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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Interact Exercises"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from matplotlib import pyplot as plt\n",
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.html.widgets import *\n",
      "from IPython.display import display"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "String sorting"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Write a `sort_string` function that takes a string as its input and prints a new string consisting of the original one, sorted.  Add a `reverse` keyword argument with a default of `False` to allow for the sorting to be done in reverse.\n",
      "\n",
      "Then, use `interact` to create a user interface for exploring your `sort_string` function."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load soln/string_sorting.py"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Plotting with parameters"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Write a `plot_sin` function that plots $sin(ax+b)$ over the interval $[0,4\\pi]$.\n",
      "\n",
      "Then use `interact` to create a user interface for exploring your function:\n",
      "\n",
      "* `a` should be a floating point number over the interval $[0.0,5.0]$.\n",
      "* `b` should be a floating point number over the interval $[-5.0,5.0]$."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load soln/param_plot_1.py"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In matplotlib, the line style and color can be set with a third argument to `plot`. Examples of this argument:\n",
      "\n",
      "* dashed red: `r--`\n",
      "* blue circles: `bo`\n",
      "* dotted black: `k.`\n",
      "\n",
      "Add a `style` argument to your `plot_sin` function that allows you to set the line style of the plot.\n",
      "\n",
      "Use `interact` to create a UI for `plot_sin` that has a drop down menu for selecting the line style between a **dotted red** line and a **dashed black line**. This time use `interact` as a decorator."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load soln/param_plot_2.py"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Simple data explorer"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In this exercise, you will use interact to build a UI for exploring correlations between different features in the [Iris dataset](http://en.wikipedia.org/wiki/Iris_flower_data_set) in [sklearn]http://scikit-learn.org/stable/(http://scikit-learn.org/stable/). This data contains 4 different measurements (called features in this content) of 150 different iris flowers of three different species.\n",
      "\n",
      "Load the dataset:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.datasets import load_iris\n",
      "iris_data = load_iris()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The actual data is stored as a NumPy array under the `data` attribute:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "iris_data.data.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "(150, 4)"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You can see the meanings of the 4 columns of data by looking at the `feature_names` attribute:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "iris_data.feature_names"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "['sepal length (cm)',\n",
        " 'sepal width (cm)',\n",
        " 'petal length (cm)',\n",
        " 'petal width (cm)']"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Write a `plot_iris` function that creates a scatter plot (using `plt.scatter`) of two columns of this dataset. Your function should have the following signature:\n",
      "\n",
      "```python\n",
      "def plot_iris(a, col1, col2):\n",
      "    ...\n",
      "```\n",
      "\n",
      "where `a` is the NumPy array of data and `col1/col2` are the two columns to use for the scatter plot.\n",
      "\n",
      "Use `interact` to build a UI to explore the iris dataset using your `plot_iris` function. You will need to use the `fixed` function when passing the dataset to the function."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load soln/data_explorer.py"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}