Neighbour pattern averaging

This example shows how to average each pattern in a scan with its nearest neighbours using average_neighbour_patterns().

More details are given in the pattern processing tutorial and the feature maps tutorial.

Imports.

import hyperspy.api as hs
import matplotlib.pyplot as plt

import kikuchipy as kp

# Silence progressbars
hs.preferences.General.show_progressbar = False

Load Ni patterns and subtract static and dynamic background.

s = kp.data.nickel_ebsd_large()
print(s)

s.remove_static_background()
s.remove_dynamic_background()

<EBSD, title: patterns Scan 1, dimensions: (75, 55|60, 60)>

Get image quality before averaging

iq0 = s.get_image_quality()

Keep one pattern for comparison.

x, y = (50, 8)
pattern0 = s.inav[x, y].deepcopy()

Average in a (3, 3) window with a Gaussian kernel with a standard deviation of 1.

s.average_neighbour_patterns(window="gaussian", std=1)

iq1 = s.get_image_quality()
pattern1 = s.inav[x, y]

Plot pattern and histograms of image qualities before and after averaging.

fig, axes = plt.subplots(2, 2, height_ratios=[3, 1.5], layout="tight")
for ax, pattern, title in zip(
    axes[0],
    [pattern0, pattern1],
    ["Static + Dynamic", "Static + Dynamic + Averaging"],
):
    ax.imshow(pattern, cmap="gray")
    ax.set_title(title)
    ax.axis("off")
for ax, iq in zip(axes[1], [iq0, iq1]):
    ax.hist(iq.ravel(), bins=100)

Estimated memory usage: 968 MB