{
 "metadata": {
  "name": "001-Introduction-Python"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Introduction to the interactive notebook\n",
      "\n",
      "The platform for this workshop will be files such as this one, which interleave explanation text, code and data visualizations. \n",
      "\n",
      "To execute each cell of the notebook, hit *`shift-enter`*. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "That will put you in the next cell. There are two types of input cells, controlled by the menu bar at the top of this page. One type are cells like this one, called \"markdown cells\". They are called that, because they contain a simple, minimalistic form of markup as input to the browser, which then  renders these cells in a useful way. You can find a pretty good markdown cheat-sheet [here](https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet). See - for example - that was a hyper-link to a web page! "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "These cells can also be used to write math-y stuff, using [$\\LaTeX$](http://www.latex-project.org/). To write math that will be rendered in this way, we simply enter a piece of syntactically correct latex between two `$` signs. Like so: $f(x) = \\frac{1}{\\sigma\\sqrt{2\\pi}} e^{\\frac{(x-\\mu)^2}{2\\sigma^2}}$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Another type of input cell in the notebook is a code cell. These cells contain code and when they are executed, this piece of code is sent to the python interpter. For example: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = 1\n",
      "print a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# A whirlwind introduction to Python\n",
      "\n",
      "## Python is object-oriented \n",
      "\n",
      "In contrast to some other commonly used scripting languages (notably Matlab), Python is an object-oriented language. That means that everything is an object. [And there's a big difference between having an `object` data-type and having *almost everything* be an object].\n",
      "\n",
      "For example, if you type `a.` and hit the `tab` key, you will see all the attributes of this object: \n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print a.real\n",
      "print a.imag"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1\n",
        "0\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "So far, that looks a lot like the matlab `struct` array. However, an important difference is that some of these attributes can be functions that can be called on/from the object. These are also referred to as 'methods' of the object. For example: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print a.bit_length"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<built-in method bit_length of int object at 0x100310338>\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To tell what this method does, we can get ipython to give us a brief description of this function. This is done by attaching a `?` after it:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a.bit_length?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This should open a sub-window at the bottom of this window with the 'docstring' of this method. If the author of the function was kind enough to provide useful information in the docstring, we can use this information to know what this method does and what are its expected inputs. This function has no inputs, so to call it, we simply provide the method with empty parentheses:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a.bit_length()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "1"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There is a lot more to say about objects and object-orientation (and what it is good for). For now, this will suffice."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Python uses a few simple data structures\n",
      "\n",
      "Some of them look deceivingly like structures in other languages. For example, here's a 'list':  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b = [1,2,3,4]\n",
      "print len(b)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "4\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here's a classic gotcha. Python indexes start at 0:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(b[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Lists can be changed: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b[0] = 2\n",
      "print b"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[2, 2, 3, 4]\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "They have a ton of interesting attributes and methods. Feel free to explore them as you just learned above. Importantly, though they look a lot like a Matlab vector, they are not the Python analogue of that. Wait just a few more cells for that one to show up. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print b * 2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[2, 2, 3, 4, 2, 2, 3, 4]\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print b+1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "TypeError",
       "evalue": "can only concatenate list (not \"int\") to list",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-10-5bd19596e089>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mprint\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;31mTypeError\u001b[0m: can only concatenate list (not \"int\") to list"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print b + b "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[2, 2, 3, 4, 2, 2, 3, 4]\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's look at one useful class method and notice some interesting (and possibly surprising) behaviors:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "new_b = b.append(5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "What do you expect `new_b` to look like?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print new_b"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "None\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This is because the `append` method has no output. It changes the object on which it is called and doesn't return anything. We'll see more about what that means when we examine functions below. In the meanwhile, here's what `b` not looks like: \n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print b"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[2, 2, 3, 4, 5]\n"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "'tuples' are very similar to lists: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "c = (1,2,3,4)\n",
