1ZYF0JE3amVRQX1lB8onzZBhNPvftLN6ey743\nXuHQvr3yPkVFKu69N5ydOwP5/PNKkpPdlU179+GrCWj6px91WW5B9yIsHIGgi3h7Y3dd9DKMJtLL\naxioUHCsWmJtyADCQ3RU1pi4YLZ4dAEAKD1f1ObcmPrSEoYEaLx+1pYF0l4w39vUzjXZJXxOMBHR\nMZcc+G+rI3TS5FvdrBpHZ+fO38fBj7Z5PaYvDH8TOBAKRyDoAr4yuYx2BegVZBhN7C+vYUpkCOnl\nNcRixZBzmqaYgZjq6lgyJIKUbAMrXebFrM4qZF6cnvQ2XGMapYLJ4SEex67IKuSel9ueNNpWSx5v\nCuGlhJhu6xDgK/ZSWxPOvfeGu/VAaw9f9yGmbPZ9hMIRCNrAV9zB1xt78ply0EeS3qxs9rv8+/I1\nQwB4NaeKJH0kAGk5BpQokLDjTBjNP5FF2sIHvcY5gqIHkhRi9Ti2PizykiwQp0JwWmUqhQKb3U5l\nZOeTWL09s9bKQLIr2FH0az46k8yyFY1tWjUdRUzZ7Pt0+VdcW1srjwgQCH6KtFWP4uuNPToqmpSL\nVtQKBenlNaxMiCO1lTVia1YsSXqdm1tt7YlC9pfX8GZCHNiNXrPPZi5+jBdS1vNsXMuxzxtqmbfy\n6Uu6V6tKTUaZwypzlXVNdombi+/Qvr0c/OwTaDR7VYi+nlnM5BmkpO9hZZiSovpYVmY9Q65Zy8vr\n07nngesvSXYnrd1tCv8Abn1sjkgY6EO0W4djNBr505/+xPnz5xk/fjx33303GzZsIDc3F71ez/Ll\nyxk+fHhvydstiDqc7qM/ytnRGpa26lHsdrvXz1ZWq9AoFFTm56KUJOYOjuRghYknR7XUMDjdba4L\n+8ZsAxWNFlKvGer1eq5urUP79pLuEsNwBvMvpZPBoX172bp6GZuu9uy07ry+hzIBUi5KTH10mXyt\ntp7ZzbPvZ1NqFf8+9RuuH/kZj6/ScvMvpnZYxs7SH7+bfZleqcPZvHkzQUFBzJ07lwMHDrBu3Tpu\nvPFG1qxZw9dff80HH3zA888/f8mCCAS9QWeq6Nuq+Zh0/zwP983ywmpC1GqejQuCcUMASMk2UN3k\nfh6nZbLk6FmGB2k5UWchavBgbMZKvNE66O2tZU3re1r97FOcnv0ADz3esdlQE6feyleDrgI8M+Cc\n1+/IKOi2YjW/f2s2DSoF/9hTxciRt3dILsFPi3bTorOzs3nkkUe47rrrWLRoEUVFRfzqV78iKCiI\nmTNnUlRU1BtyCgTdQmdSZ60q7+9jNrUfE6feytRHl5HmF81rCj1pftFow8Idysb13AlxNEkSKdkG\nt+37ymuYOziSJ0fFknjddaR+9jcGXX2Nz+t19p42JMTw74+3uaUdt0dgeESb1+9IQWjrZybZFWwv\nvJuPDr7Orbc28sUXorPzlUy7CkelUtHQ0ABAfX09kiTR2NgIgMViQS2mHQn6EZ2pom+vdmXi1Ft5\natNmlr+7lac2bSY81FFXk2E0kZpt4NWcYlKzDVQq/DBGxvLQ0XxezSkmLccxidNp6TgX9Om/ebBL\nIwV83dMIv47VoDjrbyrLSlmdXeLz+pXVNV6Pd1WI0WMS5XMU1cdy/3eb+EPBf/Hy+nSSky89MUDQ\nv2n313/DDTewbt06xo0bR3Z2NhMnTuStt95i6tSp7N+/n8TExN6QUyDoFnylzl6oqvbZSbmjjSh9\nBd6fOmXg1489CeARA3HNorpl+gwaGho63fjS1z1J2NutQXFzx+kVZBBE8o/niY6/iialikalkoMf\nbWP3W69TVmwg5YLV7d7W5htpDFPy7JxZGM6fx2ZpJEyp4J5Dt3K8ejVDQt5kSXI99zzwWJtyCK4M\n2k0aaGpq4ssvv6S8vJxp06YRExPDli1bKCwsZPjw4cydO7ffZauJpIHuo7/J6S3w7RZ7aaZ1MBza\nTzY4tG8v7z31BO9cc5XH9Rf8WMzCl1IBPAL+znN09Vke2reXz599qqW3Go4khKmRIRyIHuGzjubQ\nvr1s/d0aNo2K9PhsZbWKCKnJPUEg20C0Vk15oxUlCoyWJiolBW+53O/aE0qOVP2B8kZ/Vo1+mrvj\nyntk2mdH6G/fzb5OdyQNiG7RfZT+8iXsj3K2zvKqqqxkfYinieC6UHpTVGuyL9AUHcc9S5bKSmPR\npAm8OdbzD/OJ4/kMvGqwhxLzJWNHcFWAJ7LP0FR9kQiNCrPNTlJ4ECW6CG59bLnX6znvR11e4pZB\n5+SRH4t552ee29NyDCwf5bBwUrMNrGi2dpx1NWk5ixkb8jrbb/g//D63iFsiQvigvIERo0f3+uya\n/vjd7Mtc1m7Ru3fv5pe//CX+/v6XLATAsWPH2Lp1K5IkMW3aNGbNmuX2ucFgYNOmTRQUFHDfffdx\n5513yp8tWbKEgIAAlEolKpWKDRs2dItMgp8mrbO80hY+6Kh7aYWrO8p7Jf5A0nLOsc81y81HooGx\n0QqFhby7ZgW8lMrEqbd6WEy3P7iAhoaGdq2o/Z/soLKsFEV5aYtVMyqclGwzU5pjQ2uySxj9X7e1\n21kgtcz7+6bS0uh9O4qW56Nw/N9ZV1NvDWDXxAX8rfhb1MpYKsxN7dYVCa4s2lU4J06c8Nhmt9v5\n4osviIqKIjg4mKuvvvqShJAkiffee49nnnkGvV7P6tWrmTBhAvHxLTUBOp2O+fPn8/3333s9x3PP\nPdfvXHuCvkFHWqL4CswrUbDcJTU4MDqGlOxzbnGOlZkFJI+IkZMEVq1dzrbgAQRKNlIHNzfwtMJv\nHn4QrVrFWF0AVrudyZEhbsrM1cpKLS+WrQv5OglxpOUYSNLreCkhhiWf/ZnSU1my0nIqq/rKCgry\n8lilVqBSKlj0Qx5vX99SS7cx24Dez3s+kUSLgspptLO98G5eObOIRcM+YMGQj1ArbfI+NTYbLycM\ncZcxTMnat9/wUKpwaXVEgv5Buwpn3bp1hIWFoWrVtrW+vp5t27ahVCp58803L0mI3NxcYmJiiIqK\nAmDSpEkcPnzYTeGEhIQQEhLCkSNHvJ7jCvQMCrqJjrREaSswDy3W0D1LlrLzpedJyzFQa7VRWNfI\nouExbh0FXh7jUAySHTKMDithW0EZMVoNac31O+CImUyJRO6t5mplOa0LcGTF7TpfQb3NjmS38/SJ\nQmbG6hmOlSetZaS88QqnMzMpaa70JxQyhkSwraCMKK0fNrudR37IJUEXiISdqZEhnDbVszyrkLTE\nljEIzxtqqYu+ij+q9ZTXD+SU4mN+KGxg500PMzK4wPHcmmNHq7MvoPQP8Pq8W88BWvHS8+jUqpYY\nmg9L6HKM6RZ0L+0qnHvuuYd///vfPPDAA4wfP17evnDhQjZs2MCAAQMuWQij0Uh4eLj8s16vJzc3\nt8PHKxQK1q1bh1KpZPr06UyfPv2SZRL89HFdwMoVataaVISH6Lxmh3lVSs2LK7RYQ85jdr/9Bobs\nUySGBnntCq1EwS2ROt7LLyVA5TjnPYPC3fZxWiyGyhMc2rfXzcpytsdxKptIf42bVfXCyXPUWh37\nrwxTkvyXj9k0MlI+Zn95DZuua7Fq1mQVMinC0S4nw2ii1Gzlnji93K8tt0nipvvmMe/RpezcqedP\n6/1ITq4lMeHv/HVXA3UVSsrKytBFxnIgOoa7HnOMC8BL14HWc4CizCZPa61VQWlfGNMtuHTaVTiz\nZ8/m5ptv5k9/+hPffPMNDz30EBERjgIxhctb1uXEaYXV1NSwbt064uLiGDNmjM/9dTrPBaCvodFo\nhJyd5MA3e/jmw61yMsD03zzILdNnAJ5yHvhmD+lvvsqKAc3f4QGQVmVj+m8Xyce4ctudMwkICOB3\nf3iFqvxcBmmUci3NC8V13PXsb+Xz33bnTG67cyZzx43G5mN2TV5tA+WNTbx7/Qh5m7M41FVBKVEQ\nZ7eQ/uar1PhpodkImBzp6BitAKL8NR4L9rNjB7H2RGHLeRpbYjLprdK2AV5KHCy741w/d5Xl+WNG\nHnggCrNZyddfN1B27p988+HHaOwSqugYfr1slduzCwgIIC3tRZYPaFknVmeXMCvSfQ6Qysc6orFL\n8jM9+NknXgt2f//Zp9x250zvx/eh72Zb9Bc5u4MOJQ3ExMSwZs0aDh06xLp167jllluw2WzdJoRe\nr6eysqWtR2VlJXq9vsPHh4WFAQ6324033khubm6bCqc/ZIT0l8yVviKn1zfg1PU0NDQwceqtHnL+\nY+uWFmXTzPIBCtK2vce1STd5nNtpCdlsNhrUGsDKwQoTBypqMPnraGho8HgOIXGDmGy96DFK4PmT\n52iU7GxwcVdlGE0ogI/PVZBeXsPkZmWWW2tm3pBIkgYomJuVxwokUhOvkhXB1oIyRgZ7d13pNS0x\nKFODWf6/rwX+bK2ZJUfPYra5K8mWzs6LWbayjmXLlPzzy91tPm+Aa5NuomHJk251RdZoJUkD3M9v\n8+EOtyiULc+00ex1H3uj53N30le+m+3Rn+S8VDqVpTZx4kSuvfZadu7cSXh4uEdcp6sMHz6ckpIS\nysrK0Ov1fPvttyxd6r0HVOtYTWNjI5IkERAQgNlsJjMzk9mzZ3eLXIL+Q0f6fLnS0Y4DHoosBFIu\nWLklIsS907OXQPjMxY/xj5T1/DIyhLUnCqmzSsQFaqi1WhkbEigf662ZZ0q2gS1nS7k5ssXNpbNa\nuGiTHG62egtxgRr0GrXPBdsZX9qYbaBJqSTlosTKMKXP/YcFa1k+Ko7VWYVkGE0k6XVyBlqDTcuM\nxEUsXvwqKpWuw8/bW9+31q7JUm0wLxjq3OqgOhpDE7Nu+hftKpw///nPXHfddYwaNQqFQkFAQABz\n587tViFUKhXz58/nxRdflNOi4+Pj2bNnDwAzZsygqqqK1atXU19fj1Kp5Msvv+S1116jurqatLQ0\nwJHtdvPNNzNu3LhulU/Q9/GlQCpKS3h58QL8sdOIQg40t17AnHNgipXlvLx4AdFjEik9lUXBjyd4\nMyHK7Zyu2WBOLuTmYPdTUm+VsNjtZH2Xwegpv8CiC+Xj0gtY/AKwhgRRFTYA2wUDNnuLYvPm4lqZ\nEMfc785QZrayKrMAhULB3MGRbCsoo8LcRFWTlVCrijqbjaK6Rg8ranlmAWqQ2+gUKRsdvd8+/Yjz\nAbWsOFFE6tUtBZuu8agNiYNZ/EM+OaYH5Qy0i5YULmpbkng60yLIFW/dG+asecJjW+sYWvSYRFbv\n2uFe3Cpm3fQ72lU4Wq2WHTt2cOHCBRITExk/fjzXXnttt/scx48f75aUAA5F42TAgAG89dZbXuVL\nTU3tVlkE/Q9vb8AZRhPK8jpW6FtcSM5As2sSgIeFYS1j9afbmBUdikrhOx3a9TrhKgUvXt3iInvh\n5DmK9u1xpBuHOIL1KRclpi56lP2f7GByWZ6sJHy5uHR+SlY0z9OZ3DzEbd6QKLdhbs5r5dU2sOTo\nWTRKBUEqJUrsvNw86mBFZgHlTRJ/fm4NFm0gYQMGYA0OIvlMOeqmJq5S2916uxXVx5Jx8VUyLgby\nXzG/otZ6hmlRIXw7ILTN5w0dszh8Tez0Ffw/tG8vJel7mBUZ5JHEIBIG+hftKpy77rqLu+66i9ra\nWo4fP86RI0fYvn07kZGRXHfddYwfP55hw4b1hqwCgU+ixySy+tNtbBjT8ha+Jb+Mzde7z2pyun2c\nHQTSPv2I7GIDm692tzA2jIknLceAr2x713qULQXlbL7O/W/g2bGDSD5ylldzirE119Ss1OtY+/Yb\nzFz0qOxqS8sxkF/nvchyaJAWcMRcfA1zc17LtQMAwOIjeTx5PJ9aq0S91cqnSQnNirWKlYOCAAXo\nI5l/NJ/lYx2KyRmreeXMIoYE/oHPJn7J4YvVpJfDwQoTuRXZHNq3l9vunEn0mESS/7yNERqlfH/7\nCOTWx+6/5PTl1seXVVSQGqYE3AfWpZ32rBEU9G06HMMJDg5m0qRJTJo0CUmSyMvL4+jRo2zevBmj\n0ci8efPirpevAAAgAElEQVT4+c9/3pOyCgQ+KT2VxazoULexy2F+3mOMhpMn3KZYGo57r+1SomBS\npM4z6N9cj/KaIhSb2g+N1nurpBHBWrltzIrMfHYXG7loldj91uuMun0mf/s2nTqFmVqVxMpTBlLG\ntFxjRWYBYCfDaMJmt8tWkC9ryNXiAhjeHI9xbT/jzXUXrnLEi+4bdIPcLeCX0b/iQtOPHL4Y4Rlb\neuMVzmafpiR9j9uwtjXZJYye7egOcinpy96SP9aUXiBDCvZIL2/PfSfoe3SptY1SqWTkyJGMHDmS\nOXPmUFVVJY8wEAh6C9c34YLTp5kcGSC/5WcYTWwr8KwBAYizW+QK/v2f7CBQ8u42M1qa5EUuLcdA\nTr2VJruESqVGU9dA+M1TeejxpTz+n7f5PN4pi3udjJWU9D3MdOmrdmjfXuY/9SShkpW4QA2z48NJ\n0juUXZPdRkGtBfCd0eVqcbnGYwwNFnm7N2UV46+lzvYw09MfZ0LYJm6OeJspkUH8QzWcbaWlbPJS\nH7Pkz9t5c6T77JyXEmJIO32C0lNZnUrecMVXM1FHCyGDh8IRCQP9j3YVztGjRwkICGD06NEAlJSU\n8MYbb3Du3DlGjRpFcnIyYWFh3VIAKhB0FI834YQotzqW9OZ4R2vrZHVWIbPi9CQ1L4JqmxVL84C0\n1iOfLZIkn++dvBIitBpSEltGQK/6eCvvA+qgQI92Nq7He00KCFOS/NwaDo5KkN1OgVotr7VabFcm\nxDHvuzOE+ilZdjyfOYMiPGR98ng+ZeYmnjyeT2WjFa1KgZ9CwYGKGoLULYt/a2VVVB/LZ4bfE+E/\ngL/fvBCjJYv0chsHK0yUYiYkwvtANi3elV5bFkdHxySMwPt+BmtLYodKoeCMRWLif/+izXMK+h7t\nKpxPPvmEhx56SP75rbfeIigoiMcff5y9e/eyfft2Hn/88R4VUiAAd4vmTHa2x5uwa/aYSqFws06c\nbja73S5vV1mbsKrUDNT6MylC57bf1MgQPj5Xwas5xUjYsdohxaVuBuDlnw1i7vvvEqRWMfuqcI/j\nD1Y4ait8ucFG2Jt4wqWppdli8brfuAFBTIrQset8BQcqajA2NrHk6FnAzkWLjUiNmsdGDiS9vIb4\nAH9OmeoJ91cxb3A0GUYTq7MK2ZA4WC4WXT4qXu7sPCjw91w34F2MFh1fl1Tx7NhBLc/zlIEMQjws\nCzPe78em9nOULXQhmaC9ZqIMCOev5VW85OreS9/DoWuuEYkD/Yh2FU5paSnDhzsCr1VVVZw+fZpN\nmzYRHh7OyJEjWb58eY8LKfjp05FZM64Wzau+3oQbLLyaU0xWcyA+Sd8q0JzTMurZpvZjypz72br6\nCE+22g/gQEWNHINZdCTP6/WClDAswM/jOs7joWNusJVhSh654LthZnp5DanXtFhXzrf9JslMsJ+K\n9/JL5QLQUcEB/L8KE6d0Uaj99BhDlSzJKUOjUFDldzXj//U7NARyS8R/EqDK5j8GRrD5bClbJoxw\nu27KmDj+57szfKypwGK3E6hSYA/Vc8t//w8pe/7us/dce33pvOFMs3YqRVcLbtXJ81h0YbzmkhLt\nfGYdcdUJ+g7tKhyFQiG3sDlz5gxRUVFy37Pg4GDMZu8VwAJBR+lIn6zWhYa+FvG4AA1PjoplldEu\nFzo6cY1trDp5nrH3/oKJU2/l9H/PY82uHbzkZYCZk3qr9xY1SoXC6yK5PLOAkgYLyUfyqLNJrD1R\nxHofdS9OArVaVmYWkOKS8rwqq5AxIVpOVTeQmm1ApVBwwdyITq2WrZEMo4kvDEa39jYp2QYqVGo2\nfPwZAJIE27cHkpqq4467MgkpWMgqvZ1thYG8nVeCRuld2UX4q3lt3FD5nIZGCz+7djzDEkbLdTOV\nNSZ5MqhVpSZm8gzSTp/o0tTS1lZpXp2ZuYMj+biyHrmvjwsicaB/0a7CGTZsGF9++SW/+MUv+Oc/\n/+lWK1NWVnbF9AAS9BwdqVpvXWjobZF3XcQjomOYPOd+0j79CMPJEwQ11mORJLkdzV0xofzt23R4\nfCkPPb6U94GFH29HZTFT12QjWK3kYHM6cnZtA2a1n+yakmXMLOBGvaM552lTPclHzjIiWIuEnXvi\nw/nCYKS8sYltN4wkw2hqqSFpblcDyEqk0tKEodbM2JAAko/mEaJSEaH14644PZ+dr0ClUMoKxTXz\nDBwxog2t3H0rE+JYknMBgKIiFcuWDaCs1MR/XptMpCmfSrWKR4qqGVDXxPs3jGx20Xlikexu50zL\nMfDPD7ex7PW3PEYmyDNv0ve0OWjOG651UU5rcWO2gbmDI0nS6/ig3HtSkkgc6F+0q3DmzZvHxo0b\n+fDDD4mJiWHhwoXyZ+np6YwdO7ZHBRT89OlI1XrrQkPnm/DDPxYTrLATp0ZWNotPnCdmkJL9n+xg\nyr0PcPCjbY5YSSu2HzvDirvvxKZUoasz8duBOnYXN6FVKIgN0Mjdk1dmFmC2Kym1K1ly9Cz+SgW1\nVokKswUlkHwkD5VCwdAgf/kYp4zJR866yZteXkOUVs3rZ4oZpQt0i5k4xhGEyNlpzvY5HxSW8+b4\nFoVS16qPoa8YkR9Knlt7nu07Ehgd8zY3qF9lQ0Ak2IEQeMpQxa9jHMWcgSqFVwUepHI/txIFSmtL\nrKmzLYV84dw3+bk1jLA3yXEw53MLihlIysWmTrvqBH2LdhXOoEGDeOONN6ipqUGn07l1iL7jjjtQ\nq7s8NFQgADpWte5tPMA+Ann4pWcB2LXpj2w5V4TW2sSwAD8m26pJskqkvPEKhbUNMMjTHTMk0A8q\nirHbkSv5XbsFOLPeUq4ZwpKjZwkaNhIAQ14u/nY7AwM1XGyyMX9olNzvbFtBGZ+dr6TWaiNErcJm\nl1iRmc/sePealtZWCjgsiLUnCh1KziUBotrSxNMnCqm3ShibrPi1UjDe3ItF9bHsL34d+8f+3B19\nJzVNJ9h49TC3TK8IJXx2voIkvY7Z8RHsOl/hlvhQ3mjhnnj3TDUJO5JaI//c1RY3PmN2z73kMcp7\no9HG7Mfab38j6Pu0qy02b97M2LFjGTNmjMc4gqAgzz9igaCzdGQAmrceXM4F59C+vURITaRc7R7D\nAFip17GguJo1WRW85OJ2crrfPj9fickmUVBYzpvj3bsFuC76kt2OsbAANXbGBWk8Gm1+U3YRQ30T\nI4IDsNnt3N3sUvt5hI6TNQ1syith2w0j5WN8WSVGi1UeoKZEwcOHc4nSamRF6Gxz42qNTI4M4anM\nAjZeM0TuFvDCqYVE+P+eXw18n1WjY3g1R8u2wlIyKmtlGSdHhrDrfCXbCkuZNzgagA8KyxkepKXK\nYqXJZndLhNiYbaBcG8y838yTtxVfrCK11OEWdJ4zSa9r09XVkZidL8UiFEz/pl2FY7PZ2LlzJxcu\nXCAqKkpWPmPHjpUndAoEl0J7i4zrft4WHK9uHRdlEaiACksTyUfz0CqVmCWJJL1jHLlSoeDN8cN4\nNcd7twBnBf/wYK3c5qa1ZTKleeGeNyRKth62FZQRF+jHvrIaBgf5Y7c7ug0M1Ppjs9upavJuGYCC\nmbF69pfXcPxiLVq1klfHtWSn+Ur3rm6y8uyPCr4pe42aJi03hP0HYZpcVo12KNHTpnpCzWq3oWsp\n2QZmx4ezraCc0TpHd+itBWVyZl6G0eTocm2TqLbYsGkDWPLCc9wyfQYmk4lD+/aiqzN5JCvsNEly\nQ05vtOeG8/V7FvR/2lU4ixYtAqC6uppTp05x6tQpvvrqK95++23CwsIYM2aMqMMRXHL/LG9t7F9e\nvKBD5/Pl1nEqizJTLWN1AW7xktVZhew2XOSznzsKmttKXX7+5DmGBGk4drGeQYH+Hvukl9cwOz7c\now3MsuMFRPirCVGrSQxVUdZokWM8KzLzWZVVyMutrK55QyL5wmBkdIgWtVIpX0/uZt1gka0cZ1cF\nya7gzoOT+Vf508yOf48Bfn9AqbBRWId8rAIFqS7Zb9CilEcEa/mgsJy/FRuptNpZllnAK9cMcQve\nqxUWVHGxbr+D/Z/scBsp4DznWpNfm7/7rrrhBP2fDgdgQkNDuemmm7jpppsoKiri8OHDfPXVVxw8\neFAonCuc7h7/25nzHdq3l1MnTsDPYj3OI2FnY7aBGK3aTdmAowX//O9z5YU8r7aBZccLeGXcEHmf\nlZkFFNU3EqxWUWq2EKT2PkvGtbmmK6+MG0JajkG2GFKyDXLMJPWaodz372y3zDZnkNyZbGCz2ylu\nsPiclwMwUJvAvf9eSYBKx99vXsif8vcTrdXyg9FMdZPEqznF5NY2MDDAu4vLaSFZUSD5+fPpjYOZ\n+12ORxFrkl7HkjMVbsf6UhzhITr5d+PtJUTMtrlyaVfh2O12zp49y6lTpzh58iRnzpwhIiKC0aNH\ns2DBAhISEnpDTkEfprsylTp7vkP79vKPlPX8dqBng80VmQUosHN3fIRc8d+aALWC/eU1TIkMwY7D\nNeZcaE/W1BOkUvLxTS3f7xdOnuN4VS3JR/Lc4iBnahu8Tt3MMJrIr2uUO0ZPiQzhg8Jy+fNQPxVK\nBbJCcmVEsJbjVXX8dmg0f8ov451WXa+Xj4rntgO3UFS/ltnx7/HC2F2olTY2JA5m7YlCNEoVH9zY\nckyyj8JVZ4r2ydoKuQ7JOYitNa1d6G0pjrZeGjoSsxP8NGlX4Tz44IOEh4czceJEbr/9dh5//HG0\nWm1vyCboJ1SWlZJaXuwROO6qi6SjLpfWLh3XYkFJsvPgUMcCmVvrvYajzip5tPx3xke8ZZH9MmYA\nZ+vMbsrmrwYj8YF+HtdwWiWuiQgp2Qa38c2NNjsR/t47WkvY8W9OSXZph0aG0cTfikP5V/mr1DT5\nsyHxAe6OK3c7tqLR6pjD48K8IVGsySp0S5xYnllAUngQu85XYpVaUq2DfEzyDYpwbyXUluJo66XB\ndTSEyDi7smhX4UycOJHTp0+Tnp5OaWkpFRUVjB49mthYz7cywZXHgW/2oCgv9QgcA9iio7t0zo66\nXFwVk2trmVdzinlyVCwrM/OJ8Nd4beK5MdtAjL/jfM6MMdeU4eIG975mTgWy1SXTLCXbwK/i9Byo\nqCEp3N9tQfc1xfO3h3Pl66sUjgSB1gWlzgy6Yxfr+KvByJBAxwveocpaNuXNIqtmFYuGfcDDQ3fw\nSs45Mozu/c7qbZJcUOr6AvD5+UpZKZ82NaDETnmjFQV2EnUB8n1WNVlZeuws8QH+8rHPG2oZevsv\nPKanOqeItlYcBz/a5vV363xpEIkBVyZdShr4+uuvuXjxIgkJCYwePZo77rijxwUV9E2++XCr29hf\ncCysySfO82AXXSQddbn4UkzOHmWVFhsp17Qs+q3bpaSX1zTv1+QRJ0nNNrid05cCST5yFq1Kgd0O\nQX4K/ue7M4T6qeRO0a1RNssRo1VTWKfgYIWJ6iYrCw7nMjYkUI6Z7DxfiUqp4KXEwWQYTaw4ruBg\n5RYitaHsvOlhRgYXyDK4tu5/8ng+/iqF1xcAk00iCgU5pgZ+21w7tDHbwN3NtTYrMvOJ9Ne41SKt\nySrks/MVVGkCUfxjt5tFmfLGK0x9dJlssXTkdyPiNFc23hsoecGZNPDQQw+xZMkSbr/9dk6fPs0H\nH3zQk/IJ+ji+3GbRg67q8hvsxKm3Ot6c/aJ5TaEnzS+aWx9b7nG+