norm_to_uniform#

Import packages#

[1]:
import numpy as np
import porespy as ps
import scipy.ndimage as spim
import matplotlib.pyplot as plt
import skimage
ps.visualization.set_mpl_style()

Generate image for testing#

[2]:
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');
../../../_images/examples_tools_reference_norm_to_uniform_4_0.svg

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:

[3]:
im1 = ps.tools.norm_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');
../../../_images/examples_tools_reference_norm_to_uniform_6_0.svg

scale#

The output can be scale to a specific range:

[4]:
im2 = ps.tools.norm_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');
../../../_images/examples_tools_reference_norm_to_uniform_8_0.svg