      "print c[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In contrast to lists, they are immutable: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "c[0] = 2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "TypeError",
       "evalue": "'tuple' object does not support item assignment",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-16-5673658116c7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And they are also not analogous to Matlab vectors: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print c + c"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(1, 2, 3, 4, 1, 2, 3, 4)\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "So far, looks kinda useless. It'll become more interesting in just a minute, after we introduce 'dictionaries', or as they are affectionately known 'dicts':"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = {'key_the_first':1000.0, 'key_the_second':'woot!', 10:1 , (10,11):12}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This data structure holds key-value pairs, such that when you refer to one, you can get the other: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print d['key_the_first']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1000.0\n"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print d[10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1\n"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Note that while lists cannot be keys to a dict, tuples can:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print d[(10,11)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "12\n"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In CS-speak, a thing such as a dict is sometimes referred to as a hash-table and is considered a very useful thing to have. For example, think what you would have to do in order to count the frequency of appearance of certain words in a big text file."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Python uses a few simple control structures\n",
      "\n",
      "As in other languages you might know, there are `if`, `else`, `elif` (*not* `elseif`), `for` and `while` control statements. There are a few interesting idiosyncracies. First of all, if you have something that has several components, you can probably just loop over it, using the `for` loop: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for x in b:\n",
      "    print x"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2\n",
        "2\n",
        "3\n",
        "4\n",
        "5\n"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Second, you might notice that there's no explicit delimiter to that for loop. How does python know where it started and where it ended? The answer is that the indentation signals that. In contrast to other languages, indentation in Python is not only a stylistic preference, but a syntactical requirement. That is, if you do not indent the contents of a control block, you will get an error: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for x in b: \n",
      "print x"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "IndentationError",
       "evalue": "expected an indented block (<ipython-input-23-b651ccce9731>, line 2)",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-23-b651ccce9731>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    print x\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This level of stickler-ish insistence on such things as where the white-space appears have made people call it a \"bondage and discipline\" language. But maybe that's your thing? \n",
      "\n",
      "Obviously, we can nest several different control structures in each other:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for lookfor in range(10):\n",
      "    if lookfor in b:\n",
      "        print 'I think we have a %s in b'%lookfor"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "I think we have a 2 in b\n",
        "I think we have a 3 in b\n",
        "I think we have a 4 in b\n",
        "I think we have a 5 in b\n"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Also demonstrating:\n",
      "\n",
      "- Use of `range`\n",
      "- Different uses of the builtin `in` \n",
      "- String comprehenrsion "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Python gives users control of their namespace\n",
      "\n",
      "The idea here is that apart from very few built-in constructs and types, when you start a python interperter, it knows very little. To allow it to know more, you need to assign variables (which we did above), you need to load things from files (which we'll see later), or you need to import modules that contain more objects, functions, etc. For example, let's import a favorite module of mine, the `os` module"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import os"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "`import`ing this name into our name-space now makes a lot of other names available through it. Try to type `os.` and tab-completing on that. And some of the names under that have additional names under them. For example: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "curdir = os.path.curdir\n",
      "listdir = os.listdir(curdir)\n",
      "print os.path.join(curdir, listdir[1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "./001-Introduction-Python.ipynb\n"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- Example: count words in a file.\n",
      "- Read the paradigm file and plot it and what-not\n",
      "- Where do I get help?\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Some interesting namespaces\n",
      "\n",
