all_to_uniform

Import packages

import matplotlib.pyplot as plt
import numpy as np
import scipy.ndimage as spim

import porespy as ps

ps.visualization.set_mpl_style()

Generate image for testing

im = np.random.rand(200, 200)
strel = ps.tools.ps_disk(20, smooth=False)
im = spim.convolve(im, weights=strel)
fig, ax = plt.subplots(1, 2, figsize=[8, 4])
ax[0].axis(False)
ax[0].imshow(im)
ax[1].hist(im.flatten(), edgecolor='k', bins=25)
ax[1].set_xlabel('Value')
ax[1].set_ylabel('Counts');

Demonstrate function

The correlated noise field generated above has approximatetly normally distributed values. It’s not perfectly normal, but it’s pretty close. This can be converted to uniformly distributed values as follows:

im1 = ps.tools.all_to_uniform(im=im)
fig, ax = plt.subplots(1, 2, figsize=[8, 4])
ax[0].axis(False)
ax[0].imshow(im1)
ax[1].hist(im1.flatten(), edgecolor='k', bins=25)
ax[1].set_xlabel('Value')
ax[1].set_ylabel('Counts');

scale

The output can be scale to a specific range:

im2 = ps.tools.all_to_uniform(im=im, scale=[0, 1])
fig, ax = plt.subplots(1, 2, figsize=[8, 4])
ax[0].axis(False)
ax[0].imshow(im2)
ax[1].hist(im2.flatten(), edgecolor='k', bins=25)
ax[1].set_xlabel('Value')
ax[1].set_ylabel('Counts');