Kfc+wAuGOrdtG5sr9AECVS1f\n7yS9juWj4nhyVCzDg7Qk6XVyPYtFkthWUOamUJyfyffpq+NzsJZXxw1lRUIc1RaJGK2aN8YPw+Yl\n6S3DaMJit1NrtZFRWcu8IVE8OSqWTdcN56pAf0rMjeTXmdlytpR74sMZFxqEZFeQY3qQLy58xQDN\nHj67aYGsbJw4G5am5Rg432Bh8/XuTThXJsSxraCcuYMjeXJULBalik8qGkjLMbglKmiUSg+l+lLi\nYKK0GkYqrJ4ZaWFK0j/9yOtzmXLvA6RcdFe6G402Js8RcZormU4nDWRnZ2M2mxk+fDjTpk3rttY2\nx44dY+vWrUiSxLRp05g1a5bb5waDgU2bNlFQUMB9993HnXfe2eFjBT2HTe3n9U22tb+/s3TE5eL8\n/InUDVhKixka5E+MVs2fz5XzXn4pZptjYqazC4DTXZZb1yBvB/j4XAVRWvc3b+dnc787g85Phd3H\nDBjXjs/OjgQAVrtdduNlGE3sLjZS3WTj6pBA2U3lOr/n2bGDWHL0LAP81LzY3Mbmb8Wh3P/dmzTY\ntEyPvBMbp/hjbqCbmwwgSOXIniuoayTMT+V2b06itGrZookaPJjwqGiWW90H1IVrfGey+ZhIQEVp\nidf09Y7WVgmuLDqUNCBJEqNGjWLMmDHccccdjBw5Eo1G096hHUaSJN577z2eeeYZ9Ho9q1evZsKE\nCcTHt4yw1el0zJ8/n++//77Txwp6jum/eZCU1PXdlnHkK5XWub2yrJSaigqio6MJDI8gekwisVdd\nRYVd4vD588Rp/fj9OEegPsNoYkt+qdxqJik8mDKzlRFBAWwrKOO0qZ55g6PZWlBGrNbz+5yk1/Gq\nVExiYCCVjU0+Yy2uWCU7v8nIRqlQcqK6jvv+nc1Vgf5uHaDlLgit3GHDg1piNW/l/YpDxpUMDfwj\n9121jdM1taRcM8LjHB+fq6DBamO9l5Y8zvNmGE0U1jXyxPF8AlVKpObn2tptecbi3Q3omCPkuT3D\naEJZXscKfbM2apW+LuI0gta0q3Cefvpphg0b1qM903Jzc4mJiZHTLidNmsThw4fdlEZISAghISEc\nOXKk08cKeo5bps+goaGhW95kfaXSns7MpCR9D1Psdewvr+HlhDhAAmsZqz/dxqzoUJLidKTWBsmx\nC2dMZvP1I2Tr5ntjHaF+KmbG6nlyVCzLjhdw34VsEgcEUNZo8UgseOSHXIYGad3OufCHPGID/Cgz\nW5k3JNLDklApFAzQ+MnXSS+v8dozzalolC6mw9GLtaiVQ9he9DphfgP48uZHGBlcwMIfqnjXi5ts\nydGzGBub+PNNCT7Pn2E08Zfzlbzvkuzw1MlCdr75BxJbjRGY+N+/ICV9j9eRDjtNEi8Y6tzcaluL\nq3nrave/MzGjRtAW7WqRUaNG9bgQRqNRnrEDoNfryc3N7fFjBd1Dd73J+kqlfeiDLbw/fiip2Z6B\n+w1j4t2mfDpxBvnbKpqcMyic9/JLKaprolGSyDOZmVtdT7i/miCVEsmOWycAZ6wjLcfAfwwJY395\njZvCWZVV6KaEUrIN1Lfq7OzEqWicLrmXTxdTa1tIvulpHhm6neqmFIyWIEDHaF2g13MMD9I6Jmx6\nIa/WzKs5xWRW17ll1gFsHBtP8pFcsqqruGfN79xrm665hrRPP6KuotwxfiQylgPRMcxptljTPv0I\njV3ColASM0gJeFpFomOAwBdXZKvn/jDDR6PRXHFy+vuIkzh1kK/AvXPxdu0C4NzXV3bZ2hOFhPip\n3SyHlGwDJ6rqCFQp0Wv8qLT4bpnTup9ZVnU9i4ZHuymglQlxJB/1XnApYWd1ViF2u51nf1SQXvEp\nRksAMwf+ilrrGW6NCnGkW+t1bbbdMfkYDHfRYiWrug6tynte0IhgLVazic/ffpPb7pwpb7/tzplu\nP7fmtjtnotFosFgs/O7BB8Ds2YNO4R/QJ767V+LfUF+nTygcvV5PZWWl/HNlZSV6vb7HjjWZvFee\n9yV0Ol2/lPNSeqo1+ohMm5tTvnwtvLnNb/NljRZeOHmOZ8cOkvf1paTqrBLrr/ae5uzamdnX9aAl\nRrK1oIwgtVJOs3ZVOiEqlddpoErs3BUXSW7tPF7OfoTEkDf55pb/g1ppA+JYk1WIwexoazM5MoQn\nj+e7NfHcmG2guL4Ru13yOP/So2fluJGve5CwNw9pO9/p75nzdz7p7nt9pK/P6RPf3f76N9RX6Q6l\n2CGFI0kS+/bt4+abb+7WZAEnw4cPp6SkhLKyMvR6Pd9++y1Lly71um9rF0JnjhX0LJfaU81r/U22\ngaTwIFKyDURr1V6nbrq6sRb9kMeCUyVY6swsyyyQiztbY/GhvEYEt3TR8DVVNCk8iLQcA0V1ZjRK\nFW+16sAMLUonQutHjqmBJ47nMyjAHwk7TTYJi30wf8h9h3prIDMHzmJDYr2bHC8lDiYtx8D+8hrO\n1pqptTpqdfyUCiySHa1SQaBayYdJCW7TRCXsGJtsbG/ucODtHp4/eY7bYwYAoLTZOtwktTUiE03Q\nWTqkcJRKJdu2bWPatGk9IoRKpWL+/Pm8+OKLcmpzfHw8e/bsAWDGjBlUVVWxevVq6uvrUSqVfPnl\nl7z22mtotVqvxwp6n672VDu0by+733qdC4UF1DWYma9SEhSopUGChwc60n+3FZaSXWNmVpyetBwD\nhnoLjZLdI3D/9vXDSfOL5qlNmzm0by9pjy32aOmyOqsQk9W7u8zYXATqbOhZ1WTjoe/PoFUpMdsk\nFg2PaRm2Vmvmjy7NPsE9YL86q5D8OjP/MXAA8wZHO+JJZSaqrAv5fxUrGBvyOsODNlFrtQLDPGRR\nomB5Qiy/ycghLtCfjYkt10rJNnC6xtFOx7XLAsDjx1rGRbu6/oobLMQGaKizWuXtDRYLK5zp0Z14\nQXC1ZO0qNZPunycUjaBdOuxSmzBhAocPH2bChAk9Isj48eMZP36827YZM2bI/x8wYABvvfVWh48V\n9D5daTvvbMB5Z4DEfo2ClYktMZW1+UZ2mewk6aHMbJWVhnPB/7iowiNLDKD4xyxeXryA6DGJhAf6\n02SXeOj7MySGBiFhZ1acw+XqzXrJqzXzp/wy/JWg81PztkuMZ9nxAn6fU0x8oIbqJhvBau89x86Y\nzCw+kofUbEWN1gXKPdBy6z6k3hrAP255hM8NB6lotBHp7znyAFoSCnR+KjdlAw7F9j/fnfF6nNVu\nd6s7ctbtHKioYfmoOHn2z4qTBh4e5O5+7sgLwoFv9nRrd3DBlUOHFY7FYuGVV14hISEBvV4vT/9U\nKBQ8+uijPSagoP/QlXYmzgacqa0Wf4D1Q/WsNfmR5qfngsK9SDFJr2N3sdHrORvrauHHo6T/8D1b\nxjlaw2wvLPPalfm3h3PRqhSE+akZqdMyJEjLhsTBXpt3vjJuiKMQVK0m9ZqhPuMjI3Ut3ZZTsg3s\nPFdJQf1DnKl9Wu6BplbaWJkQxxPH83267pw1PvVWSe447VrwOTzY38PF+FRWATUWK58bjG4Zdquz\nChkd4nAX5qEmzS8aVYydJL1njEtlbWozFvfNh1s9LNkp9jq2/m4NBxMSujQPSXBl0GGFM2jQIAYN\napkpolAosNvtHmOnBVcu7fVA87aIOa0iX8F9P8nGU5s28/LiBdDs+nG+vdskOysyC9wGi23MNjB3\nsKPLwemzpfJCHern+VVP0uv4W7GR9Vc74iVlZqu8eLs29Nx1voJ6mx2NQoFS4eganWE0UdZo8XDX\ntS4GvW/QDdyXsZI6awC7Jnq2pXEm5pU1WnjieD5Nkp0hQf5yy5mnsgpIHhHjlmp92lRPmdlKdZON\nQJWSB/6dTZRWQ1yghl/HhXut/dnQHBPaaLQx7/kNTJx6q9szdaWyxtSmBdPaYnWmnm9KiAO7UVg8\nAp90WOHMmTOnJ+UQ/ARoK4jsK6GgRqWBEN8ZaOcK8ll976/xs0sknztPks6PUrNVtgYyjCaSj5xF\nobAzNEgrL/b7y2t4b0KLO2xFZj5rTxSx/uqr5G3PnzzHSJ2W1GwD+XVmbPaWVjjO/+86X0Gkv6bV\nrJ182cpxBuwL6xoxNdlY3KwcJLuCHUW/Ji1nMcOD/0io+g+MDG7JMnMSF6hhy9lSorR+1FklJLud\n0zUNKFGwraDcI0Y1pXkkgquSW5FZwOz4cHk/X/N/DAoN/+3Sk87XC0KjUtlmLK51OyOvqeeiAFTg\nhU6lRR8/fpyDBw9SU1PDqlWryMvLo6Ghgauvvrqn5BP0M3wVgfpKKFhZbecFQx2/9OJWWnuikDCF\nxPqQ5tUtNJ4VWUXMjgsDWiydEcFasqrriPRXy2OYYwM0bj3FZsdHsDW/zC2by9DgSMR2nQbqzDKb\nHBnClvxSRgUHeFgLUf4aeZtrwP7B78+QpNdRVB/LyqxnqG+2ataf3IO/UknykTzmDYmS93daQ/l1\njW4dmldkFjApQkeNS3Dfye5id2UDkHrNELcWOb6Ut8VuZ/8nOzx+T61fEA5+tM1hqbTCadm0bmfk\nyzoVBaCC1nRY4Xz11Vd8+eWXTJs2jYyMDAD8/Px4//33Wb9+fY8JKOiftHaf1VdWQKjnfgMHhNIQ\nP5hNe/cg2WzM++4MASolEf5qGiWJ1GvcrYLUxKtIy3EoBdcOAhlGE18YjG7xDFf3U0F9I2+1Gkrm\nLU7jzDJbPiqOz85Xel1MXbe5BeclBXd9O5WzdWvlWM3aH8+6ucSeyirgzdwL6PzUVFuayKttQKVQ\n8ND3Z/BXKhgeHMDs+HD+VmzE2KrwNMNoot5Hoaeh3iIr2NYxoQyjia0FZURr/eDHo+x8KZfTmZmU\nnsrymmW2/5MdbcbiWrczyqXcc2fEKAKBJx1WOH//+9959tlniYqKYvfu3QDEx8djMHgPnAquXLy5\nz5LPnYdQz3T1C1XVqE6fZNsE91qWKZEhPl1DShQebpz08ho3ZQMthZybrhsmZ2a54uvN3Nnuv7yx\niUgvdTxOC8K1bc65+oF8d/FZiupVjAiaQYPNwOPHzB4usY2JDmsk0l/N6Rqzm8yrswqJ0qr5uqSK\nM7Vmhga6JwWkl9cQG+C9Di4uUMN+l8LT9/PLWHuikCJzE/H+fh61Qnu2vst2pwJuFXPpyDwiVwvp\n0L69YmS0oEN0eB6O2Wx261kGYLVa8fMTbzECd7y5z+bFhrIiq9Bt26qT52mqrfM6wO1ARY1P19Dp\n2kYPZdHWvBrw7mbydf4Gq8STo2KJ8FdzttbsNhcH4Hx9I2tPFJFeXsPyUfFsL7yb/zq4nSkRh8iY\nthi6tyIAACAASURBVJiYgDyeHBXLiGCt17RtJQoyKms9FOSGxMF8Z6zj2bGDGByoIdzfD7vdzkPf\nn2HxkTyKzRaPOT3gUFS3RISwMiGODwrLWXL0LCF+SnSx8QQEBHq44FYmxBGidH9errNtOjqPyEln\n9xdcuXTYwhk9ejRffPEFd999t7ztq6++4mc/+1mPCCbov3irx2k94ljCzl0xoXxcaQK0HvsXN1hQ\nK/AI9D/14zkmhQfyr7Iat/0rLd7jBbm1Zrk9TOsYUW5tg9wKx+kaO9fQiFmS+O3hXCrMFvzVKupr\n6pn/vaPKX7LbuTlSx4nqejKrB3L/d5vkWI0zA62pedpnWz3QfPU4UwBPnyikobmdz13NyQArMwvw\nU3r2cJOwY7NLbiMOTteaYfAIbk1+nJLn1ni9jkbpPR3aSWcbsopRBIKO0GGFM3/+fDZu3Mg///lP\nzGYzS5cuRavVsmrVqp6UT9AP8VWPE6H1k+tTnHxQ3uD1HGoF+CmVVFiaSD6aR4hKRY1VYt6QSL4p\nu0iTTZJTkjOMJkrNTTxxLJ/YAI1cq7Ix28C8IZHsL69hSmQIUyJDWJmZT6XFkU5cb3MkDsz7/gzR\n/n5uM2ueOJ6Pn0LD2y7jDYobLFxssvL/ymtplBbybeVKlo/aIdfVOLHbHVbHrDi9h5Jzbt98ttTr\nfddZbbwxvqXrwAsnz7HrfAUDtf6crKlnRWY+qdcMdUs8cI6IBsiqaWDAwDgGDgh1xGICvfe/CvKi\n8FrHXC6lL55A4A2F3Vd/cy9IkkReXh7l5eVEREQwYsQIlMoOe+X6DMXFnv78vkZ/aujXWk6PGA6w\nKruEuyKDPFxMK6tVREhNbvs+/EMeA7QaUn/mnj1mbGxiTGgAJ6obSL1mCBlGE5+dr8COwq0WZ01W\nIVa7xN3xEW4WwS0RIXx2vtJNsazKKqS4wcIHN7q38Hc9xjU5YXdxKOtOPUdswADmDl5JXu0xN4Xy\nVFYBhvpG4gP8idD6UWFuwmSTiPRXE6xWkVlVR2JoEKdM9YzWBXoUe1Y2WkhplSjhTGIAhwKqtVoJ\nVKsobmjit0Nbst5WnDRgUWv4w6iWaasrCmuw11aT5vIs154o9EjIcMRclrvFZVr/DlMuSkx9dBm3\n3Tmz3343+yL9Rc7YWM/C6c7SKYVjtVrJycmhqqqKn//855jNjq65Wq2nS6QvIxRO9+FLzkP79pLu\nkm4bNfpqSloP92pe5AB538oaE+fP5rF13FUe51xwOBeb3e42TMxbphm4L9Lg6C9marK5HevkieP5\nvDbOs0bGWTS6IiFOrqtZd2ohT476kIeH7kClkMgwmjhQUUOOqQGrBH5KKDU3EapRs83LtZxyPXb0\nLL8ZHMmBihrZNXZLhCNRonVHhFdzit22LfohjxE6LZH+asobrShRkKfQEBgZReoAzyy2JefrsVcb\nGeGnlK+z0ySh1UcQHqLDpvZj8hz3ppsvL17Q0mPNVX6/aNZv/7hffzf7Gv1Fzu5QOB12qRUVFbFx\n40b8/PyorKzk5z//OSdPnmT//v088cQTlyyI4KeFN5++c7iXt87CzuLQnS89T5jd+9AyjVKBnY4l\nC7imCQNYJUgMDfK6r0XyHWtRKRTNdTVrabAFMCf+LhYNa+ns7KzDeeSHPK4ODSCzug6VQkGD1XNs\ngGsXgqsC/T2abgIcqHCPTTnlcEWhwE2Zrs4uYd7zL/qsnxkxKJ5JTz0tK/UDaj/mtNPVuSt98QSC\n9uiwwtm8eTNz5sxhypQpPPTQQwCMHTuWd955p8eEE/Qv2vP5txdY3v/JDqLMJvCR+jssWEtre9xX\nYN41TXhfeQ0haqXPfYNUCg/lsCKzgOzqBqyKRbxzdg3jB2ziqYSP+PuFCmCwxzlG6rSyFeIcpXCw\nwuTWpXmqSx+0yZEhHm1x1p4opLzRfUFv3SoHHDGiJUfPMjxIi4Sdi1od+z/ZQcHp05AQ5SHbmZxs\nJgFPbdrs9f690ZW+eAJBe3RY4Zw/f57Jkye7bfP398disXS7UIL+x6XOwgHHW7VdoWBShM5DAazM\nLMCOnSC1yq1if3JkCE9lFrCxVT815+K+5OhZ5g6OZNf5Crn32a/i9HKx5pnaBm4KD2a0LtBtEY8L\nGMWe0pfxUwZxT/xdhPqdYWtBAwFKpVwn5DzHSVM9Q4NalKSzeDTUz9FNulGyeyRLJOl1fHa+grQc\nAzmmBiQ7hGlUzB8aLWegnaypJ0yjcq/jyTZQb5NYMrRlTIKxvI4V1jIyIgO8WlXzIkPY18nfRUdq\ncQSCztJhhRMREUFeXh4jRrT0p8rLyyMmJqaNowRXCl2dheOKVaUGu90j9ddoacLUZHPrFLDseD6b\n8kpIDA1kbGiAm7JwtSSc3TF1ajXPjh3EtsJS/mKo9Jgtk1VdwdzBkdwQFsKOol/z/KmFTBiwiR1J\nf0OlkIA4OcPsm7KL7Dpf6ZaosDqrkG2FpcwbHA04am1mx0fwdUkVcwdHsiIzH0BuAmqy2rglUse8\nwdG8mlNMrdVGYX2jm5vN2cvNNQU6t7aBX0S33N/W4mreutpRUOv63AwNFuJcrKok6NTvQgxXE/QE\nHVY49913Hxs3bmT69OlYrVb+8pe/sGfPHh555JGelE/QT+iMz9+X6y16TCL/+ve3HunNj/yQxzut\n2tK8Mm4oyUfOckuEY58ys8Hr+IFGm50/nrnAjqRRgGOujrfZMmtPFBKrHcX93z1LvTWApLD/YEeS\newB+Q+Jg1p4oxGixsum64R6fLTl6lnnNHrK8OjOVlibKG5v47HwFpiYbw4ID3KyPF06eI8NoQsLO\ns2MHMf/7XLdzOhXIu2dLCVGriAvUcHOEjvJGK/970kDwoCHEDLoKkNyOSdLrPBINfP0u2kLU1gi6\nmw4rnOuvv541a9bwzTffMHbsWCoqKlixYgXDhnlOKhRceXTU5+/L9XY6M5OCf+xmq0uH5+WZBbyS\nbWCAxvvXdESwlq9LqgCI0qp5+IdcxugC5bkx/yipIkyjQmNVkJptYHJkiNckA8mu4EjVXH6RvobZ\n8X/ivwa+z7aCC/z/9t48Pqry7P9/z5LJvjBZTUCWhEAQECyCGpWloN8qttCiVGyJgKCAVtGwWfBR\nEBGIIqIosopYkaqlVvvTwqMsD1j2GBBIGEgiBJMwGbIvk5k5vz8mc5iTmUkC2cP9fr36snPm3Odc\ncxLmk+u+r/tzQazLuSUWK6UW90UN3jWbKeedyGaw3p+krpEsT8+hqNpCbIDOpZrupT5dmHHsPEnd\n7GXMflq1S3+bHTkmpvWwZ037jMUUVFnJVHmTtGRJnS0GahcagFh/EbQ+DRIcq9XKc889x5tvvsnU\nqVObOyZBO6Shc/6ept6mbfuID25R/kX+cOdQtl8oIMrH/RelIzOYeewcXf19WOfUnXPuiSyumC18\n4HRs/olsjLUW5S+U38TsEwu5XKXl67uf5IPzu3nXYKabv/sunF38vKl2759JodlKSkYOY2P07Llc\nzEFTidxkLVTnvpouwkcrZzLd/L25JyyIGcfOE+Gj5VxFNRqViv3GEllEd+NHUj0tBl7JKaXUW1n9\nJtZfBG2BBgmORqNBpVJhNpuFd5rALQ2d8/c09aa1uBaf7L1czBu3duPD7DxmHDtHXIDv1S/ey8Vy\n9ValTXLpx+IwyXRmab+uzEnLZHl6Dsnxnfn459/zxtmn6BXwDqsGfELPAD9W9O/GrB8ziQ/0cdvc\nbVh4EPmVBS6ZyLwT2QyNCJTXcIboA+WWAWab5LFCztGm2rnQYYg+kKQTOfSMCuWlmKul3C+m59J7\n3BiXyr/az/03c2a5HBPrL4K2QIOn1B588EHeeustxowZQ2hoqKLTZ2RkZKMDSU1NZfPmzdhsNkaM\nGMGYMWNcztm4cSOpqal4e3szY8YMune3b9abOXMmvr6+qNVqNBoNS5cubXQ8gmunIXP+nqbeKtzs\nhdGoVDW2Nco1kxdPZNMr6KoxptbDXhw1rsejfLyJC0jg7t0vU2rxZfRNv+N30XmKSjCT2UJepQWQ\nFAv2DkFwNEZzribrHqCTxab2/S02G+dqTECdhfH5HzO5YrYw4b/pRPh6sd9ot9C5NzyIAJ2XQmwA\nXusVRcqZky6fydNzFwIjaGs0WHA2btwIQFpamst7n376aaOCsNlsbNiwgYULF6LX65k/fz6DBg2i\nc+erdvbHjh0jLy+Pt99+m7Nnz7J+/XqWLFkiv//yyy8TEBDQqDgEzY+nqTev0HCXrOFsaQVWyTV7\nea1fV2YcO0/vwBJ2Xy6m3Op+jsuGpOhXY7HBD6YJfJD5Ijf7rqKn/2pe69sDuFoVtvdyMRrshQSO\n9gPJva5O9c07kS23HHAWqZnHz8vrRI7jJnM1fzqYwU2+OsZ1DuXzi0ZmHj+P1SZRZrX7uQ2LCFJ0\nMAV71Vyl2X1GJDZeCtozDRacxopKXRgMBqKiooiIsG9aS0xM5MiRIwrBOXLkCEOHDgWgZ8+elJWV\nUVhYSEhICADX4NAjaEU8Tb0NB9YvnMeMY+fxVquI8dPR2c+LrPIqZZOzmim1uAAf/plj4rK5mpGR\nQS7TX6+cukBORRU2yS4eji6cWeVqBoX8hq1DrBw0hctZh3NvG0fvnNrl2RklFVTYbC7uAGB3aX4+\nPlpuHfD3iwUUmS3cGuKv6A7qYMHJbF7t25UVtbIesMc7Je2C2+cnFv4F7ZlrajHdXJhMJkWvHb1e\nj8FgqPOc0NBQTCYTISEhqFQqFi9ejFqtZuTIkYwcObLFYhdcOx6n3ha/zoalr3I59xKVVhvdA3yQ\npGqFeSbYMwBjjcnlgpPZ9A7040DNrv6ccjNVNomkbuF8cuFyTb+a35OSMZ2nemxh6+0f85bhZyBG\nISgni8rZXON95rze4pzJzDx+nk41ay61MZRW8mF2Hipgc1Y+ZRYbM+OiPDaR09ZUtHmy5vHB3nrb\neVpNLPwL2jsNFhyLxcJ//vMfTp06RUlJiZxRqFQqXnnllWYL0BlPWcyiRYvQ6/UUFxezePFiYmJi\nSEhI8HidwED3lu1tCZ1O16Hj3LdrJ7u2bpaznJF/epz7Hvot+z//lFk3+/PXk9nM6RUj/9cZx74Z\nAL3Oi72Xi1lbU43m2Cy553Ix5dVd3farcfZZc4jJ2ZKrbRLc9c5JTsui0mJD0qjkHjoOlqXnMCTU\n36WD5/L0HC5XuXfiyK+0cNBU4rGYoLuPhoKgTrzlE47aYsam1fHAnCTuGTmqoY+42ejov5stTXuJ\nsylosOBs2bKFEydOMHLkSLZt28Yf//hH/vOf/5CYmNjoIPR6PQUFBfLrgoIC9Hp9g89x/DcoKIjB\ngwdjMBjqFJz24MzaXhxkrydOt3txVrxKRUUFVNkdyEN1Xor/1kZfc9yGRE7F1S/1IfpAbJKK5emj\n+bFoPrPd9Ktx9lkD+Da3kLgAX8U1ANl2RqtSK1pFP3v8PBMPnWVAiL9cTOCpxfWCk9nu7Wa6hfNB\n5mVCvVTMO5HN605jHRVrB4ICeGH1e4prtoXfiY78u9katKc4G0uDBefgwYO8+uqrhIeHs337dh58\n8EEGDBjABx980OggYmNjyc3NJT8/H71ez4EDB3j22WcV5wwaNIhvv/2WxMREMjIy8Pf3JyQkhKqq\nKmw2G76+vlRWVpKWlsa4ceMaHZOg+ajLBkeqqWJz/OVfV9fMOWlZgIS/9uq1fi6PZpVhIUXVOu7Q\n/4YSywW0alfHZkfZ8hWzhSV97U3canuknSmuQK2CNbcpNzevGtiDyYcNhHprOFhQyqUKMyazRWFt\n46DMYsNiq2bGsfOoVBKdvLQ8FK1niD5QbkUwOy3TbTXcPrFeI+hgNFhwzGazvIbi7e1NZWUl0dHR\nZGZmNjoIjUbD5MmTWbJkiVwW3blzZ3bu3AnAqFGjuO222zh+/DjPPPMMPj4+TJ8+HYDCwkJSUlIA\ne7Xb3Xffza233tromATNR3mBEYJdj2ss1SROSGL5O28wNDyIRacucH9UiNuumVbJvpd+XE23y2lH\nzlFpm8Z/TXO5LeRdYnxX8nRcOF9esjD92DnUKhX+GpWiKZuhtBJbjaAN0QdypqScf+aYFA7OL57I\nVrQ5cFBcbeFMcaWiXLu2nxrYsynn5mn3R4XI13K4AYzrHOZSDVfXeo3oxClorzRYcKKjozl//jxx\ncXH06NGDzz77DB8fH8VCfmMYOHAgAwcOVBwbNUo5Xz1lyhSXcZGRkaxYsaJJYhA0ntpfhpEJ/cg7\nfUL5+sLPENzZZaxV68Wdw4ZzJi2Ndz9cj8pqYY0hF6tkY+Khs/QI8CZAq2FMjF7+0n7sv+mUWLpy\nqepLon068fXd0+gZkEXSISt7LhezpK9yTcWZMJ2WXyqvlhnnV1oUYgP2EmzHBk5n/