snow_partitioning_parallel

Similar to snow_partitioning except that it performs SNOW algorithm in parallel and serial mode to save computational time and memory requirement respectively.

Import packages

import time

import matplotlib.pyplot as plt
import numpy as np

import porespy as ps

ps.visualization.set_mpl_style()
np.random.seed(10)

im

Works on 2D and 3D images. We use 2D here because it is easier to visualize.

im = ps.generators.blobs(shape=[800, 800])

plt.figure(figsize=[6, 6])
plt.axis(False)
plt.imshow(im);

parallel_kw

The parallel_kw argument takes a dictionary with the following keywords: overlap, divs, and cores. First, the user supplied or default parallel_kw dictionary will be checked for available instruction on how to perform parallelization. If not provided, the settings in porespy.settings will be used to set parllelization arguments!

overlap, divs, and cores

cores is the number of cores to use. The more cores the faster the snow_partitioning performs. If overlap is None it is estimated using porespy.tools.estimate_overlap method. The domain is divided by 2 in each direction as supplied to divs.

start = time.time()
parallel_kw = {"divs": 2, "overlap": None, "cores": 1}
x1 = ps.filters.snow_partitioning_parallel(im, r_max=5, sigma=0.4, parallel_kw=parallel_kw)
pause = time.time()
parallel_kw = {"divs": 2, "overlap": None, "cores": 4}
x2 = ps.filters.snow_partitioning_parallel(im, r_max=5, sigma=0.4, parallel_kw=parallel_kw)
stop = time.time()

print('OPERATION TIME:')
print('cores=1:', pause-start, 'seconds')
print('cores=4:', stop-pause, 'seconds')

OPERATION TIME:
cores=1: 5.9344024658203125 seconds
cores=4: 0.4298257827758789 seconds

The snow algorithm returns several images

print(x1)

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Results of snow_partitioning_parallel generated at Sat Jul 12 16:19:09 2025
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im                        Array of size (800, 800)
dt                        Array of size (800, 800)
regions                   Array of size (800, 800)
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Plot the results

fix, ax = plt.subplots(1, 2, figsize=[12, 12])
ax[0].axis(False)
ax[0].imshow(x1.dt/im)
ax[0].set_title('Distance Transform', fontdict={'fontsize': 18})
ax[1].axis(False)
ax[1].imshow(x1.regions/im)
ax[1].set_title('Regions', fontdict={'fontsize': 18});