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homcloud

  • Openness:3 ★★★
  • Document quality:3 ★★★

A Python package for extracting structural features from point cloud and image data using the mathematical framework of persistent homology. In the field of materials science, it is used to characterize structural differences between liquids and glasses, as well as for dimensionality reduction of microscope images. It is also useful for obtaining structural descriptors for machine learning.

Information
Last Update:2025/11/26

Official site

https://homcloud.dev/index.en.html

License

GPL-3.0-or-later

Availability

Works on Windows/Linux/Mac with Python installed.
Core Developers
  • Ippei Obayashi (main developer)
  • Tomohide Wada (Window version, documentation)
  • Tian Tunhua (Tutorial, new features, debug)
  • Some parts are outsourced to Trans NT, Inc.
  • Ken Nakashima (Translation of tutorials)
  • Jun Miyanaga (documentation)
  • Yasuaki Hiraoka and his laboratory members
Target substance/model
  • 2D or 3D image/volumetric data
  • Point cloud data with/without periodic boundary conditions
Document
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