map_to_regions#

Maps pore values from a network onto the image from which it was extracted

import numpy as np
import porespy as ps
import openpnm as op
import matplotlib.pyplot as plt

ws = op.Workspace()
ws.settings['loglevel'] = 50
np.random.seed(10)
ps.visualization.set_mpl_style()

Create image and extract network#

im = ps.generators.blobs(shape=[400, 400], porosity=0.6)
ps.imshow(im);
snow_output = ps.networks.snow2(im, voxel_size=1)
pn = op.io.network_from_porespy(snow_output.network)
../../../_images/23f80af904fa6615c9670a3a8dad2e0d8489e8d2fa6df3bbd6e037f8867e0f29.png

Plot the pore network#

fig, ax = plt.subplots()
op.visualization.plot_connections(pn, c='w', linewidth=2, ax=ax)
op.visualization.plot_coordinates(pn, c='w', s=100, ax=ax)
plt.imshow(snow_output.regions.T, origin='lower')
plt.axis('off');
../../../_images/fdb247bcac2b96c915d6b3fdbb7bc81d327c543ee2f84284cf1fc2975411465e.png

Now assign some values to the network:

pn['pore.values'] = np.random.rand(pn.Np)

And now assign these values to the image regions:

reg = ps.networks.map_to_regions(regions=snow_output.regions.T, values=pn['pore.values'])
plt.imshow(reg, origin='lower');
../../../_images/e1ab918f01353067a1f5434e0bafade42fcbec39083b39d78168a2dc1664acc7.png