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Constructors refactor: undef, sparsezeros, sparserand and sparserand! (#33)
* Add `sprand` and `sprand!` * Be a bit more mindful with constructors * Add `spzeros` * update `sprand` to take in `rfn` function * update docstrings * update README * Bump v0.3.0 * Bump skeleton * move functions * export new functions * slightly re-organize constructors * update registrator action * enable (some of) Aqua * fix undefined export * remove tested exports (included in aqua testset) * update testfunction * change `rfn` to `rand_function` * Add `SparseArrayDOK` docstrings * Revert "remove tested exports (included in aqua testset)" This reverts commit e7053a8. * update tested exports * merge export lists * change `rand_function` to `randfun` * change `spzeros`, `sprand` and `sprand!` to `sparsezeros`, `sparserand` and `sparserand!` * sort exports alphabetically
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‎.github/workflows/IntegrationTestRequest.yml‎

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name: "Integration Test Request"
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on:
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issue_comment:
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types: [created]
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jobs:
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integrationrequest:
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if: |
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github.event.issue.pull_request &&
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contains(fromJSON('["OWNER", "COLLABORATOR", "MEMBER"]'), github.event.comment.author_association)
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uses: ITensor/ITensorActions/.github/workflows/IntegrationTestRequest.yml@main
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with:
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localregistry: https://github.com/ITensor/ITensorRegistry.git

‎LICENSE‎

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MIT License
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Copyright (c) 2024 ITensor developers
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Copyright (c) 2025 ITensor developers
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal

‎Project.toml‎

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name = "SparseArraysBase"
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uuid = "0d5efcca-f356-4864-8770-e1ed8d78f208"
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authors = ["ITensor developers <[email protected]> and contributors"]
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version = "0.2.11"
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version = "0.3.0"
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[deps]
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Accessors = "7d9f7c33-5ae7-4f3b-8dc6-eff91059b697"
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FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b"
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LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
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MapBroadcast = "ebd9b9da-f48d-417c-9660-449667d60261"
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Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
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[compat]
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Accessors = "0.1.41"
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FillArrays = "1.13.0"
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LinearAlgebra = "1.10"
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MapBroadcast = "0.1.5"
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Random = "1.10.0"
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SafeTestsets = "0.1"
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Suppressor = "0.2"
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Test = "1.10"

‎README.md‎

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zero!
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using Test: @test, @test_throws
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a = SparseArrayDOK{Float64}(2, 2)
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a = SparseArrayDOK{Float64}(undef, 2, 2)
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````
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AbstractArray interface:
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@test b == [12, 0]
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@test storedlength(b) == 1
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a = SparseArrayDOK{Float64}(2, 2)
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a = SparseArrayDOK{Float64}(undef, 2, 2)
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a .= 2
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for I in eachindex(a)
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@test a[I] == 2
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end
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@test storedlength(a) == length(a)
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a = SparseArrayDOK{Float64}(2, 2)
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a = SparseArrayDOK{Float64}(undef, 2, 2)
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fill!(a, 2)
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for I in eachindex(a)
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@test a[I] == 2
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end
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@test storedlength(a) == length(a)
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a = SparseArrayDOK{Float64}(2, 2)
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a = SparseArrayDOK{Float64}(undef, 2, 2)
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fill!(a, 0)
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@test iszero(a)
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@test iszero(storedlength(a))
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a = SparseArrayDOK{Float64}(2, 2)
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a = SparseArrayDOK{Float64}(undef, 2, 2)
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a[1, 2] = 12
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zero!(a)
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@test iszero(a)
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@test iszero(storedlength(a))
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a = SparseArrayDOK{Float64}(2, 2)
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a = SparseArrayDOK{Float64}(undef, 2, 2)
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a[1, 2] = 12
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b = zero(a)
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@test iszero(b)

‎docs/make.jl‎

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edit_link="main",
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assets=String[],
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),
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pages=["Home" => "index.md"],
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pages=["Home" => "index.md", "Reference" => "reference.md"],
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)
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deploydocs(;

‎docs/src/reference.md‎

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# Reference
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```@autodocs
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Modules = [SparseArraysBase]
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```

