Porosity is the void volume divided by the bulk volume. In a boolean image this can be calculated with
im.sum()/im.size, assuming the void voxels are labeled
True. It can be slightly more complicated however, if the image does not fill up the full array (i.e.
im.size is not the bulk volume) or if the there are other values in the image besides
True (i.e. it’s not obvious what is void space). The
porosity function works as
np.sum(im == 1)/np.sum(im == 0). This means that
any voxels marked something else are ignored. It’s still very simple, but is more robust which comes in handy.
import matplotlib.pyplot as plt import numpy as np import porespy as ps np.random.seed(0)
import inspect inspect.signature(ps.metrics.porosity)
In its basic form a binary image is fine:
im = ps.generators.blobs(shape=[200, 200]) e = ps.metrics.porosity(im) print(e)
However, if the image is has some unfilled space, the around a cylindrical tomogram, then it can be labelled as 2 so it’s ignored:
im = ps.generators.blobs(shape=[100, 100, 100], porosity=0.5, blobiness=2).astype(int) cyl = ps.generators.cylindrical_plug(shape=im.shape, axis=0) im[~cyl] = 2 plt.imshow(im[50, ...], interpolation='none', origin='lower') e = ps.metrics.porosity(im) print(e)
The porosity specified when generatign the blobsimage was 50%, and the computed porosity is also 50%, despite having a regions of 2’s around the outside.