      "Let's look at some modules that are particularly useful for neuroimaging stuff. Let's start with `numpy` - a library which implements an array data structure, which resembles Matlab's vector data structures (there it is!):"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "arr1 = np.array([1,2,3,4])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print arr1 + 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[2 3 4 5]\n"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print arr1 * 2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[2 4 6 8]\n"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print arr1 + arr1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[2 4 6 8]\n"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "`numpy` arrays can represent data with several dimensions: \n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "arr2 = np.array([[1,0,0,0],[0,1,0,0],[0,0,1,0],[0,0,0,1]])\n",
      "print arr2\n",
      "print arr2.shape\n",
      "print arr2[0,0]\n",
      "print arr2[1,0]\n",
      "print arr2.dtype"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[1 0 0 0]\n",
        " [0 1 0 0]\n",
        " [0 0 1 0]\n",
        " [0 0 0 1]]\n",
        "(4, 4)\n",
        "1\n",
        "0\n",
        "int64\n"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In contrast to matlab, matrix computations are not implicitly assumed: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print arr2 * arr1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[1 0 0 0]\n",
        " [0 2 0 0]\n",
        " [0 0 3 0]\n",
        " [0 0 0 4]]\n"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print np.dot(arr2, arr1)\n",
      "print np.dot(arr2*2, arr1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[1 2 3 4]\n",
        "[2 4 6 8]\n"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There are a lot of useful functions in `numpy`, go check them out with `np.` - *tab*. Another useful set of functions exists in a rather large library called `scipy`:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import scipy"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scipy?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 35
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Notice that because it's rather large, the sub-modules of `scipy` aren't imported automatically and you need to explicitely import them. For example: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import scipy.stats as stats"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rv = stats.norm(0, 1)\n",
      "x = np.linspace(-3, 3)\n",
      "pdf = rv.pdf(x)\n",
      "pdf"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "array([ 0.00443185,  0.00635135,  0.00896675,  0.01247075,  0.01708592,\n",
        "        0.02306069,  0.03066159,  0.04016108,  0.05182083,  0.0658706 ,\n",
        "        0.08248352,  0.10174921,  0.12364689,  0.1480211 ,  0.17456307,\n",
        "        0.20280069,  0.2320998 ,  0.26167871,  0.29063661,  0.31799518,\n",
        "        0.34275126,  0.36393672,  0.38068082,  0.39226937,  0.39819528,\n",
        "        0.39819528,  0.39226937,  0.38068082,  0.36393672,  0.34275126,\n",
        "        0.31799518,  0.29063661,  0.26167871,  0.2320998 ,  0.20280069,\n",
        "        0.17456307,  0.1480211 ,  0.12364689,  0.10174921,  0.08248352,\n",
        "        0.0658706 ,  0.05182083,  0.04016108,  0.03066159,  0.02306069,\n",
        "        0.01708592,  0.01247075,  0.00896675,  0.00635135,  0.00443185])"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "At this point, we might need some plotting functions to look at this. One way to make sure that useful functions from `numpy` and `matplotlib` are available upon initialization is to set the notebook to `pylab` mode: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n",
        "For more information, type 'help(pylab)'.\n"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This means means that ipython automagically imports numpy as `np` and pyplot as `plt`. It also imports `matplotlib`, `pylab` and `mlab` (All three are part of the [matplotlib](http://matplotlib.org/) graphics library. It also imports all names from numpy and pylab (`from numpy import *`), so these are available without explicit reference to the namespace. On the one hand, this goes a little bit against the philosophy that you shouldn't have anything in your namespace that you didn't put there (and we generally recommend against this usage - you usually want to know where the functions you are using come from!). On the other hand, it's convenient for a lot of the things we do. The `inline` means that figures will get inserted into the notebook as we go along. OK - we're ready to look at that PDF: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(pdf)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 41,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10755b690>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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UmnqZmPUesbGqtPSyZbojEd5Ekr1okX//G/bvh6FDdUciTnXPPfDaa7qjEN6k0WSfn59P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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1072e7890>"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## You can easily define Python functions\n",
      "\n",
      "No need for other files, compilation steps, or anything. Just define it and it's there: \n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def add_two_numbers(num1, num2):\n",
      "    \"\"\"\n",
      "    This is the function docstring. Ideally it is informative. For example: \n",
      "\n",
      "    Add two numbers to each other. \n",
      "\n",
      "    Parameters\n",
      "    ----------\n",
      "    num1 : int/float\n",
      "       A number\n",
      "    \n",
      "    num2 : int/float\n",
      "       Another number\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    sum_it : the sum of the inputs\n",
      "    \n",
      "    \"\"\"\n",
      "    sum_it = num1 + num2\n",
      "    return sum_it"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "add_two_numbers(1,2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "3"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "add_two_numbers?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 44
