ENH: created practical on using jupyter/ipython

getting_started/07_jupyter.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Jupyter notebook and IPython\n",
+    "Our main interaction with python so far has been through the [Jupyter notebook](http://jupyter.org/).\n",
+    "These notebooks are extremely popular these days within the python scientific community, however they support many more languages, such as R and octave (and even matlab with the right [plugin](https://github.com/Calysto/matlab_kernel)).\n",
+    "They allow for interactive analysis of your data interspersed by explanatory notes (including LaTeX) with inline plotting.\n",
+    "However, they can not be called as scripts on the command line or be imported from other python code, which makes them rather stand-alone.\n",
+    "This makes them more useful for analysis that needs to be reproducible, but does not need to be replicated on different datasets (e.g., making a plot for a paper).\n",
+    "\n",
+    "For more ad-hoc analysis it can be useful to just use the command line (i.e., a REPL).\n",
+    "We strongly recommend to use the IPython (available as `ipython` in the terminal after you install `ipython` using `pip` or `conda`) rather than default python REPL (available through `python` or `fslpython`)\n",
+    "\n",
+    "Both Ipython and the jupyter notebook offer a whole range of magic commands, which all start with a `%` sign.\n",
+    "* A magic command starting with a single `%` sign will only affect the single line.\n",
+    "* A magic command starting with two '%' signs will affect the whole block of code.\n",
+    "\n",
+    "Note that the normal python interpreter will not understand these magic commands, so you will have to take them out when writing a python script or library.\n",
+    "\n",
+    "Here we will discuss some of the many features available to you in Ipython and the Jupyter notebook\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## Getting help\n",
+    "To get the documentation for any object or method simply append a question mark"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import string\n",
+    "string.capwords?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Alternatively you can put two questions marks to get the complete code for the method or object class"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import string\n",
+    "string.capwords??"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Both Ipython and Jupyter also come with autocomplete, which is available at any time by pressing <tab>\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## Running shell commands\n",
+    "Commands starting with a `!` will be sent to the shell rather than the python interpreter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!fslstats ${FSLDIR}/data/standard/FMRIB58_FA_1mm.nii.gz -r"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## Running other programming languages\n",
+    "In the notebook you can include a whole code block using another language by using `%%<language>` (for many languages you will have to install a toolkit first, just google your favorite language besides python)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "for filename in `ls *.md` ; do\n",
+    "    head -n 1 ${filename}\n",
+    "done"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## Timing code\n",
+    "We can time a line of code with `%time` or a whole code block using `%%time`.\n",
+    "To get the time needed to calculate the sine of a million random numbers:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "numbers = np.random.rand(int(1e6))\n",
+    "%time np.sin(numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For very fast evaluation, you might need to run it multiple times to get an accurate estimate. The `%timeit` (or `%%timeit` for a code block) takes care of this for you."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "numbers = np.random.rand(10)\n",
+    "%timeit np.sin(numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## Debugging\n",
+    "Despite your best efforts in many cases some error will crop up"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "def total(a_list):\n",
