msund/AQ NB

## AQ NB
{
 "metadata": {
  "name": "Untitled1"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "%pylab inline\nimport matplotlib.pyplot as plt # so we don't have to look at mpl's backend\nimport matplotlib.gridspec as gridspec # for subplots",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "Populating the interactive namespace from numpy and matplotlib\n"
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "WARNING: pylab import has clobbered these variables: ['power', 'random', 'fft', 'linalg', 'info']\n`%pylab --no-import-all` prevents importing * from pylab and numpy\n"
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "from matplotlylib import fig_to_plotly\nusername = 'IPython.Demo'\napi_key = '1fw3zw2o13'",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig1 = plt.figure()\n\nfrom __future__ import print_function\n\"\"\"\nEdward Tufte uses this example from Anscombe to show 4 datasets of x\nand y that have the same mean, standard deviation, and regression\nline, but which are qualitatively different.\n\nmatplotlib fun for a rainy day\n\"\"\"\n\nfrom pylab import *\n\nx =  array([10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5])\ny1 = array([8.04, 6.95, 7.58, 8.81, 8.33, 9.96, 7.24, 4.26, 10.84, 4.82, 5.68])\ny2 = array([9.14, 8.14, 8.74, 8.77, 9.26, 8.10, 6.13, 3.10, 9.13, 7.26, 4.74])\ny3 = array([7.46, 6.77, 12.74, 7.11, 7.81, 8.84, 6.08, 5.39, 8.15, 6.42, 5.73])\nx4 = array([8,8,8,8,8,8,8,19,8,8,8])\ny4 = array([6.58,5.76,7.71,8.84,8.47,7.04,5.25,12.50,5.56,7.91,6.89])\n\ndef fit(x):\n    return 3+0.5*x\n\n\n\nxfit = array( [amin(x), amax(x) ] )\n\nsubplot(221)\nplot(x,y1,'ks', xfit, fit(xfit), 'r-', lw=2)\naxis([2,20,2,14])\nsetp(gca(), xticklabels=[], yticks=(4,8,12), xticks=(0,10,20))\ntext(3,12, 'I', fontsize=20)\n\nsubplot(222)\nplot(x,y2,'ks', xfit, fit(xfit), 'r-', lw=2)\naxis([2,20,2,14])\nsetp(gca(), xticklabels=[], yticks=(4,8,12), yticklabels=[], xticks=(0,10,20))\ntext(3,12, 'II', fontsize=20)\n\nsubplot(223)\nplot(x,y3,'ks', xfit, fit(xfit), 'r-', lw=2)\naxis([2,20,2,14])\ntext(3,12, 'III', fontsize=20)\nsetp(gca(), yticks=(4,8,12), xticks=(0,10,20))\n\nsubplot(224)\n\nxfit = array([amin(x4),amax(x4)])\nplot(x4,y4,'ks', xfit, fit(xfit), 'r-', lw=2)\naxis([2,20,2,14])\nsetp(gca(), yticklabels=[], yticks=(4,8,12), xticks=(0,10,20))\ntext(3,12, 'IV', fontsize=20)\n\n#verify the stats\npairs = (x,y1), (x,y2), (x,y3), (x4,y4)\nfor x,y in pairs:\n    print ('mean=%1.2f, std=%1.2f, r=%1.2f'%(mean(y), std(y), corrcoef(x,y)[0][1]))\n\nshow()\n\nfig_to_plotly(fig1, username, api_key, notebook= True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "mean=7.50, std=1.94, r=0.82\nmean=7.50, std=1.94, r=0.82\nmean=7.50, std=1.94, r=0.82\nmean=7.50, std=1.94, r=0.82\n"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": "<matplotlib.figure.Figure at 0x10dde4f10>"
      },
      {
       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/2767/600/450\" width=\"650\"></iframe>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": "<IPython.core.display.HTML at 0x10db8b7d0>"
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}
	{
	"metadata": {
	"name": "Untitled1"
	},
	"nbformat": 3,
	"nbformat_minor": 0,
	"worksheets": [
	{
	"cells": [
	{
	"cell_type": "code",
	"collapsed": false,
