pip-audit VS yapf

Next up is making sure, none of the dependencies used throughout the project brings with it any already identified security issue. The makefile target audit, invokes the handy tool pip-audit.

Here is my "one true" Makefile for Python projects[1]. The skeleton gets tweaked slightly each time, but it's served me well for 4+ years.

[1]: https://github.com/pypa/pip-audit/blob/main/Makefile

Why use this over the established https://pypi.org/project/pip-audit/ ?

https://pypi.org/project/pip-audit/ details usage and the GitHub Action install.

If it's pure Python, the only packaging file you need is `pyproject.toml`. You can fill that file with packaging metadata per PEP 518 and PEP 621, including using modern build tooling like flit[1] for the build backend and build[2] for the frontend.

With that, you entire package build (for all distribution types) should be reducible to `python -m build`. Here's an example of a full project doing everything with just `pyproject.toml`[3] (FD: my project).

[1]: https://github.com/pypa/flit

[2]: https://github.com/pypa/build

[3]: https://github.com/pypa/pip-audit

- repo: https://github.com/trailofbits/pip-audit rev: v2.4.3 hooks: - id: pip-audit args: [ "-r", "requirements.txt" ] ci: # Leave pip-audit to only run locally and not in CI # pre-commit.ci does not allow network calls skip: [ pip-audit ]

This is really nicely written; kudos to the author for compiling a great deal of information in a readable format.

If I can be forgiven one nitpick: Poetry does not use a PEP 518-style[1] build configuration by default, which means that its use of `pyproject.toml` is slightly out of pace with the rest of the Python packaging ecosystem. That isn't to say that it isn't excellent, because it is! But you the standards have come a long way, and you can now use `pyproject.toml` with any build backend as long as you use the standard metadata.

By way of example, here's a project that's completely PEP 517 and PEP 518 compatible without needing a setup.py or setup.cfg[2]. Everything goes through pyproject.toml.

[1]: https://peps.python.org/pep-0518/

[2]: https://github.com/trailofbits/pip-audit/blob/main/pyproject...

Checking could be done if something like this eventually shows up in safety or pip-audit.

Something like python's pip-audit. For commercial solutions I know there's Snyk and Jfrog we can always purchase, but I'm interested to see if there's an open-source tool that can do this.

YAPF (Yet Another Python Formatter): YAPF takes a different approach in that it’s based off of ‘clang-format’, a popular formatter for C++ code. YAPF reformats Python code so that it conforms to the style guide and looks good.

I think I agree about the testing and labor of complicated translation rules.

But it doesn't appear that almost every pretty printer uses the Wadler pretty printing paper. It seems like MOST of them don't?

e.g. clang-format is one of the biggest and best, and it has a model that includes "unwrapped lines", a "layouter", a line break cost function, exhaustive search with memoization, and Dijikstra's algorithm:

https://llvm.org/devmtg/2013-04/jasper-slides.pdf

The YAPF Python formatter is based on this same algorithm - https://github.com/google/yapf

The Dart formatter used a model of "chunks, rules, and spans"

https://journal.stuffwithstuff.com/2015/09/08/the-hardest-pr...

It almost seems like there are 2 camps -- the functional algorithms for functional/expression-based languages, and other algorithms for more statement-based languages.

Though I guess Prettier/JavaScript falls on the functional side.

I just ran across this survey on lobste.rs and it seems to cover the functional pretty printing languages influenced by Wadler, but functional style, but not the other kind of formatter ("Google" formatters perhaps)

https://arxiv.org/pdf/2310.01530.pdf

To get all your code into a consistent format the next step is to run a formatter. I recommend black, the well-known uncompromising code formatter, which is the most popular choice. Alternatives to black are autoflake, prettier and yapf, if you do not agree with blacks constraints.

Use yapf to format code -> https://github.com/google/yapf

Google is surprisingly rigorous when it comes to code formatting. I have been a software engineer at Amazon and it was nothing like what the book says happens at Google. So the conventions you see for python docstring formatting are primarily designed to integrate with Google's internal tooling. By using docstrings following the Google conventions, you will ultimately end up with automated documentation and other fancy automated things (like type checking which they did in the docstring before there were type hints). Also notably, Google has an open source python formatting tool that they use internally called YAPF (which stands for "Yet Another Python Formatter". So if you really want to go all-in on Google python style, grab that, too.

https://github.com/google/yapf has configs, do ctrl+f SPLIT_COMPLEX_COMPREHENSION in the readme

Only as recommendation: If the lines of the source code (here: you C code you aim to document) are kept short, in manageable bytes (similar to entries parser.add_argument in Clark's "Tiny Python Projects", example seldomly pass beyond the frequently recommended threshold of 80 characters/line), reporting with listings becomes easier (equally, the reading of the difference logs/views by git and vimdiff), than with lines of say 120 characters per line. Though we no longer are constrained to 80 characters per line by terminals/screens and punch cards (when Fortran still was FORTRAN), this is a reason e.g., yapf for Python allows you to choose between 4 spaces/indentation (PEP8 style), or 2 spaces/indentation (Google style).

There is also a formatter for Python files called yapf that your team can use to avoid arguing over formatting conventions. Plus, Google also provides a settings file for Vim, noting that the default settings should be enough if you're using Emacs.