About PhasorPy

The PhasorPy project’s mission is to advance and democratize phasor analysis through the open-source PhasorPy library.

Founding and funding

The PhasorPy project was established in 2023 by the Advanced Bioimaging Unit at the University of the Republic and Institut Pasteur de Montevideo, and the Laboratory for Fluorescence Dynamics at the University of California, Irvine.

The project was supported by the Essential Open Source Software for Science (EOSS) program at Chan Zuckerberg Initiative (grant number 2022-252604; 2022-2024).

Project goals

The original goals were to create an open-source Python library based on sound software engineering principles, establish a community of users and developers, and reimplement core phasor-based functionality of the discontinued closed-source Globals for Images - SimFCS software by Enrico Gratton.

The current goals are to improve community sustainability and to advance the PhasorPy library toward a stable, extensible platform that supports large-scale automated workflows, AI-assisted analysis, and robust integration with the scientific Python ecosystem, while continuing to serve as a reliable base for graphical applications and training resources.

Governance and contributors

PhasorPy is developed collaboratively by contributors to the PhasorPy repository and governed by members of the PhasorPy organization.

Technical direction is developed through open discussion and consensus-building in issues, pull requests, and community meetings. Members of the PhasorPy organization make final technical and release decisions.

Infrastructure hosting, funded activities, and institutional partnerships are coordinated through the Advanced Bioimaging Unit.

See the Contributing guide to get involved.

Acknowledgments

We thank the PhasorPy user community for feedback, bug reports, feature suggestions, and data files that have helped improve the library.

We acknowledge the open-source scientific Python ecosystem that PhasorPy builds on, including Python, NumPy, SciPy, Matplotlib, and scikit-learn, the microscopy file-format stack (including tifffile, ptufile, and related tools), and the documentation toolchain based on Sphinx and Sphinx-Gallery.

We also acknowledge GitHub for providing a platform for collaborative development and hosting documentation, PyPI and conda-forge for package distribution, and Zenodo for hosting sample data.

Citation

If PhasorPy contributes to research that leads to a publication, please cite doi: 10.5281/zenodo.13862586.