{
 "metadata": {
  "name": "",
  "signature": "sha256:0c67908c53af094d11707ef17096da64bbfc5cb02845cf7eda284c5ce2447de2"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Modules and scripts"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Compatibility with Python 3\n",
      "from __future__ import print_function  # print('me') instead of print 'me'\n",
      "from __future__ import division  # 1/2 == 0.5, not 0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Show figures inside the notebook\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np  # the Python array package\n",
      "import matplotlib.pyplot as plt  # the Python plotting package"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import nibabel as nib"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's say I have a function that loads a 4D image, and a list with mean values across all voxels in each volume:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def vol_means(image_fname):\n",
      "    img = nib.load(image_fname)\n",
      "    data = img.get_data()\n",
      "    means = []\n",
      "    for i in range(data.shape[-1]):\n",
      "        vol = data[..., i]\n",
      "        means.append(np.mean(vol))\n",
      "    return means"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's check that works:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "my_means = vol_means('ds107_sub012_t1r2.nii')\n",
      "plt.plot(my_means)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "[<matplotlib.lines.Line2D at 0x106d29910>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Wr33HHfW9yLfeskycxlIudwaSefa//71582eeWf9W+NBD7bvwCSv2YB2ZH32U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\nEUJI7AsUefZCiCAS+wJF2Th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       "text": [
        "<matplotlib.figure.Figure at 0x101e34110>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This seems like a useful function to me.  I want to make a module called `mymodule` so I can do this:\n",
      "\n",
      "```\n",
      "import mymodule\n",
      "means = mymodule.vol_means('ds107_sub012_t1r2.nii')\n",
      "```"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In fact, what I would really like to do, is to be able to have a script called `my_script.py` that prints the scan means out to the terminal window like this:\n",
      "\n",
      "```\n",
      "import mymodule\n",
      "means = mymodule.vol_means('ds107_sub012_t1r2.nii')\n",
      "print(means)\n",
      "```\n",
      "\n",
      "And I want to be able to run it like this from the command line:\n",
      "\n",
      "```\n",
      "python my_script.py\n",
      "```"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A Python *module* is a Python file with functions (and other things) that other modules and scripts may use.  These other modules and scripts will `import` the module.\n",
      "\n",
      "A script is a file that you can run with `python my_script.py` that will do some processing, and usually print something to the screen, put up a plot or save some data to the filesystem.\n",
      "\n",
      "You store modules in files ending in `.py`.  For example, the module `mymodule` would be in a file called `mymodule.py`.\n",
      "\n",
      "A module needs to 'import' everything it uses."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You can store scripts with any extension, but it is common to use `.py` extensions for Python scripts as well."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<hr/>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now let's go to the text editor and the terminal, and create the module `mymodule.py` and the script `my_script.py`."
     ]
    }
   ],
   "metadata": {}
  }
 ]
}