11 unstable releases (4 breaking)
0.17.5 | Aug 12, 2024 |
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0.17.3 | Aug 11, 2024 |
0.16.0 | Aug 4, 2024 |
0.15.0 | Aug 4, 2024 |
0.13.1 | Jul 18, 2020 |
#299 in Data structures
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415KB
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SLoC
Overview
This library provides an alternative to the std Range
types which supports finite interval normalization. When used with a bounded, finite data type, the interval will support set operations (unions, intersections, etc), and iteration over potentially disjoint sets of intervals can be represented efficiently as a tree of interval bounds.
Representations for infinite intervals
This library was previously designed to support infinite and finite data types, utilizing trait specialization to make normalization of infinite intervals a no-op. To allow building on stable, this feature has been disabled, so Interval<T>
and Selection<T>
are only usable if T: Finite
. As such, there are many methods defined for selections and intervals which are essentially useless, as all intervals will be normalized into a closed representation after construction.
What is interval normalization?
Interval normalization ensures that equivalent intervals have the same representation. For instance, if we have an Interval<i32>
covering (0, 15], the left bound is exclusive, and due to the finiteness of i32
, the interval will be equivalent to [1, 15]. In this way, intervals over finite types can always be 'normalized' as closed finite intervals. Additionally, unions of nearby intervals may overlap if denormalized. [0, 4] union [5, 6] selects the same points as [0, 6], even though the intervals do not share bounds. Thus we also have to normalize intervals with respect to set operations.
How is interval normalization achieved?
Interval<T>
is implemented as a normalizing wrapper around RawInterval<T>
. Any type which implements Normalize
will be automatically normalized after any operation performed on Interval<T>
. Dynamic unions of intervals are implemented through Selection<T>
, which is a normalizing wrapper around TineTree<T>
, which ensures that interval operations are performed on the broadest 'denormalized' set of intervals possible before normalization occurs.
Dependencies
~170KB