LQalym02n5q\nDrNPBy/16SJfy94B1b5xVWG6qdIR06evx42aTeHKLRC0Fg0WnEmTJsntpCdOnMj69euprKxk2rRp\nzRacoH3h7stw/vYPGRMZbP9StcCc7VuwVFW69IKZn57L2EX2HfIn9n1PX38dQ8PD+PKSiXKLDbMk\n8ZuoToov/h8KSim3PcWZ0vnM6rlVsVbjr1W7FBsMDQ/iw+x89htLOF1Sjgq7/5kjg/JUMZZVVqV4\nPTstC1+N+3MlSeLNjEvYkJCcnK8dZFZUM+PsZToPuos9PxsYUnN8iD6QveoAHp35fL09gxrryi0Q\ntBYNFhzntgTR0dG89NJLzRKQoPlprikZd1+GSxM6KzKE5Qn2XjH3hAXJZcz+WjVS9M1yDOV5uQwN\nD/KYoQzRB3Kh/CZeSJtDmK4Tj3Qew/TYcsV9I3y8FPt3fqmsIlCrZc1A5RTYpcoqThWXM/HQWQK9\n3Jc8h3hpmHb0HDq13X261GLFW6N2e65KpZJdmmtb6wB09/UiuWc4y382EHXvKFLOnJQr9f5f8hQG\nDLmjzmcsOnEK2jPu/9U4cebMGbZu3er2va1bt5KRkdHkQQmaD0cWMtuSzyzJxGxLPrvfeYMfdn/f\n6Gt7+jKsbTGjRsUQfSDJ8TGsHNCdEJ2WsMgoOb7K8jLWn89zWw69OdNI0uGR/HrvZvw1u/jizilI\nnFacd9BUQuqVUt47l8svlWYMpRVUS5KilBnsU2D9gv3ZfHtPIny8GKz3d5l2m38imyqbjSndIyiz\n2Ijx0+GtUTM8Ioj5J7IV5zpcosG+sdNQUqmMPy2LcG/733hzOqnJP3OSuWvWkfzBZuauWdegkmeL\nxv3fiGJDqKA9UG+G849//IP777/f7Xu33HILX3zxBfPmzWvywATNQ3NOyXjySattlV/79UUL3Na7\nL/PH/56yi9lEeanJtVhdHAZ6ByaQVryDUF0nvr77SZad2cXRwkjMNhuz0zKJ8NZRUFVNToWZXoF+\nii6gM4+fdxuzQwxT+tvNPoeGBzH1iIGAmv4zvYN8yK+0sO1nI1qVit9EdWK/sYSkrpF8mJ3HzON2\nZ4RSi42CSjMR3l7MOHYef62KSd0jFNVnf+gcyj7j1XLs68lKRCdOQXumXsHJyspiwIABbt/r168f\n7733ntv3BG2TpmiU5gl3X4bzTl1kbNTVkjRHWbIz1UGdyN27k1c7qaGPPat56ug5vs0t5P6oEPbk\nl/BTSRJrzyfT3f9tvrjzW7RqK1N7RLIhM48p3SP5NreQ2b1iWJGeQ7HFKouNQ7R0KpWL1xkoxS+r\nrIrk+EA+zMpn5YDuCrsbB86bOZO6RsoFAgBPHDGQGBZIYlgg2342uvitAQrngevJSkQnTkF7pl7B\nqaiowGKxoNPpXN6zWq32zXqCdkNjG6WB52ood1+Gfcb/mn1nTnLAUk1BcQlVPsov4WUmK0gSqvxf\nePPyVa+02AAf7g0P4l+XgjCUfUS5xY//756nWJ+5m6OFofKX+cbMPD67aCTCW8ebGZfIKq+SbWM8\nCQbY14Fqi98Vs4U/HcyQ12f21ox1zrRUQLnF5nYzp16nYZ+xmOT4GL68ZHL7jM6VVbIiPYc8nwAe\neXGW23PqQ3TiFLRX6hWc6OhoUlNTGTx4sMt7aWlpCoNNQdunsY3S6pt6q+/L8Ifd3ysE6aahfbmy\nfQuzE5SiUFBp4V3DGNKK5/Fk94/kCjRHnxuHaBVVWwnVeSm6aU46fBa4KhiKz9ArhpnHz7P2XB5P\nxkbK15l/Its+NWa1EeTkc+ZOtF48kU2kj5aZx88T6+8jb9bcbyzhvAVWqvRUd9GwKKdY6YWWnsPE\nrnbHgkU5ZR6fkUDQUalXcEaPHs26deuw2WwMHjwYtVqNzWbj0KFDbNiwgaSkpJaIU9BENLZRWmOq\noVym6B6ZwJ5PP+b1BKUoPNplEL89kAxSAL+J+h0VVgMrz0rydNilCrM9S6gyowKXvTPDIoKYm5ZF\npI9rVg52Z+fTxeV8mHWZTZn5WCSJe8IDWdqvK386mEFStwiWp+egwr1oOdojONvdgL0FtE2SSJyQ\nxJ3DhsvimnPqJDGSWXYQAHgpxl+UMgtuOOoVnLvvvpvCwkLWrFnDqlWrZN8frVbL+PHjufvuu1si\nTkET0phGaddbDeVpiq5CrZVdB2ySSu7CGa57i37B77O0XxfAXmbsmA6L9rXv3p98+Cw+GjVvZlyS\np+KG6O1dN8cdOKPY1OmMobSSJ3rYs5sFJ3/moehO7LlczIfZeQTrrrZ8/vyikXKre2sdq6RsU7Cs\nZm0nqXMYe2uExPG/lGmPM0tynWITpcyCG40G7cMZPXo0I0aMICMjg5KSEgIDA4mPj8fPz6+54xO0\nEk1dDeVpim5GRh4Eh3Oh/CZmn1hIucWPv98xldfP7OLNW+OU5/eK4amj55jUPYKDphIifXQu7sxg\nX5/ppNPyRI9Ikn/MIuXWbvI5809kE+OnZe/lYvYbSwj20vD5RSPL+3dnxrHzdPf3lq8xRB/IE0eU\nbTIcSKhIOmxAr9Ngtkn4a1Q8XGObc6CWkDS1eAsE7ZUGb/z08/PzWK0m6Hg0dTWUpyk6q86HP/x3\nBOdK58trNQt+Oo9Fct/F07Hf0pM7c0pGDp9dLODucLtgvJlxiRnHzuOjUVFUbeXe8ECyy8zM7nN1\nmmxuWhYHTSVISC6tCcJ0WrcFAladN126duPVINfPVVtIRCmzQGCnTTRgE7RNmrIayt1f+RfKb+JQ\n5io6qQN4oGat5i2DxNgYPe8act1ep7u/D/uMxR5taE4XV2CTJAqqdDxxxECQViO7Pa9Iz+F4YRlv\n3ap0f17WvxsLTmZTbrEpfc3KzVRJNsaGh7q4OWsi47j3kQkuQpKcXYRPJy0p0x53KSUXpcyCGx0h\nOIIWwfmvfJuk4tXTD7Ll52eIDVzDiNC1DIvwZ4jevlZz0FSCr0bl4rfmKGPeb3TfuOygqYROXlrF\nFNry9BwOmux7XzLLKnEvU1BgtmCx2UhOy+LhzqFIEgTrNGSVWfjsopEV/bvL576SU8pvHpngIiS/\nFBYRpNXyUpAFJJNLKbkQGMGNjkry1EazA3Pp0qXWDqFe2lNTpobG+cPu7/nbW5+xO+15rFZfRt/0\nPOHeBqySRH6VmXE1ayAr0nOY3SuG2WmZRPro5MzinjB7UcBTR88R5KUGVCx3chOYetTAul/Fudx3\nwclsgry0FFVbCPHSKkqoHTx19BwqFVwqryI+yI8VTtedk5bFhSoLUSHB+EdF89unnnYrHq9Pf4LZ\nlnyX4ylekcxds67e59MRf+atiYizaYmOjq7/pHoQGY6gRbDZ4NtdPfn38Y1083mLETEbmNc7CucK\ntM8v2nfnOzp4uusTMzsti0ndI+wW/2lZiqmuGF/3ZdBlFhuv9o1hVmomv412bR89Oy2LIC81hdU2\nwnx0CrEBWN6/GzMz8nl79w91fkZhrCkQ1I0QHEGz8/PPGl54IYRzp0r4+i57Bdq83q4VaJMOn2Xm\n8fOE1Lg2O6+nqFFxrqySwXp/9lwuZog+kFBvL7m5GdjXaNxhrknizU7tApyF6mJ5FYFeGlSAnwcX\naFtFOa9Pf6JOex9RjSYQ1E29btECwfVis8HLCy4yfKgPatMW7gr7DT0DstCp3a+k+GjU6FQqfqm2\nMeeniwCyq/SFiipMVdUcLCjFWGVmwclsCqqqedHJsTnCR0vyj1mKaz7/Yyb5Nftx/DT23jeOaz4f\nH41VArNNQh0WwdpfxRHkoUVBqEZVr7P20PGPsfyKsrpumcnKvY+IajSBAESGI2gmdmw/xCsvx2Ct\n1PL1HVPoGZDFinS77161zf2yocUmEROgY2V8DB9m5/H4kXP4aFRUWqwMjwgi6dbuzE7LJLzGFbrY\nYiVAq2HG8XMYq6rp7OvN+C5hcvaSUVLBnWEBlFRbef7HTMZ3CeOzi0ZFdnO+tILYW24hPDSUg/nn\nMNtszDuRzetOxQqvnLrAQ9H2zp112fuIajSBoG5E0UAbpb0sJNaO02aDRS9d5KOPenJb0DtsHfyV\n3IXT4UsW6aPlZFGFYq1kdloWhdUWnugeCeDiXzb/RDZXzNVIQJi3F+UWG9G+Otld4IUfM3mkS5iL\nO/OCk9m82rcrTx41oEJFiJeGEquNcG8t+ZUWylRqZqas4kxaGqe3f8jShM4cNJWwz1hMZlkVnbw0\nPBStV1x3pUpP8gebm/1ZtlVEnE1Le4mzQxUNpKamsnnzZmw2GyNGjGDMmDEu52zcuJHU1FS8vb2Z\nMWMG3bt3b/BYQfPyw+7v+XLjLr5PfZbCYi33hT+ITp3OyrNXRWGIPpAzJeXsNpVTZZOYeOgs/l5a\nCi1W1EjEeHvxYVY+VVaJ3kG+HDSVyF/0S/t1ZeLhDPoG+bt1f37j1u4KU0+Hw7PJbGFFeg7Gymo0\nGjVVNhteahXZZVWEe2vxD7ZnLnmnT7A0wW5E69xWwPmaDsSajEBwfbQJwbHZbGzYsIGFCxei1+uZ\nP38+gwYNUjhRHzt2jLy8PN5++23Onj3L+vXrWbJkSYPGCpqX/d99z7svZZN2cSUP3rSOoOBV1WRg\nYgAAHwlJREFUNRVo9j8InLto5lVa2DTw6p6W2dnFhNosvNpdLx9bdOoC+VVmtv1s5MtLJn5bk2F4\nqdRu3Z8douBopubO4XlOVTW3BPuSX2lBo1Kx31iEBOgLC3gv+S9460Ohq7JPD0BOrSIA4RAgEFw/\nbUJwDAYDUVFRREREAJCYmMiRI0cUonHkyBGGDh0KQM+ePSkrK6OwsJD8/Px6xwqaj6wsFc/P6ktY\n9a18ducT7MjZ77LPxSEKkoSLYERUlric/1KfLqRk5MgVaMvTczhTUo5NklyMOuFq184zJfY1IncO\nz8v7d2PGsfOsua0HH2bnER/op7DGmZOWzYfZFSR1jVSM8+/SjRQvvViTEQiagDYhOCaTidDQUPm1\nXq/HYDDUeU5oaCgmk6lBYwXNw8cf+/H66370iTjElm7r0aqtHi1nzpeb0Xj7uBz3dL5ztlJUbSG7\nvIo+QX4khtmnu5yNOm1IzDmRRWJYACkZOfxSaXZ7zbgA+/0PFpSy5rZYxXvL+3dl0mGDQnCWmaz8\n9plZQmAEgiaiTQhOQ2mq+obAwMD6T2pldDpdm48zOFjLd99Z+HDRv9BW2gsD3FnOAIT37E1IaChU\nKgs2PJ1vQ5Knxpb0dXWEdmRNf79YQH5lNSFealksPO3HcbST9vGw1ybQS01KRg6X1N7c3PdWHpiT\nxD0jR3n6+E1Oe/iZg4izqWkvcTYFbUJw9Ho9BQUF8uuCggL0en2DzrFYLPWOrU17qAhpD5Uro0fb\n40z8w3jZJ6222zLYM4UHn5kB4GJ2mVZayey0LEXFmsMzzVPHTseaTU6FGa0Kfh0ZxL9/KWRFeg4a\nlYr8KjOLTl3gpT5d5HEOjzSASqt7J2qrBMnxMaxU6Xlh9XtAy/6utIefOYg4m5r2FGdjaROCExsb\nS25uLvn5+ej1eg4cOMCzzz6rOGfQoEF8++23JCYmkpGRgb+/PyEhIQQGBtY7VtC8KPafhOoxaopY\nUKLGy2YlLz+PoNAw9nz6MUPHP8awp19QmF121XfioWAvxd4ZGxJze8Ww3+j+H6Fjui3GV8c9YUFs\nzsznliA/xVrQ7LRMJh0+i06tpmegD510ar7IKWCIPpAYPy/mpmWxrJZfWrSv/Z+DqEITCJqHNrMP\n5/jx44rS5rFjx7Jz504ARo2yT2ts2LCB1NRUfHx8mD59Oj169PA4ti7EPpymw1OcLh0+geVXbAx7\n+gVZoDyZXS44mU2ITktmWRXvDuzh8n5KRg5WCTkLAtwaci44mU2wk1mnwww0s6yKiV3D2WcsVhiD\n7jMWYw2LYvgzya2ybtPef+ZtDRFn09Kh9uEMHDiQgQMHKo45hMbBlClTGjxW0Lp46vDpvEvfk9ml\nXufF8/HRHDSVuLQomH8iG6tk4w81ztKesiDHdRLDAnkhLZs3+nflJh9vno+P5s2MS4q9Ng62FVTy\naCuJjUBwI9BmBEfQsagtJo6NmL+o8mUTTE9ml47F/SH6QN7I+IWkI+fwq7G40apUrBt01fjzbGkF\nPfxdq98c19lxuZRfLBIpGTlkllUBngsVYvr0VYjND7u/Z8+nH6O1WlyaqQkEgmtHCI6gWXAWE5eN\nmJZ8lr/zBlH3jmLRN1/yUoy/PM5RMAB2D7Oufl6K5mdTDhtIycih1GLlcpUFSZI4X1rpUqjwQloW\nGiT+0DmM/SpJzpiWp+cw1ENhg/OGTpcpwVrN1AQCwbUjBEfQLDh3+HRbbdZJzYIDe6myWOWCAZO5\nGkNpJV4qFfuMxZRaLAqxAegd5KtoSQB2QVtzLpcpRwzo1CrKbDCzR4Q8ZeZY53G83mcsxlRVzeTj\nWXTt0QP/sHCXDZ0NmRIUCATXhhAcQbPgXLn2i8q1MACgLPcSK3uGA1ctZd7MuESFzYKhpAofjZpJ\nh88SF+jNX3vfDMC94UHMTcvi951D2Xu5GI1KxdnSCpAk9N5aSi02NCinzJxLtR1rN8tMVn5fx3qN\naKYmEDQ9QnAEzcadw4Zz57DhvD79CbDky+s4GpUKqyRhqnLdD3PsSgkxvt5sur2nfGxuWhZLzvzM\nX3vfzBB9IBsz8/j8YoGivfQLP2bKLaoBhROB49jMs0bi4uPrtKhxrNtcykhnhc2ssNABUTItEDQG\nITiCZmfo+MeY/dorhFeWKA01T+coHKEBtGq1Yn8MwLL+3Zh82G5XtOBkNlU2SSE2YHeLXnAyW76W\n8wZRgL3qAJJefqnO6TDFuk3PcEApXMK4UyBoHEJwBM3OncOG8+V7q5kTpHRjXp4Qw4yfLioEx5Pt\njLdGRUpGDmabjU5e7n9ty2o5CORUmHk+I5/oW/rx/5KnMGDIHcDVLKYgP49io5HIyEj8QsMoMpl4\ntfa6Ta8YZmbksy8yThh3CgSNRAiOoEUIDQ4CyeRyPLLzzSwo0VB2IYsYrWfbmSqrRHJ8DPPSsiis\ndr+OYq7VSbTMYsNfpVJ48DmymKFSGXsuF/N6rxjABpZ8Zl3MgT6uG0jjevUmec26a/i0AoHAHUJw\nBC2Cpz032efO4a1RodeouCdMT5XNSnJaFilOU2bJaVnEBuo4aCpBrVLxVGyUa1lzeg4Wm032UztV\nXM494YEkdQ0HSz4pKUuomPm8XH22It21ci5a4z52sW4jEDQNQnAELYJzmbSDeSeyebKbfaH/oKmE\njZn5eKuhwibJpdI2JIqqLfQLDmR9Zj7rfnW1rYDjnHNllVRYrIR5eyksbpanX10jSg5RkbL9b3L1\nmbu2CPeGB/Fi+i+81usm+ZhYtxEImg4hOK3IjbST3blMOufUSapKSkjqFi6LzZ7Lxaz9VSwr0nPc\n+qI9ftiAj86LFek53FuzMVSSABVUSFAFLoUEtQsHNJZqOdNy5zYwRB/I55oQUrzCRMM1gaAZEILT\nStyIO9kdZdIp0x5HSj+p2JjpmN7y1JCtf7Afz8fbzQNnp2USqNXK7QcOmkpYez7PbTfQUotVvoZV\n68XQRyaw/J03PLoNjBMN1wSCZkMITivRkXey15e5WTTamvTEjrPI1NWQzUGEt07OghzZ0UYnfzXn\nUubLVfYptBWFEkNnXs1W9m7/GyZymXnWSEREhFu3AYFA0LQIwWklOupOdneZ26Llr/L3d1cR3SkE\ni0ZLZEI/TpwzyBmGs8i4beDm5K8GSoGqq0nb7svFVGm8SPGKVJRFOzItgUDQsgjBaSU8VW2194qo\n2pnbQVMJvqXFXDHmY8nTcW94EHsu59Jv9FhO/t9uZmb8QqVZIvlENin9uspTYY8dzCBAq0atUqHX\nKX9NrR6yI2dyKsz8sUsYmsg45q5Z1256jggEHRkhOK2Eu6qtjlAR5Zy5ubhEQ41bM+w7c5I3tv9D\nPr7p7VXM2PYhcV5qciur6O7vw6t9b1aMA/s02cXyKrmFtMdWA746duPH8Efa9/MUCDoSQnBaCUVb\n5g5UEeWcudU13aUJVU4dTvrLs/Tu35+92/9G2ckTLO8V4TJu5vHz7DPanZ8vVlQx5YgBqyQx90QW\ny/p1k8+dn/4L1qibGTfjL+3+eQoEHQkhOK1IR1xLcM7cPE13qVG5nTp0rmJz50oQ6+8jV6qlZOTI\nbQpmp2Wy4GQ25Tpfom/px9hFotJMIGiLCMERNCnOmVum5L4tgcFs4/E6prpqN29zOEynFpbxl+Pn\n6OLnQ0HV1QxpRf/upGTkEHJLP+YKCxqBoM3S6oJTWlrKypUrMRqNhIeHM2vWLPz9/V3OS01NZfPm\nzdhsNkaMGMGYMWMA2L59O9999x1BNcaQEyZMYMCAAS36GQRKHJnKD7u/d1mnmp+eyx2PJsnvuyuf\ndmRJDr+z2mtA94YH8c8ck8Jp+qIFJoj1GoGgTdPqgrNjxw769+/P7373O3bs2MGOHTt47LHHFOfY\nbDY2bNjAwoUL0ev1zJ8/n0GDBtG5c2dUKhWjR49m9OjRrfQJBJ5wt07lmO7a9PYq/vvJh8Tp1Fhq\nNmvurrXx9b3kv7BpoLLjp2MN6LV+XRUuAgFdutUpYgKBoPVpdcE5cuQIL7/8MgDDhg3j5ZdfdhEc\ng8FAVFQUERH2heTExESOHDlC586dARRuwIK2hbt1qh92f8/pzz5mTd/O8jFH9dremo2vdw4bzjY3\nmS7Y14Cc/7vMZOW3z8yq073hvod+29QfTSAQXCPum4+0IEVFRYSEhAAQHBxMUVGRyzkmk4nQ0FD5\ntV6vx2S6uqj8zTffMHv2bN577z3KysqaP2hBo9jz6ccs7RWlODanVwz7jMWKja+VuC86cLgOnDZL\npHhFMrymVbQn94a92//WxJ9AIBBcDy2S4SxevJjCwkKX448++qjitcpDVVNd3HfffYwbNw6ATz/9\nlC1btjB9+vQ6xwQGBtb5fltAp9N1uDj37drJrq2byT39k9v2zWpUqLx95evdO2EiL360jtcSrmZC\nDteBZek5hHXtzqsfbZPf88Z9pquTbO3iebaHGEHE2dS0lzibghYRnIULF3p8Lzg4mMLCQkJCQrhy\n5QrBwcEu5+j1egoKCuTXBQUF6PV6ebyDESNGsGzZsnrjaQ87ztvLzviGxllf+2aoqV77wyPy9R57\ncjrmKjOTt6zHD4lyi40ArYp9RhgWHsSBoADFvas8ZERmlRqz2dzmn2dH+5m3NiLOpqUpRLHVp9QG\nDRrE7t27AdizZw+33367yzmxsbHk5uaSn5+PxWLhwIEDDBo0CIArV67I5x06dIibb77ZZbyg9XE7\n3VUzjQbK6jVnJv3lWRJuH8I7A3uw8fY43h4YS3J8DEP0gS57eYaOf4zlV5QdQ5eZrNwrqtcEgjZB\nqxcNjBkzhpUrV/L999/LZdFgX7dZu3Yt8+fPR6PRMHnyZJYsWSKXRTsKBj7++GOysrJQqVSEh4cz\nbdq01vw4Ag94Miu9hN1cs67Nmg21Aeqo7g0CQUdBJd2AJV6XLl1q7RDqpa2l2Z7KjRsa5+vTn2C2\nxXUjaIpXZIM2a/6w+3v2OgnJvY9cm5C0tefpjvYQI4g4m5r2Emd0dHSjr9HqGY6g7dMU5caespSb\nhvbl9elP1LtvpiPaAAkENxpCcAT1UlezuIYKjrvprpuG9iV3784bquupQHAjIwRHUC9N1Syudpby\n+vQnOmzXU4FA4IoQHEG9NEWzOHdrQB2166lAIHBPq5dFC9o+jS03dqwBzbbkM0syMduSz+533qCg\nqNjt+e2966lAIHCPyHAE9dLYcmNPa0BziiSWX7G5LXcWJpwCQcdDCI6gQTSmSszT1NlNIcEkTkhy\nETLAY1WcEB2BoP0iBEfQ7NS1BuROyEQxgUDQMRGCI2h2GuoU4MBTRlRmvNygPTsCgaBtIgRH0OzU\ntwZUe72mqKgYglyvk3fxZ9YE1xQviGk2gaDdIQRH0CJ4WgNy52Iw+0oxi0o0vBRztQHbvPRckm5S\nOomLaTaBoH0hBEfQqrirYFvRNYg5RRpSvMLkjIhwiSF61/YDYs+OQNB+EIIjaFXqqmBLdjL1fH36\nE+DG/FPs2REI2g9CcAQthru9NQ11MbjWwgOBQND2EIIjaBE8OU5H3TuK5c4Gnnje/Bl17yhSzpwU\nvW4EgnaKEBxBi+DRcfrMSYY9/ULDNn/u3cmwp18QIiMQtFOE4AhahLqMOsXmT4HgxkAIjqBFuBbH\n6R92f0/WTyd5U2XBKkncGx7EEH0gIKrSBIL2TKsLTmlpKStXrsRoNBIeHs6sWbPw9/d3OW/NmjUc\nP36coKAg3njjjWseL2hdGrro71jrebdXhHxseXoOAEP0gaIqTSBox7R6e4IdO3bQv39/Vq1aRd++\nfdmxY4fb84YPH86LL7543eMFrcudw4bb12q8Ilmp0pPiFcnwZ5JdpsfcrvX0imGfsfiaWiIIBIK2\nR6sLzpEjRxg6dCgAw4YN4/Dhw27PS0hIcJu5NHS8oPW5c9hw5q5ZR/IHm5m7Zp3btRhPaz05Kp1b\ngRIIBO2HVhecoqIiQkJCAAgODqaoqKhFxwvaFhaN+1nemD59hdgIBO2cFlnDWbx4MYWFhS7HH330\nUcVrlcrVuuRaaOx4QesjNngKBB2XFhGchQsXenwvODiYwsJCQkJCuHLlCsHBwR7Pbarx0dHR13SP\n1iIwMLC1Q2gQTRnnHyY8xh8mPKY49k4TXbs9PM/2ECOIOJua9hJnY2n1KbVBgwaxe/duAPbs2cPt\nt9/eouMFAoFA0DKoJEmSWjMAT2XNJpOJtWvXMn/+fADeeustTp8+TUlJCcHBwTzyyCMMHz5clEUL\nBAJBO6HVBUcgEAgENwatPqUmEAgEghsDITgCgUAgaBFa3dqmOWgvdjkNvU9qaiqbN2/GZrMxYsQI\nxowZA8D27dv57rvvCAoKAmDChAkMGDCgSWLzdE9nNm7cSGpqKt7e3syYMYPu3bs3eGxT0Zg4Z86c\nia+vL2q1Go1Gw9KlS1stzpycHNasWUNWVhZ//OMfeeihh67pM7aFONvS89y3bx9ffvklkiTh6+vL\nE088QdeuXRs0tq3E2VLPs74YDx8+zPbt21GpVGg0GpKSkujdu3eDxrogdUA++ugjaceOHZIkSdI/\n/vEPaevWrW7PO3XqlHT+/Hnp+eefv67xLRGn1WqVnn76aSkvL0+qrq6WkpOTpQsXLkiSJEnbt2+X\n/vWvfzV5XHXd08HRo0el1157TZIkScrIyJBefPHFBo9tC3FKkiTNmDFDKikpaZbYrjXOoqIiyWAw\nSJ988on05ZdfXtPYthCnJLWt55meni6VlZVJkiRJx48fb7O/n57ilKSWeZ4NibGiokL+/9nZ2dJz\nzz3X4LG16ZBTau3FLqch9zEYDERFRREREYFWqyUxMZEjR47I70vNUPNR3z1rx96zZ0/KysooLCxs\n0Ni2EKeD5nh+1xNnUFAQsbGxaDSaax7bFuJ00FaeZ3x8PH5+fgDExcVRUFDQ4LFtIU4Hzf08GxKj\nj4+P/P8rKyvlDfbX8yw75JRae7HLach9TCYToaGh8mu9Xo/BYJBff/PNN+zdu5cePXowceLEJpn6\nq++e7s4JDQ3FZDI1aGxT0Zg4Q0JCUKlULF68GLVazciRIxk5cmSrxdkcY6+Vxt6rrT7P7777joED\nB17X2NaKE1rmeTY0xkOHDvHJJ59QVFQkb1W5nmfZbgWnvdjlNGec9913H+PGjQPg008/ZcuWLUyf\nPv36Ar0OWuKv2abAU5yLFi1Cr9dTXFzM4sWLiYmJISEhoYWj6zgsXryYTp06tannefLkSb7//nsW\nL17cqnHUh7s429LzHDx4MIMHD+b06dNs27atTveYumi3gtPW7HKaK069Xq9IswsKCtDr9fJ4ByNG\njGDZsmXXHWdD71nfORaLpd6xTUVj4nS8B/ZposGDB2MwGJrlH3RD4myOsddKY+/VqVMnoO08z+zs\nbNauXctf//pXAgICrmlsa8cJLfM8r/V5JCQkkJ+fT2lp6XU9yw65htNe7HIacp/Y2Fhyc3PJz8/H\nYrFw4MABBg0aBMCVK1fk8w4dOsTNN9/cJHHVdU/n2Pfu3QtARkYG/v7+hISENGhsU9GYOKuqqqio\nqADs89JpaWlN9vyuJ04HtbOxtvY8PcXZ1p6n0WgkJSWFZ555hqioqGsa2xbibKnn2ZAYc3Nz5Z/3\n+fPnsVgsBAQEXNez7JBOA+3FLqehcR4/flxRejh27FgA3nnnHbKyslCpVISHhzNt2jR5TaixuLvn\nzp07ARg1ahQAGzZsIDU1FR8fH6ZPn06PHj3qjLc5uN448/LySElJAcBms3H33Xe3apyFhYXMnz+f\n8vJy1Go1Pj4+rFy5Eh8fnzb1PD3FWVRU1Kae5/vvv8+hQ4cICwsDUJQVt6Xn6SnOlvz9rC/Gf/7z\nn+zduxeNRoNOp+PPf/4zvXr18ji2Ljqk4AgEAoGg7dEhp9QEAoFA0PYQgiMQCASCFkEIjkAgEAha\nBCE4AoFAIGgRhOAIBAKBoEUQgiMQCASCFkEIjkAgEAhaBCE4AkEDePfdd9m2bRsAp0+f5rnnnmuR\n+44fP568vLwWuZdA0Ny0Wy81gaA2M2fOpKioCLVajbe3NwMHDmTy5MkKe/XrRaVSyQarCQkJvPXW\nW/WO2b17N9999x2LFi1q9P1r83//9398/vnnrFy5Uj62ePFiTCaTy7F+/foxZswYzpw5w9atW7l4\n8SJqtZqYmBgef/xxYmNj5fM/+OADevTooXAmXrNmDXv27OHtt98mMjISsLtkrFu3jpMnTwJw6623\nMnXqVHx9fZv8swo6DiLDEXQo5s2bx5YtW1i2bBnnzp3jiy++cDnHarVe17XbkilHnz59uHTpEiUl\nJYD9M2VnZ1NdXU1xcbF87OzZs/Tp04fy8nJef/11HnjgATZt2sT777/Pww8/jJeXl+K6qamp3Hbb\nbfLrM2fOkJ+f73L/bdu2UV5ezrvvvsvq1aspKiri73//ezN+YkFHQGQ4gg6JXq9nwIABXLhwAbBP\nTU2ePJmvv/4aSZJYvXo1R48eZdu2bRiNRjp37szUqVNlg8TMzEzef/99cnNzFT1KAH766Sfeeecd\n3nvvPcBuwLh582bOnDmDJEkkJiZy//33s27dOqxWKxMnTkSj0bBp0yaqq6v55JNP+O9//0t1dTWD\nBw8mKSkJnU4HwJdffsnXX3+NSqXikUceqfPzRUREcOrUKYYMGUJmZiZdunQhPDyc06dPy8ckSSIu\nLo7MzExUKhV33XUXADqdjv79+yuumZ2djb+/v+z4a7Va2bRpEzNnzmT27NmKcy9fvsztt98uZ4+3\n3347R48eva6fleDGQWQ4gg6FIwsxGo2kpqbSvXt3+b0jR46wdOlS3nzzTVlQnnzySTZu3MjIkSNZ\ntmwZFosFi8XCihUrGDp0KJs2beKOO+7g4MGDbnsW2Ww2li1bRkREBGvWrOH9998nMTGRmJgYpk6d\nSnx8PFu2bGHTpk0AfPzxx+Tm5rJixQpWr16NyWTis88+A+zZxb/+9S8WLlzIqlWrOHHiRJ2fNSEh\ngdOnTwNw6tQpEhIS6NWrl+JYfHw8arWam266CbVazbvvvktqaiqlpaUu1zt+/Lgiu/n6669JSEhw\n61J8//33c/ToUcrKyigtLeXgwYMuwiwQ1EYIjqBDsWLFCiZNmsT//M//0KdPH4V77ZgxY/D398fL\ny4tdu3YxcuRI4uLiUKlUDB06FC8vLzIyMsjIyMBqtfLAAw+gVqu54447iIuLc3s/g8HAlStX+NOf\n/oROp8PLy0t20q2NJEn87//+L0lJSfj7++Pj48PYsWM5cOAAAAcOHGD48OF07twZb2/vOjMcsE+r\nOcTlzJkzJCQkKETIcQzAz8+PRYsWoVKpWLt2LVOnTmX58uWKLrPHjx+XRcNoNLJr1y7Gjx/v9t7d\nunXDYrEwefJkpkyZglar5b777qszXoFATKkJOhRz5syhb9++bt9zbodrNBrZu3cv33zzjXzMYrFQ\nWFiIJEkujaQc9vG1cbSWUKvr/9utuLgYs9nMvHnz5GOSJMlZWWFhoULYPN3TQUJCAu+//z5lZWWc\nPXuWZ599Fm9vb65cuUJZWRnp6emMHj1aPj8mJoYZM2YAcOnSJVavXs3mzZt59tlnKSsrIycnRxbL\nzZs3M27cOHx9feX4nNewVq5cSbdu3Zg7dy42m42PPvqI1atXM2vWrHqfg+DGRQiO4IbBeUosNDSU\nsWPH8vvf/97lvFOnTmEymRTHjEajokGWg7CwMIxGIzabrV7RCQwMRKfT8eabb8rdHJ0JCQnBaDQq\n7lkXkZGRdOrUiV27dhEWFoa3tzcA8fHx7Ny5k8rKSnr27Ol2bHR0NEOHDmXXrl0A/Pjjj/Tt21d+\nRj/99BPp6els3bpVHrNgwQImTZpEYmIi2dnZTJ06VV57GjVqFC+99FKd8QoEYkpNcEMycuRIdu7c\nicFgQJIkKisrOXbsGJWVlcTHx6PRaPj3v/+NxWLh4MGDGAwGt9eJi4sjJCSEjz/+mKqqKsxmM+np\n6YBdQAoKCrBYLACo1Wp+/etfs3nzZrmSzGQy8eOPPwJw1113sXv3bi5evEhVVVWDqr4SEhL46quv\nFK2He/fuzddff01sbKxchXbp0iW++uorWUiNRiP79+8nPj4egGPHjinWb1atWkVKSgorVqxg+fLl\ngL0C0NGVNjY2ll27dmE2mzGbzezatYuuXbs28OkLblREhiO4IenRowdPPvkkGzZsIDc3F51OR+/e\nvenTpw9arZbk5GTWrl3Lp59+ysCBAxkyZIjb66jVaubOncumTZvk6ap77rmHXr160bdvX7p06cK0\nadNQq9WsX7+exx57jM8++4y//vWvFBcXo9fruf/++7n11lsZMGAADzzwAIsWLUKtVjN+/Hj2799f\n5+fo06cP+/fvp3fv3vKx3r1789FHHzFixAj5mI+PD2fPnuWrr76irKwMf39/fvWrX/HnP/8ZSZJI\nS0tj4sSJ8vlBQUEu93JkaADTp09n06ZNTJ8+HUmS6NmzJzNnzmz4D0BwQyI6fgoENzgGg4FNmzax\nZMmS1g5F0MERU2oCgYCHH364tUMQ3ACIDEcgEAgELYLIcAQCgUDQIgjBEQgEAkGLIARHIBAIBC2C\nEByBQCAQtAhCcAQCgUDQIgjBEQgEAkGL8P8DZr/AK8Gr2zYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f39a2a30210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(estAll.predict(X_rookie),Y,'o')\n",
    "plt.plot(np.arange(0,0.25,0.01),np.arange(0,0.25,0.01),'b-')\n",
    "plt.ylabel('Career WS/48')\n",
    "plt.xlabel('Predicted WS/48');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The blue line above is NOT the best-fit line. It's the identity line. I plot it to help visualize where the model fails. The model seems to primarily fail in the extremes - it tends to overestimate the worst players. \n",
    "\n",
    "All in all, This model does a remarkably good job given its simplicity (linear regression), but it also leaves a lot of variance unexplained. \n",
    "\n",
    "One reason this model might miss some variance is there's more than one way to be a productive basketball player. For instance, Dwight Howard and Steph Curry find very different ways to contribute. One linear regression model is unlikely to succesfully predict both players. \n",
    "\n",
    "In a [previous post](http://www.danvatterott.com/blog/2016/02/21/grouping-nba-players/), I grouped players according to their on-court performance. These player groupings might help predict career performance. \n",
    "\n",
    "Below, I will use the same player grouping I developed in my previous post, and examine how these groupings impact my ability to predict career performance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "df = pd.read_pickle('nba_bballref_career_stats_2016_Mar_15.pkl')\n",
    "df = df[df['G']>50]\n",
    "df_drop = df.drop(['Year','Name','G','GS','MP','FG','FGA','FG%','3P','2P','FT','TRB','PTS','ORtg','DRtg','PER','TS%','3PAr','FTr','ORB%','DRB%','TRB%','AST%','STL%','BLK%','TOV%','USG%','OWS','DWS','WS','WS/48','OBPM','DBPM','BPM','VORP'],1)\n",
    "X = df_drop.as_matrix() #take data out of dataframe\n",
    "ScaleModel = StandardScaler().fit(X)\n",
    "X = ScaleModel.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "reduced_model = PCA(n_components=5, whiten=True).fit(X)\n",
    "\n",
    "reduced_data = reduced_model.transform(X) #transform data into the 5 PCA components space\n",
    "final_fit = KMeans(n_clusters=6).fit(reduced_data) #fit 6 clusters\n",
    "df['kmeans_label'] = final_fit.labels_ #label each data point with its clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See my other post for more details about this clustering procedure. \n",
    "\n",
    "Let's see how WS/48 varies across the groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vwwhd5Dx3+selIIiITIp3AAKQf+Uz8Z5Ilysh81hEpDdMAAYntz9e7b58ItIfdgEREZkU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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f39a2a02110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "WS_48 = [df[df['kmeans_label']==x]['WS/48'] for x in np.unique(df['kmeans_label'])] #create a vector of ws/48. One for each cluster\n",
    "plt.boxplot(WS_48);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some groups perform better than others, but there's lots of overlap between the groups. Importantly, each group has a fair amount of variability. Each group spans at least 0.15 WS/48. This gives the regression enough room to successfully predict performance in each group. \n",
    "\n",
    "Now, lets get a bit of a refresher on what the groups are. Again, my previous post has a good description of these groups. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3v/0tv/rVr3zy/Ka0uwrvqKlJDBwUTHRIAJdcdRwuqvB3SCIiIiIi0kF98EEu/7n2PYb0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LNMI7IsoygKL774ImlpaWzYsIHh4WG2b9+OXq/nwQcfxO/309/fL3p2Q0RlZSUvv/wyX/3q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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f39a00a3050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "TS = [np.mean(df[df['kmeans_label']==x]['TS%'])*100 for x in np.unique(df['kmeans_label'])] #create vectors of each stat for each cluster\n",
    "ThreeAr = [np.mean(df[df['kmeans_label']==x]['3PAr'])*100 for x in np.unique(df['kmeans_label'])]\n",
    "FTr = [np.mean(df[df['kmeans_label']==x]['FTr'])*100 for x in np.unique(df['kmeans_label'])]\n",
    "RBD = [np.mean(df[df['kmeans_label']==x]['TRB%']) for x in np.unique(df['kmeans_label'])]\n",
    "AST = [np.mean(df[df['kmeans_label']==x]['AST%']) for x in np.unique(df['kmeans_label'])]\n",
    "STL = [np.mean(df[df['kmeans_label']==x]['STL%']) for x in np.unique(df['kmeans_label'])]\n",
    "TOV = [np.mean(df[df['kmeans_label']==x]['TOV%']) for x in np.unique(df['kmeans_label'])]\n",
    "USG = [np.mean(df[df['kmeans_label']==x]['USG%']) for x in np.unique(df['kmeans_label'])]\n",
    "\n",
    "Data = np.array([TS,ThreeAr,FTr,RBD,AST,STL,TOV,USG])\n",
    "ind = np.arange(1,9)\n",
    "\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.plot(ind,Data,'o-',linewidth=2)\n",
    "plt.xticks(ind,('True Shooting', '3 point Attempt', 'Free Throw Rate', 'Rebound', 'Assist','Steal','TOV','Usage'),rotation=45)\n",
    "plt.legend(('Group 1','Group 2','Group 3','Group 4','Group 5','Group 6'))\n",
    "plt.ylabel('Percent');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I've plotted the groups across a number of useful categories. For information about these categories see [basketball reference's glossary](http://www.basketball-reference.com/about/glossary.html).\n",
    "\n",
    "Here's a quick rehash of the groupings. See my [previous post](http://www.danvatterott.com/blog/2016/02/21/grouping-nba-players/) for more detail.\n",
    "\n",
    "**The group explanation below are likely not in the correct order as the group order changes each time this script is run.**\n",
    "\n",
    "<ul>\n",
    "<li><b>Group 1:</b> These are the distributors who shoot a fair number of threes, don't rebound at all, dish out assists, gather steals, and ...turn the ball over.</li> \n",
    "<li><b>Group 2:</b> These are the scorers who get to the free throw line, dish out assists, and carry a high usage.</li> \n",
    "<li><b>Group 3:</b> These are the bench players who don't score...or do much in general.</li>\n",
    "<li><b>Group 4:</b> These are the 3 point shooters who shoot tons of 3 pointers, almost no free throws, and don't rebound well.</li>\n",
    "<li><b>Group 5:</b> These are the mid-range shooters who shoot well, but don't shoot threes or draw free throws</li>\n",
    "<li><b>Group 6:</b> These are the defensive big men who shoot no threes, rebound lots, and carry a low usage.</li>\n",
    "</ul>\n",
    "\n",
    "On to the regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rookie_df = pd.read_pickle('nba_bballref_rookie_stats_2016_Mar_15.pkl')\n",
    "rookie_df = rookie_df.drop(['Year','Career Games','Name'],1)\n",
    "\n",
    "X = rookie_df.as_matrix() #take data out of dataframe\n",
    "ScaleRookie = StandardScaler().fit(X) #scale data\n",
    "X = ScaleRookie.transform(X) #transform data to scale\n",
    "\n",
    "reduced_model_rookie = PCA(n_components=10).fit(X) #create pca model of first 10 components. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You might have noticed the giant condition number in the regression above. This indicates significant [multicollinearity](https://en.wikipedia.org/wiki/Multicollinearity) of the features, which isn't surprising since I have many features that reflect the same abilities. \n",
    "\n",
    "The multicollinearity doesn't prevent the regression model from making accurate predictions, but does it make the beta weight estimates irratic. With irratic beta weights, it's hard to tell whether the different clusters use different models when predicting career ws/48. \n",
    "\n",
    "In the following regression, I put the predicting features through a PCA and keep only the first 10 PCA components. Using only the first 10 PCA components keeps the component score below 20, indicating that multicollinearity is not a problem. I then examine whether the different groups exhibit a different patterns of beta weights (whether different models predict success of the different groups). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cluster_labels = df[df['Year']>1980]['kmeans_label'] #limit labels to players after 1980\n",
