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‎.github/workflows/Downstream.yml‎

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name: IntegrationTest
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on:
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push:
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branches: [master]
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tags: [v*]
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pull_request:
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jobs:
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test:
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name: ${{ matrix.package.repo }}/${{ matrix.package.group }}/${{ matrix.julia-version }}
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runs-on: ${{ matrix.os }}
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env:
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GROUP: ${{ matrix.package.group }}
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strategy:
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fail-fast: false
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matrix:
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julia-version: [1]
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os: [ubuntu-latest]
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package:
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- {user: SciML, repo: DataInterpolations.jl, group: Core}
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- {user: SciML, repo: ModelingToolkit.jl, group: InterfaceI}
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steps:
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- uses: actions/checkout@v4
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- uses: julia-actions/setup-julia@v1
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with:
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version: ${{ matrix.julia-version }}
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arch: x64
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- uses: julia-actions/julia-buildpkg@latest
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- name: Clone Downstream
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uses: actions/checkout@v4
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with:
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repository: ${{ matrix.package.user }}/${{ matrix.package.repo }}
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path: downstream
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- name: Load this and run the downstream tests
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shell: julia --color=yes --project=downstream {0}
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run: |
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using Pkg
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try
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# force it to use this PR's version of the package
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Pkg.develop(PackageSpec(path=".")) # resolver may fail with main deps
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Pkg.update()
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Pkg.test(coverage=true) # resolver may fail with test time deps
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catch err
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err isa Pkg.Resolve.ResolverError || rethrow()
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# If we can't resolve that means this is incompatible by SemVer and this is fine
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# It means we marked this as a breaking change, so we don't need to worry about
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# Mistakenly introducing a breaking change, as we have intentionally made one
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@info "Not compatible with this release. No problem." exception=err
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exit(0) # Exit immediately, as a success
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end
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- uses: julia-actions/julia-processcoverage@v1
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- uses: codecov/codecov-action@v3
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with:
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file: lcov.info

‎Project.toml‎

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name = "FindFirstFunctions"
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uuid = "64ca27bc-2ba2-4a57-88aa-44e436879224"
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authors = ["Chris Elrod <[email protected]> and contributors"]
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version = "1.0.0"
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version = "1.1.0"
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[compat]
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julia = "1.8"

‎README.md‎

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# FindFirstFunctions
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[![Build Status](https://github.com/SciML/FindFirstFunctions.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/SciML/FindFirstFunctions.jl/actions/workflows/CI.yml?query=branch%3Amain)
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FindFirstFunctions.jl is a package for faster `findfirst` type functions. These are specailized to improve performance
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over more generic implementations.
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## Functions
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### `findfirstequal`
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```julia
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findfirstequal(x::Int64,A::DenseVector{Int64})
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```
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Finds the first value in `A` equal to `x`
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### `bracketstrictlymontonic`
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```julia
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bracketstrictlymontonic(v, x, guess; lt=<comparison>, by=<transform>, rev=false)
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```
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Starting from an initial `guess` index, find indices `(lo, hi)` such that `v[lo] ≤ x ≤
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v[hi]` according to the specified order, assuming that `x` is actually within the range of
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values found in `v`. If `x` is outside that range, either `lo` will be `firstindex(v)` or
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`hi` will be `lastindex(v)`.
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Note that the results will not typically satisfy `lo ≤ guess ≤ hi`. If `x` is precisely
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equal to a value that is not unique in the input `v`, there is no guarantee that `(lo, hi)`
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will encompass *all* indices corresponding to that value.
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This algorithm is essentially an expanding binary search, which can be used as a precursor
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to `searchsorted` and related functions, which can take `lo` and `hi` as arguments. The
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purpose of using this function first would be to accelerate convergence in those functions
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by using correlated `guess`es for repeated calls. The best `guess` for the next call of
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this function would be the index returned by the previous call to `searchsorted`.
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See `sort!` for an explanation of the keyword arguments `by`, `lt` and `rev`.
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### `searchsortedfirstcorrelated(v::AbstractVector, x, guess)`
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```julia
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searchsortedfirstcorrelated(v::AbstractVector, x, guess)
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```
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An accelerated `findfirst` on sorted vectors using a bracketed search. Requires a `guess`
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to start the search from.

‎src/FindFirstFunctions.jl‎

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end
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"""
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bracketstrictlymontonic(v, x, guess; lt=<comparison>, by=<transform>, rev=false)
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Starting from an initial `guess` index, find indices `(lo, hi)` such that `v[lo] ≤ x ≤
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v[hi]` according to the specified order, assuming that `x` is actually within the range of
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values found in `v`. If `x` is outside that range, either `lo` will be `firstindex(v)` or
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`hi` will be `lastindex(v)`.
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Note that the results will not typically satisfy `lo ≤ guess ≤ hi`. If `x` is precisely
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equal to a value that is not unique in the input `v`, there is no guarantee that `(lo, hi)`
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will encompass *all* indices corresponding to that value.
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This algorithm is essentially an expanding binary search, which can be used as a precursor
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to `searchsorted` and related functions, which can take `lo` and `hi` as arguments. The
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purpose of using this function first would be to accelerate convergence in those functions
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by using correlated `guess`es for repeated calls. The best `guess` for the next call of
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this function would be the index returned by the previous call to `searchsorted`.
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See [`sort!`](@ref) for an explanation of the keyword arguments `by`, `lt` and `rev`.
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"""
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function bracketstrictlymontonic(v::AbstractVector,
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x,
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guess::T,
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o::Base.Order.Ordering)::NTuple{2,keytype(v)} where {T<:Integer}
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bottom = firstindex(v)
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top = lastindex(v)
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if guess < bottom || guess > top
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return bottom, top
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# # NOTE: for cache efficiency in repeated calls, we avoid accessing the first and last elements of `v`
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# # on each call to this function. This should only result in significant slow downs for calls with
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# # out-of-bounds values of `x` *and* bad `guess`es.
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# elseif lt(o, x, v[bottom])
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# return bottom, bottom
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# elseif lt(o, v[top], x)
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# return top, top
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else
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u = T(1)
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lo, hi = guess, min(guess + u, top)
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@inbounds if Base.Order.lt(o, x, v[lo])
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while lo > bottom && Base.Order.lt(o, x, v[lo])
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lo, hi = max(bottom, lo - u), lo
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u += u
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end
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else
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while hi < top && !Base.Order.lt(o, x, v[hi])
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lo, hi = hi, min(top, hi + u)
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u += u
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end
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end
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end
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return lo, hi
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end
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function searchsortedfirstcorrelated(v::AbstractVector, x, guess)
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lo, hi = bracketstrictlymontonic(v, x, guess, Base.Order.Forward)
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searchsortedfirst(v, x, lo, hi, Base.Order.Forward)
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end
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function searchsortedlastcorrelated(v::AbstractVector, x, guess)
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lo, hi = bracketstrictlymontonic(v, x, guess, Base.Order.Forward)
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searchsortedlast(v, x, lo, hi, Base.Order.Forward)
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end
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searchsortedfirstcorrelated(r::AbstractRange, x, _) = searchsortedfirst(r, x)
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searchsortedlastcorrelated(r::AbstractRange, x, _) = searchsortedlast(r, x)
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end

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