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Proposal for AbstractWrappedArray in order to mitigate "AbstractArray-fallback" #31563

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Proposal for AbstractWrappedArray in order to mitigate "AbstractArray-fallback" #31563

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391c7b0
introduce AbstractWrappedArray
KlausC Apr 5, 2019
c78b781
removed additional type parameter
KlausC Jun 21, 2019
e663327
removed additional type parameter - edit error
KlausC Jun 21, 2019
be69ed2
Merge branch 'master' into krc/abstractwrapped
KlausC Sep 20, 2019
2f10704
Merge branch 'master' into krc/abstractwrapped
KlausC Nov 6, 2019
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introduce AbstractWrappedArray
  • Loading branch information
@KlausC
KlausC committed Apr 5, 2019
commit 391c7b000cc68cf031cd7810799da374039b403f
21 changes: 21 additions & 0 deletions base/abstractarray.jl
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Expand Up @@ -2226,3 +2226,24 @@ function hash(A::AbstractArray, h::UInt)

return h
end

"""
AbstractWrappedArray{T,N,P,B}

Supertype for `N`-dimensional array-like types with elements of type `T` which are
'wrapping' another [`AbstractArray`](@ref) of type `P`. The ultimate unwrapped base
type is `B`.
[`Adjoint`](@ref), [`Symmetric`](@ref), [`SubArray`](@ref) and other types are subtypes
of this. Main purpose of this type is to allow for dispatching on `B`, which is
appropriate for sparse arrays. Typically `B == basetype(P)`.
"""
abstract type AbstractWrappedArray{T,N,P,S} <: AbstractArray{T,N}; end

"""
basetype(::Type)

Return the base type of an `AbstractWrappedArray`, the type itself otherwise.
"""

basetype(::Type{<:AbstractWrappedArray{T,N,X,S}}) where {T,N,X,S} = S
basetype(::Type{P}) where P = P
9 changes: 6 additions & 3 deletions base/subarray.jl
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Expand Up @@ -5,15 +5,18 @@ const ViewIndex = Union{Real, AbstractArray}
const ScalarIndex = Real

# L is true if the view itself supports fast linear indexing
struct SubArray{T,N,P,I,L} <: AbstractArray{T,N}
struct SubArray{T,N,P,I,L,B} <: AbstractWrappedArray{T,N,P,B}
parent::P
indices::I
offset1::Int # for linear indexing and pointer, only valid when L==true
stride1::Int # used only for linear indexing
function SubArray{T,N,P,I,L}(parent, indices, offset1, stride1) where {T,N,P,I,L}
function SubArray{T,N,P,I,L,B}(parent, indices, offset1, stride1) where {T,N,P,I,L,B}
@_inline_meta
check_parent_index_match(parent, indices)
new(parent, indices, offset1, stride1)
new{T,N,P,I,L,B}(parent, indices, offset1, stride1)
end
function SubArray{T,N,P,I,L}(parent, indices, offset1, stride1) where {T,N,P,I,L}
SubArray{T,N,P,I,L,basetype(P)}(parent, indices, offset1, stride1)
end
end
# Compute the linear indexability of the indices, and combine it with the linear indexing of the parent
Expand Down
1 change: 1 addition & 0 deletions stdlib/LinearAlgebra/src/LinearAlgebra.jl
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Expand Up @@ -18,6 +18,7 @@ import Base: USE_BLAS64, abs, acos, acosh, acot, acoth, acsc, acsch, adjoint, as
using Base: hvcat_fill, IndexLinear, promote_op, promote_typeof,
@propagate_inbounds, @pure, reduce, typed_vcat, require_one_based_indexing
using Base.Broadcast: Broadcasted
using Base: AbstractWrappedArray, basetype

