07_jupyter.md

Jupyter notebook and IPython

Our main interaction with python so far has been through the Jupyter notebook. 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). 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 or fslipython) 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

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Getting help

To get the documentation for any object or method simply append a question mark

import string
string.capwords?

This also works for any of the magic commands discussed below

%run?

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 the tab key

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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

You can even capture the output from the shell command in a variable:

r = !fslstats ${FSLDIR}/data/standard/FMRIB58_FA_1mm.nii.gz -r
r_lower, r_upper = [float(element) for element in r[0].split()]
print('Bounds are ({:.0f}, {:.0f})'.format(r_lower, r_upper))

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Running python scripts

We could run a python script as a shell command above. However, it will often be more convenient to use %run instead.

%run <python script> <arguments...>

Arguments are provided in exactly the same way as if you called python in the shell. The main advantages are:

• Any top-level variables will be made available to you after the script finishes
• All the debugging/timing/profiling tools discussed below will be available to you A common workflow, when writing a python script is to have an Ipython terminal open next to your text editor and regularly use %run to test the script

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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

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Timing/profiling 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)  # this will take a few seconds to run

Finally, if you want to figure out what part of the code is actually slowing you down you can use %prun, which gives you an overview of how long the interpreter spent in each method:

import nibabel as nib
import os.path as op
%prun nib.load(op.expandvars('${FSLDIR}/data/standard/FMRIB58_FA_1mm.nii.gz'))

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Debugging

Despite your best efforts in many cases some error will crop up

import numpy as np
def total(a_list):
    """Calculate the total of a list.

    This is a very naive (not recommended) and bugged implementation
    """
    # 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

%debug

Try to check the value of a_list and local_list from within the debugger.

WARNING: you need to quit the debugger before any further commands will run (type q into the prompt)!

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 reverse this)

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Enabling plotting

By far the most popular scientific plotting library is matplotlib. You can enable plotting in Ipython or the jupyter notebook using %matplotlib <backend>, where 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 exiting the python interpreter and restarting python (note that in the jupyter notebook you can just press Restart in the Kernel menu).

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 *

The last line imports everything from the matplotlib.pylab module into the namespace.

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-')

The main disadvantage is that it will not be obvious to the naive reader of this code, whether functions like linspace, sin, or plot are originate from numpy, matplotlib, or are built-in. This is why we dont recommend from <module> import * statements in any longer code or code you intend to share.

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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

%run script_from_notebook.py

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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.