Skip to main content

Automated graph theory analysis of digital structural networks images

Project description

StructuralGT_RC: 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_RC 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 is the same software as StructuralGT, except with the added functionality to perform Ricci Curvature calculation. The two versions are separated due to issues with some systems installing the dependency NetworKit. This application converts digital images of nano-/micro-/macro-scale structures into a graph theoretical 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.

See the README for detail information.

https://github.com/drewvecchio/StructuralGT/tree/StructuralGT_RC

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: vecdrew@umich.edu

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

StructuralGT_RC-1.0.1a2.tar.gz (46.6 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

StructuralGT_RC-1.0.1a2-py3-none-any.whl (107.5 kB view details)

Uploaded Python 3

File details

Details for the file StructuralGT_RC-1.0.1a2.tar.gz.

File metadata

  • Download URL: StructuralGT_RC-1.0.1a2.tar.gz
  • Upload date:
  • Size: 46.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/3.10.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.59.0 CPython/3.8.2

File hashes

Hashes for StructuralGT_RC-1.0.1a2.tar.gz
Algorithm Hash digest
SHA256 1ebaf9f63dd0587dd465059d26e6343e8ec01dced1ae30845590587256e7ed12
MD5 a2da1d38f1345559c2ca3e26b3370143
BLAKE2b-256 b03669fa9059452e9bb0def0b83263c25cd444a769e6cde73cafdbe52fb50ea5

See more details on using hashes here.

File details

Details for the file StructuralGT_RC-1.0.1a2-py3-none-any.whl.

File metadata

  • Download URL: StructuralGT_RC-1.0.1a2-py3-none-any.whl
  • Upload date:
  • Size: 107.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/3.10.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.59.0 CPython/3.8.2

File hashes

Hashes for StructuralGT_RC-1.0.1a2-py3-none-any.whl
Algorithm Hash digest
SHA256 e52f3a500e96b6efcc48e8fdf31de017f1b3212f4fe0d2d785515fe3ea3e97f4
MD5 9e3d8e3739cc69043612e16d04c00df8
BLAKE2b-256 73ed9a9b3a0d59da4e495cc9e4358accdcc0ba245ee60808725a4106968b7e84

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page