    "rookie_df_drop['kmeans_label'] = cluster_labels #label each data point with its clusters\n",
    "\n",
    "estHold = [[],[],[],[],[],[]]\n",
    "\n",
    "for i,group in enumerate(np.unique(final_fit.labels_)):\n",
    "           \n",
    "    Grouper = df['kmeans_label']==group #do regression one group at a time\n",
    "    Yearer = df['Year']>1980\n",
    "    \n",
    "    Group1 = df[Grouper & Yearer]\n",
    "    Y = Group1['WS/48'] #get predicted data\n",
    "    \n",
    "    Group1_rookie = rookie_df_drop[rookie_df_drop['kmeans_label']==group] #get predictor data of group\n",
    "    Group1_rookie = Group1_rookie.drop(['kmeans_label'],1)\n",
    "\n",
    "    X = Group1_rookie.as_matrix() #take data out of dataframe\n",
    "    X = ScaleRookie.transform(X) #scale data\n",
    "    \n",
    "    X = reduced_model_rookie.transform(X) #transform data into the 10 PCA components space\n",
    "    \n",
    "    X = sm.add_constant(X)  # Adds a constant term to the predictor\n",
    "    est = sm.OLS(Y,X) #create regression model\n",
    "    est = est.fit()\n",
    "    #print(est.summary())\n",
    "    estHold[i] = est \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dan-laptop/anaconda/lib/python2.7/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
     "data": {
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FBZowYYLGjBmjNWvWXNe0uUNqaqqSkpIktTd/DgkJ0blz5zz2GvhFExQAAAAEh4MHD6q5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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f39a0106110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6)) #plot the beta weights\n",
    "width=0.12\n",
    "for i,est in enumerate(estHold):\n",
    "    plt.bar(np.arange(11)+width*i,est.params,color=plt.rcParams['axes.color_cycle'][i],width=width,yerr=(est.conf_int()[1]-est.conf_int()[0])/2)\n",
    "    \n",
    "plt.xlim(right=11)\n",
    "plt.xlabel('Principle Components')\n",
    "plt.legend(('Group 1','Group 2','Group 3','Group 4','Group 5','Group 6'))\n",
    "plt.ylabel('Beta Weights');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above I plot the beta weights for each principle component across the groupings. This plot is a lot to look at, but I wanted to depict how the beta values changed across the groups. They are not drastically different, but they're also not identical. Error bars depict 95% confidence intervals. \n",
    "\n",
    "Below I fit a regression to each group, but with all the features. Again, multicollinearity will be a problem, but this will not decrease the regression's accuracy, which is all I really care about."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5xJB9Szik68yCHE+O5blRlGPE62QyxQY98avmotH6IYzFeLcLJ6jf3QCcXvMy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u/YBIUFqKf9u9KuLexZILizKGlXUXb+gbI7HNXo1+Hurjc+6Ra4YRz5nUSM4Osv9f29KNkZKJHh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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f39a2a02790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = rookie_df.as_matrix() #take data out of dataframe\n",
    "\n",
    "cluster_labels = df[df['Year']>1980]['kmeans_label']\n",
    "rookie_df_drop['kmeans_label'] = cluster_labels #label each data point with its clusters\n",
    "\n",
    "plt.figure(figsize=(8,6));\n",
    "\n",
    "estHold = [[],[],[],[],[],[]]\n",
    "\n",
    "for i,group in enumerate(np.unique(final_fit.labels_)):\n",
    "           \n",
    "    Grouper = df['kmeans_label']==group #do one regression at a time\n",
    "    Yearer = df['Year']>1980\n",
    "    \n",
    "    Group1 = df[Grouper & Yearer]\n",
    "    Y = Group1['WS/48'] #get predictor data\n",
    "    \n",
    "    Group1_rookie = rookie_df_drop[rookie_df_drop['kmeans_label']==group]\n",
    "    Group1_rookie = Group1_rookie.drop(['kmeans_label'],1) #get predicted data\n",
    "\n",
    "    X = Group1_rookie.as_matrix() #take data out of dataframe    \n",
    "    \n",
    "    X = sm.add_constant(X)  # Adds a constant term to the predictor\n",
    "    est = sm.OLS(Y,X) #fit with linear regression model\n",
    "    est = est.fit()\n",
    "    estHold[i] = est\n",
    "    #print est.summary()\n",
    "    \n",
    "    plt.subplot(3,2,i+1) #plot each regression's prediction against actual data\n",
    "    plt.plot(est.predict(X),Y,'o',color=plt.rcParams['axes.color_cycle'][i])\n",
    "    plt.plot(np.arange(-0.1,0.25,0.01),np.arange(-0.1,0.25,0.01),'-')\n",
    "    plt.title('Group %d'%(i+1))\n",
    "    plt.text(0.15,-0.05,'$r^2$=%.2f'%est.rsquared)\n",
    "    plt.xticks([0.0,0.12,0.25])\n",
    "    plt.yticks([0.0,0.12,0.25]); "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plots above depict each regression's predictions against actual ws/48. I provide each model's r^2 in the plot too. \n",
    "\n",
    "Some regressions are better than others. For instance, the regression model does a pretty awesome job predicting the bench warmers...I wonder if this is because they have shorter careers... The regression model does not do a good job predicting the 3-point shooters.\n",
    "\n",
    "Now onto the fun stuff though. \n",
    "\n",
    "Below, create a function for predicting a players career WS/48. First, I write a function that finds what cluster a player would belong to, and what the regression model predicts for this players career (with 95% confidence intervals). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZcHtvJN24hbBH5nLHzQsLMCPv3BCVAkK63VrpZc5AYu6iCBVs6TZ+IE59tQf/t3cnrly6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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f39a00fe150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the residuals. there's obviously a problem with under/over prediction\n",
    "\n",
    "plt.figure(figsize=(8,6));\n",
    "\n",
    "for i,group in enumerate(np.unique(final_fit.labels_)):\n",
    "           \n",
    "    Grouper = df['kmeans_label']==group #do one regression at a time\n",
    "    Yearer = df['Year']>1980\n",
    "    \n",
    "    Group1 = df[Grouper & Yearer]\n",
    "    Y = Group1['WS/48'] #get predictor data\n",
    "    resid = estHold[i].resid\n",
    "        \n",
    "    plt.subplot(3,2,i+1) #plot each regression's prediction against actual data\n",
    "    plt.plot(Y,resid,'o',color=plt.rcParams['axes.color_cycle'][i])\n",
    "    plt.title('Group %d'%(i+1))\n",
    "    plt.xticks([0.0,0.12,0.25])\n",
    "    plt.yticks([-0.1,0.0,0.1]); \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "in the plots above i look at the residuals across the predicted variable. There is an obvious correlation in the residuals this is not great..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def player_prediction__regressionModel(PlayerName):\n",
    "    from statsmodels.sandbox.regression.predstd import wls_prediction_std\n",
    "    \n",
    "    clust_df = pd.read_pickle('nba_bballref_career_stats_2016_Mar_05.pkl')\n",
    "    clust_df = clust_df[clust_df['Name']==PlayerName]\n",
    "    clust_df = clust_df.drop(['Name','G','GS','MP','FG','FGA','FG%','3P','2P','FT','TRB','PTS','ORtg','DRtg','PER','TS%','3PAr','FTr','ORB%','DRB%','TRB%','AST%','STL%','BLK%','TOV%','USG%','OWS','DWS','WS','WS/48','OBPM','DBPM','BPM','VORP'],1)\n",
    "    new_vect = ScaleModel.transform(clust_df.as_matrix()[0])\n",
    "    reduced_data = reduced_model.transform(new_vect) \n",
    "    Group = final_fit.predict(reduced_data)\n",
    "    clust_df['kmeans_label'] = Group[0]\n",
    "\n",
    "    Predrookie_df = pd.read_pickle('nba_bballref_rookie_stats_2016_Mar_15.pkl')\n",
    "    Predrookie_df = Predrookie_df[Predrookie_df['Name']==PlayerName]\n",
    "    Predrookie_df = Predrookie_df.drop(['Year','Career Games','Name'],1)\n",
    "    predX = Predrookie_df.as_matrix() #take data out of dataframe\n",
    "    predX = sm.add_constant(predX,has_constant='add')  # Adds a constant term to the predictor\n",
    "    prstd_ols, iv_l_ols, iv_u_ols = wls_prediction_std(estHold[Group[0]],predX,alpha=0.05)\n",
    "    return {'Name':PlayerName,'Group':Group[0]+1,'Prediction':estHold[Group[0]].predict(predX),'Upper_CI':iv_u_ols,'Lower_CI':iv_l_ols}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here I create a function that creates a list of all the first round draft picks from a given year. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def gather_draftData(Year):\n",
    "    \n",
    "    import urllib2\n",
    "    from bs4 import BeautifulSoup\n",
    "    import pandas as pd\n",
    "    import numpy as np\n",
    "    \n",
    "    draft_len = 30\n",
    "    \n",
    "    def convert_float(val):\n",
    "        try:\n",
    "            return float(val)\n",
    "        except ValueError:\n",
    "            return np.nan\n",
    "    \n",
    "    url = 'http://www.basketball-reference.com/draft/NBA_'+str(Year)+'.html'\n",
    "    html = urllib2.urlopen(url)\n",
    "    soup = BeautifulSoup(html,\"lxml\")\n",
    "    \n",
    "    draft_num = [soup.findAll('tbody')[0].findAll('tr')[i].findAll('td')[0].text for i in range(draft_len)]\n",
    "    draft_nam = [soup.findAll('tbody')[0].findAll('tr')[i].findAll('td')[3].text for i in range(draft_len)]\n",
    "    games = [convert_float(soup.findAll('tbody')[0].findAll('tr')[i].findAll('td')[6].text) for i in range(draft_len)]\n",
    "        \n",
    "    draft_df = pd.DataFrame([draft_num,draft_nam,games]).T\n",
    "    draft_df.columns = ['Number','Name','Games']\n",
    "    df.index = range(np.size(df,0))\n",
    "    return draft_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below I create predictions for each first-round draft pick from 2015. The spurs' first round pick, Nikola Milutinov, has yet to play so I do not create a prediction for him. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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8889r9uzZCggI0FNPPeX8WmZmptq2bWtII9u3b9fcuXPlcDjUr18/paSkVPr6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NAAAg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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f398d3f9290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.patches as mpatches\n",
    "\n",
    "draft_df = gather_draftData(2015)\n",
    "\n",
    "draft_df['Name'][14] =  'Kelly Oubre Jr.' #annoying name inconsistencies \n",
    "\n",
    "plt.subplots(figsize=(14,6));\n",
    "plt.xticks(range(1,31),draft_df['Name'],rotation=90)\n",
    "\n",
    "draft_df = draft_df.drop(17, 0) #Sam Dekker has received little playing time making his prediction highly erratic\n",
    "\n",
    "for name in draft_df['Name']:\n",
    "        \n",
    "    draft_num = draft_df[draft_df['Name']==name]['Number']\n",
    "    try: int(draft_df[draft_df['Name']==name]['Games']) \n",
    "    except:continue\n",
    "        \n",
    "    predict_dict = player_prediction__regressionModel(name)\n",
    "    yerr = (predict_dict['Upper_CI']-predict_dict['Lower_CI'])/2\n",
    "        \n",
    "    plt.errorbar(draft_num,predict_dict['Prediction'],fmt='o',label=name,\n",
    "                color=plt.rcParams['axes.color_cycle'][predict_dict['Group']-1],yerr=yerr);\n",
    "\n",
    "plt.xlim(left=0,right=31)\n",
    "patch = [mpatches.Patch(color=plt.rcParams['axes.color_cycle'][i], label='Group %d'%(i+1)) for i in range(6)]\n",
    "plt.legend(handles=patch,ncol=3)\n",
    "plt.ylabel('Predicted WS/48')\n",
    "plt.xlabel('Draft Position');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot above is ordered by draft pick. The error bars depict 95% confidence interbals...which are a little wider than I would like. It's interesting to look at what clusters these players fit into. Lots of 3-pt shooters! It could be that rookies play a limited role in the offense - just shooting 3s. \n",
    "\n",