	"input": "%pylab inline\nimport matplotlib.pyplot as plt # so we don't have to look at mpl's backend\nimport matplotlib.gridspec as gridspec # for subplots",
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"output_type": "stream",
	"stream": "stdout",
	"text": "Populating the interactive namespace from numpy and matplotlib\n"
	},
	{
	"output_type": "stream",
	"stream": "stderr",
	"text": "WARNING: pylab import has clobbered these variables: ['power', 'random', 'fft', 'linalg', 'info']\n`%pylab --no-import-all` prevents importing * from pylab and numpy\n"
	}
	],
	"prompt_number": 15
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": "from matplotlylib import fig_to_plotly\nusername = 'IPython.Demo'\napi_key = '1fw3zw2o13'",
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 16
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": "fig1 = plt.figure()\n\nfrom __future__ import print_function\n\"\"\"\nEdward Tufte uses this example from Anscombe to show 4 datasets of x\nand y that have the same mean, standard deviation, and regression\nline, but which are qualitatively different.\n\nmatplotlib fun for a rainy day\n\"\"\"\n\nfrom pylab import \n\nx = array([10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5])\ny1 = array([8.04, 6.95, 7.58, 8.81, 8.33, 9.96, 7.24, 4.26, 10.84, 4.82, 5.68])\ny2 = array([9.14, 8.14, 8.74, 8.77, 9.26, 8.10, 6.13, 3.10, 9.13, 7.26, 4.74])\ny3 = array([7.46, 6.77, 12.74, 7.11, 7.81, 8.84, 6.08, 5.39, 8.15, 6.42, 5.73])\nx4 = array([8,8,8,8,8,8,8,19,8,8,8])\ny4 = array([6.58,5.76,7.71,8.84,8.47,7.04,5.25,12.50,5.56,7.91,6.89])\n\ndef fit(x):\n return 3+0.5x\n\n\n\nxfit = array( [amin(x), amax(x) ] )\n\nsubplot(221)\nplot(x,y1,'ks', xfit, fit(xfit), 'r-', lw=2)\naxis([2,20,2,14])\nsetp(gca(), xticklabels=[], yticks=(4,8,12), xticks=(0,10,20))\ntext(3,12, 'I', fontsize=20)\n\nsubplot(222)\nplot(x,y2,'ks', xfit, fit(xfit), 'r-', lw=2)\naxis([2,20,2,14])\nsetp(gca(), xticklabels=[], yticks=(4,8,12), yticklabels=[], xticks=(0,10,20))\ntext(3,12, 'II', fontsize=20)\n\nsubplot(223)\nplot(x,y3,'ks', xfit, fit(xfit), 'r-', lw=2)\naxis([2,20,2,14])\ntext(3,12, 'III', fontsize=20)\nsetp(gca(), yticks=(4,8,12), xticks=(0,10,20))\n\nsubplot(224)\n\nxfit = array([amin(x4),amax(x4)])\nplot(x4,y4,'ks', xfit, fit(xfit), 'r-', lw=2)\naxis([2,20,2,14])\nsetp(gca(), yticklabels=[], yticks=(4,8,12), xticks=(0,10,20))\ntext(3,12, 'IV', fontsize=20)\n\n#verify the stats\npairs = (x,y1), (x,y2), (x,y3), (x4,y4)\nfor x,y in pairs:\n print ('mean=%1.2f, std=%1.2f, r=%1.2f'%(mean(y), std(y), corrcoef(x,y)[0][1]))\n\nshow()\n\nfig_to_plotly(fig1, username, api_key, notebook= True)",
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"output_type": "stream",
	"stream": "stdout",
	"text": "mean=7.50, std=1.94, r=0.82\nmean=7.50, std=1.94, r=0.82\nmean=7.50, std=1.94, r=0.82\nmean=7.50, std=1.94, r=0.82\n"
	},
	{
	"metadata": {},
	"output_type": "display_data",
	"png": 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	"text": "<matplotlib.figure.Figure at 0x10dde4f10>"
	},
	{
	"html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/2767/600/450\" width=\"650\"></iframe>",
	"metadata": {},
	"output_type": "pyout",
	"prompt_number": 17,
	"text": "<IPython.core.display.HTML at 0x10db8b7d0>"
	}
	],
	"prompt_number": 17
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": "",
	"language": "python",
	"metadata": {},
	"outputs": []
	}
	],
	"metadata": {}
	}
	]
	}