Quantitative Image Analysis of Porous Materials

Warning

As of February 12th, 2021, we are actively working on version 2.0. The dev branch will no longer be backwards compatible with previous versions of PoreSpy. We expect this conversion to be complete by winter’s end.


What is PoreSpy? stars

PoreSpy is a collection of image analysis functions used to extract information from 3D images of porous materials (typically obtained from X-ray tomography). There are other packages that offer generalized image analysis tools (i.e skimage and scipy.ndimage in the Python environment, ImageJ, MatLab’s Image Processing Toolbox), but they all require building up complex scripts or macros to accomplish tasks of specific use to porous media. Porespy includes predefined functions to accomplish many of these routine analyses rapidly and conveniently.


Capabilities

PoreSpy consists of the following modules:

  • generators: Routines for generating artificial images of porous materials useful for testing and illustration

  • filters: Functions that accept an image and return an altered image

  • metrics: Tools for quantifying properties of images

  • networks: Algorithms and tools for analyzing images as pore networks

  • visualization: Helper functions for creating useful views of the image

  • io: Functions for outputting image data in various formats for use in common software

  • tools: Various useful tools for working with images


How To Cite

If you use PoreSpy in a publication, please add the following citation:

Citation

Gostick J, Khan ZA, Tranter TG, Kok MDR, Agnaou M, Sadeghi MA, Jervis R. PoreSpy: A Python Toolkit for Quantitative Analysis of Porous Media Images. Journal of Open Source Software, 2019. doi:10.21105/joss.01296