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Deprecate speye.
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Sacha0 committed Oct 27, 2017
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7 changes: 7 additions & 0 deletions base/deprecated.jl
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Expand Up @@ -2056,6 +2056,13 @@ end
# deprecate bits to bitstring (#24263, #24281)
@deprecate bits bitstring

# deprecate speye
@deprecate speye(n::Integer) sparse(1.0I, n)
@deprecate speye(m::Integer, n::Integer) sparse(1.0I, m, n)
@deprecate speye(::Type{T}, n::Integer) where {T} sparse(UniformScaling(one(T)), n)
@deprecate speye(::Type{T}, m::Integer, n::Integer) where {T} sparse(UniformScaling(one(T)), m, n)
@deprecate speye(S::SparseMatrixCSC{T}) where {T} sparse(UniformScaling(one(T)), size(S)...)

# issue #24167
@deprecate EnvHash EnvDict

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2 changes: 1 addition & 1 deletion base/essentials.jl
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Expand Up @@ -119,7 +119,7 @@ true
Similarly, if `T` is a composite type and `x` a related instance, the result of
`convert(T, x)` may alias part or all of `x`.
```jldoctest
julia> x = speye(5);
julia> x = sparse(1.0I, 5);
julia> typeof(x)
SparseMatrixCSC{Float64,Int64}
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1 change: 0 additions & 1 deletion base/exports.jl
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Expand Up @@ -1267,7 +1267,6 @@ export
sparse,
sparsevec,
spdiagm,
speye,
spones,
sprand,
sprandn,
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2 changes: 1 addition & 1 deletion base/linalg/arnoldi.jl
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Expand Up @@ -145,7 +145,7 @@ final residual vector `resid`.
# Examples
```jldoctest
julia> A = speye(4, 4); B = Diagonal(1:4);
julia> A = sparse(1.0I, 4); B = Diagonal(1:4);
julia> λ, ϕ = eigs(A, B, nev = 2);
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2 changes: 1 addition & 1 deletion base/sparse/cholmod.jl
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Expand Up @@ -16,7 +16,7 @@ export
Factor,
Sparse

import ..SparseArrays: AbstractSparseMatrix, SparseMatrixCSC, increment, indtype, sparse, speye,
import ..SparseArrays: AbstractSparseMatrix, SparseMatrixCSC, increment, indtype, sparse,
spzeros, nnz

#########
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2 changes: 1 addition & 1 deletion base/sparse/sparse.jl
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Expand Up @@ -31,7 +31,7 @@ import Base: @get!, acos, acosd, acot, acotd, acsch, asech, asin, asind, asinh,

export AbstractSparseArray, AbstractSparseMatrix, AbstractSparseVector,
SparseMatrixCSC, SparseVector, blkdiag, droptol!, dropzeros!, dropzeros,
issparse, nonzeros, nzrange, rowvals, sparse, sparsevec, spdiagm, speye, spones,
issparse, nonzeros, nzrange, rowvals, sparse, sparsevec, spdiagm, spones,
sprand, sprandn, spzeros, nnz, permute

