{
 "metadata": {
  "name": "introduction_to_python"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Introduction to the interactive notebook\n",
      "\n",
      "By Ariel Rokem, late of this university\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 0x100311c88>\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;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": [
        "./.gitignore\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": [
      "## 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": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "add_two_numbers(1,2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "3"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "add_two_numbers?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "If we have time, let's look at a slightly more interesting example: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np # The array library - we'll come back to this one\n",
      "\n",
      "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": 30
    },
    {
     "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": 31
    },
    {
     "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": [
        "[557 492 491 336 213 209 198 149 148 134 134 120 105  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' 'by' 'response' 'attention' 'for' 'be' 'BOLD'\n",
        " 'human' 'signal' 'responses' 'on' 'not' 'are' 'et' 'neurons' 'stimulus'\n",
        " 'as' 'effects' 'from' 'This' 'electrophysiological' 'signals' 'cortex'\n",
        " 'between' 'macaque' 'or' 'an' 'neuronal' 'effect' 'but' 'The' 'spatial'\n",
        " 'time' 'this' 'primary' 'may']\n"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "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": [
      "%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": 33
    },
    {
     "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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VKyxbtkyeqlLRMNSsjIuLi2DHX6poUzby8/Px66+/Cqqzd+/enF+qL168QEREhKD6VJEp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\n31+wXFRUFPV/dnY2vHjxQhRPOuggBjpBQwcddNCBhl69eonSUcjMzITU1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      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "For help: tab complete, ``something?``, and 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": {}
  }
 ]
}