export
# Modules
Expand Down
14 changes: 8 additions & 6 deletions stdlib/LinearAlgebra/src/adjtrans.jl
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Expand Up @@ -32,12 +32,13 @@ julia> adjoint(A)
9-2im 4-6im
```
"""
struct Adjoint{T,S} <: AbstractMatrix{T}
struct Adjoint{T,S,B} <: AbstractWrappedArray{T,2,S,B}
parent::S
function Adjoint{T,S}(A::S) where {T,S}
function Adjoint{T,S,B}(A::S) where {T,S,B}
checkeltype_adjoint(T, eltype(A))
new(A)
end
Adjoint{T,S}(A::S) where {T,S} = Adjoint{T,S,basetype(S)}(A)
end
"""
Transpose
Expand All @@ -63,17 +64,18 @@ julia> transpose(A)
9+2im 4+6im
```
"""
struct Transpose{T,S} <: AbstractMatrix{T}
struct Transpose{T,S,B} <: AbstractWrappedArray{T,2,S,B}
parent::S
function Transpose{T,S}(A::S) where {T,S}
function Transpose{T,S,B}(A::S) where {T,S,B}
checkeltype_transpose(T, eltype(A))
new(A)
end
Transpose{T,S}(A::S) where {T,S} = Transpose{T,S,basetype(S)}(A)
end

function checkeltype_adjoint(::Type{ResultEltype}, ::Type{ParentEltype}) where {ResultEltype,ParentEltype}
Expected = Base.promote_op(adjoint, ParentEltype)
ResultEltype === Expected || error(string(
ResultEltype >: Expected || error(string(
"Element type mismatch. Tried to create an `Adjoint{", ResultEltype, "}` ",
"from an object with eltype `", ParentEltype, "`, but the element type of ",
"the adjoint of an object with eltype `", ParentEltype, "` must be ",
Expand All @@ -83,7 +85,7 @@ end

function checkeltype_transpose(::Type{ResultEltype}, ::Type{ParentEltype}) where {ResultEltype, ParentEltype}
Expected = Base.promote_op(transpose, ParentEltype)
ResultEltype === Expected || error(string(
ResultEltype >: Expected || error(string(
"Element type mismatch. Tried to create a `Transpose{", ResultEltype, "}` ",
"from an object with eltype `", ParentEltype, "`, but the element type of ",
"the transpose of an object with eltype `", ParentEltype, "` must be ",
Expand Down
18 changes: 12 additions & 6 deletions stdlib/LinearAlgebra/src/symmetric.jl
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@@ -1,13 +1,16 @@
# This file is a part of Julia. License is MIT: https://julialang.org/license

# Symmetric and Hermitian matrices
struct Symmetric{T,S<:AbstractMatrix{<:T}} <: AbstractMatrix{T}
struct Symmetric{T,S<:AbstractMatrix{<:T},B} <: AbstractWrappedArray{T,2,S,B}
data::S
uplo::Char

function Symmetric{T,S}(data, uplo) where {T,S<:AbstractMatrix{<:T}}
function Symmetric{T,S,B}(data, uplo) where {T,S<:AbstractMatrix{<:T},B}
require_one_based_indexing(data)
new{T,S}(data, uplo)
new(data, uplo)
end
function Symmetric{T,S}(data, uplo) where {T,S<:AbstractMatrix{<:T}}
Symmetric{T,S,basetype(S)}(data, uplo)
end
end
"""
Expand Down Expand Up @@ -76,13 +79,16 @@ function symmetric_type(::Type{T}) where {S, T<:AbstractMatrix{S}}
end
symmetric_type(::Type{T}) where {T<:Number} = T

struct Hermitian{T,S<:AbstractMatrix{<:T}} <: AbstractMatrix{T}
struct Hermitian{T,S<:AbstractMatrix{<:T},B} <: AbstractWrappedArray{T,2,S,B}
data::S
uplo::Char

function Hermitian{T,S}(data, uplo) where {T,S<:AbstractMatrix{<:T}}
function Hermitian{T,S,B}(data, uplo) where {T,S<:AbstractMatrix{<:T},B}
require_one_based_indexing(data)
new{T,S}(data, uplo)
new(data, uplo)
end
function Hermitian{T,S}(data, uplo) where {T,S<:AbstractMatrix{<:T}}
Hermitian{T,S,basetype(S)}(data, uplo)
end
end
"""
Expand Down
11 changes: 7 additions & 4 deletions stdlib/LinearAlgebra/src/triangular.jl
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Expand Up @@ -3,19 +3,22 @@
## Triangular