+    "    # create local copy befor changing the input\n",
+    "    local_list = list(a_list)\n",
+    "    total = 0.\n",
+    "    while len(local_list) > 0:\n",
+    "        total += local_list.pop(1)  # returns element at index=1 and removes it\n",
+    "    return total\n",
+    "\n",
+    "print(total([2, 3, 4]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can always open a debugger at the location of the last error by using the `%debug` magic command. You can find a list of commands available in the debugger [here](http://www.georgejhunt.com/olpc/pydebug/pydebug/ipdb.html)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%debug"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Try to check the value of `a_list` and `local_list` from within the debugger.\n",
+    "\n",
+    "If you always want to enter the debugger when an error is raised you can call `%pdb on` at any time (call `%pdf off` to rever this)\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## Enabling plotting\n",
+    "By far the most popular scientific plotting library is [matplotlib](https://matplotlib.org/).\n",
+    "You can enable plotting in Ipython or the jupyter notebook using `%matplotlib <backend>`, where [backend](https://matplotlib.org/faq/usage_faq.html#what-is-a-backend) is the system that will be used to display the plots.\n",
+    "When failing to provide a backend it will simply use the default (which is usually fine).\n",
+    "* In the jupyter notebook use the `nbagg` backend for interactive plots or the `inline` backend for non-interactive plots\n",
+    "* Otherwise on Mac OSx use the `macosx` backend"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib nbagg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> Keep in mind that as soon as you have started plotting you can no longer change your backend without restarting python.\n",
+    "\n",
+    "To do the equivalent in a python script would look like"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib as mpl\n",
+    "mpl.use(<backend>)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For interactive use it can be handy to have all the `numpy` numeric functions and `matplotlib` plotting functions directly available without importing them explicitly.\n",
+    "This can be achieved using the `%pylab <backend>` magic command."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pylab nbagg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is equivalent in python code to:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib as mpl\n",
+    "mpl.use(<backend>)\n",
+    "from matplotlib.pylab import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I start most of my notebooks or terminals with the `%pylab` command, because afterwards I can just do stuff like:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = linspace(0, pi, 301)\n",
+    "y = sin(x)\n",
+    "plot(x, y, 'r-')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## Exporting code from the Jupyter notebook\n",
+    "If you have a code cell in the jupyter notebook, that you want to convert into a script, you can use the %%writefile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile script_from_notebook.py\n",
+    "# a bunch of imports\n",
+    "import numpy as np\n",
+    "from datetime import datetime\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Any additional code cells need to contain the `-a` flag to stop jupyter from overwriting the original code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile -a script_from_notebook.py\n",
+    "\n",
+    "print('today is ', datetime.now())\n",
+    "print('sin(3) is ', np.sin(3))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now run this script"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!python script_from_notebook.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## Exporting code from the Ipython terminal\n",
+    "You can access the full history of your session using `%history`.\n",
+    "To save the history to a file use `%history -f <filename>`\n",
+    "You will probably have to clean a lot of erroneous commands you typed from that file before you are able to run it as a script."
+   ]
+  }
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

