StructuralGT 1.0.1rc1

Automated graph theory analysis of digital structural networks images

StructuralGT: An automated python package for graph theory analysis of structural networks.

Designed for processing digital micrographs of complex network materials.

For example, analyzing SEM images of polymer network.

StructuralGT is designed as an easy-to-use python-based application for applying graph theory (GT) analysis to structural networks of a wide variety of material systems. This application converts digital images of nano-/micro-/macro-scale structures into a GT representation of the structure in the image consisting of nodes and the edges that connect them. Fibers (or fiber-like structures) are taken to represent edges, and the location where a fiber branches, or 2 or more fibers intersect are taken to represent nodes. The program operates with a graphical user interface (GUI) so that selecting images and processing the graphs are intuitive and accessible to anyone, regardless of programming experience. Detection of networks from input images, the extraction of the graph object, and the subsequent GT analysis of the graph is handled entirely from the GUI, and a PDF file with the results of the analysis is saved. Also see StructuralGT_RC for added Ricci Curvature analysis.

See the README for detail information.

https://github.com/drewvecchio/StructuralGT

Copyright (C) 2021, The Regents of the University of Michigan.

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.

Contributers: Drew Vecchio, Samuel Mahler, Mark D. Hammig, Nicholas A. Kotov

Contact email: [e-mail]