Calculate pattern similarity between two patterns

[hakonanes]

Useful e.g. when deciding with which adjacent patterns to average each pattern, to avoid averaging across a grain boundary, as per the discussion in #13. A pattern difference map in itself is rich in information, and is comparable to e.g. a kernel average misorientation map or grain boundary map.

The 'pattern difference' procedure as explained by Wright et al. (https://doi.org/10.1016/j.ultramic.2014.10.002):

1. Convert each pattern to a column vector.
2. Subtract the average intensity of each pattern from each vector component.
3. Normalise the column vector.
4. Calculate difference between two patterns by calculating the dot product between the two normalised column vectors.
5. Subtract the resulting difference value from 1 so that 0 indicates no difference and 1 represents the maximum difference.
6. Average the resulting eight difference values for each pattern to its eight adjacent patterns to obtain final difference value

Edit

This is similar to obtaining the normalized cross-correlation (NCC) coeffienct or normalized dot product (NDP). We should have general implementations of both. Should implement them together since they are mainly used for the same puproses. Implementations should be so that they can be used in multiple applications in KikuchiPy. See #116 and #117 for example uses.

To do:

• general implementation of NCC: see xcdskd's implementation https://github.com/wiai/xcdskd/blob/24e9109eeea567676eb0089ea87d7ecb74528852/src/aloe/image/nxcc.py#L23 + https://xcdskd.readthedocs.io/en/latest/cross_correlation/cross_correlation_coefficient.html
• general implementation of NDP: see xcdskd's implementation https://github.com/wiai/xcdskd/blob/24e9109eeea567676eb0089ea87d7ecb74528852/src/aloe/image/nxcc.py#L81 + https://xcdskd.readthedocs.io/en/latest/cross_correlation/cross_correlation_coefficient.html#Application-as-an-Image-Similarity-Measure
• Tests
• Documentation

[hakonanes]

Note that normalized and zero-normalized correlation is already present in the kikuchipy.util.pattern_similarity, however it is not used by the EBSD class yet. Future similarity methods should be implemented there.

[hakonanes]

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