twat-labs is a plugin for the twat ecosystem, a collection of tools and libraries available on PyPI. This specific package, twat-labs, is designed to host experimental features, new extensions, or specialized utilities that are under development or evaluation before potential integration into the core twat offerings or other specialized twat packages.

It's part of the twat collection of Python packages.

twat-labs serves as a proving ground for innovative or niche functionalities related to the broader twat system. While the core twat project provides stable and well-tested tools, twat-labs offers a space for developers and users to explore cutting-edge capabilities.

Currently, in its initial phase, twat-labs primarily establishes the plugin structure and includes basic versioning. As it evolves, it will house more concrete experimental features.

• Users of the twat ecosystem: If you use twat tools and are interested in trying out upcoming features or need specialized functionalities that are not yet in the main releases, twat-labs is for you.
• Python Developers: If you are contributing to the twat ecosystem or developing tools that integrate with it, twat-labs provides examples of plugin structure and a place to potentially contribute experimental modules.

• Access to Experimental Features: Get early access to new tools and functionalities being developed for the twat ecosystem.
• Extensibility: Provides a clear mechanism for extending the twat system with new capabilities.
• Feedback Loop: Allows developers to gather feedback on new features before they are finalized.

You can install twat-labs using pip:

pip install twat-labs

This will install the latest stable version from PyPI.

As a Python library, twat-labs can be imported into your Python projects. Currently, its primary exposed feature is its version information, which is a common practice for plugins to allow host systems to identify them:

import twat_labs

print(f"twat-labs version: {twat_labs.__version__}")

# Future functionalities will be accessible here
# For example:
# if hasattr(twat_labs, 'experimental_feature_x'):
#     twat_labs.experimental_feature_x()

It is anticipated that twat-labs, as a plugin for the twat ecosystem, may extend a twat CLI if one exists. For example, if twat is a command-line tool, installing twat-labs might automatically make new subcommands or options available.

Example (hypothetical, depends on the main twat tool):

# Assuming 'twat' is the main CLI tool
twat labs-feature --option

The exact CLI usage will depend on how the main twat application discovers and integrates its plugins. Please refer to the documentation of the core twat tool for details on how it manages plugins.

This section provides a more detailed look into the internal workings of twat-labs, its development practices, and how to contribute.

• Plugin Registration: twat-labs is registered as a plugin within the twat ecosystem using Python's entry points mechanism. This is defined in the pyproject.toml file under the [project.entry-points."twat.plugins"] table:

  [project.entry-points."twat.plugins"]
  labs = "twat_labs"

  This allows the main twat application to discover and load twat-labs if it's installed in the same Python environment.

• Current Functionality: As of now, the primary functionality within the src/twat_labs/__init__.py module is to expose its version:

  from twat_labs.__version__ import version as __version__
  
  __all__ = ["__version__"]

  The actual version string is dynamically generated at build time by hatch-vcs based on Git tags and stored in src/twat_labs/__version__.py.

• Project Structure:

  • src/twat_labs/: Contains the main source code for the plugin.
  • tests/: Contains test suites for the project, written using pytest.
  • pyproject.toml: The heart of the project's build system and metadata, compliant with PEP 621.
  • .github/workflows/: Defines CI/CD pipelines using GitHub Actions for automated testing and releases.

twat-labs uses modern Python packaging standards and tools:

• pyproject.toml (PEP 621): This file defines all project metadata, dependencies, and tool configurations.

• hatch: The project uses Hatch as its primary tool for development workflow management. Key hatch commands include:

  • hatch shell: Activates the project's isolated development environment with all dependencies installed.
  • hatch run test: Executes the test suite using pytest.
  • hatch run test-cov: Runs tests with coverage reporting.
  • hatch run lint: Runs linters (ruff) and type checkers (mypy).
  • hatch run format: Formats code using ruff format. Refer to the [tool.hatch.envs.default.scripts] and [tool.hatch.envs.lint.scripts] sections in pyproject.toml for a full list of available scripts.

• Versioning with hatch-vcs: The project's version is dynamically determined from Git tags (e.g., v1.0.0). hatch-vcs generates the src/twat_labs/__version__.py file during the build process. This means manual updates to the version in source files are not required; versioning is managed by creating new Git tags.

To maintain code quality and consistency, twat-labs adheres to the following practices:

• Linting and Formatting with ruff:

  • Ruff is used for extremely fast Python linting and formatting.
  • Configuration is stored in the [tool.ruff] section of pyproject.toml.
  • Key rules include adherence to PEP 8, various flake8 plugins, and import sorting (isort conventions).
  • Run hatch run lint:style or hatch run lint:fmt to check and format code.

• Static Type Checking with mypy:

  • The project enforces static type checking using Mypy.
  • Configuration is in the [tool.mypy] section of pyproject.toml.
  • Strict rules are enabled (e.g., disallow_untyped_defs, no_implicit_optional).
  • Run hatch run lint:typing or hatch run type-check to perform type checks.
  • All new code contributions must include type hints.

• Testing with pytest:

  • Tests are located in the tests/ directory and are written using the pytest framework.
  • Comprehensive tests are expected for all new features and bug fixes.
  • Run tests with hatch run test. Coverage reports can be generated with hatch run test-cov.
  • The pyproject.toml file contains configurations for pytest under [tool.pytest.ini_options].

• Pre-commit Hooks:

  • The project uses pre-commit hooks configured in .pre-commit-config.yaml. These hooks typically run linters and formatters automatically before each commit, ensuring that contributions adhere to the project's coding standards.

• Changelog (CHANGELOG.md):

  • All notable changes, including new features, bug fixes, and improvements, must be documented in CHANGELOG.md.
  • The changelog follows the principles of Keep a Changelog.

• CI/CD with GitHub Actions:

  • The workflows defined in .github/workflows/ (e.g., push.yml, release.yml) automate testing across different Python versions and operating systems upon pushes and pull requests.
  • Successful merges to the main branch that include new version tags can trigger automated releases to PyPI.

• Contribution Process:

  1. Fork the repository on GitHub.
  2. Create a new branch for your feature or bug fix: git checkout -b feature/your-feature-name or git checkout -b fix/issue-number.
  3. Make your changes, adhering to the coding standards (linting, typing, testing).
  4. Add tests for your changes.
  5. Update CHANGELOG.md with a description of your changes.
  6. Commit your changes with a clear and descriptive commit message.
  7. Push your branch to your fork: git push origin feature/your-feature-name.
  8. Open a Pull Request to the main twat-labs repository.
  9. Ensure all CI checks pass.

By following these guidelines, contributors can help ensure that twat-labs remains a high-quality, robust, and maintainable project.