CIndices

Julia, Matlab, etc. index arrays starting at 1. C, python, etc. index starting at 0. In a dense array, we can simply subtract one from the index, and in fact, this is what Julia does under the hood when you pass a vector between C and Julia.

However, for sparse array formats, it's not just a matter of subtracting one from the index, as the internal lists of indices, positions, etc all start from zero as well. To remedy the situation, this package exports a handy zero-indexed integer type called CIndex. The internal representation of CIndex is one less than the value it represents, and we can use CIndex as the index or position type of a sparse array to represent arrays in other languages.

For example, if idx_c, ptr_c, and val are the internal arrays of a CSC matrix in a zero-indexed language, we can represent that matrix as a one-indexed CIndex array without copying by calling

julia> using CIndices, SparseArrays

julia> m = 4; n = 3; ptr_c = [0, 3, 3, 5]; idx_c = [1, 2, 3, 0, 2]; val = [1.1, 2.2, 3.3, 4.4, 5.5];

julia> ptr_jl = unsafe_wrap(Array, reinterpret(Ptr{CIndex{Int}}, pointer(ptr_c)), length(ptr_c); own = false)
4-element Vector{CIndex{Int64}}:
 1
 4
 4
 6

julia> idx_jl = unsafe_wrap(Array, reinterpret(Ptr{CIndex{Int}}, pointer(idx_c)), length(idx_c); own = false)
5-element Vector{CIndex{Int64}}:
 2
 3
 4
 1
 3

julia> A = SparseMatrixCSC{Float64, CIndex{Int}}(m, n, ptr_jl, idx_jl, val)
4×3 SparseMatrixCSC{Float64, CIndex{Int64}} with 5 stored entries:
  ⋅    ⋅   4.4
 1.1   ⋅    ⋅ 
 2.2   ⋅   5.5
 3.3   ⋅    ⋅