include("abstractsparse.jl")
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106 changes: 31 additions & 75 deletions base/sparse/sparsematrix.jl
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Expand Up @@ -59,11 +59,11 @@ Returns the number of stored (filled) elements in a sparse array.
# Examples
```jldoctest
julia> A = speye(3)
3×3 SparseMatrixCSC{Float64,Int64} with 3 stored entries:
[1, 1] = 1.0
[2, 2] = 1.0
[3, 3] = 1.0
julia> A = sparse(I, 3)
3×3 SparseMatrixCSC{Int64,Int64} with 3 stored entries:
[1, 1] = 1
[2, 2] = 1
[3, 3] = 1
julia> nnz(A)
3
Expand All @@ -83,17 +83,17 @@ modifications to the returned vector will mutate `A` as well. See
# Examples
```jldoctest
julia> A = speye(3)
3×3 SparseMatrixCSC{Float64,Int64} with 3 stored entries:
[1, 1] = 1.0
[2, 2] = 1.0
[3, 3] = 1.0
julia> A = sparse(I, 3)
3×3 SparseMatrixCSC{Int64,Int64} with 3 stored entries:
[1, 1] = 1
[2, 2] = 1
[3, 3] = 1
julia> nonzeros(A)
3-element Array{Float64,1}:
1.0
1.0
1.0
3-element Array{Int64,1}:
1
1
1
```
"""
nonzeros(S::SparseMatrixCSC) = S.nzval
Expand All @@ -108,11 +108,11 @@ nonzero values. See also [`nonzeros`](@ref) and [`nzrange`](@ref).
# Examples
```jldoctest
julia> A = speye(3)
3×3 SparseMatrixCSC{Float64,Int64} with 3 stored entries:
[1, 1] = 1.0
[2, 2] = 1.0
[3, 3] = 1.0
julia> A = sparse(I, 3)
3×3 SparseMatrixCSC{Int64,Int64} with 3 stored entries:
[1, 1] = 1
[2, 2] = 1
[3, 3] = 1
julia> rowvals(A)
3-element Array{Int64,1}:
Expand Down Expand Up @@ -1453,8 +1453,6 @@ julia> spones(A)
[3, 3] = 1.0
[2, 4] = 1.0
```
Note the difference from [`speye`](@ref).
"""
spones(S::SparseMatrixCSC{T}) where {T} =
SparseMatrixCSC(S.m, S.n, copy(S.colptr), copy(S.rowval), ones(T, S.colptr[end]-1))
Expand Down Expand Up @@ -1487,54 +1485,12 @@ function spzeros(::Type{Tv}, ::Type{Ti}, sz::Tuple{Integer,Integer}) where {Tv,
spzeros(Tv, Ti, sz[1], sz[2])
end

speye(n::Integer) = speye(Float64, n)
speye(::Type{T}, n::Integer) where {T} = speye(T, n, n)
speye(m::Integer, n::Integer) = speye(Float64, m, n)

"""
speye(S)
Create a sparse identity matrix with the same size as `S`.
# Examples
```jldoctest
julia> A = sparse([1,2,3,4],[2,4,3,1],[5.,4.,3.,2.])
4×4 SparseMatrixCSC{Float64,Int64} with 4 stored entries:
[4, 1] = 2.0
[1, 2] = 5.0
[3, 3] = 3.0
[2, 4] = 4.0
julia> speye(A)
4×4 SparseMatrixCSC{Float64,Int64} with 4 stored entries:
[1, 1] = 1.0
[2, 2] = 1.0
[3, 3] = 1.0
[4, 4] = 1.0
```
Note the difference from [`spones`](@ref).
"""
speye(S::SparseMatrixCSC{T}) where {T} = speye(T, size(S, 1), size(S, 2))
eye(S::SparseMatrixCSC) = speye(S)

"""
speye([type,]m[,n])
Create a sparse identity matrix of size `m x m`. When `n` is supplied,
create a sparse identity matrix of size `m x n`. The type defaults to [`Float64`](@ref)
if not specified.
`sparse(I, m, n)` is equivalent to `speye(Int, m, n)`, and
`sparse(α*I, m, n)` can be used to efficiently create a sparse
multiple `α` of the identity matrix.
"""
speye(::Type{T}, m::Integer, n::Integer) where {T} = speye_scaled(T, oneunit(T), m, n)
eye(S::SparseMatrixCSC{T}) where {T} = sparse(UniformScaling{T}(one(T)), size(S)...)

function one(S::SparseMatrixCSC{T}) where T
m,n = size(S)
if m != n; throw(DimensionMismatch("multiplicative identity only defined for square matrices")); end
speye(T, m)
speye_scaled(one(T), m, m)
end

speye_scaled(diag, m::Integer, n::Integer) = speye_scaled(typeof(diag), diag, m, n)
Expand Down Expand Up @@ -3120,13 +3076,13 @@ Concatenate matrices block-diagonally. Currently only implemented for sparse mat
# Examples
```jldoctest
julia> blkdiag(speye(3), 2*speye(2))
5×5 SparseMatrixCSC{Float64,Int64} with 5 stored entries:
[1, 1] = 1.0
[2, 2] = 1.0
[3, 3] = 1.0
[4, 4] = 2.0
[5, 5] = 2.0
julia> blkdiag(sparse(I, 3), sparse(2I, 2))
5×5 SparseMatrixCSC{Int64,Int64} with 5 stored entries:
[1, 1] = 1
[2, 2] = 1
[3, 3] = 1
[4, 4] = 2
[5, 5] = 2
```
"""
function blkdiag(X::SparseMatrixCSC...)
Expand Down Expand Up @@ -3578,6 +3534,6 @@ end

## Uniform matrix arithmetic

(+)(A::SparseMatrixCSC, J::UniformScaling) = A + J.λ * speye(A)
(-)(A::SparseMatrixCSC, J::UniformScaling) = A - J.λ * speye(A)
(-)(J::UniformScaling, A::SparseMatrixCSC) = J.λ * speye(A) - A
(+)(A::SparseMatrixCSC, J::UniformScaling) = A + sparse(J, size(A)...)
(-)(A::SparseMatrixCSC, J::UniformScaling) = A - sparse(J, size(A)...)
(-)(J::UniformScaling, A::SparseMatrixCSC) = sparse(J, size(A)...) - A
16 changes: 5 additions & 11 deletions doc/src/manual/arrays.md
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Expand Up @@ -781,19 +781,13 @@ stored zeros. (See [Sparse Matrix Storage](@ref man-csc).).

### Sparse Vector and Matrix Constructors

The simplest way to create sparse arrays is to use functions equivalent to the [`zeros`](@ref)
and [`eye`](@ref) functions that Julia provides for working with dense arrays. To produce
sparse arrays instead, you can use the same names with an `sp` prefix:
The simplest way to create a sparse array is to use a function equivalent to the [`zeros`](@ref)
function that Julia provides for working with dense arrays. To produce a
sparse array instead, you can use the same name with an `sp` prefix:

