# Feature maps¶

This section details methods for extracting information from pattern intensities, called feature maps (for lack of a better description).

## Image quality¶

The image quality metric $$Q$$ presented by Krieger Lassen [Lassen1994] can be calculated for an EBSD object with get_image_quality(), or, for a single pattern (numpy.ndarray), with get_image_quality(). Following the notation in [Marquardt2017], it is given by

\begin{split}\begin{align} Q &= 1 - \frac{J}{J_{\mathrm{res}}w_{\mathrm{tot}}},\\ J &= \sum_{h = -N/2}^{N/2} \sum_{k = -N/2}^{N/2} w(h, k) \left|\mathbf{q}\right|^2,\\ J_{\mathrm{res}} &= \frac{1}{N^2} \sum_{h = -N/2}^{N/2} \sum_{k = -N/2}^{N/2} \left|\mathbf{q}\right|^2,\\ w_{\mathrm{tot}} &= \sum_{h = -N/2}^{N/2} \sum_{k = -N/2}^{N/2} w(h, k). \end{align}\end{split}

The function $$w(h, k)$$ is the Fast Fourier Transform (FFT) power spectrum of the EBSD pattern, and the vectors $$\mathbf{q}$$ are the frequency vectors with components $$(h, k)$$. The sharper the Kikuchi bands, the greater the high frequency content of the power spectrum, and thus the closer $$Q$$ will be to unity. To visualize parts of the computation, we compute the power spectrum of a pattern in an EBSD object s and the frequency vectors, shift the zero-frequency components to the centre, and plot them:

>>> import kikuchipy as kp
>>> import matplotlib.pyplot as plt
>>> import numpy as np

>>> p = s.inav[0, 0].data
>>> plt.figure()
>>> plt.imshow(p)
>>> plt.colorbar()

>>> p_fft = kp.pattern.fft(p, shift=True)
>>> plt.figure()
>>> plt.imshow(np.log(kp.pattern.fft_spectrum(p_fft)))
>>> plt.colorbar()

>>> q = kp.pattern.fft_frequency_vectors(shape=p.shape)
>>> plt.figure()
>>> plt.imshow(np.fft.fftshift(q))
>>> plt.colorbar() A background corrected EBSD pattern (left), the logarithm of the shifted power spectrum $$w(h, k)$$ of its FFT (middle), and the shifted frequency vectors $$\mathbf{q}$$ (right).

If we don’t want the EBSD patterns to be zero-mean normalized before computing $$Q$$, we must pass normalized=False. Let’s compute the image quality $$Q$$ and plot it for a Nickel data set from [Anes2019]:

>>> iq = s.get_image_quality(normalize=True)  # Default
>>> plt.figure()
>>> plt.imshow(iq)
>>> plt.colorbar() The image quality map of a Nickel data set from [Anes2019].

If we want to use this map to navigate around in when plotting patterns, we can easily do that as explained in the visualizing patterns guide.