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#171: Chilled out Python decorators with PEP 614

Published Thu, Mar 5, 2020, recorded Wed, Feb 26, 2020

Sponsored by 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: button.clicked.connect
    • Under current restrictions you can’t do @button[0].clicked.connect
    • Workarounds involve assigning list element to a variable first:
      • button0 = buttons[0]
      • @button0.clicked.connect
  • Author points out grammar is already loose enough to hack around:
    • Define function def _(x): return x
    • Then use _ as your decorator: @_(buttons[0].clicked.connect)
    • 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 if, elif, or while blocks (as opposed to valid string input to eval)
    • 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?
  • Tools:
    • 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
  • announcement post, repo
  • suggested from @OpensourceF:
  • From
    • 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:
    1. You write standard markdown files for content.
    2. Markdown files can be shared and imported into your top-level markdown.
    3. Fragments of HTML can be used when css classes and other specializations are needed, but generally HTML is avoided.
    4. A dictionary of variables and their values to replace in the merged markdown is processes.
    5. Markdown content is converted to HTML and embedded in your larger site layout (e.g. within a Jinja2 template).
    6. 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

  •, 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 pyproject.toml
    • 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.
  • FlakeHell:
    • 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.






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