Basic notebook checks. Do they run? Do they contain lint?
|Latest dev release|
Basic notebook smoke tests: Do they run ok? Do they contain lint?
$ pip install nbsmoke
Or get the latest pre-release:
$ pip install --pre nbsmoke
$ conda install -c conda-forge nbsmoke
Check all notebooks run without errors:
$ pytest --nbsmoke-run
Check all notebooks run without errors, and store html to look at afterwards:
$ pytest --nbsmoke-run --store-html=/scratch
Lint check notebooks:
$ pytest --nbsmoke-lint
Lint failures as warnings only:
$ pytest --nbsmoke-lint --nbsmoke-lint-onlywarn
Instead of all files in a directory, you can specify a list e.g.:
$ pytest --nbsmoke-run notebooks/Untitled*.ipynb
If you want to restrict pytest to running only your notebook tests, use -k, e.g.:
$ pytest --nbsmoke-run -k ".ipynb"
Additional options are available by standard pytest 'ini' configuration in setup.cfg, pytest.ini, or tox.ini:
[pytest] # when running, seconds allowed per cell (see nbconvert timeout) nbsmoke_cell_timeout = 600 # notebooks to skip running; one case insensitive re to match per line nbsmoke_skip_run = ^.*skipme\.ipynb$ ^.*skipmetoo.*$ # case insensitive re to match for file to be considered notebook; # defaults to ``^.*\.ipynb`` it_is_nb_file = ^.*\.something$ # flakes you don't want to hear about (regex) nbsmoke_flakes_to_ignore = .*hvplot.* imported but unused.* # line magics to treat as being flakes (i.e. magics you don't want in your notebooks) nbsmoke_flakes_line_magics_blacklist = pylab # cell magics to treat as being flakes (i.e. magics you don't want in your notebooks) nbsmoke_flakes_cell_magics_blacklist = bash ruby # add your own magic handlers (python file containing line_magic_handlers and cell_magic_handlers as dictionaries magic_name: callable) nbsmoke_magic_handlers = path/to/file.py
# noqa comments to mark that something should be
ignored during lint checking.
nbsmoke_skip_run list in a project's config can be ignored by
--ignore-nbsmoke-skip-run (useful if sometimes you want to run
all notebooks for a project where many are typically skipped).
What's the point?
Although more sophisticated testing of notebooks is possible (e.g. see nbval), just checking that notebooks run from start to finish without error in a fresh kernel (or on a neutral CI service) can be useful during development. Practical experience of working on several projects with notebooks confirms this, but that's all the evidence I have.
Checking notebooks for lint might seem trivial/pointless, but it frequently uncovers unused names (typically unused imports). It's also quite common to find python 2 vs 3 problems, and sometimes undefined names - in a way that's faster than running the notebook (over multiple versions of python).
Unused imports/names themselves might seem trivial, but they can hinder understanding of a notebook by readers, or add dependencies that are not required.
Hopefully you don't have mysterious (unused) imports in your notebook,
but if you do, you can add
# noqa: explanation to stop flake errors.
E.g. if you're importing something for its side effects, it's very
helpful to inform the reader of that.
Pyflakes is used as the underlying linter because "Pyflakes makes a simple promise: it will never complain about style, and it will try very, very hard to never emit false positives."
First, install using
pip install -e .. Then run the tests using
pytest -v tests/.
New release to PyPI:
git tag -a vX.Y.Z -m "Something about release" && git push --tags.
Then a PR will auto-open on conda-forge, which should be merged.
Get some help to debug apparently incorrect flakes by adding
pytest -v --nbsmoke-lint --nbsmoke-lint-debug examples.
Distributed under the terms of the BSD-3 license, "nbsmoke" is free and open source software.
If you encounter any problems, please file an issue (ideally including a copy of any problematic notebook).
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