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Python implementation of the active-finite-Voronoi (AFV) model

Project description

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Tests on all platforms pytest codecov License: MIT

PyAFV

Python code that implements the active-finite-Voronoi (AFV) model in 2D. The AFV framework was introduced and developed in, for example, Refs. [13].

Installation

To install PyAFV with pip, run:

pip install pyafv

The package supports Python ≥ 3.10 and < 3.15. To verify that the installation was successful and that the correct version is installed, run the following in Python:

import pyafv
print(pyafv.__version__)

Usage

The following example demonstrates how to construct a finite-Voronoi diagram:

import numpy as np
import pyafv as afv

N = 100                                          # number of cells
pts = np.random.rand(N, 2) * 10                  # initial positions
params = afv.PhysicalParams()                    # use default parameter values
sim = afv.FiniteVoronoiSimulator(pts, params)    # initialize the simulator
sim.plot_2d(show=True)                           # visualize the Voronoi diagram

To compute the conservative forces and extract detailed geometric information (e.g., cell areas, vertices, and edges), call:

diag = sim.build()

The returned object diag is a Python dict containing these quantities.

More information

GitHub: https://github.com/wwang721/pyafv

See important issues for additional context, such as:

Full documentation on readthedocs!

References

[1] J. Huang, H. Levine, and D. Bi, Bridging the gap between collective motility and epithelial-mesenchymal transitions through the active finite Voronoi model, Soft Matter 19, 9389 (2023).
[2] E. Teomy, D. A. Kessler, and H. Levine, Confluent and nonconfluent phases in a model of cell tissue, Phys. Rev. E 98, 042418 (2018).
[3] W. Wang (汪巍) and B. A. Camley, Divergence of detachment forces in the finite-Voronoi model, manuscript in preparation (2026).

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