constellaration 0.2.1

Code for analyzing and evaluating stellarator plasma shapes

ConStellaration: A dataset of QI-like stellarator plasma boundaries and optimization benchmarks

ConStellaration is a dataset of diverse QI-like stellarator plasma boundary shapes, paired with their ideal-MHD equilibria and performance metrics. The dataset is available on Hugging Face. The repository contains a suite of tools and notebooks for exploring the dataset, including a forward model for plasma simulation, scoring functions for optimization evaluation and data-driven generative modeling.

Installation

The following instructions have been tested on Ubuntu 22.04. Other platforms may require additional steps and have not been validated.

The system dependency libnetcdf-dev is required for running the forward model. On Ubuntu, please ensure it is installed before proceeding, by running:

sudo apt-get update
sudo apt-get install build-essential cmake libnetcdf-dev

Install from PyPI

The package can be installed directly from PyPI:

pip install constellaration

Install by cloning the repository

1. Clone the repository:

git clone https://github.com/proximafusion/constellaration.git
cd constellaration

2. Install the required Python dependencies:

pip install .

Running with Docker

If you prefer not to install system dependencies, you can use the provided Dockerfile to build a Docker image and run your scripts in a container.

1. Build the Docker image:

docker build -t constellaration .

2. Run your scripts by mounting a volume to the container:

docker run --rm -v $(pwd):/workspace constellaration python relative/path/to/your_script.py

Replace your_script.py with the path to your script. The $(pwd) command mounts the current directory to /workspace inside the container.

Explanation Notebook

You can explore the functionalities of the repo through the Boundary Explorer Notebook.

Contributing

To be able to run unit tests, please install the test and lint environment:

pip install -e ".[test,lint]"

Note: The development and test environment currently supports Python 3.10 only. Other Python versions are not guaranteed to work.

Linting

We use pre-commit to automatically lint and format code before each commit. Linting is static code analysis that catches style issues and potential errors. If any hook fails, the commit will be blocked until you fix the reported issues and re-stage your changes.

Install the hook (once per clone):

pip install pre-commit
pre-commit install

You can run all pre-commit hooks against all files like this:

pre-commit run --all-files

Unit tests

To locally run all unit tests (while in the top directory of the repo)

pytest .

Optimization baseline

The optimization baseline can be executed by running the individual files within the folder

optimization_examples

Citation

If you find this work useful, please cite us: