{
 "metadata": {
  "name": "",
  "signature": "sha256:4e9f4ce8fa9be2e33ffdae399daec11bf3ef63a6f0065b1d59739310ebfe8008"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Image Browser"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This example shows how to browse through a set of images with a slider."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.html.widgets import interact"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn import datasets"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We will use the digits dataset from [scikit-learn](http://scikit-learn.org/stable/)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "digits = datasets.load_digits()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def browse_images(digits):\n",
      "    n = len(digits.images)\n",
      "    def view_image(i):\n",
      "        plt.imshow(digits.images[i], cmap=plt.cm.gray_r, interpolation='nearest')\n",
      "        plt.title('Training: %s' % digits.target[i])\n",
      "        plt.show()\n",
      "    interact(view_image, i=(0,n-1))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "browse_images(digits)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {
        "png": {
         "height": 390,
         "width": 370
        }
       },
       "output_type": "display_data",
       "png": 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AQDFRDgAAxUQ5AAAUE+UAAFBMlAMAQDFRDgAAxUQ5AAAUE+UAAFBMlAMAQDFRDgAAxUQ5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\nAJM1b5RfPTsetqD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       "text": [
        "<matplotlib.figure.Figure at 0x111c03f50>"
       ]
      }
     ],
     "prompt_number": 15
    }
   ],
   "metadata": {}
  }
 ]
}