{
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Get me at http://tinyurl.com/sklcds\n",
      "========================================"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The IPython Notebook!\n",
      "========================\n",
      "Press shift + enter to run a cell.\n",
      "\n",
      "You can go back to previous cells, change them and re-run them."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(\"Hello World\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X = 112"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(X)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "range(10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      " IPython notebook allows tab-completion and shows docstrings (by pressing tab [shift-tab in latest versions] after the opening parantheses), or using ?\n",
      " "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "range?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Cells can be arbitrary long or short, and can define functions that will be available in other cells."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def fib(n):\n",
      "    if n in [0, 1]:\n",
      "        return n\n",
      "    return fib(n - 1) + fib(n - 2)\n",
      "\n",
      "for x in range(5):\n",
      "    print(fib(x))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Numpy\n",
      "======\n",
      "Numpy array are the most common numeric data type.\n",
      "\n",
      "As in other environments, it is very beneficial to vectorize your code (array-based computing) to make use of fast C and Fortran implementations."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "np.ones(10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.ones((10, 10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.arange(10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Numpy allows *broadcasting* over rows, leading to practical short-hand notations."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X = np.ones((10, 10)) + np.array([3, 5, 1, 10, 6, 12, 98, 1, 0, 3])\n",
      "print(X)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Most libraries in Python use object oriented interfaces."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X.mean(axis=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Numpy has all standard array functions, linear algebra, and *fancy indexing*."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X[:3, 1:4]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X[:, ::2]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X = np.random.randint(10, size=(32, 103))\n",
      "X"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X[np.random.randint(32, size=10)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X[np.random.randint(32, size=10)].shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [

      "Matplotlib\n",
      "=============\n",
      "For all of your plotting needs!"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Enable in-line plotting (can be done in config file)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(np.random.uniform(size=10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.bar(np.arange(10), np.random.uniform(size=10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.hist(np.random.normal(size=1000))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x, y = np.random.uniform(size=(2, 10))\n",
      "plt.scatter(x, y, marker=\"x\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(np.eye(5))\n",
      "plt.matshow(np.eye(5))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Tasks\n",
      "======\n",
      "\n",
      "1. Plot the function ``f(x) = x ** 2`` using lines. How can you get a smooth plot?\n",
      "2. Visualize a two-dimensional gaussian distribution from samples. How does your approach look with very many samples?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}