Sponsored by Datadog: pythonbytes.fm/datadog
Special guest: David Amos
David #1: PEP 614 – Relaxing Grammar Restrictions on Decorators
- Python currently requires that all decorators consist of a dotted name, optionally followed by a single call.
- E.g., can’t use subscripts or chained calls
- PEP proposes allowing any valid expression.
- Motivation for limitation is not a technical requirement:
- “I have a gut feeling about this one. I'm not sure where it comes from, but I have it... So while it would be quite easy to change the syntax in the future, I'd like to stick to the more restricted form unless a real use case is presented where [changing the syntax] would increase readability.”
- (Guido van Rossom, Source)
- Use case highlighted by PEP:
- List of Qt buttons:
buttons = [button0, button1, …]
- Decorator is a method on a class attribute:
- Under current restrictions you can’t do
- Workarounds involve assigning list element to a variable first:
button0 = buttons
- Author points out grammar is already loose enough to hack around:
- Define function
def _(x): return x
- Then use _ as your decorator:
- That’s less readable than just using the subscript
- PEP proposes relaxing grammar to “any valid expression” (sort of), i.e. anything that you can use as a test in
while blocks (as opposed to valid string input to
- Some things wouldn’t be allowed, though
- E.g., tuples require parentheses,
@f, g doesn’t make sense
- Does a tuple as a decorator make sense in the first place, though?
- CPython implementation on GitHub:
Michael #2: Create a macOS Menu Bar App with Python (Pomodoro Timer)
- by Camillo Visini
- Nice article: Learn how to create your very own macOS Menu Bar App using Python, rumps and py2app
- The mac menu bar is super useful. I leverage the heck out of this thing. Why not write Python for it?
- Python 3 and PyCharm as an IDE
- Rumps → Ridiculously Uncomplicated macOS Python Statusbar apps
- py2app → For creating standalone macOS apps from Python code (how cool is that?)
- Get started with the code:
app = rumps.App("Pomodoro", "🍅")
- Then easily use Py2App to convert this into a full macOS app.
- Would love to see somebody try to submit one of these to the mac app store.
Brian #3: Conditional Coverage
- Nikita Sobolev - CTO of wemake.services
- announcement post, repo
- suggested from @OpensourceF:
- From README.md:
- Conditional coverage based on any rules you define!
- Some project have different parts that relies on different environments:
- Python version, some code is only executed on specific versions and ignored on others
- OS version, some code might be Windows, Mac, or Linux only
- External packages, some code is only executed when some 3rd party package is installed
- Traditional method:
- combine coverage data before reporting. This works ok on CI systems or with tox for multiple Python/package version.
- Doesn’t help much locally if wanting split is due to OS dependencies
- Requires multiple test runs to get full coverage
- New coverage plugin
- allows you to maintain coverage while developing locally.
- single test run and a reasonable coverage report
- So cool.
- Recommend to keep conditionals to a minimum and somewhat isolated. I wouldn’t want this all over my code base.
- Still want real full coverage on CI.
David #4: Pycel – A library for compiling excel spreadsheets to python code & visualizing them as a graph
- Compile an Excel file with formulas as a Python object
- The compiler converts formulas in the spreadsheet to executable code
- Once compiled, you can set values for cells and inspect the output in other cells
- This is all happening in Python now, not touching Excel anymore
- You can visualize all of the formulas as a graph to explore how formulas depend on one another
- The author of the package wrote it to solve a problem in civilian aerospace engineering
- Finally, with all the formulas compiled, the package can solve for variables using an optimization process
- In original use case this was to optimize engineering parameters to produce aircraft that could actually fly
- Author describes how using Python he increased the cases that could be optimized from 65% to 98% and reduced calculation time from 10 minutes to around 30 seconds to 1 minute.
Michael #5: markdown-subtemplate
- A template engine to render Markdown with external template imports and basic variable replacements.
- Choice between data-driven server apps (typical Flask app), CMSes that let us edit content on the web such as WordPress, and even flat file systems like Pelican.
- This should not be a black and white decision.
- Here's how it works:
- You write standard markdown files for content.
- Markdown files can be shared and imported into your top-level markdown.
- Fragments of HTML can be used when css classes and other specializations are needed, but generally HTML is avoided.
- A dictionary of variables and their values to replace in the merged markdown is processes.
- Markdown content is converted to HTML and embedded in your larger site layout (e.g. within a Jinja2 template).
- Markdown transforms are cached to achieve very high performance regardless of the complexity of the content.
- Extensible logging and caching. Extensible storage coming soon.
- PRs and contributions are welcome. More to come
Brian #6: FlakeHell
- wemake.services, from Conditional Coverage, also makes the wemake-python-styleguide, and recommends using FlakeHell
- Allows you to configure flake8 and plugins more easily in pyproject.toml files.
- Provides a ramp to start using linting tools with “legacy first”:
flakehell baseline > .flakehell_baseline
- specify that file in your
- flakehell lint will run your liniting tools and only report new failures
- you can start fixing older stuff later, or just apply style guide to new code.
- Lots of awesome shortcuts for configuration with wildcards and such.
- Can specify a shared config in one repo and use it multiple projects as a starting point with local changes.
- It's a Flake8 wrapper to make it cool.
- Shareable and remote configs.
- Legacy-friendly: ability to get report only about new errors.
- Caching for much better performance.
- Use only specified plugins, not everything installed.
- Manage codes per plugin.
- Enable and disable plugins and codes by wildcard.
- Make output beautiful.
- pyproject.toml support.
- Show codes for installed plugins.
- Show all messages and codes for a plugin.
- Check that all required plugins are installed.
- Syntax highlighting in messages and code snippets.
- PyLint integration.
- Allow codes intersection for different plugins.