aswift_figures

This repository contains the notebooks, simulation scripts, and figure exports used to generate ASWIFT manuscript figures. Its purpose is transparency and reproducibility: each figure notebook is kept with the data-processing and simulation code used to produce it.

The main ASWIFT software repository is available at Soh-Lab/aswift.

Setup

Set up Python 3.12 and install a C++ compiler.

Install requirements:

pip install -r requirements.txt

Figure Notebooks

Primary figure notebooks live in figures/figure1.ipynb through figures/figure5.ipynb. Supporting information notebooks live in figures/SI_1.ipynb onward.

Rendered outputs are collected in figures/figure_exports/.

Peak Extraction

To extract peaks and generate JSON/CSV outputs, edit peak_extraction/config/example_config.toml, then run:

python -m peak_extraction.app -c peak_extraction/config/example_config.toml --save

To visualize individual fits:

streamlit run analysis/fit_visualization.py "peak_extraction/config/example_config.toml"

Use a different config path in the command when needed.

Simulations and Statistics

Run one simulation config directly:

python -m simulation.simul_app -c simulation/simulation_config/simul_config.toml --save

Run the manuscript simulation and statistics workflow:

python -m simulation.workflow -c simulation/workflow_config/paper_simulations.toml

To recompute only the statistics from existing simulation outputs:

python -m simulation.workflow -c simulation/workflow_config/paper_simulations.toml --skip-simulations

The binding-curve summary uses kind = "peak" in the workflow config; all other configured summaries use peak-height error statistics.

Citation

If you use ASWIFT in your research, please cite:

Yates, M., Ji, J., Yee, S., & Soh, H. T. (2026). Robust Regularization Enables Automated, Real-Time Square-Wave Voltammetry Signal Quantification. bioRxiv. https://doi.org/10.64898/2026.07.25.740173

Machine-readable citation metadata is available in the main ASWIFT repository's CITATION.cff.