User Guide

PoreSpy is a collection of functions that are especially useful for analyzing binary images of porous materials, typically produced by X-ray tomography. The functions in this collection are often simple combinations of other standard image analysis functions, so really only offer the convenience of organizing the functions into one place, and sparing you the trouble of working them out.

Modules

This package consists of several modules, the purposes of which are given below:

binarization	Convert greyscale images to binary
filters	Process images based on structural features
generators	Make artificial images for testing & illustration
metrics	Extract information from images
network_extraction	Extract pore network models from images
simulations	Performing complex simulations directly on an image
tools	Utilities for altering & manipulating images
visualization	Quickly and easily visualiz 3D images

Example Usage

>>> import porespy as ps
>>> import matplotlib.pyplot as plt
>>> im = ps.generators.blobs([100, 100])
>>> mip = ps.filters.porosimetry(im)
>>> PcSw = ps.metrics.pore_size_distribution(mip)
>>> fig = plt.plot(*PcSw)

Related Packages

PoreSpy relies heavily on two general image analysis packages: scipy.ndimage and scikit-image also known as skimage. The former contains an assortment of veyr general image analysis tools such as image morphology filters, while the latter offers more complex but still general functions such as watershed segmentation. PoreSpy does not duplicate any of these general functions so you will also have to install and learn how to use them to get the most from PoreSpy. The functions in PoreSpy are generally built up using several of the more general functions offered by skimage and scipy. There are a few functions in PoreSpy that are implimented natively, but only when necessary.

Image Types

PoreSpy is meant to work on single-channel, binary images. Such images are conveniently represented by Numpy arrays, hence all references to an image is equivalent to an array. It is further assumed that the arrays are binarized, meaning 1’s or True values indicating the void space, and 0’s or False values for the solid.

Limitations

Although scikit-image and scipy.ndimage have a wide assortment of functions, they are not always the fastest implementation. It is often faster to use ImageJ for many things, such as distance transforms and image morphology. The advantage of PoreSpy is the flexibility offered by the Python environment.