{
 "metadata": {
  "name": ""
 },
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 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Interact Demos"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This Notebook shows basic demonstrations of IPython `interact` module. This provides a high-level interface for creating user interface controls to use in exploring code and data interactively."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.html.widgets.interact import interact, interactive\n",
      "from IPython.html import widgets\n",
      "from IPython.display import clear_output, display, HTML"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Basic interact"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here is a simple function that displays its arguments as an HTML table:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def show_args(**kwargs):\n",
      "    s = '<h3>Arguments:</h3><table>\\n'\n",
      "    for k,v in kwargs.items():\n",
      "        s += '<tr><td>{0}</td><td>{1}</td></tr>\\n'.format(k,v)\n",
      "    s += '</table>'\n",
      "    display(HTML(s))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "show_args(a=10, b='Hi There', c=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's use this function to explore how `interact` works."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "interact(show_args,\n",
      "         Temp=(0,10),\n",
      "         Current=(0.,10.,0.01),\n",
      "         z=(True,False),\n",
      "         Text=u'Type here!',\n",
      "         Algorithm=['This','That','Other'],\n",
      "         a=widgets.FloatRangeWidget(min=-10.0, max=10.0, step=0.1, value=5.0)\n",
      "         )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The keyword arguments to `interact` can be any `Widget` instance that has a `value` and `description` attribute, or one of the shorthand notations shown above."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Factoring polynomials"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here is an example that uses [SymPy](http://sympy.org/en/index.html) to factor polynomials."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sympy import Symbol, Eq, factor, init_printing\n",
      "init_printing(use_latex=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = Symbol('x')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def factorit(n):\n",
      "    display(Eq(x**n-1, factor(x**n-1)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Notice how the output of the `factorit` function is properly formatted LaTeX."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "interact(factorit, n=(2,40))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "A simple 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": [
      "from sklearn import datasets"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "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": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def browse_images(digits):\n",
      "    n = len(digits.images)\n",
      "    def view_image(i):\n",
      "        imshow(digits.images[i], cmap=cm.gray_r, interpolation='nearest')\n",
      "        title('Training: %s' % digits.target[i])\n",
      "        show()\n",
      "    interact(view_image, i=(0,n-1))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "browse_images(digits)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Explore random graphs"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In this example, we build a simple UI for exploring random graphs with [NetworkX](http://networkx.github.io/)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import networkx as nx"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plot_random_graph(n, p, generator):\n",
      "    g = generator(n,p)\n",
      "    nx.draw(g)\n",
      "    show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "interact(plot_random_graph, n=(2,30), p=(0.0, 1.0, 0.001),\n",
      "        generator={'gnp': nx.gnp_random_graph,\n",
      "                   'erdos_renyi': nx.erdos_renyi_graph,\n",
      "                   'binomial': nx.binomial_graph})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Image manipulation"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This example builds a simple UI for performing basic image manipulation with [scikit-image](http://scikit-image.org/)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import skimage\n",
      "from skimage import data, filter, io"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "i = data.coffee()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "io.Image(i)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def edit_image(image):\n",
      "    def apply_filter(sigma, r, g, b):\n",
      "        new_image = filter.gaussian_filter(image, sigma=sigma)\n",
      "        new_image[:,:,0] = r*new_image[:,:,0]\n",
      "        new_image[:,:,1] = g*new_image[:,:,1]\n",
      "        new_image[:,:,2] = b*new_image[:,:,2]\n",
      "        new_image = io.Image(new_image)\n",
      "        display(new_image)\n",
      "        return new_image\n",
      "    lims = (0.0,1.0,0.01)\n",
      "    return interactive(apply_filter, sigma=(0.1,10.0,0.01), r=lims, g=lims, b=lims)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "w = edit_image(i)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "display(w)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "w.arguments"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "w.result"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Playing with audio"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This example uses the `Audio` object and Matplotlib to explore the phenomenon of beat frequencies."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import Audio\n",
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def beat_freq(f1=220.0, f2=224.0):\n",
      "    max_time = 3\n",
      "    rate = 8000.0\n",
      "    times = np.linspace(0,max_time,rate*max_time)\n",
      "    signal = np.sin(2*np.pi*f1*times) + np.sin(2*np.pi*f2*times)\n",
      "    print f1, f2, abs(f1-f2)\n",
      "    display(Audio(data=signal, rate=rate))\n",
      "    return signal"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "v = interactive(beat_freq, f1=(200.0,300.0), f2=(200.0,300.0))\n",
      "display(v)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(v.result[0:6000])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}