getting_started/07_jupyter.md

+# Jupyter notebook and IPython
+Our main interaction with python so far has been through the [Jupyter notebook](http://jupyter.org/).
+These notebooks are extremely popular these days within the python scientific community, however they support many more languages, such as R and octave (and even matlab with the right [plugin](https://github.com/Calysto/matlab_kernel)).
+They allow for interactive analysis of your data interspersed by explanatory notes (including LaTeX) with inline plotting.
+However, they can not be called as scripts on the command line or be imported from other python code, which makes them rather stand-alone.
+This makes them more useful for analysis that needs to be reproducible, but does not need to be replicated on different datasets (e.g., making a plot for a paper).
+
+For more ad-hoc analysis it can be useful to just use the command line (i.e., a REPL).
+We strongly recommend to use the IPython (available as `ipython` in the terminal after you install `ipython` using `pip` or `conda`) rather than default python REPL (available through `python` or `fslpython`)
+
+Both Ipython and the jupyter notebook offer a whole range of magic commands, which all start with a `%` sign.
+* A magic command starting with a single `%` sign will only affect the single line.
+* A magic command starting with two '%' signs will affect the whole block of code.
+
+Note that the normal python interpreter will not understand these magic commands, so you will have to take them out when writing a python script or library.
+
+Here we will discuss some of the many features available to you in Ipython and the Jupyter notebook
+
+---
+
+## Getting help
+To get the documentation for any object or method simply append a question mark
+```
+import string
+string.capwords?
+```
+
+Alternatively you can put two questions marks to get the complete code for the method or object class
+```
+import string
+string.capwords??
+```
+
+Both Ipython and Jupyter also come with autocomplete, which is available at any time by pressing <tab>
+
+---
+
+## Running shell commands
+Commands starting with a `!` will be sent to the shell rather than the python interpreter.
+```
+!fslstats ${FSLDIR}/data/standard/FMRIB58_FA_1mm.nii.gz -r
+```
+
+---
+
+## Running other programming languages
+In the notebook you can include a whole code block using another language by using `%%<language>` (for many languages you will have to install a toolkit first, just google your favorite language besides python)
+```
+%%bash
+for filename in `ls *.md` ; do
+    head -n 1 ${filename}
+done
+```
+
+---
+
+## Timing code
+We can time a line of code with `%time` or a whole code block using `%%time`.
+To get the time needed to calculate the sine of a million random numbers:
+```
+import numpy as np
+numbers = np.random.rand(int(1e6))
+%time np.sin(numbers)
+```
+
+For very fast evaluation, you might need to run it multiple times to get an accurate estimate. The `%timeit` (or `%%timeit` for a code block) takes care of this for you.
+```
+import numpy as np
+numbers = np.random.rand(10)
+%timeit np.sin(numbers)
+```
+
+---
+
+## Debugging
+Despite your best efforts in many cases some error will crop up
+```
+import numpy as np
+def total(a_list):
+    # create local copy befor changing the input
+    local_list = list(a_list)
+    total = 0.
+    while len(local_list) > 0:
+        total += local_list.pop(1)  # returns element at index=1 and removes it
+    return total
+
+print(total([2, 3, 4]))
+```
+
+You can always open a debugger at the location of the last error by using the `%debug` magic command. You can find a list of commands available in the debugger [here](http://www.georgejhunt.com/olpc/pydebug/pydebug/ipdb.html)
+```
+%debug
+```
+Try to check the value of `a_list` and `local_list` from within the debugger.
+
+If you always want to enter the debugger when an error is raised you can call `%pdb on` at any time (call `%pdf off` to rever this)
+
+---
+
+## Enabling plotting
+By far the most popular scientific plotting library is [matplotlib](https://matplotlib.org/).
+You can enable plotting in Ipython or the jupyter notebook using `%matplotlib <backend>`, where [backend](https://matplotlib.org/faq/usage_faq.html#what-is-a-backend) is the system that will be used to display the plots.
+When failing to provide a backend it will simply use the default (which is usually fine).
+* In the jupyter notebook use the `nbagg` backend for interactive plots or the `inline` backend for non-interactive plots
+* Otherwise on Mac OSx use the `macosx` backend
+```
+%matplotlib nbagg
+```
+> Keep in mind that as soon as you have started plotting you can no longer change your backend without restarting python.
+
+To do the equivalent in a python script would look like
+```
+import matplotlib as mpl
+mpl.use(<backend>)
+```
+
+For interactive use it can be handy to have all the `numpy` numeric functions and `matplotlib` plotting functions directly available without importing them explicitly.
+This can be achieved using the `%pylab <backend>` magic command.
+```
+%pylab nbagg
+```
+
+This is equivalent in python code to:
+```
+import matplotlib as mpl
+mpl.use(<backend>)
+from matplotlib.pylab import *
+```
+
+I start most of my notebooks or terminals with the `%pylab` command, because afterwards I can just do stuff like:
+```
+x = linspace(0, pi, 301)
+y = sin(x)
+plot(x, y, 'r-')
+```
+
+---
+
+## Exporting code from the Jupyter notebook
+If you have a code cell in the jupyter notebook, that you want to convert into a script, you can use the %%writefile
+
+```
+%%writefile script_from_notebook.py
+# a bunch of imports
+import numpy as np
+from datetime import datetime
+
+```
+
+Any additional code cells need to contain the `-a` flag to stop jupyter from overwriting the original code
+```
+%%writefile -a script_from_notebook.py
+
+print('today is ', datetime.now())
+print('sin(3) is ', np.sin(3))
+```
+
+We can now run this script
+```
+!python script_from_notebook.py
+```
+
+---
+
+## Exporting code from the Ipython terminal
+You can access the full history of your session using `%history`.
+To save the history to a file use `%history -f <filename>`
+You will probably have to clean a lot of erroneous commands you typed from that file before you are able to run it as a script.
+

