# find_trapped_regions#

This is meant to find regions of defending phase that are trapped during an invasion simulation. It works for both ordinary and invasion percolation type simulations.

[1]:

import matplotlib.pyplot as plt
import numpy as np
import porespy as ps
import inspect
ps.visualization.set_mpl_style()
inspect.signature(ps.filters.find_trapped_regions)

[1]:

<Signature (seq, outlets=None, bins=25, return_mask=True)>

[2]:

np.random.seed(7)
im = ps.generators.blobs(shape=[100, 100], porosity=0.6)
inlets = np.zeros_like(im)
inlets[0, :] = True
outlets = np.zeros_like(im)
outlets[-1, :] = True
sizes = ps.filters.porosimetry(im, inlets=inlets)
fig, ax = plt.subplots(1, 1, figsize=[6, 6])
ax.imshow(sizes, interpolation='none', origin='lower')
ax.axis(False);


## seq#

Given the sequence at which each voxel was invaded, this finds all voxels that were invaded after they were cutoff from the outlet. The output of porosimetry however is in the reverse order, since the largest sizes are invaded first. PoreSpy has a function for this, called size_to_seq:

[3]:

seq = ps.filters.size_to_seq(sizes)
fig, ax = plt.subplots(1, 1, figsize=[6, 6])
ax.imshow(seq, interpolation='none', origin='lower')
ax.axis(False);


Now we can pass this result into find_trapped_regions:

[4]:

trapped = ps.filters.find_trapped_regions(seq=seq, outlets=outlets)
fig, ax = plt.subplots(1, 1, figsize=[6, 6])
ax.imshow(trapped/im, interpolation='none', origin='lower')
ax.axis(False);


In the above image the trapped regions are indicated by True, so this can be used as a mask to remove invading voxels from the invasion image.

## outlets#

It’s possible to specify the which voxels are treated as the outlets. If the outlets were on the right we’d see the following:

[5]:

outlets = np.zeros_like(im)
outlets[:, 0] = True
trapped = ps.filters.find_trapped_regions(seq=seq, outlets=outlets)
fig, ax = plt.subplots(1, 1, figsize=[6, 6])
ax.imshow(trapped/im, interpolation='none', origin='lower')
ax.axis(False);


## bins#

The function works by iterating backwards through the invasion sequence. If a lot of small steps were taken, such as occurs in the invasion percolation function (ibip) this can be unreasonably slow. It’s possible to specify the number of steps analyzed, or even which steps to use via the bins argument. The default is 25. Specifying None results in all bins being used. Passing a list of bins sizes is used directly.

[6]:

outlets = np.zeros_like(im)
outlets[-1, :] = True
trapped = ps.filters.find_trapped_regions(seq=seq, outlets=outlets, bins=2)
fig, ax = plt.subplots(1, 1, figsize=[6, 6])
ax.imshow(trapped/im, interpolation='none', origin='lower')
ax.axis(False);