xray#

Visualize a 3D image as a 2D image in the style of an xray radiograph, with the brightness corresponding inversely to the amount of material the xray passed through.

import porespy as ps
import matplotlib.pyplot as plt
ps.visualization.set_mpl_style()
[03:25:59] ERROR    PARDISO solver not installed, run `pip install pypardiso`. Otherwise,          _workspace.py:56
                    simulations will be slow. Apple M chips not supported.                                         

Create a test image of fibers since the orientation is useful for visualization:

im = ps.generators.cylinders(shape=[200, 200, 200], r=6, porosity=0.7)
fig, ax = plt.subplots(1, 3, figsize=[8, 12])
ax[0].imshow(im[50, :, :])
ax[0].axis(False)
ax[1].imshow(im[:, 50, :])
ax[1].axis(False)
ax[2].imshow(im[:, :, 50])
ax[2].axis(False);
../../../_images/43c73c29748d1adee836f8453b8c7e5a4f28d34d6e986fa304312f97f6d9b3c9.png

axis#

The default behavior is the view the sample as though the xrays passed along the x-axis, but this is adjustable:

fig, ax = plt.subplots(1, 3, figsize=[8, 12])

im1 = ps.visualization.xray(im)
ax[0].imshow(im1, cmap=plt.cm.plasma)
ax[0].axis(False)

im2 = ps.visualization.xray(im, axis=1)
ax[1].imshow(im2, cmap=plt.cm.nipy_spectral)
ax[1].axis(False)

im2 = ps.visualization.xray(im, axis=2)
ax[2].imshow(im2, cmap=plt.cm.bone)
ax[2].axis(False);
../../../_images/a3e04440c4080cbb4a7b11dd390bf661806020b2df522b93657ca07fb1079c60.png