getting_started/README.md

@@ -9,6 +9,5 @@ This directory contains the "Getting started" practical.
 4. Numpy (PM)
 5. Nifti images (MJ)
 6. Image manipulation (MC)
-7. Moving from the Jupyter notebook to a text editor and terminal (MC)
+7. Jupyter notebook and Ipython(MC)
 8. Writing a callable script (MJ)
-9. Debugging (MC)

getting_started/basics.ipynb

@@ -483,13 +483,13 @@
     {
      "ename": "IndexError",
      "evalue": "list index out of range",
-     "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
       "\u001b[0;32m<ipython-input-78-f4cf4536701c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mIndexError\u001b[0m: list index out of range"
-     ]
+     ],
+     "output_type": "error"
     }
    ],
    "source": [
@@ -504,13 +504,13 @@
     {
      "ename": "IndexError",
      "evalue": "list index out of range",
-     "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
       "\u001b[0;32m<ipython-input-79-52d95fbe5286>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mIndexError\u001b[0m: list index out of range"
-     ]
+     ],
+     "output_type": "error"
     }
    ],
    "source": [
@@ -647,13 +647,13 @@
     {
      "ename": "TypeError",
      "evalue": "list indices must be integers or slices, not list",
-     "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
       "\u001b[0;32m<ipython-input-84-aad7915ae3d8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: list indices must be integers or slices, not list"
-     ]
+     ],
+     "output_type": "error"
     }
    ],
    "source": [

getting_started/scripts.ipynb

@@ -18,7 +18,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "#!/usr/bin/env python"
@@ -36,7 +38,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "#!/usr/bin/env fslpython"
@@ -56,7 +60,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "import subprocess as sp\n",
@@ -73,7 +79,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "spobj = sp.run(['ls'], stdout = sp.PIPE)"
@@ -89,7 +97,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "spobj = sp.run('ls -la'.split(), stdout = sp.PIPE)\n",
@@ -109,7 +119,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "import os\n",
@@ -131,7 +143,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "commands = \"\"\"\n",
@@ -162,7 +176,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "import sys\n",
@@ -184,10 +200,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Usage: bash <input image> <output image>\n"
+     ]
+    }
+   ],
    "source": [
+    "%%bash\n",
     "#!/bin/bash\n",
     "if [ $# -lt 2 ] ; then\n",
     "  echo \"Usage: $0 <input image> <output image>\"\n",
@@ -213,9 +238,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "IndexError",
+     "evalue": "list index out of range",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-2-f7378930c369>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0mspobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfsldir\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'/bin/fslstats'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'-V'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstdout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPIPE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0msout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mspobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'utf-8'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mvol_vox\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m \u001b[0mvol_mm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Volumes are: '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvol_vox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m' in voxels and '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvol_mm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m' in mm'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
+     ],
+     "output_type": "error"
+    }
+   ],
    "source": [
     "#!/usr/bin/env fslpython\n",
     "import os, sys\n",
@@ -235,9 +272,55 @@
     "vol_mm = float(sout.split()[1])\n",
     "print('Volumes are: ', vol_vox, ' in voxels and ', vol_mm, ' in mm')"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
   }
  ],
- "metadata": {},
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.2"
+  },
+  "toc": {
+   "colors": {
+    "hover_highlight": "#DAA520",
+    "running_highlight": "#FF0000",
+    "selected_highlight": "#FFD700"
+   },
+   "moveMenuLeft": true,
+   "nav_menu": {
+    "height": "105px",
+    "width": "252px"
+   },
+   "navigate_menu": true,
+   "number_sections": true,
+   "sideBar": true,
+   "threshold": 4.0,
+   "toc_cell": false,
+   "toc_section_display": "block",
+   "toc_window_display": false
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 2
 }