Cite as:
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:10.5281/zenodo.2633284
PoreSpy is a collection of image analysis tool used to extract information from 3D images of porous materials (typically obtained from X-ray tomography). There are many packages that offer generalized image analysis tools (i.e Skimage and Scipy.NDimage in the Python environment, ImageJ, MatLab's Image Processing Toolbox), but the all require building up complex scripts or macros to accomplish tasks of specific use to porous media. The aim of PoreSpy is to provide a set of pre-written tools for all the common porous media measurements.
PoreSpy relies heavily on two general image analysis packages: scipy.ndimage and scikit-image also known as skimage. The former contains an assortment of 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 implemented natively, but only when necessary.
PoreSpy consists of the following modules:
generators: Routines for generating artificial images of porous materials useful for testing and illustrationfilters: Functions that accept an image and return an altered imagemetrics: Tools for quantifying properties of imagessimulations: More complex calculations based on physical processesnetworks: Tools for analyzing images as pore networksvisualization: Helper functions for creating useful views of the imageio: Functions for output image data in various formats for use in common softwaretools: Various useful tools for working with images
PoreSpy depends heavily on the Scipy Stack. The best way to get a fully functional environment is the Anaconda distribution. Be sure to get the Python 3.6+ version.
Once you've installed Conda, you can then install PoreSpy. It is available on the Python Package Index and can be installed by typing the following at the conda prompt:
pip install porespy
On Windows, you should have a shortcut to the "anaconda prompt" in the
Anaconda program group in the start menu. This will open a Windows command
console with access to the Python features added by Conda, such as
installing things via pip.
On Mac or Linux, you need to open a normal terminal window, then type
source activate {env} where you replace {env} with the name of the
environment you want to install PoreSpy. If you don't know what this means,
then use source activate root, which will install PoreSpy in the root
environment which is the default.
If you think you may be interested in contributing to PoreSpy and wish to both use and edit the source code, then you should clone the repository to your local machine, and install it using the following PIP command:
pip install -e "C:\path\to\the\local\files\"
For information about contributing, refer to the contributors guide
The following code snippets illustrate generating a 2D image, applying several filters, and calculating some common metrics. A set of examples is included in this repo, and can be browsed here.
PoreSpy offers several ways to generate artificial images, for quick testing and developmnet of work flows, instead of dealing with reading/writing/storing of large tomograms.
import porespy as ps
import matplotlib.pyplot as plt
im = ps.generators.blobs(shape=[200, 200], porosity=0.5, blobiness=2)
plt.imshow(im)
A common filter to apply is the local thickness, which replaces every voxel with the radius of a sphere that overlaps it. Analysis of the histogram of the voxel values provides information about the pore size distribution.
lt = ps.filters.local_thickness(im)
plt.imshow(lt)
A less common filter is the application of chords that span the pore space in a given direction. It is possible to gain information about anisotropy of the material by looking at the distributions of chords lengths in each principle direction.
cr = ps.filters.apply_chords(im)
cr = ps.filters.flood(cr, mode='size')
plt.imshow(cr)
The metrics sub-module contains several common functions that analyze binary tomogram directly. Examples are simple porosity, as well as two-point correlation function.
data = ps.metrics.two_point_correlation_fft(im)
fig = plt.plot(*data, 'bo-')
plt.ylabel('probability')
plt.xlabel('correlation length [voxels]')
The metrics sub-module also contains a suite of functions that produce plots based on values in images that have passed through a filter, such as local thickness.
mip = ps.filters.porosimetry(im)
data = ps.metrics.pore_size_distribution(mip, log=False)
plt.imshow(mip)
# Now show intrusion curve
plt.plot(data.R, data.cdf, 'bo-')
plt.xlabel('invasion size [voxels]')
plt.ylabel('volume fraction invaded [voxels]')
