timefold-solver

I written a data structure for grouping intervals into disjoint connected clusters. Each cluster in the data structure is a connected component of the interval space (see https://en.wikipedia.org/wiki/Connected_space for the definition of connected component). This is useful to determining maximum concurrent use of a resource; for instance, if there are only 3 projectors, a maximum of 3 lessons that require projectors can overlap at any given time. The basic idea is to use a sorted multi-set of each interval's end points, doing case analysis on insertion to determine what interval clusters to merge; removal re-computes the entire cluster the removed range was in, splitting the cluster into two or more when gaps are encountered. For the implementation, see https://github.com/TimefoldAI/timefold-solver/blob/main/core... As a side effect of

Let us know how we can improve! Feel free to start a discussion (https://github.com/TimefoldAI/timefold-solver/discussions) or submit an issue (https://github.com/TimefoldAI/timefold-solver-python/issues). We aim to treat Python as a first-class citizen (while keeping it maintainable). For instance, many of the Java methods are mapped to properties on the Python classes with more Pythonic names (see `ScoreExplanation` for an example). I suspect what gives the Java smell is probably the `SolverFactory`/`SolverConfig`, which is a lot of boilerplate code. A lot of the code can be generated, although we would need to design an API for that. The fluent (method chaining) API might also be giving the Java smell; I don't see many fluent API being used in Python. In particular, needing to either end lines with `\` or surround the statement with br