```jldoctest
julia> spzeros(3)
3-element SparseVector{Float64,Int64} with 0 stored entries
julia> speye(3,5)
3×5 SparseMatrixCSC{Float64,Int64} with 3 stored entries:
[1, 1] = 1.0
[2, 2] = 1.0
[3, 3] = 1.0
```

The [`sparse`](@ref) function is often a handy way to construct sparse arrays. For
Expand Down Expand Up @@ -867,7 +861,7 @@ You can go in the other direction using the [`Array`](@ref) constructor. The [`i
function can be used to query if a matrix is sparse.

```jldoctest
julia> issparse(speye(5))
julia> issparse(spzeros(5))
true
```

Expand Down Expand Up @@ -895,7 +889,7 @@ section of the standard library reference.
|:-------------------------- |:---------------------- |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| [`spzeros(m,n)`](@ref) | [`zeros(m,n)`](@ref) | Creates a *m*-by-*n* matrix of zeros. ([`spzeros(m,n)`](@ref) is empty.) |
| [`spones(S)`](@ref) | [`ones(m,n)`](@ref) | Creates a matrix filled with ones. Unlike the dense version, [`spones`](@ref) has the same sparsity pattern as *S*. |
| [`speye(n)`](@ref) | [`eye(n)`](@ref) | Creates a *n*-by-*n* identity matrix. |
| [`sparse(I, n)`](@ref) | [`eye(n)`](@ref) | Creates a *n*-by-*n* identity matrix. |
| [`Array(S)`](@ref) | [`sparse(A)`](@ref) | Interconverts between dense and sparse formats. |
| [`sprand(m,n,d)`](@ref) | [`rand(m,n)`](@ref) | Creates a *m*-by-*n* random matrix (of density *d*) with iid non-zero elements distributed uniformly on the half-open interval ``[0, 1)``. |
| [`sprandn(m,n,d)`](@ref) | [`randn(m,n)`](@ref) | Creates a *m*-by-*n* random matrix (of density *d*) with iid non-zero elements distributed according to the standard normal (Gaussian) distribution. |
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2 changes: 0 additions & 2 deletions doc/src/stdlib/arrays.md
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Expand Up @@ -188,8 +188,6 @@ Base.SparseArrays.issparse
Base.SparseArrays.nnz
Base.SparseArrays.spzeros
Base.SparseArrays.spones
Base.SparseArrays.speye(::Type, ::Integer, ::Integer)
Base.SparseArrays.speye(::SparseMatrixCSC)
Base.SparseArrays.spdiagm
Base.SparseArrays.sprand
Base.SparseArrays.sprandn
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2 changes: 1 addition & 1 deletion test/arrayops.jl
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Expand Up @@ -1008,7 +1008,7 @@ end
@test m[1,2] == ([2,4],)

# issue #21123
@test mapslices(nnz, speye(3), 1) == [1 1 1]
@test mapslices(nnz, sparse(1.0I, 3), 1) == [1 1 1]
end

@testset "single multidimensional index" begin
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2 changes: 1 addition & 1 deletion test/hashing.jl
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Expand Up @@ -73,7 +73,7 @@ vals = Any[
Dict(x => x for x in 1:10),
Dict(7=>7,9=>9,4=>4,10=>10,2=>2,3=>3,8=>8,5=>5,6=>6,1=>1),
[], [1], [2], [1, 1], [1, 2], [1, 3], [2, 2], [1, 2, 2], [1, 3, 3],
zeros(2, 2), spzeros(2, 2), eye(2, 2), speye(2, 2),
zeros(2, 2), spzeros(2, 2), eye(2, 2), sparse(1.0I, 2),
sparse(ones(2, 2)), ones(2, 2), sparse([0 0; 1 0]), [0 0; 1 0],
[-0. 0; -0. 0.], SparseMatrixCSC(2, 2, [1, 3, 3], [1, 2], [-0., -0.])
]
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2 changes: 1 addition & 1 deletion test/linalg/arnoldi.jl
