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abstractsparse.jl
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abstractsparse.jl
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# This file is a part of Julia. License is MIT: https://julialang.org/license
"""
AbstractSparseArray{Tv,Ti,N}
Supertype for `N`-dimensional sparse arrays (or array-like types) with elements
of type `Tv` and index type `Ti`. [`SparseMatrixCSC`](@ref), [`SparseVector`](@ref)
and `SuiteSparse.CHOLMOD.Sparse` are subtypes of this.
"""
abstract type AbstractSparseArray{Tv,Ti,N} <: AbstractArray{Tv,N} end
"""
AbstractSparseVector{Tv,Ti}
Supertype for one-dimensional sparse arrays (or array-like types) with elements
of type `Tv` and index type `Ti`. Alias for `AbstractSparseArray{Tv,Ti,1}`.
"""
const AbstractSparseVector{Tv,Ti} = AbstractSparseArray{Tv,Ti,1}
"""
AbstractSparseMatrix{Tv,Ti}
Supertype for two-dimensional sparse arrays (or array-like types) with elements
of type `Tv` and index type `Ti`. Alias for `AbstractSparseArray{Tv,Ti,2}`.
"""
const AbstractSparseMatrix{Tv,Ti} = AbstractSparseArray{Tv,Ti,2}
"""
AbstractSparseMatrixCSC{Tv,Ti<:Integer} <: AbstractSparseMatrix{Tv,Ti}
Supertype for matrix with compressed sparse column (CSC).
"""
abstract type AbstractSparseMatrixCSC{Tv,Ti<:Integer} <: AbstractSparseMatrix{Tv,Ti} end
"""
issparse(S)
Returns `true` if `S` is sparse, and `false` otherwise.
# Examples
```jldoctest
julia> sv = sparsevec([1, 4], [2.3, 2.2], 10)
10-element SparseVector{Float64, Int64} with 2 stored entries:
[1 ] = 2.3
[4 ] = 2.2
julia> issparse(sv)
true
julia> issparse(Array(sv))
false
```
"""
function issparse(A::AbstractArray)
# Handle wrapper arrays: sparse if it is wrapping a sparse array.
# This gets compiled away during specialization.
p = parent(A)
if p === A
# have reached top of wrapping without finding a sparse array, assume it is not.
return false
else
return issparse(p)
end
end
issparse(A::DenseArray) = false
issparse(S::AbstractSparseArray) = true
indtype(S::AbstractSparseArray{<:Any,Ti}) where {Ti} = Ti
function Base.reinterpret(::Type, A::AbstractSparseArray)
error("""
`reinterpret` on sparse arrays is discontinued.
Try reinterpreting the value itself instead.
""")
end
# The following two methods should be overloaded by concrete types to avoid
# allocating the I = findall(...)
_sparse_findnextnz(v::AbstractSparseArray, i) = (I = findall(!iszero, v); n = searchsortedfirst(I, i); n<=length(I) ? I[n] : nothing)
_sparse_findprevnz(v::AbstractSparseArray, i) = (I = findall(!iszero, v); n = searchsortedlast(I, i); !iszero(n) ? I[n] : nothing)
function findnext(f::Function, v::AbstractSparseArray, i)
# short-circuit the case f == !iszero because that avoids
# allocating e.g. zero(BigInt) for the f(zero(...)) test.
if nnz(v) == length(v) || (f != (!iszero) && f(zero(eltype(v))))
return invoke(findnext, Tuple{Function,Any,Any}, f, v, i)
end
j = _sparse_findnextnz(v, i)
while j !== nothing && !f(v[j])
j = _sparse_findnextnz(v, nextind(v, j))
end
return j
end
function findprev(f::Function, v::AbstractSparseArray, i)
# short-circuit the case f == !iszero because that avoids
# allocating e.g. zero(BigInt) for the f(zero(...)) test.
if nnz(v) == length(v) || (f != (!iszero) && f(zero(eltype(v))))
return invoke(findprev, Tuple{Function,Any,Any}, f, v, i)
end
j = _sparse_findprevnz(v, i)
while j !== nothing && !f(v[j])
j = _sparse_findprevnz(v, prevind(v, j))
end
return j
end
"""
findnz(A::SparseMatrixCSC)
Return a tuple `(I, J, V)` where `I` and `J` are the row and column indices of the stored
("structurally non-zero") values in sparse matrix `A`, and `V` is a vector of the values.
# Examples
```jldoctest
julia> A = sparse([1 2 0; 0 0 3; 0 4 0])
3×3 SparseMatrixCSC{Int64, Int64} with 4 stored entries:
1 2 ⋅
⋅ ⋅ 3
⋅ 4 ⋅
julia> findnz(A)
([1, 1, 3, 2], [1, 2, 2, 3], [1, 2, 4, 3])
```
"""
function findnz end
widelength(x::AbstractSparseArray) = prod(Int64.(size(x)))