random_cantor_dust
¶
This generates a fractal image by iteratively and randomaly removing segments at successively large scales.
import porespy as ps
import matplotlib.pyplot as plt
import numpy as np
import inspect
inspect.signature(ps.generators.random_cantor_dust)
<Signature (shape, n, p=2, f=0.8)>
shape
¶
The shape of the image can either be 2D or 3D:
fig, ax = plt.subplots(1, 1, figsize=[6, 6])
im = ps.generators.random_cantor_dust(shape=[500, 500], n=8)
ax.imshow(im, interpolation='none')
ax.axis(False);
n
¶
The number of scales to bisect the image, with higher numbers leading to large features. The algorithm starts by dividing a p x p
region and randomly setting some quadrants to False
. It proceeds by increasing the region size and repeating. n
controls the number of iterations at larger scales.
fig, ax = plt.subplots(1, 2, figsize=[12, 6])
n=4
im = ps.generators.random_cantor_dust(shape=[500, 500], n=n)
ax[0].imshow(im, interpolation='none')
ax[0].axis(False)
ax[0].set_title(f'n = {n}')
n=8
im = ps.generators.random_cantor_dust(shape=[500, 500], n=n)
ax[1].imshow(im, interpolation='none')
ax[1].axis(False)
ax[1].set_title(f'n = {n}');
f
¶
The probability that a quadrant survives the process (i.e. set to True
)
fig, ax = plt.subplots(1, 2, figsize=[12, 6])
f=0.7
im = ps.generators.random_cantor_dust(shape=[500, 500], n=n, f=f)
ax[0].imshow(im, interpolation='none')
ax[0].axis(False)
ax[0].set_title(f'f = {f}')
f=0.9
im = ps.generators.random_cantor_dust(shape=[500, 500], n=n, f=f)
ax[1].imshow(im, interpolation='none')
ax[1].axis(False)
ax[1].set_title(f'f = {f}');
p
¶
The size of the initial region, which is scaled by a factor of p
on each step
fig, ax = plt.subplots(1, 2, figsize=[12, 6])
p=2
im = ps.generators.random_cantor_dust(shape=[500, 500], n=8, p=p)
ax[0].imshow(im, interpolation='none')
ax[0].axis(False)
ax[0].set_title(f'p = {p}')
p=3
im = ps.generators.random_cantor_dust(shape=[500, 500], n=8, p=p)
ax[1].imshow(im, interpolation='none')
ax[1].axis(False)
ax[1].set_title(f'p = {p}');