{
 "metadata": {
  "kernelspec": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "display_name": "IPython (Python 2)",
   "language": "python",
   "name": "python2"
  },
  "name": "",
  "signature": "sha256:a43729325678a34b35adcd136402bb1b203e6115706c344b0911188df089f974"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Please See PS7 pdf for questions"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "using PyPlot"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "INFO: Loading help data...\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n=2000\n",
      "a=randn(n,n)\n",
      "ev=sqrt(eigvals(a'*a))/sqrt(n);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f=figure(figsize=(5,5))\n",
      "plt.hist(ev, bins=40, normed=\"true\",color=\"y\")\n",
      "x=0:.01:2;\n",
      "plot(x,sqrt(4-x.^2)/pi,\"r\",linewidth=3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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",
       "text": [
        "Figure(PyObject <matplotlib.figure.Figure object at 0x7f84741925d0>)"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "1-element Array{Any,1}:\n",
        " PyObject <matplotlib.lines.Line2D object at 0x7f84824661d0>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "function ellipse(A)\n",
      "for t=[0:.1:2pi]\n",
      "   plot(cos(t),sin(t),\"b.\")\n",
      "   plot((A*[cos(t);sin(t)])... ,\"r.\")\n",
      "end\n",
      "axis(\"equal\")\n",
      "end"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "ellipse (generic function with 1 method)"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ellipse([6 2; 2 1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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",
       "text": [
        "Figure(PyObject <matplotlib.figure.Figure object at 0x7f8482466990>)"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "(-8.0,8.0,-3.0,3.0)"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}