pyCE
How much information does it take to describe a localized field configuration? Marcelo Gleiser and Nikitas Stamatopoulos posed that deceptively simple question in 2012, and answered it with a new measure, the configurational entropy, built from the Shannon entropy of the configuration’s power spectrum. Where the energy of a configuration tells you what it costs to assemble, its configurational entropy tells you how that cost is organized across scales. The measure has since been put to work on solitons, compact stars, phase transitions, and the cosmic microwave background.
pyCE is a Python library for doing this kind of work. It grew out of research in Marcelo Gleiser’s group in the Department of Physics and Astronomy at Dartmouth College, and it gathers in one place the machinery those projects share: radial Fourier transforms in arbitrary spatial dimension, modal fractions, and entropy and divergence measures for both discrete and continuous spectra.
The library is organized around four physical arenas. The instantons
module solves for the bounce profiles that mediate false-vacuum decay and
computes their configurational entropy. The bosonstars module solves the
Einstein-Klein-Gordon system for self-gravitating complex scalar fields.
The oscillons module simulates
long-lived, localized oscillations of a real scalar field. The
polytropes module solves the Lane-Emden equation for self-gravitating
spheres of gas. The cosmology module brings the same
information-theoretic lens to the angular power spectrum of the cosmic
microwave background — WMAP data is bundled, Planck data is a download away.
Underneath all four sits pyCE.math, where the radial Fourier transform
lives.
If you’re new here, start with Getting Started, then work through whichever guide matches your problem.
Guide
Reference
Citing
Sowinski DR. pyCE [computer software]. Version 0.2.0. 2018. Accessed July 21, 2026. https://github.com/EternalTime/pyCE
@software{sowinski_pyce,
author = {Sowinski, Damian R.},
title = {pyCE: Configurational entropy tools for classical field configurations},
year = {2018},
version = {0.2.0},
url = {https://github.com/EternalTime/pyCE}
}
References
The configurational entropy program this library serves is developed in the following papers; the PDFs are hosted on the author’s website.
D. R. Sowinski, Complexity and Stability for Epistemic Agents: The Foundations and Phenomenology of Configurational Entropy, Ph.D. thesis, Dartmouth College (2016).
M. Gleiser & D. Sowinski, Information-entropic stability bound for compact objects: Application to Q-balls and the Chandrasekhar limit of polytropes, Phys. Lett. B 727, 272 (2013).
M. Gleiser & D. Sowinski, Information-entropic signature of the critical point, Phys. Lett. B 747, 125 (2015).
D. Sowinski & M. Gleiser, Information dynamics at a phase transition, J. Stat. Phys. 167, 1221 (2017).
D. Sowinski & M. Gleiser, Configurational information approach to instantons and false vacuum decay in D-dimensional spacetime, Phys. Rev. D 98, 056026 (2018).
M. Gleiser, M. Stephens & D. Sowinski, Configurational entropy as a lifetime predictor and pattern discriminator for oscillons, Phys. Rev. D 97, 096007 (2018).
D. R. Sowinski, S. Kelty & G. Ghoshal, Configurational information measures, phase transitions, and an upper bound on complexity, arXiv:2503.02980 (2025).