Optimize adaptive histogram equalization

[hakonanes]

The implementation of adaptive histogram equalization (calling scikit-image's equalize_adapthist()) should be optimized.

Potential optimizations include:

• Use HyperSpy's improved map() function (seems like this function handles chunks better than the current use of Dask's map_blocks() in kikuchipy)
• Use the more restricted (and thus faster) private function for rescaling intensities after equalization (https://github.com/pyxem/kikuchipy/blob/develop/kikuchipy/pattern/_pattern.py#L88)
• Use scikit-image's private function _clahe() directly instead of via the public API (dangerous since the function can change whenever)
• Use another implementation? See https://stackoverflow.com/questions/43569566/adaptive-histogram-equalization-in-python, https://gist.github.com/sadimanna/52c320ce5c49e200ce398f800d39a2c1.

The implementation is spread across two files:

https://github.com/pyxem/kikuchipy/blob/c5df967f13e8058e05d8b5e5dd97594a889cf3ca/kikuchipy/signals/ebsd.py#L800-L888

which calls

https://github.com/pyxem/kikuchipy/blob/c5df967f13e8058e05d8b5e5dd97594a889cf3ca/kikuchipy/pattern/chunk.py#L364-L411

[hakonanes]

This method is also applicable to the EBSD master pattern. Applying it to the master pattern before dictionary generation with EBSDMasterPattern.get_patterns() seems to produce comparable relative intensities within patterns to the case where equalization is done to each individual pattern after dictionary generation (without equalization applied to the master pattern).

[hakonanes]

I don't adaptive histogram equalization myself, and have not encountered someone who finds this method in kikuchipy useful. If you're that person, feel free to say so and I'll re-open this issue!