# could be renamed to Triangular when that name has been fully deprecated
abstract type AbstractTriangular{T,S<:AbstractMatrix} <: AbstractMatrix{T} end
abstract type AbstractTriangular{T,S<:AbstractMatrix,B} <: AbstractWrappedArray{T,2,S,B} end

# First loop through all methods that don't need special care for upper/lower and unit diagonal
for t in (:LowerTriangular, :UnitLowerTriangular, :UpperTriangular,
:UnitUpperTriangular)
@eval begin
struct $t{T,S<:AbstractMatrix{T}} <: AbstractTriangular{T,S}
struct $t{T,S<:AbstractMatrix{T},B} <: AbstractTriangular{T,S,B}
data::S

function $t{T,S}(data) where {T,S<:AbstractMatrix{T}}
function $t{T,S,B}(data) where {T,S<:AbstractMatrix{T},B}
require_one_based_indexing(data)
checksquare(data)
new{T,S}(data)
new(data)
end
function $t{T,S}(data) where {T,S<:AbstractMatrix{T}}
($t){T,S,basetype(S)}(data)
end
end
$t(A::$t) = A
Expand Down
30 changes: 15 additions & 15 deletions stdlib/LinearAlgebra/test/adjtrans.jl
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Expand Up @@ -31,7 +31,7 @@ using Test, LinearAlgebra, SparseArrays
intvecvec = [[1, 2], [3, 4]]
intmatmat = [[[1 2]] [[3 4]] [[5 6]]; [[7 8]] [[9 10]] [[11 12]]]
@test (X = Adjoint{Adjoint{Int,Vector{Int}},Vector{Vector{Int}}}(intvecvec);
isa(X, Adjoint{Adjoint{Int,Vector{Int}},Vector{Vector{Int}}}) && X.parent === intvecvec)
isa(X, Adjoint{Adjoint{Int,Vector{Int}}}) && X.parent === intvecvec)
@test (X = Adjoint{Adjoint{Int,Matrix{Int}},Matrix{Matrix{Int}}}(intmatmat);
isa(X, Adjoint{Adjoint{Int,Matrix{Int}},Matrix{Matrix{Int}}}) && X.parent === intmatmat)
@test (X = Transpose{Transpose{Int,Vector{Int}},Vector{Vector{Int}}}(intvecvec);
Expand Down Expand Up @@ -63,10 +63,10 @@ end

@testset "Adjoint and Transpose add additional layers to already-wrapped objects" begin
intvec, intmat = [1, 2], [1 2; 3 4]
@test (A = Adjoint(Adjoint(intvec))::Adjoint{Int,Adjoint{Int,Vector{Int}}}; A.parent.parent === intvec)
@test (A = Adjoint(Adjoint(intmat))::Adjoint{Int,Adjoint{Int,Matrix{Int}}}; A.parent.parent === intmat)
@test (A = Transpose(Transpose(intvec))::Transpose{Int,Transpose{Int,Vector{Int}}}; A.parent.parent === intvec)
@test (A = Transpose(Transpose(intmat))::Transpose{Int,Transpose{Int,Matrix{Int}}}; A.parent.parent === intmat)
@test (A = Adjoint(Adjoint(intvec))::Adjoint{Int,<:Adjoint{Int,Vector{Int}}}; A.parent.parent === intvec)
@test (A = Adjoint(Adjoint(intmat))::Adjoint{Int,<:Adjoint{Int,Matrix{Int}}}; A.parent.parent === intmat)
@test (A = Transpose(Transpose(intvec))::Transpose{Int,<:Transpose{Int,Vector{Int}}}; A.parent.parent === intvec)
@test (A = Transpose(Transpose(intmat))::Transpose{Int,<:Transpose{Int,Matrix{Int}}}; A.parent.parent === intmat)
end