    "As a t-wolves fan, I am relatively happy about the high prediction for Karl-Anthony Towns. His predicted ws/48 is between Marc Gasol and Elton Brand. Again, the CIs are quite wide, so the model says there's a 95% chance he is somewhere between Lebron James ever and a player that averages less than 0.1 ws/48. \n",
    "\n",
    "Karl-Anthony Towns would have the highest predicted ws/48 if it were not for Kevin Looney who the model loves. Kevin Looney has not seen much playing time though, which likely makes his prediction more erratic. Keep in mind I did not use draft position as a predictor in the model.\n",
    "\n",
    "Sam Dekker has a pretty huge error bar, likely because of his limited playing time this year. \n",
    "\n",
    "While I fed a ton of features into this model, it's still just a linear regression. The simplicity of the model might prevent me from making more accurate predictions.\n",
    "\n",
    "I've already started playing with some more complex models. If those work out well, I will post them here. I ended up sticking with a plain linear regression because my vast number of features is a little unwieldy in a more complex models. If you're interested (and the models produce better results) check back in the future. \n",
    "\n",
    "For now, these models explain between 40 and 70% of the variance in career ws/48 from only a player's rookie year. Even predicting 30% of variance is pretty remarkable, so I don't want to trash on this part of the model. Explaining 65% of the variance is pretty awesome. The model gives us a pretty accurate idea of how these \"bench players\" will perform. For instance, the future does not look bright for players like Emmanuel Mudiay and Tyus Jones. Not to say these players are doomed. The model assumes that players will retain their grouping for the entire career. Emmanuel Mudiay and Tyus Jones might start performing more like distributors as their career progresses. This could result in a better career. \n",
    "\n",
    "One nice part about this model is it tells us where the predictions are less confident. For instance, it is nice to know that we're relatively confident when predicting bench players, but not when we're predicting 3-point shooters. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For those curious, I output each groups regression summary below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  WS/48   R-squared:                       0.518\n",
      "Model:                            OLS   Adj. R-squared:                  0.426\n",
      "Method:                 Least Squares   F-statistic:                     5.589\n",
      "Date:                Sun, 20 Mar 2016   Prob (F-statistic):           1.11e-19\n",
      "Time:                        19:32:55   Log-Likelihood:                 655.20\n",
      "No. Observations:                 292   AIC:                            -1214.\n",
      "Df Residuals:                     244   BIC:                            -1038.\n",
      "Df Model:                          47                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.6481      0.218      2.967      0.003         0.218     1.078\n",
      "x1            -0.0026      0.001     -2.148      0.033        -0.005    -0.000\n",
      "x2            -0.0003      0.000     -1.338      0.182        -0.001     0.000\n",
      "x3            -0.0002      0.000     -1.263      0.208        -0.001     0.000\n",
      "x4          -2.87e-06   1.15e-05     -0.249      0.803     -2.56e-05  1.98e-05\n",
      "x5             0.0622      0.071      0.878      0.381        -0.077     0.202\n",
      "x6             0.0348      0.040      0.862      0.390        -0.045     0.114\n",
      "x7             0.3493      0.507      0.688      0.492        -0.650     1.349\n",
      "x8            -0.0106      0.060     -0.178      0.859        -0.128     0.107\n",
      "x9            -0.0234      0.041     -0.578      0.564        -0.103     0.056\n",
      "x10           -0.0113      0.017     -0.667      0.505        -0.044     0.022\n",
      "x11           -0.0150      0.058     -0.261      0.794        -0.128     0.098\n",
      "x12           -0.0437      0.040     -1.091      0.276        -0.123     0.035\n",
      "x13           -0.3398      0.437     -0.778      0.437        -1.200     0.520\n",
      "x14            0.0373      0.028      1.329      0.185        -0.018     0.092\n",
      "x15           -0.0035      0.012     -0.299      0.765        -0.027     0.020\n",
      "x16           -0.0458      0.042     -1.077      0.282        -0.129     0.038\n",
      "x17           -0.0082      0.038     -0.214      0.831        -0.084     0.067\n",
      "x18            0.0260      0.038      0.689      0.492        -0.048     0.100\n",
      "x19           -0.0227      0.036     -0.624      0.533        -0.094     0.049\n",
      "x20            0.0070      0.010      0.710      0.478        -0.012     0.027\n",
      "x21           -0.0373      0.041     -0.913      0.362        -0.118     0.043\n",
      "x22           -0.0270      0.019     -1.398      0.163        -0.065     0.011\n",
      "x23           -0.0029      0.015     -0.189      0.850        -0.033     0.027\n",
      "x24            0.0070      0.003      2.309      0.022         0.001     0.013\n",
      "x25           -0.0054      0.028     -0.196      0.844        -0.060     0.049\n",
      "x26            0.0038      0.002      2.242      0.026         0.000     0.007\n",
      "x27           -0.0064      0.002     -2.885      0.004        -0.011    -0.002\n",
      "x28            0.0110      0.006      1.916      0.057        -0.000     0.022\n",
      "x29           -0.3450      0.412     -0.838      0.403        -1.156     0.466\n",
      "x30           -0.0680      0.186     -0.367      0.714        -0.434     0.298\n",
      "x31           -0.0447      0.057     -0.788      0.431        -0.156     0.067\n",
      "x32            0.0165      0.016      1.051      0.294        -0.014     0.047\n",
      "x33            0.0043      0.011      0.374      0.708        -0.018     0.027\n",
      "x34           -0.0121      0.026     -0.458      0.648        -0.064     0.040\n",
      "x35           -0.0012      0.002     -0.488      0.626        -0.006     0.004\n",
      "x36            0.0178      0.030      0.595      0.553        -0.041     0.077\n",
      "x37            0.0113      0.011      1.027      0.305        -0.010     0.033\n",
      "x38            0.0008      0.002      0.347      0.729        -0.004     0.005\n",
      "x39           -0.0123      0.005     -2.528      0.012        -0.022    -0.003\n",
      "x40           -0.0124      0.036     -0.342      0.733        -0.084     0.059\n",
      "x41           -0.0173      0.036     -0.477      0.634        -0.089     0.054\n",
      "x42            0.0263      0.037      0.713      0.476        -0.046     0.099\n",
      "x43           -1.2830      0.410     -3.133      0.002        -2.090    -0.476\n",
      "x44            0.0412      0.037      1.111      0.268        -0.032     0.114\n",
      "x45            0.0524      0.037      1.427      0.155        -0.020     0.125\n",
      "x46           -0.0441      0.037     -1.208      0.228        -0.116     0.028\n",
      "x47           -0.0050      0.007     -0.763      0.446        -0.018     0.008\n",
      "==============================================================================\n",
      "Omnibus:                        5.349   Durbin-Watson:                   2.029\n",
      "Prob(Omnibus):                  0.069   Jarque-Bera (JB):                5.364\n",
      "Skew:                           0.242   Prob(JB):                       0.0684\n",
      "Kurtosis:                       3.456   Cond. No.                     4.91e+05\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 4.91e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  WS/48   R-squared:                       0.607\n",
      "Model:                            OLS   Adj. R-squared:                  0.460\n",
      "Method:                 Least Squares   F-statistic:                     4.136\n",
      "Date:                Sun, 20 Mar 2016   Prob (F-statistic):           1.21e-10\n",
      "Time:                        19:32:55   Log-Likelihood:                 358.04\n",
      "No. Observations:                 174   AIC:                            -620.1\n",
      "Df Residuals:                     126   BIC:                            -468.5\n",
      "Df Model:                          47                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.0938      0.434      0.216      0.829        -0.764     0.952\n",
      "x1            -0.0011      0.002     -0.525      0.600        -0.005     0.003\n",
      "x2             0.0006      0.000      1.189      0.237        -0.000     0.001\n",
      "x3          3.789e-05      0.000      0.133      0.894        -0.001     0.001\n",
      "x4         -1.886e-05   1.86e-05     -1.013      0.313     -5.57e-05   1.8e-05\n",
      "x5             0.0602      0.113      0.533      0.595        -0.163     0.284\n",
      "x6             0.0881      0.070      1.262      0.209        -0.050     0.226\n",
      "x7             1.1696      0.880      1.329      0.186        -0.572     2.911\n",
      "x8            -0.0880      0.111     -0.791      0.430        -0.308     0.132\n",
      "x9            -0.0881      0.071     -1.234      0.219        -0.229     0.053\n",
      "x10           -0.0504      0.023     -2.162      0.032        -0.097    -0.004\n",
      "x11           -0.1112      0.099     -1.124      0.263        -0.307     0.085\n",
      "x12           -0.0635      0.071     -0.897      0.371        -0.204     0.077\n",
      "x13           -0.6204      0.571     -1.086      0.280        -1.751     0.510\n",
      "x14           -0.0179      0.048     -0.374      0.709        -0.113     0.077\n",
      "x15            0.0048      0.018      0.270      0.788        -0.030     0.040\n",
      "x16            0.1898      0.109      1.742      0.084        -0.026     0.405\n",
      "x17           -0.1190      0.077     -1.544      0.125        -0.272     0.033\n",
      "x18           -0.1231      0.066     -1.873      0.063        -0.253     0.007\n",
      "x19            0.0970      0.064      1.517      0.132        -0.030     0.224\n",
      "x20           -0.0215      0.018     -1.184      0.239        -0.057     0.014\n",
      "x21            0.0408      0.075      0.542      0.589        -0.108     0.190\n",
      "x22           -0.0225      0.023     -1.000      0.319        -0.067     0.022\n",
      "x23            0.0259      0.031      0.834      0.406        -0.036     0.087\n",
      "x24            0.0100      0.007      1.514      0.133        -0.003     0.023\n",
      "x25            0.0044      0.046      0.095      0.924        -0.086     0.095\n",
      "x26            0.0006      0.004      0.130      0.897        -0.008     0.009\n",
      "x27            0.0006      0.004      0.161      0.873        -0.007     0.009\n",
      "x28            0.0292      0.013      2.305      0.023         0.004     0.054\n",
      "x29           -1.0776      0.934     -1.154      0.251        -2.925     0.770\n",
      "x30            0.3468      0.195      1.776      0.078        -0.040     0.733\n",
      "x31            0.1407      0.131      1.071      0.286        -0.119     0.401\n",
      "x32           -0.0587      0.035     -1.684      0.095        -0.128     0.010\n",
      "x33           -0.0524      0.021     -2.548      0.012        -0.093    -0.012\n",
      "x34            0.1172      0.050      2.321      0.022         0.017     0.217\n",
      "x35            0.0002      0.004      0.048      0.962        -0.008     0.008\n",
      "x36           -0.0495      0.055     -0.902      0.369        -0.158     0.059\n",
      "x37           -0.0073      0.016     -0.466      0.642        -0.039     0.024\n",
