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.
See 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
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 ConfigParser library.
Regarding the execution, roughly, the script initiates an
IPython Kernel with a shell and
an iopub sockets. The shell is needed to execute the cells in
the notebook (it sends requests to the Kernel) and the iopub provides
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
https://jupyter-client.readthedocs.io/en/latest/messaging.html#messaging
To execute this plugin, you need to execute py.test with the nbval flag
to differentiate the testing from the usual python files:
py.test --nbval
You can also specify --nbval-lax, which runs notebooks and checks for
errors, but only compares the outputs of cells with a #NBVAL_CHECK_OUTPUT
marker comment.
py.test --nbval-lax
The commands above will execute all the .ipynb files and 'pytest' tests in the current folder.
Specify -p no:python if you would like to execute notebooks only. Alternatively, you can execute a specific notebook:
py.test --nbval my_notebook.ipynb
By default, each .ipynb file will be executed using the kernel
specified in its metadata. You can override this behavior by passing
either --nbval-kernel-name mykernel to run all the notebooks using
mykernel, or --current-env to use a kernel in the same environment
in which pytest itself was launched.
If the output lines are going to be sanitized, an extra flag, --nbval-sanitize-with
together with the path to a confguration file with regex expressions, must be passed,
i.e.
py.test --nbval my_notebook.ipynb --nbval-sanitize-with path/to/my_sanitize_file
where my_sanitize_file has the following structure.
[Section1]
regex: [a-z]*
replace: abcd
regex: [1-9]*
replace: 0000
[Section2]
regex: foo
replace: bar
The 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.
The narrative documentation for nbval can be found at https://nbval.readthedocs.io.
The 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
verbose -v option. Nbval's options can be found under the
Jupyter Notebook validation section.
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 py.test system.
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
FAQs
A py.test plugin to validate Jupyter notebooks
We found that nbval demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 5 open source maintainers collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
Research
The Socket Research Team breaks down a malicious wrapper package that uses obfuscation to harvest credentials and exfiltrate sensitive data.
Research
Security News
Attackers used a malicious npm package typosquatting a popular ESLint plugin to steal sensitive data, execute commands, and exploit developer systems.
Security News
The Ultralytics' PyPI Package was compromised four times in one weekend through GitHub Actions cache poisoning and failure to rotate previously compromised API tokens.