@testset "Adjoint and Transpose basic AbstractArray functionality" begin
Expand Down Expand Up @@ -290,8 +290,8 @@ end
@test hcat(Transpose(vec), Transpose(vec))::Transpose{Complex{Int},Vector{Complex{Int}}} == hcat(tvec, tvec)
@test hcat(Transpose(vec), 1, Transpose(vec))::Transpose{Complex{Int},Vector{Complex{Int}}} == hcat(tvec, 1, tvec)
# for arrays with concrete array eltype
@test hcat(Adjoint(vecvec), Adjoint(vecvec))::Adjoint{Adjoint{Complex{Int},Vector{Complex{Int}}},Vector{Vector{Complex{Int}}}} == hcat(avecvec, avecvec)
@test hcat(Transpose(vecvec), Transpose(vecvec))::Transpose{Transpose{Complex{Int},Vector{Complex{Int}}},Vector{Vector{Complex{Int}}}} == hcat(tvecvec, tvecvec)
@test hcat(Adjoint(vecvec), Adjoint(vecvec))::Adjoint{<:Adjoint{Complex{Int},Vector{Complex{Int}}},Vector{Vector{Complex{Int}}}} == hcat(avecvec, avecvec)
@test hcat(Transpose(vecvec), Transpose(vecvec))::Transpose{<:Transpose{Complex{Int},Vector{Complex{Int}}},Vector{Vector{Complex{Int}}}} == hcat(tvecvec, tvecvec)
end

