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This small package contains:

  • cavity! and cavity: Functions to compute the N all-but-one operations between N elements in time O(N). The operation is arbitrary and needs only to be associative. This is equivalent to computing [reduce(op, (src[j] for j in eachindex(src) if i != j); init) for i in eachindex(src)] which however would need N*(N-1) evaluations of op. If op is commutative with exact inverse invop, you could obtain the same result of cavity(src, op, init), also in time O(N), with invop.(reduce(op, src; init), src).

  • Accumulator: An a = Accumulator(v::AbstractVector) works as a replacement for v with extra tracking computations.

    • Construction of a requires time O(N) where N == length(v).
    • sum(a) requires time O(1).
    • cumsum(a), cavity(a) require time O(1) and return respectively a CumSum and Cavity objects that are linked to a.
  • c::CumSum(a::Accumulator): keeps a live-updated cumsum of a.

    • Create it with c = cumsum(a::Accumulator)
    • Retrieval c[i] takes time O(log N).
    • collect(c) takes time O(N)
    • searchsortedfirst(r, c) takes time O(log N)
  • c::Cavity(a::Accumulator): keeps a live-updated cavity of a.

    • Create it with c = cavity(a::Accumulator).
    • Retrieval c[i] takes time O(log N).
    • collect(c) takes time O(N) (but is slower than cavity(v::Vector)).
  • Q::ExponentialQueueDict{K}(): Dict-like interface to a collection of events with associated independent probability rates, intended for sampling on a Gillespie-like scheme.

    • Events are of type K.
    • Dict-like contruction Q = ExponentialQueueDict([:a => 0.1, :b => 0.2, :c => 0.3]) is allowed
    • Rates can be queried by getindex (i.e. r = Q[k]) and updated via setindex! (i.e. Q[k] = r), both in time O(log N) where N is the number of stored events.
    • Next event type and time can extracted from the queue by k,t = pop!(Q) or k,t = peek(Q). On pop!, event k is then removed from the collection. pop! and peek take time O(log N).
    • If event time is unneeded, next event alone can be extracted with k = peekevent(Q) (taking also time O(log N)).
  • Q::ExponentialQueue(): Like ExponentialQueue{Int} but events are stored on a vector instead of a Dict, so it may be slightly more efficient. Event indices are positive integers (note that the memory space needed scales with the maximum index, so use ExponentialQueueDIct{Int} if you need very large indices).