galform-execution 0.2.1

GALFORM execution helper utilities

galform_execution

A Python-based utility to manage GALFORM N-body simulation submissions to SLURM on COSMA.

galform_execution automates the generation and submission of SLURM batch scripts for GALFORM runs. It handles environment setup, parameter injection into .input.ref files, and conditional post-processing (NETA, LUM_FUN, etc.), providing a robust and reproducible workflow for large-scale galaxy formation simulations.

Key Features

• Dynamic Script Generation: Generates self-contained tcsh scripts with resolved simulation and model parameters.
• Config-Driven: Manage simulations, models, and dust parameters via JSON configurations.
• SLURM Optimized: Automatic job array support and remapping of high subvolume indices.
• Robustness: Built-in retries with exponential backoff for transient scheduler errors.

Prerequisites

• Environment: Access to the COSMA HPC cluster (Durham University).
• Tooling: uv is recommended for fast installation and dependency management.

Installation

# Install the package and its dependencies directly from PyPI using uv
uv pip install galform_execution

For development installation from source:

git clone https://github.com/OscarHickman/galform_execution.git
cd galform_execution
uv pip install -e .

Quick Start

1. Identify your GALFORM directory

You need a compiled version of GALFORM with the standard directory structure (containing build/galform2, *.input.ref, etc.).

2. Preview a submission (Dry Run)

Check the generated SLURM script without submitting to the queue:

submit-galform-job /path/to/your/galform --nbody-sim Mill2 --model lc16 --iz 40 --nvol 1-64 --dry-run

3. Submit a job array

submit-galform-job /path/to/your/galform --nbody-sim Mill2 --model lc16 --iz 40 --nvol 1-64 --output-folder-name MyProject

Configuration

Configurations are stored in galform_execution/config/:

• simulations/: JSON files defining N-body simulation parameters (tree paths, cosmology).
• models.json: Maps model names to base input files and dust profiles.
• redshift_lists/: Redshift mappings for various simulations.

Development

Running Tests

uv run pytest tests

Linting

uv run ruff check galform_execution

Citing this software

If you use galform_execution in your research, please cite it. GitHub will show a "Cite this repository" button (top-right of the repo page) once CITATION.cff is committed.

After connecting Zenodo (see below), each release gets a permanent DOI. Replace YOUR_ZENODO_DOI with the DOI minted on first release:

@software{hickman_galform_execution,
  author    = {Hickman, Oscar},
  title     = {galform\_execution},
  url       = {https://github.com/OscarHickman/galform_execution},
  doi       = {YOUR_ZENODO_DOI},
  license   = {MIT}
}

Setting up citation infrastructure

Zenodo (DOIs for every release) — already connected: Push a new version tag and the release workflow will create a GitHub Release, which Zenodo archives automatically and mints a DOI. Update CITATION.cff and this README with the DOI badge Zenodo provides.

Conda-forge:

1. Get the sha256 of the PyPI source tarball:

   curl -s https://pypi.org/pypi/galform_execution/json \
     | python -c "import sys,json; d=json.load(sys.stdin); \
       [print(f['digests']['sha256']) for v in d['releases'].values() \
        for f in v if f['filename'].endswith('.tar.gz')]" | tail -1
   

2. Paste it into recipe/meta.yaml.
3. Fork conda-forge/staged-recipes, copy recipe/meta.yaml to recipes/galform_execution/meta.yaml, and open a PR.

License

Distributed under the MIT License. See LICENSE for more information.