PUI_HW3_YLai.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "ename": "IOError",
     "evalue": "[Errno 2] No such file or directory: '/Users/ericlaibuffalo/PUI2015/ylai_matplotlibrc.json'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIOError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-60-f2b3f9d9497e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetenv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"PUI2015\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\"/ylai_matplotlibrc.json\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0mpl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrcParams\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIOError\u001b[0m: [Errno 2] No such file or directory: '/Users/ericlaibuffalo/PUI2015/ylai_matplotlibrc.json'"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "__author__ = 'ylai'\n",
    "\n",
    "import pylab as pl\n",
    "import numpy as np\n",
    "%pylab inline\n",
    "\n",
    "import json\n",
    "import os\n",
    "s = json.load( open(os.getenv(\"PUI2015\")+\"/ylai_matplotlibrc.json\") )\n",
    "pl.rcParams.update(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mymean = 100\n",
    "distributions = {}\n",
    "distributions['chisq'] = np.random.chisquare(mymean, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  7.,  10.,  13.,  23.,  27.,   7.,   7.,   3.,   2.,   1.]),\n",
       " array([  67.95872572,   76.4992821 ,   85.03983848,   93.58039486,\n",
       "         102.12095124,  110.66150762,  119.20206401,  127.74262039,\n",
       "         136.28317677,  144.82373315,  153.36428953]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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X1rQkvwX8MvAby4rnrS9eyWDH554kRxhs751JdjF/fQGDDL0JoMvRY0nOZIi+GEuoV9VR\n4NEkr+qK3gzcB9zC4CAh3c+bx1HfjHsQuCjJizJ4FMubgfuZz75YcoDjb/sB4NeSvCDJuQz+Bb1j\nCu2bmCR7GOyJXVJV31/20Vz1RVUdrqpdVXVuVZ3LINQu6Ibp5qovOjcDPw/Q5egLqupJhumLMR7R\n/UngS8A9DP7inA7sBD4LPAQcBHZM+8jzJF4MDvzcBxxmcGBw+7z0BXA9g2MJP2Bw07crVtt2Bv+C\nP8zgj+EvTbv9m9wXbwW+CvwHcFf3+vCc9cX/Ln0vVnz+NbqzX+axL7qM+FiXGXcCvWH7wouPJKkh\nPs5OkhpiqEtSQwx1SWqIoS5JDTHUJakhhrokNcRQl6SGGOqS1JD/B1MHbY/GO6sfAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10eb5fb10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pl.hist(distributions['chisq'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "101.058619844\n",
      "16.1781849848\n"
     ]
    }
   ],
   "source": [
    "print (distributions['chisq'].mean())\n",
    "print (distributions['chisq'].std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1818 1666 1538 1428 1333 1250 1176 1111 1052 1000  952  909  869  833  800\n",
      "  769  740  714  689  666  645  625  606  588  571  555  540  526  512  500\n",
      "  487  476  465  454  444  434  425  416  408  400  392  384  377  370  363\n",
      "  357  350  344  338  333  327  322  317  312  307  303  298  294  289  285\n",
      "  281  277  273  270  266  263  259  256  253  250  246  243  240  238  235\n",
      "  232  229  227  224  222  219  217  215  212  210  208  206  204  202]\n"
     ]
    }
   ],
   "source": [
    "mysizes = 20000/(np.array(range(1,90))+10)\n",
    "print (mysize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "distributions = {'chisq':{}}\n",
    "for n in mysizes:\n",
    "    distributions['chisq'][n] = np.random.chisquare(mymean,n)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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M7LOS3iDpzZJ+18xerXa7Eklyzv25mf2upD+XdFnSazvJzMzulvSrzrmP9Hs9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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f72e190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "means = {}\n",
    "means['chisq'] = {}\n",
    "\n",
    "fig_mu_N = pl.figure(figsize=(10,10))\n",
    "axchiq_mu_n = fig_mu_N.add_subplot(1,1,1)\n",
    "for nn in distributions['chisq']:\n",
    "    axchiq_mu_n.plot(nn, distributions['chisq'][nn].mean(), 'o')\n",
    "    axchiq_mu_n.set_xlabel('sample size')\n",
    "    axchiq_mu_n.set_ylabel('sample mean')\n",
    "    axchiq_mu_n.plot([min(mysizes), max(mysizes)], [mymean, mymean], 'k')\n"
   ]
  },
  {
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