@testset "map/broadcast over Adjoint/Transpose-wrapped vectors and Numbers" begin
Expand All @@ -305,26 +305,26 @@ end
@test map(-, Adjoint(vec))::Adjoint{Complex{Int},Vector{Complex{Int}}} == -avec
@test map(-, Transpose(vec))::Transpose{Complex{Int},Vector{Complex{Int}}} == -tvec
# unary map over wrapped vectors with concrete array eltype
@test map(-, Adjoint(vecvec))::Adjoint{Adjoint{Complex{Int},Vector{Complex{Int}}},Vector{Vector{Complex{Int}}}} == -avecvec
@test map(-, Transpose(vecvec))::Transpose{Transpose{Complex{Int},Vector{Complex{Int}}},Vector{Vector{Complex{Int}}}} == -tvecvec
@test map(-, Adjoint(vecvec))::Adjoint{<:Adjoint{Complex{Int},Vector{Complex{Int}}},Vector{Vector{Complex{Int}}}} == -avecvec
@test map(-, Transpose(vecvec))::Transpose{<:Transpose{Complex{Int},Vector{Complex{Int}}},Vector{Vector{Complex{Int}}}} == -tvecvec
# binary map over wrapped vectors with concrete scalar eltype
@test map(+, Adjoint(vec), Adjoint(vec))::Adjoint{Complex{Int},Vector{Complex{Int}}} == avec + avec
@test map(+, Transpose(vec), Transpose(vec))::Transpose{Complex{Int},Vector{Complex{Int}}} == tvec + tvec
# binary map over wrapped vectors with concrete array eltype
@test map(+, Adjoint(vecvec), Adjoint(vecvec))::Adjoint{Adjoint{Complex{Int},Vector{Complex{Int}}},Vector{Vector{Complex{Int}}}} == avecvec + avecvec
@test map(+, Transpose(vecvec), Transpose(vecvec))::Transpose{Transpose{Complex{Int},Vector{Complex{Int}}},Vector{Vector{Complex{Int}}}} == tvecvec + tvecvec
@test map(+, Adjoint(vecvec), Adjoint(vecvec))::Adjoint{<:Adjoint{Complex{Int},Vector{Complex{Int}}},Vector{Vector{Complex{Int}}}} == avecvec + avecvec
@test map(+, Transpose(vecvec), Transpose(vecvec))::Transpose{<:Transpose{Complex{Int},Vector{Complex{Int}}},Vector{Vector{Complex{Int}}}} == tvecvec + tvecvec
# unary broadcast over wrapped vectors with concrete scalar eltype
@test broadcast(-, Adjoint(vec))::Adjoint{Complex{Int},Vector{Complex{Int}}} == -avec
@test broadcast(-, Transpose(vec))::Transpose{Complex{Int},Vector{Complex{Int}}} == -tvec
# unary broadcast over wrapped vectors with concrete array eltype
@test broadcast(-, Adjoint(vecvec))::Adjoint{Adjoint{Complex{Int},Vector{Complex{Int}}},Vector{Vector{Complex{Int}}}} == -avecvec
@test broadcast(-, Transpose(vecvec))::Transpose{Transpose{Complex{Int},Vector{Complex{Int}}},Vector{Vector{Complex{Int}}}} == -tvecvec
@test broadcast(-, Adjoint(vecvec))::Adjoint{<:Adjoint{Complex{Int},Vector{Complex{Int}}},Vector{Vector{Complex{Int}}}} == -avecvec
@test broadcast(-, Transpose(vecvec))::Transpose{<:Transpose{Complex{Int},Vector{Complex{Int}}},Vector{Vector{Complex{Int}}}} == -tvecvec
# binary broadcast over wrapped vectors with concrete scalar eltype
@test broadcast(+, Adjoint(vec), Adjoint(vec))::Adjoint{Complex{Int},Vector{Complex{Int}}} == avec + avec
@test broadcast(+, Transpose(vec), Transpose(vec))::Transpose{Complex{Int},Vector{Complex{Int}}} == tvec + tvec
# binary broadcast over wrapped vectors with concrete array eltype
@test broadcast(+, Adjoint(vecvec), Adjoint(vecvec))::Adjoint{Adjoint{Complex{Int},Vector{Complex{Int}}},Vector{Vector{Complex{Int}}}} == avecvec + avecvec
@test broadcast(+, Transpose(vecvec), Transpose(vecvec))::Transpose{Transpose{Complex{Int},Vector{Complex{Int}}},Vector{Vector{Complex{Int}}}} == tvecvec + tvecvec
@test broadcast(+, Adjoint(vecvec), Adjoint(vecvec))::Adjoint{<:Adjoint{Complex{Int},Vector{Complex{Int}}},Vector{Vector{Complex{Int}}}} == avecvec + avecvec
@test broadcast(+, Transpose(vecvec), Transpose(vecvec))::Transpose{<:Transpose{Complex{Int},Vector{Complex{Int}}},Vector{Vector{Complex{Int}}}} == tvecvec + tvecvec
# trinary broadcast over wrapped vectors with concrete scalar eltype and numbers
@test broadcast(+, Adjoint(vec), 1, Adjoint(vec))::Adjoint{Complex{Int},Vector{Complex{Int}}} == avec + avec .+ 1
@test broadcast(+, Transpose(vec), 1, Transpose(vec))::Transpose{Complex{Int},Vector{Complex{Int}}} == tvec + tvec .+ 1
Expand Down
2 changes: 1 addition & 1 deletion stdlib/LinearAlgebra/test/dense.jl
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Expand Up @@ -665,7 +665,7 @@ end
@test typeof(log(A13)) == Array{elty, 2}

T = elty == Float64 ? Symmetric : Hermitian
@test typeof(log(T(A13))) == T{elty, Array{elty, 2}}
@test typeof(log(T(A13))) == T{elty, Array{elty, 2}, Array{elty, 2}}

A1 = convert(Matrix{elty}, [4 2 0; 1 4 1; 1 1 4])
logA1 = convert(Matrix{elty}, [1.329661349 0.5302876358 -0.06818951543;
Expand Down
8 changes: 4 additions & 4 deletions stdlib/LinearAlgebra/test/triangular.jl
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Expand Up @@ -548,13 +548,13 @@ end
end

