Calculate similarity between pattern in a scan and its neighbours

[hakonanes]

EBSD method to calculate similarity between each pattern in a scan (preferably with mask, most likely this feature will be implemented later) and its neighbours. Which neighbours should preferably be up to the user to decide, should start with 1st, 2nd etc. in either a cross or square. This will be similar to EMsofts average dot product (ADP) map (their implementation https://github.com/EMsoft-org/EMsoft/blob/7762e1961508fe3e71d4702620764ceb98a78b9e/Source/DictionaryIndexing/EMgetADP.f90#L360). The coefficient map can then be used e.g. to look at (how nice!), or use as input to a pattern averaging method so the similarity can be used as a weight in averaging.

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]

Scrap the comment above, however, I think it WILL be implemented in a future PR...

[hakonanes]

This was implemented (without a mask) by @onatlandsmyr in #280.