In collections.py there are some tools for working with lists, sets, dicts, and iterables more declaratively.
In data.py there are some functiions to more concisely read and write yaml in the most common use cases.
In exceptions.py there are some error handling tools. They are all callback-oriented, so not necessarily user-friendly; mostly they are intended for use in decorators.py.
In decorators.py there are some general decorator tools, in particular one to wrap a function in a debugger call on error and one to attach context to exception messages. There’s also a wraptify function that will convert decorators that use functools.wraps (and therefore clobber a lot of metadata from the target) into decorators which preserve metadata using wrapt.decorator.
In validation.py there are some general validation checks that seem to come in handy in lots of contexts. For example, making sure that a dict has all of a set of required keys, and all other keys are part of some optional set.
TODO: Figure out how to actually get changelog content.
Changelog content for this version goes here.