{
 "metadata": {
  "name": "coin-flips"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "## Reminder: use Shift-ENTER to run each cell; feel free to edit, etc, before and after running!\n\n## Reminder: go up to 'File' and 'Make a copy' before running this notebook.  Change the name to something that includes your NetID, e.g. 'coin-flips-titus'."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import random\n\n# flip a coin\nrandom.choice([0, 1])",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": "0"
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "N = 100\nflips = []\nfor i in range(N):\n    flip = random.choice([0, 1])\n    flips.append(flip)\n    \nprint flips",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "[1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0]\n"
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "print sum(flips) / float(len(flips))",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "0.47\n"
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "hist(flips)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": "(array([53,  0,  0,  0,  0,  0,  0,  0,  0, 47]),\n array([ 0. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9,  1. ]),\n <a list of 10 Patch objects>)"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ioqI3xkyLWGthWZZ18+ZNa/78+daiRYusPXv29MKU6RFrLTo6OqxJkyZZhw8ftizLuu9a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\n9bgPGs5g1QAAAABJRU5ErkJggg==\n",
       "text": "<matplotlib.figure.Figure at 0x383ba50>"
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# write a function to perform many flips\ndef flip_N(N):\n    flips = []\n    for i in range(N):\n        flip = random.choice([0, 1])\n        flips.append(flip)\n        \n    return sum(flips) / float(N)\n\navg = flip_N(100)\nprint avg",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "0.46\n"
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# now perform many trials, and show the distribution\nn_trials = 500\ntrials = []\nN = 100\n\nfor i in range(n_trials):\n    avg = flip_N(N)\n    trials.append(avg)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "hist(trials)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": "(array([  8,  19,  54, 112, 134,  94,  54,  15,   6,   4]),\n array([ 0.37 ,  0.399,  0.428,  0.457,  0.486,  0.515,  0.544,  0.573,\n        0.602,  0.631,  0.66 ]),\n <a list of 10 Patch objects>)"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": "<matplotlib.figure.Figure at 0x7f78041f7090>"
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ls",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "coin-flips-evokerr.ipynb\r\ncoin-flips-hljordt.ipynb\r\ncoin-flips.ipynb\r\ncoin-flips-lnj204.ipynb\r\ncoin-flips-mherrmannsfeldt.ipynb\r\ncoin-flips-qchrist2.ipynb\r\ncoin-flips-Richard.ipynb\r\nfilter-blast-csv-hljordt.ipynb\r\nfilter-blast-csv.ipynb\r\nfilter-blast-csv_qchrist2.ipynb\r\ngraph-blast-bitscore-Copy0.ipynb\r\ngraph-blast-bitscore-Copy6-Copy0.ipynb\r\ngraph-blast-bitscore-Copy6.ipynb\r\ngraph-blast-bitscore-evokerr.ipynb\r\ngraph-blast-bitscore.ipynb\r\ngraph-blast-bitscore-leonard.ipynb\r\ngraph-blast-bitscore-lnj204.ipynb\r\ngraph-blast-bitscore-mherrmannsfeldt.ipynb\r\ngraph-blast-bitscore-qchrist2.ipynb\r\ngraph-blast-bitscore-Richard.ipynb\r\ngraph-blast-bitscore-sr320.ipynb\r\nhljordt.ipynb\r\nkmer-abundance-hljordt.ipynb\r\nkmer-abundance.ipynb\r\nkmer-abundance-lnj204.ipynb\r\nkmer-abundance-mherrmannsfeldt.ipynb\r\nkmer-abundance-qchrist2.ipynb\r\nkmer-abundance-Richard.ipynb\r\nkmer-abundance-titus.ipynb\r\nmonty-hall-evokerr.ipynb\r\nmonty-hall-hljordt.ipynb\r\nmonty-hall.ipynb\r\nmonty-hall-lnj204.ipynb\r\nmonty-hall-mherrmannsfeldt.ipynb\r\nmonty-hall-qchrist2.ipynb\r\nmonty-hall-Richard.ipynb\r\nmonty-hall-titus.ipynb\r\nREADME.txt\r\n"
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    }
   ],
   "metadata": {}
  }
 ]
}