Calculate similarity between pattern and dictionary

[hakonanes]

EBSD method to calculate similarity between each pattern in a scan to a dictionary (preferably with a mask). Might be faster to do this as EMsoft does it, i.e. construct 2D matrices e.g. of 1024 experimental and simulated patterns, and multiplying these to collect dot products, continuously sorting them for each experimental pattern. Should perhaps start with a naive implementation an go from there.

Should be careful not to duplicate work done in pyXem: https://github.com/pyxem/pyxem/blob/master/pyxem/utils/indexation_utils.py#L44

Similarity metrics we should support:

• Normalized dot product (NDP): https://xcdskd.readthedocs.io/en/latest/cross_correlation/cross_correlation_coefficient.html#Application-as-an-Image-Similarity-Measure
• Normalized cross correlation (NCC): https://xcdskd.readthedocs.io/en/latest/cross_correlation/cross_correlation_coefficient.html#The-Normalized-Cross-Correlation-Coefficient

This will be slow, however, let's start with the correct, naive implementation first and improve from there.

Have to wait for #15.

To do:

• Naive implementation
• With mask
• Test
• Documentation

[hakonanes]

This is implemented in #231, #232, and #233.

[hakonanes]

The masking will have to wait.