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Expand Up @@ -163,7 +163,7 @@ let
# Adjust the tolerance a bit since matrices with repeated eigenvalues
# can be very stressful to ARPACK and this may therefore fail with
# info = 3 if the tolerance is too small
@test eigs(speye(50), nev=10, tol = 5e-16)[1] ones(10) #Issue 4246
@test eigs(sparse(1.0I, 50), nev=10, tol = 5e-16)[1] ones(10) #Issue 4246
end

@testset "real svds" begin
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2 changes: 1 addition & 1 deletion test/linalg/special.jl
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Expand Up @@ -178,7 +178,7 @@ end
annotations = testfull ? (triannotations..., symannotations...) : (LowerTriangular, Symmetric)
# Concatenations involving these types, un/annotated, should yield sparse arrays
spvec = spzeros(N)
spmat = speye(N)
spmat = sparse(1.0I, N)
diagmat = Diagonal(ones(N))
bidiagmat = Bidiagonal(ones(N), ones(N-1), :U)
tridiagmat = Tridiagonal(ones(N-1), ones(N), ones(N-1))
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2 changes: 1 addition & 1 deletion test/linalg/uniformscaling.jl
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Expand Up @@ -81,7 +81,7 @@ let
@test B + I == B + eye(B)
@test I + B == B + eye(B)
AA = randn(2, 2)
for SS in (sprandn(3,3, 0.5), speye(Int, 3))
for SS in (sprandn(3,3, 0.5), sparse(Int(1)I, 3))
for (A, S) in ((AA, SS), (view(AA, 1:2, 1:2), view(SS, 1:3, 1:3)))
@test @inferred(A + I) == A + eye(A)
@test @inferred(I + A) == A + eye(A)
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6 changes: 3 additions & 3 deletions test/perf/kernel/getdivgrad.jl
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Expand Up @@ -5,9 +5,9 @@
#----------------- Get the A matrix
function getDivGrad(n1,n2,n3)
# the Divergence
D1 = kron(speye(n3),kron(speye(n2),ddx(n1)))
D2 = kron(speye(n3),kron(ddx(n2),speye(n1)))
D3 = kron(ddx(n3),kron(speye(n2),speye(n1)))
D1 = kron(sparse(1.0I, n3), kron(sparse(1.0I, n2), ddx(n1)))
D2 = kron(sparse(1.0I, n3), kron(ddx(n2), sparse(1.0I, n1)))
D3 = kron(ddx(n3), kron(sparse(1.0I, n2), sparse(1.0I, n1)))
# DIV from faces to cell-centers
Div = [D1 D2 D3]

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2 changes: 1 addition & 1 deletion test/perf/sparse/fem.jl
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Expand Up @@ -7,7 +7,7 @@ function fdlaplacian(N)
# create a 1D laplacian and a sparse identity
fdl1 = spdiagm(-1 => ones(N-1), 0 => -2*ones(N), 1 => ones(N-1))
# laplace operator on the full grid
return kron(speye(N), fdl1) + kron(fdl1, speye(N))
return kron(sparse(1.0I, N), fdl1) + kron(fdl1, sparse(1.0I, N))
end

# get the list of boundary dof-indices
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2 changes: 1 addition & 1 deletion test/show.jl
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Expand Up @@ -492,7 +492,7 @@ end
# issue #12960
mutable struct T12960 end
let
A = speye(3)
A = sparse(1.0I, 3)
B = similar(A, T12960)
@test sprint(show, B) == "\n [1, 1] = #undef\n [2, 2] = #undef\n [3, 3] = #undef"
@test sprint(print, B) == "\n [1, 1] = #undef\n [2, 2] = #undef\n [3, 3] = #undef"
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