ASWIFT

ASWIFT fits square-wave voltammetry (SWV) traces and extracts peak signal, background, peak voltage, full-prominence peak width, and baseline-subtracted full-prominence peak area. Use the no-code ASWIFT Viewer for interactive analysis, or install the Python package to build ASWIFT into a custom workflow.

Installation

ASWIFT requires Python 3.12 or newer. Install the core fitting utilities with:

pip install aswift

Install the viewer, pandas CSV helpers, plotting tools, and PalmSens .pssession support with:

pip install "aswift[viewer]"

PalmSens support uses pypalmsens, which requires the Microsoft .NET 9 Runtime.

ASWIFT Viewer

Launch the viewer from an activated Python environment:

aswift-viewer

The viewer loads CSV, JSON, and PalmSens .pssession data; fits individual or batched voltammograms; visualizes fitted backgrounds and peaks; and exports raw and normalized results with their associated metadata.

Under Input, enable Peak potential range to select the most prominent peak within typed lower and upper limits in volts. Leave it unchecked for automatic peak selection. Allow enough range for peak drift. A trace with no eligible peak is reported as a failed fit.

Use Normalize with typed start and end sample indices. Both limits are included, so 0–4 uses the first five samples as the reference.

Python API

import numpy as np
from aswift import aswift_fit, plot_fit_result

volts = np.asarray([...], dtype=float)
current = np.asarray([...], dtype=float)

result = aswift_fit(volts, current)
print(result.peak_signal, result.peak_voltage)
ax = plot_fit_result(result)

The returned FitResult includes the peak signal, background, voltage, full-prominence width and area, peak profile, background profile, and combined fitted current. ASWIFT also provides dataframe and PalmSens helpers for batch workflows. Single-trace and batch fitting methods accept expected_potential_range=(-0.35, -0.20) to restrict peak selection to an inclusive potential range in volts.

Documentation and support

See the full documentation and examples on GitHub. Please open an issue for bug reports, questions, feedback, or feature requests.

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 CITATION.cff.

License

ASWIFT is licensed under the MIT License. See the license for details.