write_a_thing 0.1.0

Use LLMs to help you write your things.

Write A Thing

Use LLMs to help you write your things.

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Developer:

• Dan Saattrup Nielsen ([e-mail])

Setup

Installation

1. Run make install, which sets up a virtual environment and all Python dependencies therein.
2. Run source .venv/bin/activate to activate the virtual environment.
3. (Optional) Run make install-pre-commit, which installs pre-commit hooks for linting, formatting and type checking.

Adding and Removing Packages

To install new PyPI packages, run:

uv add <package-name>

To remove them again, run:

uv remove <package-name>

To show all installed packages, run:

uv pip list

All Built-in Commands

The project includes the following convenience commands:

• make install: Install the project and its dependencies in a virtual environment.
• make install-pre-commit: Install pre-commit hooks for linting, formatting and type checking.
• make lint: Lint the code using ruff.
• make format: Format the code using ruff.
• make type-check: Type check the code using mypy.
• make test: Run tests using pytest and update the coverage badge in the readme.
• make docker: Build a Docker image and run the Docker container.
• make docs: View documentation locally in a browser.
• make publish-docs: Publish documentation to GitHub Pages.
• make tree: Show the project structure as a tree.

A Word on Modules and Scripts

In the src directory there are two subdirectories, write_a_thing and scripts. This is a brief explanation of the differences between the two.

Modules

All Python files in the write_a_thing directory are modules internal to the project package. Examples here could be a general data loading script, a definition of a model, or a training function. Think of modules as all the building blocks of a project.

When a module is importing functions/classes from other modules we use the relative import notation - here's an example:

from .other_module import some_function

Scripts

Python files in the scripts folder are scripts, which are short code snippets that are external to the project package, and which is meant to actually run the code. As such, only scripts will be called from the terminal. An analogy here is that the internal numpy code are all modules, but the Python code you write where you import some numpy functions and actually run them, that a script.

When importing module functions/classes when you're in a script, you do it like you would normally import from any other package:

from write_a_thing import some_function

Note that this is also how we import functions/classes in tests, since each test Python file is also a Python script, rather than a module.

Features

Docker Setup

A Dockerfile is included in the new repositories, which by default runs src/scripts/main.py. You can build the Docker image and run the Docker container by running make docker.

Automatic Documentation

Run make docs to create the documentation in the docs folder, which is based on your docstrings in your code. You can publish this documentation to Github Pages by running make publish-docs. To add more manual documentation pages, simply add more Markdown files to the docs directory; this will automatically be included in the documentation.

Automatic Test Coverage Calculation

Run make test to test your code, which also updates the "coverage badge" in the README, showing you how much of your code base that is currently being tested.

Continuous Integration

Github CI pipelines are included in the repo, running all the tests in the tests directory, as well as building online documentation, if Github Pages has been enabled for the repository (can be enabled on Github in the repository settings).

Code Spaces

Code Spaces is a new feature on Github, that allows you to develop on a project completely in the cloud, without having to do any local setup at all. This repo comes included with a configuration file for running code spaces on Github. When hosted on alexandrainst/write_a_thing then simply press the <> Code button and add a code space to get started, which will open a VSCode window directly in your browser.