diff --git a/getting_started/07_jupyter.ipynb b/getting_started/07_jupyter.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..77c8df4c0500f3792a1ae02f225e89351ba8d79c
--- /dev/null
+++ b/getting_started/07_jupyter.ipynb
@@ -0,0 +1,357 @@
+{
+ "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
+}
diff --git a/getting_started/07_jupyter.md b/getting_started/07_jupyter.md
new file mode 100644
index 0000000000000000000000000000000000000000..1a9090a254fda5ce3d0da46bf6e44301f09536e5
--- /dev/null
+++ b/getting_started/07_jupyter.md
@@ -0,0 +1,169 @@
+# 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.
+
diff --git a/getting_started/README.md b/getting_started/README.md
index fe3b5f8aa6f044c91406d3e88d9a9b50d2a01753..f250beae1f7ac2a2f544f56d3929cd6b26ae6b87 100644
--- a/getting_started/README.md
+++ b/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)
diff --git a/getting_started/basics.ipynb b/getting_started/basics.ipynb
index 6a2266f2c052aae71d4339e8accfa75a6ab9d159..4cd6d7951303564e791df3d80c95220afd2ce159 100644
--- a/getting_started/basics.ipynb
+++ b/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": [
diff --git a/getting_started/scripts.ipynb b/getting_started/scripts.ipynb
index 1d3d705d5b636c281ce97e6a527431c69075847d..7b87ec6388573c9cebb864bdbea98da654593e9c 100644
--- a/getting_started/scripts.ipynb
+++ b/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
}