      "x38            0.0007      0.006      0.115      0.908        -0.012     0.013\n",
      "x39           -0.0156      0.011     -1.467      0.145        -0.037     0.005\n",
      "x40           -0.0397      0.070     -0.564      0.574        -0.179     0.100\n",
      "x41           -0.0200      0.070     -0.287      0.775        -0.158     0.118\n",
      "x42            0.0343      0.070      0.487      0.627        -0.105     0.174\n",
      "x43           -0.3365      0.757     -0.444      0.658        -1.835     1.162\n",
      "x44            0.0525      0.066      0.793      0.429        -0.079     0.184\n",
      "x45            0.0489      0.067      0.730      0.467        -0.084     0.182\n",
      "x46           -0.0518      0.066     -0.784      0.435        -0.183     0.079\n",
      "x47            0.0146      0.008      1.721      0.088        -0.002     0.031\n",
      "==============================================================================\n",
      "Omnibus:                        2.052   Durbin-Watson:                   2.145\n",
      "Prob(Omnibus):                  0.358   Jarque-Bera (JB):                1.632\n",
      "Skew:                           0.197   Prob(JB):                        0.442\n",
      "Kurtosis:                       3.264   Cond. No.                     8.73e+05\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 8.73e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  WS/48   R-squared:                       0.564\n",
      "Model:                            OLS   Adj. R-squared:                  0.454\n",
      "Method:                 Least Squares   F-statistic:                     5.137\n",
      "Date:                Sun, 20 Mar 2016   Prob (F-statistic):           3.71e-16\n",
      "Time:                        19:32:55   Log-Likelihood:                 483.11\n",
      "No. Observations:                 235   AIC:                            -870.2\n",
      "Df Residuals:                     187   BIC:                            -704.2\n",
      "Df Model:                          47                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
      "------------------------------------------------------------------------------\n",
      "const         -0.5179      0.300     -1.724      0.086        -1.110     0.075\n",
      "x1            -0.0034      0.002     -2.127      0.035        -0.007    -0.000\n",
      "x2            -0.0006      0.000     -2.061      0.041        -0.001 -2.52e-05\n",
      "x3            -0.0006      0.000     -2.240      0.026        -0.001 -7.06e-05\n",
      "x4          3.194e-05   1.85e-05      1.726      0.086     -4.57e-06  6.84e-05\n",
      "x5            -0.0015      0.086     -0.017      0.986        -0.170     0.167\n",
      "x6            -0.0521      0.048     -1.095      0.275        -0.146     0.042\n",
      "x7            -0.5453      0.528     -1.033      0.303        -1.587     0.496\n",
      "x8            -0.1367      0.074     -1.839      0.067        -0.283     0.010\n",
      "x9             0.0649      0.048      1.356      0.177        -0.030     0.159\n",
      "x10            0.0135      0.028      0.488      0.626        -0.041     0.068\n",
      "x11           -0.0626      0.062     -1.014      0.312        -0.184     0.059\n",
      "x12            0.0563      0.047      1.202      0.231        -0.036     0.149\n",
      "x13           -0.0158      0.172     -0.092      0.927        -0.355     0.324\n",
      "x14           -0.0429      0.038     -1.129      0.260        -0.118     0.032\n",
      "x15           -0.0095      0.017     -0.552      0.581        -0.044     0.025\n",
      "x16           -0.0786      0.038     -2.081      0.039        -0.153    -0.004\n",
      "x17           -0.0786      0.057     -1.382      0.169        -0.191     0.034\n",
      "x18           -0.0749      0.055     -1.363      0.174        -0.183     0.033\n",
      "x19            0.0855      0.051      1.674      0.096        -0.015     0.186\n",
      "x20            0.0254      0.010      2.480      0.014         0.005     0.046\n",
      "x21            0.0354      0.050      0.712      0.477        -0.063     0.134\n",
      "x22            0.0822      0.050      1.643      0.102        -0.016     0.181\n",
      "x23            0.0108      0.019      0.560      0.576        -0.027     0.049\n",
      "x24           -0.0017      0.005     -0.329      0.742        -0.012     0.009\n",
      "x25            0.0117      0.037      0.317      0.752        -0.061     0.085\n",
      "x26           -0.0020      0.001     -1.676      0.095        -0.004     0.000\n",
      "x27            0.0058      0.003      2.237      0.026         0.001     0.011\n",
      "x28           -0.0081      0.010     -0.819      0.414        -0.028     0.011\n",
      "x29            0.9112      0.504      1.809      0.072        -0.083     1.905\n",
      "x30           -0.0410      0.109     -0.377      0.706        -0.255     0.173\n",
      "x31            0.0887      0.102      0.871      0.385        -0.112     0.289\n",
      "x32           -0.0088      0.027     -0.323      0.747        -0.063     0.045\n",
      "x33           -0.0094      0.017     -0.537      0.592        -0.044     0.025\n",
      "x34            0.0153      0.042      0.364      0.716        -0.068     0.098\n",
      "x35           -0.0066      0.002     -2.670      0.008        -0.011    -0.002\n",
      "x36           -0.0194      0.038     -0.505      0.614        -0.095     0.056\n",
      "x37           -0.0475      0.028     -1.712      0.089        -0.102     0.007\n",
      "x38           -0.0026      0.002     -1.149      0.252        -0.007     0.002\n",
      "x39            0.0192      0.009      2.223      0.027         0.002     0.036\n",
      "x40            0.0051      0.048      0.107      0.915        -0.089     0.100\n",
      "x41            0.0047      0.048      0.099      0.921        -0.089     0.099\n",
      "x42            0.0108      0.048      0.225      0.822        -0.084     0.106\n",
      "x43            1.2759      0.448      2.851      0.005         0.393     2.159\n",
      "x44            0.1232      0.049      2.536      0.012         0.027     0.219\n",
      "x45            0.1241      0.049      2.522      0.012         0.027     0.221\n",
      "x46           -0.1220      0.049     -2.470      0.014        -0.219    -0.025\n",
      "x47           -0.0056      0.009     -0.591      0.556        -0.024     0.013\n",
      "==============================================================================\n",
      "Omnibus:                        2.419   Durbin-Watson:                   1.941\n",
      "Prob(Omnibus):                  0.298   Jarque-Bera (JB):                2.455\n",
      "Skew:                           0.242   Prob(JB):                        0.293\n",
      "Kurtosis:                       2.872   Cond. No.                     4.14e+05\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 4.14e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  WS/48   R-squared:                       0.708\n",
      "Model:                            OLS   Adj. R-squared:                  0.625\n",
      "Method:                 Least Squares   F-statistic:                     8.467\n",
      "Date:                Sun, 20 Mar 2016   Prob (F-statistic):           2.73e-25\n",
      "Time:                        19:32:55   Log-Likelihood:                 513.11\n",
      "No. Observations:                 212   AIC:                            -930.2\n",
      "Df Residuals:                     164   BIC:                            -769.1\n",
      "Df Model:                          47                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.2939      0.180      1.630      0.105        -0.062     0.650\n",
      "x1             0.0011      0.001      0.900      0.370        -0.001     0.004\n",
      "x2            -0.0003      0.000     -1.378      0.170        -0.001     0.000\n",
      "x3            -0.0004      0.000     -1.620      0.107        -0.001  9.46e-05\n",
      "x4          3.531e-05   1.79e-05      1.969      0.051     -9.18e-08  7.07e-05\n",
      "x5            -0.1301      0.085     -1.530      0.128        -0.298     0.038\n",
      "x6             0.0515      0.051      1.018      0.310        -0.048     0.151\n",
      "x7            -0.4282      0.365     -1.172      0.243        -1.149     0.293\n",
      "x8             0.0230      0.082      0.281      0.779        -0.139     0.185\n",
      "x9            -0.0695      0.052     -1.343      0.181        -0.172     0.033\n",
      "x10           -0.0084      0.020     -0.421      0.674        -0.048     0.031\n",
      "x11            0.0139      0.073      0.191      0.849        -0.130     0.158\n",
      "x12           -0.0533      0.051     -1.042      0.299        -0.154     0.048\n",
      "x13            0.7598      0.265      2.867      0.005         0.237     1.283\n",
      "x14           -0.0358      0.027     -1.340      0.182        -0.088     0.017\n",
      "x15           -0.0053      0.008     -0.690      0.491        -0.020     0.010\n",
      "x16           -0.0131      0.029     -0.447      0.655        -0.071     0.045\n",
      "x17           -0.0519      0.038     -1.349      0.179        -0.128     0.024\n",
      "x18           -0.0313      0.039     -0.805      0.422        -0.108     0.045\n",
      "x19            0.0385      0.037      1.035      0.302        -0.035     0.112\n",
      "x20            0.0305      0.014      2.237      0.027         0.004     0.057\n",
      "x21           -0.0100      0.044     -0.226      0.822        -0.097     0.077\n",
      "x22           -0.0200      0.015     -1.327      0.186        -0.050     0.010\n",
      "x23           -0.0099      0.012     -0.852      0.396        -0.033     0.013\n",
      "x24            0.0066      0.003      2.060      0.041         0.000     0.013\n",
      "x25            0.0595      0.028      2.157      0.032         0.005     0.114\n",
      "x26         9.128e-05      0.001      0.072      0.943        -0.002     0.003\n",
      "x27           -0.0032      0.002     -1.929      0.055        -0.006  7.58e-05\n",
      "x28            0.0041      0.006      0.705      0.482        -0.007     0.015\n",
      "x29           -0.2977      0.272     -1.096      0.275        -0.834     0.238\n",
      "x30           -0.0266      0.091     -0.293      0.770        -0.205     0.152\n",
      "x31           -0.0363      0.038     -0.955      0.341        -0.111     0.039\n",
      "x32           -0.0012      0.014     -0.085      0.933        -0.029     0.027\n",
      "x33           -0.0102      0.010     -1.048      0.296        -0.029     0.009\n",
      "x34            0.0156      0.023      0.680      0.497        -0.030     0.061\n",
      "x35           -0.0072      0.003     -2.190      0.030        -0.014    -0.001\n",
      "x36            0.0135      0.031      0.434      0.665        -0.048     0.075\n",
      "x37            0.0120      0.009      1.320      0.189        -0.006     0.030\n",
      "x38            0.0004      0.002      0.218      0.828        -0.003     0.004\n",
      "x39           -0.0037      0.005     -0.775      0.440        -0.013     0.006\n",
      "x40            0.0608      0.038      1.620      0.107        -0.013     0.135\n",
      "x41            0.0531      0.039      1.361      0.175        -0.024     0.130\n",
      "x42           -0.0473      0.038     -1.243      0.216        -0.122     0.028\n",
      "x43           -0.1573      0.307     -0.512      0.610        -0.764     0.450\n",
      "x44            0.0203      0.044      0.463      0.644        -0.066     0.107\n",
      "x45            0.0167      0.044      0.379      0.705        -0.070     0.104\n",
