Short repository for reproducing figures and experiments used in Principled model selection for stochastic dynamics paper.
This repository contains analysis code, plotting utilities and precomputed CSV/PKL results used to generate the figures in the paper.
Note: For daily use, up-to-date code, and a more user-friendly version, please refer to the main repository: https://github.com/ronceray/StochasticForceInference
- Clone the repository:
git clone https://github.com/odannis/PASTIS_paper.git
cd PASTIS_paper- Create and activate a virtual environment (recommended):
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt*.ipynb— analysis and figure-generation notebooks.csv/— precomputed CSV/PKL data used by the notebooks and plotting scripts.simulation_models/,SFFI/— local packages referenced by the code.
Note: every Jupyter notebook (*.ipynb) in this repository is used to create one or more figures for the paper. The notebooks load the precomputed result files (mostly .pkl and .csv) located in the csv/ directory and produce the final plot images. The .pkl files in csv/ were generated on a compute cluster using the scripts found in the script_cluster/ folder. Jobs were launched there using the start_ib_q.sh wrapper script.
If you want to re-run the full parameter sweeps (which produced the .pkl files), inspect script_cluster/ and script_cluster/start_ib_q.sh; these are the job submission and orchestration scripts used on the cluster. Reproducing those runs requires a cluster scheduler environment and may take significant compute resources — for most uses you only need the files in csv/ to reproduce the figures locally.
This repository is provided under the MIT License (file LICENSE).
If you use this code or results from this repository, please cite both the paper and the archived code release.
Code DOI (Zenodo):
Paper (arXiv preprint):
PASTIS paper: https://arxiv.org/abs/2501.10339