notebook/examples/Interactive Widgets/Image Browser.ipynb

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 "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": "iVBORw0KGgoAAAANSUhEUgAAAuUAAAMNCAYAAAA/WgqlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOQAAsJdEOQAAFBPlAABQTJQD
       "text": [
        "<matplotlib.figure.Figure at 0x111c03f50>"
       ]
      }
     ],
     "prompt_number": 15
    }
   ],
   "metadata": {}
  }
 ]
}