‎examples/README.jl‎

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zero!
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using Test: @test, @test_throws
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a = SparseArrayDOK{Float64}(2, 2)
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a = SparseArrayDOK{Float64}(undef, 2, 2)
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# AbstractArray interface:
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@test b == [12, 0]
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@test storedlength(b) == 1
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a = SparseArrayDOK{Float64}(2, 2)
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a = SparseArrayDOK{Float64}(undef, 2, 2)
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a .= 2
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for I in eachindex(a)
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@test a[I] == 2
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end
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@test storedlength(a) == length(a)
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a = SparseArrayDOK{Float64}(2, 2)
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a = SparseArrayDOK{Float64}(undef, 2, 2)
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fill!(a, 2)
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for I in eachindex(a)
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@test a[I] == 2
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end
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@test storedlength(a) == length(a)
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a = SparseArrayDOK{Float64}(2, 2)
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a = SparseArrayDOK{Float64}(undef, 2, 2)
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fill!(a, 0)
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@test iszero(a)
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@test iszero(storedlength(a))
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a = SparseArrayDOK{Float64}(2, 2)
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a = SparseArrayDOK{Float64}(undef, 2, 2)
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a[1, 2] = 12
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zero!(a)
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@test iszero(a)
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@test iszero(storedlength(a))
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a = SparseArrayDOK{Float64}(2, 2)
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a = SparseArrayDOK{Float64}(undef, 2, 2)
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a[1, 2] = 12
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b = zero(a)
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@test iszero(b)

‎src/SparseArraysBase.jl‎

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OneElementVector,
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eachstoredindex,
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isstored,
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oneelementarray,
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oneelement,
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sparserand,
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sparserand!,
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sparsezeros,
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storedlength,
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storedpairs,
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storedvalues

‎src/abstractsparsearray.jl‎

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end
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eachstoredindex(a::ReplacedUnstoredSparseArray) = eachstoredindex(parent(a))
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@derive ReplacedUnstoredSparseArray AbstractArrayOps
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# Special-purpose constructors
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# ----------------------------
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using Random: Random, AbstractRNG, default_rng
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@doc """
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sparsezeros([T::Type], dims) -> A::SparseArrayDOK{T}
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Create an empty size `dims` sparse array.
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The optional `T` argument specifies the element type, which defaults to `Float64`.
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""" sparsezeros
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sparsezeros(dims::Dims) = sparsezeros(Float64, dims)
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sparsezeros(::Type{T}, dims::Dims) where {T} = SparseArrayDOK{T}(undef, dims)
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@doc """
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sparserand([rng], [T::Type], dims; density::Real=0.5, randfun::Function=rand) -> A::SparseArrayDOK{T}
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Create a random size `dims` sparse array in which the probability of any element being stored is independently given by `density`.
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The optional `rng` argument specifies a random number generator, see also `Random`.
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The optional `T` argument specifies the element type, which defaults to `Float64`.
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The optional `randfun` argument can be used to control the type of random elements, and should support
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the signature `randfun(rng, T, N)` to generate `N` entries of type `T`.
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See also [`sparserand!`](@ref).
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""" sparserand
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function sparserand(::Type{T}, dims::Dims; kwargs...) where {T}
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return sparserand(default_rng(), T, dims; kwargs...)
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end
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sparserand(dims::Dims; kwargs...) = sparserand(default_rng(), Float64, dims; kwargs...)
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function sparserand(rng::AbstractRNG, dims::Dims; kwargs...)
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return sparserand(rng, Float64, dims; kwargs...)
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end
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function sparserand(rng::AbstractRNG, ::Type{T}, dims::Dims; kwargs...) where {T}
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A = SparseArrayDOK{T}(undef, dims)
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sparserand!(rng, A; kwargs...)
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return A
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end
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@doc """
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sparserand!([rng], A::AbstractArray; density::Real=0.5, randfun::Function=rand) -> A
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Overwrite part of an array with random entries, where the probability of overwriting is independently given by `density`.
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The optional `rng` argument specifies a random number generator, see also `Random`.
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The optional `randfun` argument can be used to control the type of random elements, and should support
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the signature `randfun(rng, T, N)` to generate `N` entries of type `T`.
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See also [`sparserand`](@ref).
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""" sparserand!
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sparserand!(A::AbstractArray; kwargs...) = sparserand!(default_rng(), A; kwargs...)
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function sparserand!(
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rng::AbstractRNG, A::AbstractArray; density::Real=0.5, randfun::Function=Random.rand
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)
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ArrayLayouts.zero!(A)
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rand_inds = Random.randsubseq(rng, eachindex(A), density)
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rand_entries = randfun(rng, eltype(A), length(rand_inds))
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@inbounds for (I, v) in zip(rand_inds, rand_entries)
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A[I] = v
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end
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end

‎src/abstractsparsearrayinterface.jl‎

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a::AbstractArray, T::Type, size::Tuple{Vararg{Int}}
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)
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# TODO: Define `default_similartype` or something like that?
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return SparseArrayDOK{T}(size...)
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return SparseArrayDOK{T}(undef, size)
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end
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# map over a specified subset of indices of the inputs.

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