pyplaid 0.1.14

A package that implements a data model tailored for AI and ML in the context of physics problems

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Physics Learning AI Datamodel (PLAID)

• Physics Learning AI Datamodel (PLAID)
  • 1. Description
  • 2. Getting started
  • 3. Call for Contributions
  • 4. Documentation

1. Description

This library proposes an implementation for a datamodel tailored for AI and ML learning of physics problems. It has been developped at SafranTech, the research center of Safran group.

• Documentation: https://plaid-lib.readthedocs.io/
• Source code: https://github.com/PLAID-lib/plaid
• Contributing: https://github.com/PLAID-lib/plaid/blob/main/CONTRIBUTING.md
• License: https://github.com/PLAID-lib/plaid/blob/main/LICENSE.txt
• Bug reports: https://github.com/PLAID-lib/plaid/issues
• Report a security vulnerability: https://github.com/PLAID-lib/plaid/issues

2. Getting started

2.1 Using the library

To use the library, the simplest way is to install it from the packages available:

• on conda-forge for Windows, macOS and Linux:

  conda install -c conda-forge plaid
  

• or on PyPi for Linux:

  pip install pyplaid
  

Note

• Only the conda-forge packages (all operating systems) and the Linux PyPI package include a bundled pyCGNS dependency. In other situations, which we have not tested, pyCGNS must be installed separately beforehand.
• On Apple Silicon, users can force an osx-64 conda environment using CONDA_SUBDIR=osx-64, allowing installation of the existing macOS-64 builds under Rosetta.

2.2 Contributing to the library

To contribute to the library, you need to clone the repo using git:

git clone https://github.com/PLAID-lib/plaid.git

2.2.1 Development dependencies

To configure an environment:

• using conda (Windows, macOS and Linux):

  conda env create -n plaid-dev python=3.12 -f environment.yml
  pip install -e . --no-deps
  

• using uv (Linux):

  uv sync --dev
  

2.2.2 Tests and examples

To check the installation, you can run the unit test suite:

pytest tests

To test further and learn about simple use cases, you can run and explore the examples:

cd examples
bash run_examples.sh  # [unix]
run_examples.bat      # [win]

2.2.3 Documentation

To compile locally the documentation, you can run:

cd docs
make html

Various notebooks are executed during compilation. The documentation can then be explored in docs/_build/html.

2.2.4 Formatting and linting with Ruff

We use Ruff for linting and formatting.

The configuration is defined in ruff.toml, and some folders like docs/ and examples/ are excluded from checks.

You can run Ruff manually as follows:

ruff --config ruff.toml check . --fix      # auto-fix linting issues
ruff --config ruff.toml format .           # auto-format code

2.2.5 Setting up pre-commit

Pre-commit is configured to run the following hooks:

• Ruff check
• Ruff format
• Pytest

The selected hooks are defined in the .pre-commit-config.yaml file.

To run all hooks manually on the full codebase:

pre-commit run --all-files

You can also run (once):

pre-commit install

This ensures that every time you commit, all the hooks are executed automatically on the staged files.

3. Call for Contributions

The PLAID project welcomes your expertise and enthusiasm!

Small improvements or fixes are always appreciated.

Writing code isn’t the only way to contribute to PLAID. You can also:

• review pull requests
• help us stay on top of new and old issues
• develop tutorials, presentations, and other educational materials
• maintain and improve our documentation
• help with outreach and onboard new contributors

If you are new to contributing to open source, this guide helps explain why, what, and how to successfully get involved.

4. Documentation

The documentation is deployed on readthedocs.