@testset "special printing of Lower/UpperTriangular" begin
@test occursin(r"3×3 (LinearAlgebra\.)?LowerTriangular{Int64,Array{Int64,2}}:\n 2 ⋅ ⋅\n 2 2 ⋅\n 2 2 2",
@test occursin(r"3×3 (LinearAlgebra\.)?LowerTriangular{Int64,Array{Int64,2},Array{Int64,2}}:\n 2 ⋅ ⋅\n 2 2 ⋅\n 2 2 2",
sprint(show, MIME"text/plain"(), LowerTriangular(2ones(Int64,3,3))))
@test occursin(r"3×3 (LinearAlgebra\.)?UnitLowerTriangular{Int64,Array{Int64,2}}:\n 1 ⋅ ⋅\n 2 1 ⋅\n 2 2 1",
@test occursin(r"3×3 (LinearAlgebra\.)?UnitLowerTriangular{Int64,Array{Int64,2},Array{Int64,2}}:\n 1 ⋅ ⋅\n 2 1 ⋅\n 2 2 1",
sprint(show, MIME"text/plain"(), UnitLowerTriangular(2ones(Int64,3,3))))
@test occursin(r"3×3 (LinearAlgebra\.)?UpperTriangular{Int64,Array{Int64,2}}:\n 2 2 2\n ⋅ 2 2\n ⋅ ⋅ 2",
@test occursin(r"3×3 (LinearAlgebra\.)?UpperTriangular{Int64,Array{Int64,2},Array{Int64,2}}:\n 2 2 2\n ⋅ 2 2\n ⋅ ⋅ 2",
sprint(show, MIME"text/plain"(), UpperTriangular(2ones(Int64,3,3))))
@test occursin(r"3×3 (LinearAlgebra\.)?UnitUpperTriangular{Int64,Array{Int64,2}}:\n 1 2 2\n ⋅ 1 2\n ⋅ ⋅ 1",
@test occursin(r"3×3 (LinearAlgebra\.)?UnitUpperTriangular{Int64,Array{Int64,2},Array{Int64,2}}:\n 1 2 2\n ⋅ 1 2\n ⋅ ⋅ 1",
sprint(show, MIME"text/plain"(), UnitUpperTriangular(2ones(Int64,3,3))))
end

Expand Down
6 changes: 3 additions & 3 deletions test/show.jl
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Expand Up @@ -636,7 +636,7 @@ Base.methodloc_callback[] = methloc
@test replstr(Matrix(1.0I, 10, 10)) == "10×10 Array{Float64,2}:\n 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0\n 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0\n 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0\n 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0\n 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0\n 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0\n 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0"
# an array too long vertically to fit on screen, and too long horizontally:
@test replstr(Vector(1.:100.)) == "100-element Array{Float64,1}:\n 1.0\n 2.0\n 3.0\n 4.0\n 5.0\n 6.0\n 7.0\n 8.0\n 9.0\n 10.0\n ⋮ \n 92.0\n 93.0\n 94.0\n 95.0\n 96.0\n 97.0\n 98.0\n 99.0\n 100.0"
@test occursin(r"1×100 (LinearAlgebra\.)?Adjoint{Float64,Array{Float64,1}}:\n 1.0 2.0 3.0 4.0 5.0 6.0 7.0 … 95.0 96.0 97.0 98.0 99.0 100.0", replstr(Vector(1.:100.)'))
@test occursin(r"1×100 (LinearAlgebra\.)?Adjoint{Float64,Array{Float64,1},Array{Float64,1}}:\n 1.0 2.0 3.0 4.0 5.0 6.0 7.0 … 95.0 96.0 97.0 98.0 99.0 100.0", replstr(Vector(1.:100.)'))
# too big in both directions to fit on screen:
@test replstr((1.:100.)*(1:100)') == "100×100 Array{Float64,2}:\n 1.0 2.0 3.0 4.0 5.0 6.0 … 97.0 98.0 99.0 100.0\n 2.0 4.0 6.0 8.0 10.0 12.0 194.0 196.0 198.0 200.0\n 3.0 6.0 9.0 12.0 15.0 18.0 291.0 294.0 297.0 300.0\n 4.0 8.0 12.0 16.0 20.0 24.0 388.0 392.0 396.0 400.0\n 5.0 10.0 15.0 20.0 25.0 30.0 485.0 490.0 495.0 500.0\n 6.0 12.0 18.0 24.0 30.0 36.0 … 582.0 588.0 594.0 600.0\n 7.0 14.0 21.0 28.0 35.0 42.0 679.0 686.0 693.0 700.0\n 8.0 16.0 24.0 32.0 40.0 48.0 776.0 784.0 792.0 800.0\n 9.0 18.0 27.0 36.0 45.0 54.0 873.0 882.0 891.0 900.0\n 10.0 20.0 30.0 40.0 50.0 60.0 970.0 980.0 990.0 1000.0\n ⋮ ⋮ ⋱ \n 92.0 184.0 276.0 368.0 460.0 552.0 8924.0 9016.0 9108.0 9200.0\n 93.0 186.0 279.0 372.0 465.0 558.0 9021.0 9114.0 9207.0 9300.0\n 94.0 188.0 282.0 376.0 470.0 564.0 9118.0 9212.0 9306.0 9400.0\n 95.0 190.0 285.0 380.0 475.0 570.0 9215.0 9310.0 9405.0 9500.0\n 96.0 192.0 288.0 384.0 480.0 576.0 … 9312.0 9408.0 9504.0 9600.0\n 97.0 194.0 291.0 388.0 485.0 582.0 9409.0 9506.0 9603.0 9700.0\n 98.0 196.0 294.0 392.0 490.0 588.0 9506.0 9604.0 9702.0 9800.0\n 99.0 198.0 297.0 396.0 495.0 594.0 9603.0 9702.0 9801.0 9900.0\n 100.0 200.0 300.0 400.0 500.0 600.0 9700.0 9800.0 9900.0 10000.0"

Expand Down Expand Up @@ -689,8 +689,8 @@ let A = reshape(1:16, 4, 4)
@test occursin(r"4×4 (LinearAlgebra\.)?Bidiagonal{Int(32|64),Array{Int(32|64),1}}:\n 1 ⋅ ⋅ ⋅\n 2 6 ⋅ ⋅\n ⋅ 7 11 ⋅\n ⋅ ⋅ 12 16", replstr(Bidiagonal(A, :L)))
@test occursin(r"4×4 (LinearAlgebra\.)?SymTridiagonal{Int(32|64),Array{Int(32|64),1}}:\n 2 7 ⋅ ⋅\n 7 12 17 ⋅\n ⋅ 17 22 27\n ⋅ ⋅ 27 32", replstr(SymTridiagonal(A + A')))
@test occursin(r"4×4 (LinearAlgebra\.)?Tridiagonal{Int(32|64),Array{Int(32|64),1}}:\n 1 5 ⋅ ⋅\n 2 6 10 ⋅\n ⋅ 7 11 15\n ⋅ ⋅ 12 16", replstr(Tridiagonal(diag(A, -1), diag(A), diag(A, +1))))
@test occursin(r"4×4 (LinearAlgebra\.)?UpperTriangular{Int(32|64),Array{Int(32|64),2}}:\n 1 5 9 13\n ⋅ 6 10 14\n ⋅ ⋅ 11 15\n ⋅ ⋅ ⋅ 16", replstr(UpperTriangular(copy(A))))
@test occursin(r"4×4 (LinearAlgebra\.)?LowerTriangular{Int(32|64),Array{Int(32|64),2}}:\n 1 ⋅ ⋅ ⋅\n 2 6 ⋅ ⋅\n 3 7 11 ⋅\n 4 8 12 16", replstr(LowerTriangular(copy(A))))
@test occursin(r"4×4 (LinearAlgebra\.)?UpperTriangular{Int(32|64),Array{Int(32|64),2},Array{Int(32|64),2}}:\n 1 5 9 13\n ⋅ 6 10 14\n ⋅ ⋅ 11 15\n ⋅ ⋅ ⋅ 16", replstr(UpperTriangular(copy(A))))
@test occursin(r"4×4 (LinearAlgebra\.)?LowerTriangular{Int(32|64),Array{Int(32|64),2},Array{Int(32|64),2}}:\n 1 ⋅ ⋅ ⋅\n 2 6 ⋅ ⋅\n 3 7 11 ⋅\n 4 8 12 16", replstr(LowerTriangular(copy(A))))
end

# Vararg methods in method tables
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