unicode-transforms VS lens

Thanks! Here's a puzzle. Profiling shows that about a third of the time in my code is spent in normalize from unicode-transforms. (Normalization is a required step in the algorithm but can be omitted if you know that the input is already in NFD form.) And when I add a benchmark that omits normalization, I see run time cut by a third. But text-icu's run time in my benchmark doesn't seem to be affected much by whether I set the normalization option. I am not sure how to square that with the benchmarks here that seem to show unicode-transforms outperforming text-icu in normalization. text-icu's documentation says that "an incremental check is performed to see whether the input data is in FCD form. If the data is not in FCD form, incremental NFD normalization is performed." I'm not sure exactly what this means, but it may mean that text-icu avoids normalizing the whole string, but just normalizes enough to do the comparison, and sometimes avoids normalization altogether if it can quickly determine that the string is already normalized. I don't see a way to do this currently with unicode-transforms.

They are in fact well structured and not random at all. Take a look at this:

https://github.com/ekmett/lens/wiki/Operators

A composition of functional operations over Kmett style lenses. Here's the original Haskell implementation, here's usage in JS via Ramda

The documentation of lenses is geared towards people know what lenses are. Of course this single fragment of documentation doesn't make sense if you don't know what lenses are. If you want to understand what lenses are the package links to a helpful wiki and tutorial: https://github.com/ekmett/lens/wiki/Overview

All within reach within seconds of discovering the function.