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "If we have time, let's look at a slightly more interesting example: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def word_count(url): \n",
      "    \"\"\" \n",
      "    Count word frequencies in the text body of a given url\n",
      "\n",
      "    Parameters \n",
      "    ----------\n",
      "    url : string\n",
      "        The address of a url to be scraped \n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    word_dict : dict\n",
      "       Frequency counts of the words \n",
      "\n",
      "    \"\"\"\n",
      "    import urllib\n",
      "    url_get = urllib.urlretrieve(url)\n",
      "    f = file(url_get[0])\n",
      "    txt = f.read()\n",
      "    \n",
      "    start_idx = txt.find('<body>')\n",
      "    end_idx = txt.find('</body>')\n",
      "    \n",
      "    new_txt = txt[start_idx:end_idx]\n",
      "    new_txt = new_txt.split(' ')\n",
      "    \n",
      "    word_dict = {}\n",
      "    for word in new_txt:\n",
      "        # Get rid of all kinds of html crud and the empty character:\n",
      "        if not('>' in word or '<' in word or '=' in word or '-' in word or '&' in word or word == ''): \n",
      "            if word in word_dict.keys():\n",
      "                word_dict[word] += 1\n",
      "            else: \n",
      "                word_dict[word] = 1\n",
      "\n",
      "    vals = np.array(word_dict.values())\n",
      "    keys = np.array(word_dict.keys())\n",
      "    sort_idx = np.argsort(vals)\n",
      "\n",
      "    return (vals[sort_idx][::-1], keys[sort_idx[::-1]])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 45
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's apply this to a paper about fMRI:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "word_arr = word_count('http://www.journalofvision.org/content/11/5/12.full?')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 46
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "For simplicity, let's examine the top 50 results:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "to_plot_vals = word_arr[0][:50]\n",
      "to_plot_words = word_arr[1][:50]\n",
      "print to_plot_vals\n",
      "print to_plot_words"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[557 492 490 336 213 209 198 149 148 134 134 120 105  91  81  77  77  77\n",
        "  76  74  71  62  62  54  53  47  46  46  42  42  41  39  38  37  35  34\n",
        "  34  34  33  33  33  32  31  30  30  30  29  29  28  27]\n",
        "['the' 'in' 'of' '\\n' 'to' 'and' 'a' 'is' 'that' 'text\"\\n' 'reference'\n",
        " 'visual' 'fMRI' 'with' 'V1' 'response' 'by' 'attention' 'for' 'be' 'BOLD'\n",
        " 'human' 'signal' 'responses' 'on' 'not' 'are' 'et' 'stimulus' 'neurons'\n",
        " 'as' 'effects' 'from' 'This' 'electrophysiological' 'signals' 'between'\n",
        " 'cortex' 'an' 'or' 'macaque' 'neuronal' 'effect' 'but' 'The' 'spatial'\n",
        " 'this' 'time' 'primary' 'stimuli']\n"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I think that it's fair to say that the first 11 words are not very interesting, so let's ignore those and plot the others, [wordle](http://www.wordle.net/)-style"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots(1)\n",
      "for word_idx in range(len(to_plot_words[11:])):\n",
      "    ax.text(np.random.rand(), np.random.rand(), to_plot_words[11:][word_idx], fontsize=to_plot_vals[11:][word_idx])\n",
      "ax.set_axis_off()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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RUu0pZWfMmDFo27YtZ11BQQHWrl2rUIeQA0UIkVseUNE8fvyYt05LS6vSA1VXV4SuO3W8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\nGIZhGKac8f8N6FgulPXltwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10756b910>"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Getting help\n",
      "\n",
      "One useful mechanism for getting help is the `np.lookfor` function, which searches through the docstrings in `numpy` for a good match. This is sometimes useful when you are looking for something in particular:\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scipy.lookfor('normal distribution')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Search results for 'normal distribution'\n",
        "----------------------------------------\n",
        "numpy.matlib.randn\n",
        "    Return a random matrix with data from the \"standard normal\" distribution.\n",
        "numpy.std\n",
        "    Compute the standard deviation along the specified axis.\n",
        "numpy.ma.mvoid.std\n",
        "    Compute the standard deviation along the specified axis.\n",
        "numpy.ma.MaskedArray.std\n",
        "    Compute the standard deviation along the specified axis.\n",
        "numpy.var\n",
        "    Compute the variance along the specified axis.\n",
        "numpy.ma.var\n",
        "    Compute the variance along the specified axis.\n",
        "numpy.ma.std\n",
        "    Compute the standard deviation along the specified axis.\n",
        "numpy.ma.mvoid.var\n",
        "    Compute the variance along the specified axis.\n",
        "numpy.ma.MaskedArray.var\n",
        "    Compute the variance along the specified axis.\n",
        "numpy.ma.mrecords.MaskedRecords.var\n",
        "    Compute the variance along the specified axis."
       ]
      }
     ],
     "prompt_number": 61
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Also: when in doubt, Google is your friend."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Summary\n",
      "\n",
      "Python is an object-oriented language with powerful libraries for representation of numerical objects, and for scientific analysis and visualization. Using the ipython notebook, we can interactively analyze and visualize data in an iterative fashion. We can now move on to examine some more interesting data. Namely, we will start looking at some MRI data, using neuroimaging-specific libraries.   "
     ]
    }
   ],
   "metadata": {}
  }
 ]
}