      "x46           -0.0211      0.044     -0.482      0.630        -0.108     0.065\n",
      "x47            0.0038      0.011      0.347      0.729        -0.018     0.025\n",
      "==============================================================================\n",
      "Omnibus:                       13.737   Durbin-Watson:                   2.123\n",
      "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               26.385\n",
      "Skew:                           0.299   Prob(JB):                     1.86e-06\n",
      "Kurtosis:                       4.622   Cond. No.                     1.88e+05\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.88e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  WS/48   R-squared:                       0.415\n",
      "Model:                            OLS   Adj. R-squared:                  0.352\n",
      "Method:                 Least Squares   F-statistic:                     6.615\n",
      "Date:                Sun, 20 Mar 2016   Prob (F-statistic):           7.94e-29\n",
      "Time:                        19:32:55   Log-Likelihood:                 1073.4\n",
      "No. Observations:                 486   AIC:                            -2051.\n",
      "Df Residuals:                     438   BIC:                            -1850.\n",
      "Df Model:                          47                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.3320      0.151      2.199      0.028         0.035     0.629\n",
      "x1            -0.0021      0.001     -2.942      0.003        -0.004    -0.001\n",
      "x2            -0.0003      0.000     -1.802      0.072        -0.001  2.34e-05\n",
      "x3         -1.564e-05      0.000     -0.133      0.894        -0.000     0.000\n",
      "x4          1.933e-06   7.42e-06      0.261      0.795     -1.26e-05  1.65e-05\n",
      "x5             0.0129      0.042      0.310      0.757        -0.069     0.095\n",
      "x6             0.0275      0.028      0.989      0.323        -0.027     0.082\n",
      "x7            -0.3138      0.340     -0.922      0.357        -0.983     0.355\n",
      "x8             0.0587      0.036      1.636      0.103        -0.012     0.129\n",
      "x9            -0.0151      0.027     -0.565      0.572        -0.067     0.037\n",
      "x10           -0.0030      0.017     -0.178      0.859        -0.037     0.030\n",
      "x11            0.0362      0.029      1.240      0.216        -0.021     0.094\n",
      "x12           -0.0212      0.027     -0.799      0.424        -0.073     0.031\n",
      "x13            0.0249      0.063      0.394      0.694        -0.099     0.149\n",
      "x14            0.0141      0.020      0.708      0.479        -0.025     0.053\n",
      "x15            0.0105      0.009      1.207      0.228        -0.007     0.028\n",
      "x16            0.0212      0.014      1.519      0.130        -0.006     0.049\n",
      "x17            0.0356      0.033      1.087      0.278        -0.029     0.100\n",
      "x18            0.0091      0.030      0.308      0.758        -0.049     0.067\n",
      "x19           -0.0336      0.028     -1.207      0.228        -0.088     0.021\n",
      "x20            0.0071      0.008      0.907      0.365        -0.008     0.022\n",
      "x21           -0.0118      0.032     -0.372      0.710        -0.074     0.051\n",
      "x22            0.0246      0.023      1.048      0.295        -0.021     0.071\n",
      "x23            0.0238      0.011      2.166      0.031         0.002     0.045\n",
      "x24            0.0026      0.003      0.909      0.364        -0.003     0.008\n",
      "x25           -0.0276      0.019     -1.439      0.151        -0.065     0.010\n",
      "x26            0.0009      0.001      1.066      0.287        -0.001     0.002\n",
      "x27           -0.0018      0.001     -1.435      0.152        -0.004     0.001\n",
      "x28            0.0031      0.005      0.616      0.539        -0.007     0.013\n",
      "x29            0.1040      0.342      0.304      0.762        -0.569     0.777\n",
      "x30           -0.1213      0.070     -1.729      0.085        -0.259     0.017\n",
      "x31           -0.0332      0.047     -0.705      0.481        -0.126     0.059\n",
      "x32           -0.0204      0.014     -1.443      0.150        -0.048     0.007\n",
      "x33           -0.0128      0.008     -1.641      0.101        -0.028     0.003\n",
      "x34            0.0399      0.019      2.086      0.038         0.002     0.077\n",
      "x35           -0.0024      0.002     -1.376      0.169        -0.006     0.001\n",
      "x36            0.0067      0.022      0.299      0.765        -0.037     0.051\n",
      "x37           -0.0131      0.012     -1.124      0.262        -0.036     0.010\n",
      "x38           -0.0013      0.001     -0.892      0.373        -0.004     0.002\n",
      "x39           -0.0065      0.004     -1.572      0.117        -0.015     0.002\n",
      "x40            0.0012      0.028      0.042      0.966        -0.054     0.056\n",
      "x41           -0.0032      0.028     -0.114      0.909        -0.058     0.052\n",
      "x42            0.0090      0.028      0.324      0.746        -0.046     0.064\n",
      "x43           -0.0012      0.216     -0.005      0.996        -0.426     0.424\n",
      "x44           -0.0178      0.026     -0.675      0.500        -0.070     0.034\n",
      "x45           -0.0208      0.026     -0.792      0.429        -0.072     0.031\n",
      "x46            0.0199      0.026      0.759      0.448        -0.032     0.071\n",
      "x47            0.0030      0.005      0.537      0.592        -0.008     0.014\n",
      "==============================================================================\n",
      "Omnibus:                       17.328   Durbin-Watson:                   2.010\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               18.550\n",
      "Skew:                           0.426   Prob(JB):                     9.37e-05\n",
      "Kurtosis:                       3.434   Cond. No.                     4.73e+05\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 4.73e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  WS/48   R-squared:                       0.432\n",
      "Model:                            OLS   Adj. R-squared:                  0.323\n",
      "Method:                 Least Squares   F-statistic:                     3.938\n",
      "Date:                Sun, 20 Mar 2016   Prob (F-statistic):           1.29e-12\n",
      "Time:                        19:32:55   Log-Likelihood:                 646.07\n",
      "No. Observations:                 291   AIC:                            -1196.\n",
      "Df Residuals:                     243   BIC:                            -1020.\n",
      "Df Model:                          47                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.2671      0.169      1.584      0.114        -0.065     0.599\n",
      "x1            -0.0020      0.001     -1.872      0.062        -0.004     0.000\n",
      "x2            -0.0002      0.000     -0.902      0.368        -0.001     0.000\n",
      "x3            -0.0001      0.000     -0.646      0.519        -0.000     0.000\n",
      "x4         -7.071e-06   1.36e-05     -0.521      0.603     -3.38e-05  1.97e-05\n",
      "x5            -0.0392      0.088     -0.445      0.657        -0.213     0.134\n",
      "x6            -0.0084      0.046     -0.184      0.854        -0.099     0.082\n",
      "x7            -0.2000      0.465     -0.430      0.668        -1.117     0.717\n",
      "x8            -0.0052      0.088     -0.059      0.953        -0.180     0.169\n",
      "x9            -0.0068      0.052     -0.130      0.896        -0.109     0.096\n",
      "x10           -0.0186      0.013     -1.485      0.139        -0.043     0.006\n",
      "x11            0.0241      0.074      0.326      0.745        -0.121     0.170\n",
      "x12           -0.0085      0.047     -0.182      0.856        -0.100     0.083\n",
      "x13           -0.0754      0.429     -0.176      0.861        -0.921     0.770\n",
      "x14           -0.0090      0.027     -0.336      0.737        -0.062     0.044\n",
      "x15           -0.0088      0.005     -1.922      0.056        -0.018     0.000\n",
      "x16           -0.0137      0.023     -0.588      0.557        -0.060     0.032\n",
      "x17            0.0205      0.039      0.518      0.605        -0.057     0.098\n",
      "x18            0.0331      0.040      0.833      0.406        -0.045     0.111\n",
      "x19           -0.0224      0.037     -0.607      0.545        -0.095     0.050\n",
      "x20            0.0292      0.018      1.623      0.106        -0.006     0.065\n",
      "x21            0.0199      0.043      0.462      0.644        -0.065     0.105\n",
      "x22           -0.0242      0.010     -2.305      0.022        -0.045    -0.004\n",
      "x23           -0.0315      0.013     -2.487      0.014        -0.057    -0.007\n",
      "x24            0.0013      0.003      0.432      0.666        -0.005     0.007\n",
      "x25            0.0223      0.028      0.811      0.418        -0.032     0.077\n",
      "x26           -0.0011      0.001     -1.305      0.193        -0.003     0.001\n",
      "x27           -0.0013      0.001     -0.881      0.379        -0.004     0.002\n",
      "x28           -0.0034      0.006     -0.590      0.555        -0.015     0.008\n",
      "x29            0.4418      0.200      2.205      0.028         0.047     0.837\n",
      "x30            0.0595      0.185      0.321      0.748        -0.306     0.425\n",
      "x31            0.0070      0.016      0.429      0.668        -0.025     0.039\n",
      "x32            0.0190      0.018      1.046      0.297        -0.017     0.055\n",
      "x33            0.0111      0.011      0.972      0.332        -0.011     0.034\n",
      "x34           -0.0268      0.028     -0.971      0.332        -0.081     0.028\n",
      "x35           -0.0037      0.004     -0.913      0.362        -0.012     0.004\n",
      "x36           -0.0074      0.031     -0.239      0.811        -0.069     0.054\n",
      "x37            0.0173      0.006      2.895      0.004         0.006     0.029\n",
      "x38           -0.0017      0.001     -1.356      0.176        -0.004     0.001\n",
      "x39            0.0050      0.006      0.805      0.422        -0.007     0.017\n",
      "x40           -0.0324      0.036     -0.902      0.368        -0.103     0.038\n",
      "x41           -0.0157      0.036     -0.436      0.663        -0.086     0.055\n",
      "x42            0.0318      0.036      0.893      0.373        -0.038     0.102\n",
      "x43           -0.1606      0.254     -0.633      0.527        -0.660     0.339\n",
      "x44           -0.0351      0.040     -0.875      0.383        -0.114     0.044\n",
      "x45           -0.0398      0.040     -1.000      0.318        -0.118     0.039\n",
      "x46            0.0350      0.040      0.876      0.382        -0.044     0.114\n",
      "x47            0.0125      0.008      1.595      0.112        -0.003     0.028\n",
      "==============================================================================\n",
      "Omnibus:                        7.440   Durbin-Watson:                   1.904\n",
      "Prob(Omnibus):                  0.024   Jarque-Bera (JB):                7.321\n",
      "Skew:                           0.340   Prob(JB):                       0.0257\n",
      "Kurtosis:                       3.376   Cond. No.                     3.86e+05\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 3.86e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "[print(i.summary()) for i in estHold];"
   ]
  }
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