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()
[03:29:32] ERROR    PARDISO solver not installed, run `pip install pypardiso`. Otherwise,          _workspace.py:56
                    simulations will be slow. Apple M chips not supported.                                         

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/e22cfea60c5b2f4effa732d56597aea6e19d48f4725d99f325b793286e0740e6.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/8a1dc5785bf80b7448195a340c428d27fd5ef38cddbedc4748dc60e3179ca739.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/a01c90084278ee0919973fb075ce24706e0062535e6803de24d4b958320930e3.png