output sparse S matrix

Manifest.toml

@@ -39,6 +39,10 @@ git-tree-sha1 = "8e924324b2e9275a51407a4e06deb3455b1e359f"
 uuid = "94ce4f54-9a6c-5748-9c1c-f9c7231a4531"
 version = "1.16.0+7"
 
+[[LinearAlgebra]]
+deps = ["Libdl"]
+uuid = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+
 [[Logging]]
 uuid = "56ddb016-857b-54e1-b83d-db4d58db5568"
 
@@ -77,6 +81,10 @@ uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
 [[Sockets]]
 uuid = "6462fe0b-24de-5631-8697-dd941f90decc"
 
+[[SparseArrays]]
+deps = ["LinearAlgebra", "Random"]
+uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
+
 [[Test]]
 deps = ["Distributed", "InteractiveUtils", "Logging", "Random"]
 uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

Project.toml

@@ -8,6 +8,7 @@ Libdl = "8f399da3-3557-5675-b5ff-fb832c97cbdb"
 Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 SBML_jll = "bb12108a-f4ef-5f88-8ef3-0b33ff7017f1"
 SHA = "ea8e919c-243c-51af-8825-aaa63cd721ce"
+SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [compat]

src/SBML.jl

 module SBML
 
 using SBML_jll, Libdl, Pkg
+using SparseArrays
 
 include("structs.jl")
 include("version.jl")

src/utils.jl

 """
-    function getS(m::Model)::Tuple{Vector{String},Vector{String},Matrix{Float64}}
+    function getS(m::Model; zeros=spzeros)::Tuple{Vector{String},Vector{String},AbstractMatrix{Float64}}
 
 Extract the vector of species (aka metabolite) identifiers, vector of reaction
 identifiers, and the (dense) stoichiometry matrix from an existing `Model`.
 Returns a tuple with these values.
-"""
-function getS(m::Model)::Tuple{Vector{String},Vector{String},Matrix{Float64}}
-    #TODO this will need a sparse version and faster row ID lookup
-    rows = [k for k in keys(m.species)] #TODO this too
+
+The matrix is sparse by default (initially constructed by
+`SparseArrays.spzeros`). You can fill in a custom empty matrix constructed to
+argument `zeros`; e.g. running with `zeros=zeros` will produce a dense matrix.
+"""
+function getS(
+    m::Model;
+    zeros = spzeros,
+)::Tuple{Vector{String},Vector{String},AbstractMatrix{Float64}}
+    rows = [k for k in keys(m.species)]
     cols = [k for k in keys(m.reactions)]
+    rowsd = Dict(k => i for (i, k) in enumerate(rows))
     S = zeros(Float64, length(rows), length(cols))
-    for ri = 1:length(cols)
-        stoi = m.reactions[cols[ri]].stoichiometry
-        S[indexin(keys(stoi), rows), ri] .= values(stoi)
+    for col = 1:length(cols)
+        stoi = m.reactions[cols[col]].stoichiometry
+        S[getindex.(Ref(rowsd), keys(stoi)), col] .= values(stoi)
     end
     return rows, cols, S
 end