Py.test plugin for validating Jupyter notebooks
The plugin adds functionality to py.test to recognise and collect Jupyter
notebooks. The intended purpose of the tests is to determine whether execution
of the stored inputs match the stored outputs of the
.ipynb file. Whilst also
ensuring that the notebooks are running without errors.
The tests were designed to ensure that Jupyter notebooks (especially those for reference and documentation), are executing consistently.
Each cell is taken as a test, a cell that doesn't reproduce the expected output will fail.
docs/source/index.ipynb for the full documentation.
Available on PyPi:
pip install nbval
or install the latest version from cloning the repository and running:
pip install .
from the main directory. To uninstall:
pip uninstall nbval
How it works
The extension looks through every cell that contains code in an IPython notebook
and then the
py.test system compares the outputs stored in the notebook
with the outputs of the cells when they are executed. Thus, the notebook itself is
used as a testing function.
The output lines when executing the notebook can be sanitized passing an
extra option and file, when calling the
py.test command. This file
is a usual configuration file for the
Regarding the execution, roughly, the script initiates an
IPython Kernel with a
iopub sockets. The
shell is needed to execute the cells in
the notebook (it sends requests to the Kernel) and the
an interface to get the messages from the outputs. The contents
of the messages obtained from the Kernel are organised in dictionaries
with different information, such as time stamps of executions,
cell data types, cell types, the status of the Kernel, username, etc.
In general, the functionality of the IPython notebook system is quite complex, but a detailed explanation of the messages and how the system works, can be found here
To execute this plugin, you need to execute
py.test with the
to differentiate the testing from the usual python files:
You can also specify
--nbval-lax, which runs notebooks and checks for
errors, but only compares the outputs of cells with a
The commands above will execute all the
.ipynb files in the current folder.
Alternatively, you can execute a specific notebook:
py.test --nbval my_notebook.ipynb
If the output lines are going to be sanitized, an extra flag,
together with the path to a confguration file with regex expressions, must be passed,
py.test --nbval my_notebook.ipynb --sanitize-with path/to/my_sanitize_file
my_sanitize_file has the following structure.
[Section1] regex: [a-z]* replace: abcd regex: [1-9]* replace: 0000 [Section2] regex: foo replace: bar
regex option contains the expression that is going to be matched in the outputs, and
replace is the string that will replace the
regex match. Currently, the section
names do not have any meaning or influence in the testing system, it will take
all the sections and replace the corresponding options.
To use notebooks to generate coverage for imported code, use the pytest-cov plugin. nbval should automatically detect the relevant options and configure itself with it.
nbval is compatible with the pytest-xdist plugin for parallel running of tests. It does
however require the use of the
--dist loadscope flag to ensure that all cells of one
notebook are run on the same kernel.
py.test system help can be obtained with
py.test -h, which will
show all the flags that can be passed to the command, such as the
-v option. The IPython notebook plugin can be found under the
This plugin was inspired by Andrea Zonca's py.test plugin for collecting unit tests in the IPython notebooks (https://github.com/zonca/pytest-ipynb).
The original prototype was based on the template in
https://gist.github.com/timo/2621679 and the code of a testing system
for notebooks https://gist.github.com/minrk/2620735 which we
integrated and mixed with the
We acknowledge financial support from
OpenDreamKit Horizon 2020 European Research Infrastructures project (#676541), http://opendreamkit.org
EPSRC’s Centre for Doctoral Training in Next Generation Computational Modelling, http://ngcm.soton.ac.uk (#EP/L015382/1) and EPSRC’s Doctoral Training Centre in Complex System Simulation ((EP/G03690X/1),
The Gordon and Betty Moore Foundation through Grant GBMF #4856,by the Alfred P. Sloan Foundation and by the Helmsley Trust.
2014 - 2017 David Cortes-Ortuno, Oliver Laslett, T. Kluyver, Vidar Fauske, Maximilian Albert, MinRK, Ondrej Hovorka, Hans Fangohr