Merge pull request #1172 from vyudu/network-analysis-touchup

network_analysis cleanup: Updating `ratematrix` to be more similar to new matrix API functions

Author: vyudu

src/network_analysis.jl

@@ -206,9 +206,6 @@ function laplacianmat(rn::ReactionSystem, pmap::Dict = Dict(); sparse = false)
     D * K
 end
 
-Base.zero(::Type{Union{R, Symbolics.BasicSymbolic{Real}}}) where R <: Real = zero(R)
-Base.one(::Type{Union{R, Symbolics.BasicSymbolic{Real}}}) where R <: Real = one(R)
-
 @doc raw"""
     fluxmat(rn::ReactionSystem, pmap = Dict(); sparse=false)
 
@@ -218,15 +215,20 @@ Base.one(::Type{Union{R, Symbolics.BasicSymbolic{Real}}}) where R <: Real = one(
     **Warning**: Unlike other Catalyst functions, the `fluxmat` function will return a `Matrix{Num}` in the symbolic case. This is to allow easier computation of the matrix decomposition of the ODEs, and to ensure that multiplying the sparse form of the matrix will work.
 """
 function fluxmat(rn::ReactionSystem, pmap::Dict = Dict(); sparse=false)
+    deps = Set()
+    for (i, rx) in enumerate(reactions(rn))
+        empty!(deps)
+        get_variables!(deps, rx.rate, species(rn))
+        (!isempty(deps)) && (error("Reaction $rx's rate constant depends on species $(join(deps, ", ")). `fluxmat` cannot support rate constants of this form."))
+    end
+
     rates = if isempty(pmap)
         reactionrates(rn)
     else
         substitutevals(rn, pmap, parameters(rn), reactionrates(rn))
     end
 
     rcmap = reactioncomplexmap(rn)
-    nc = length(rcmap)
-    nr = length(rates)
     mtype = eltype(rates) <: Symbolics.BasicSymbolic ? Num : eltype(rates)
     if sparse
         return fluxmat(SparseMatrixCSC{mtype, Int}, rcmap, rates)
@@ -295,6 +297,10 @@ function massactionvector(rn::ReactionSystem, scmap::Dict = Dict(); combinatoric
     rxs = reactions(rn)
     sm = speciesmap(rn)
 
+    for rx in rxs
+        !ismassaction(rx, rn) && error("The supplied ReactionSystem has non-mass action reaction $rx. The `massactionvector` can only be constructed for mass action networks.")
+    end
+
     specs = if isempty(scmap) 
         species(rn)
     else
@@ -936,7 +942,7 @@ function isdetailedbalanced(rs::ReactionSystem, parametermap::Dict; abstol=0, re
     complexes, D = reactioncomplexes(rs)
     img = incidencematgraph(rs)
     undir_img = SimpleGraph(incidencematgraph(rs))
-    K = ratematrix(rs, pmap)
+    K = adjacencymat(rs, pmap)
 
     spanning_forest = Graphs.kruskal_mst(undir_img)
     outofforest_edges = setdiff(collect(edges(undir_img)), spanning_forest)
@@ -993,52 +999,25 @@ function isdetailedbalanced(rs::ReactionSystem, parametermap)
 end
 
 """
-    iscomplexbalanced(rs::ReactionSystem, parametermap)
+    iscomplexbalanced(rs::ReactionSystem, parametermap; sparse = false)
 
 Constructively compute whether a network will have complex-balanced equilibrium
 solutions, following the method in van der Schaft et al., [2015](https://link.springer.com/article/10.1007/s10910-015-0498-2#Sec3). 
-Accepts a dictionary, vector, or tuple of variable-to-value mappings, e.g. [k1 => 1.0, k2 => 2.0,...]. 
-"""
-function iscomplexbalanced(rs::ReactionSystem, parametermap::Dict)
-    if length(parametermap) != numparams(rs)
-        error("Incorrect number of parameters specified.")
-    end
-
-    pmap = symmap_to_varmap(rs, parametermap)
-    pmap = Dict(ModelingToolkit.value(k) => v for (k, v) in pmap)
 
-    sm = speciesmap(rs)
-    cm = reactioncomplexmap(rs)
-    complexes, D = reactioncomplexes(rs)
+Requires the specification of values for the parameters/rate constants. Accepts a dictionary, vector, or tuple of parameter-to-value mappings, e.g. [k1 => 1.0, k2 => 2.0,...]. 
+"""
+function iscomplexbalanced(rs::ReactionSystem, parametermap::Dict; sparse = false)
     rxns = reactions(rs)
-    nc = length(complexes)
-    nr = numreactions(rs)
-    nm = numspecies(rs)
-
     if !all(r -> ismassaction(r, rs), rxns)
         error("The supplied ReactionSystem has reactions that are not ismassaction. Testing for being complex balanced is currently only supported for pure mass action networks.")
     end
 
-    rates = [substitute(rate, pmap) for rate in reactionrates(rs)]
-
-    # Construct kinetic matrix, K
-    K = zeros(nr, nc)
-    for c in 1:nc
-        complex = complexes[c]
-        for (r, dir) in cm[complex]
-            rxn = rxns[r]
-            if dir == -1
-                K[r, c] = rates[r]
-            end
-        end
-    end
-
-    L = -D * K
-    S = netstoichmat(rs)
+    D = incidencemat(rs; sparse)
+    S = netstoichmat(rs; sparse)
 
     # Compute ρ using the matrix-tree theorem
     g = incidencematgraph(rs)
-    R = ratematrix(rs, rates)
+    R = adjacencymat(rs, parametermap; sparse)
     ρ = matrixtree(g, R)
 
     # Determine if 1) ρ is positive and 2) D^T Ln ρ lies in the image of S^T
@@ -1069,50 +1048,81 @@ function iscomplexbalanced(rs::ReactionSystem, parametermap)
 end
 
 """
-    ratematrix(rs::ReactionSystem, parametermap)
+    adjacencymat(rs::ReactionSystem, pmap = Dict(); sparse = false)
 
     Given a reaction system with n complexes, outputs an n-by-n matrix where R_{ij} is the rate 
     constant of the reaction between complex i and complex j. Accepts a dictionary, vector, or tuple 
     of variable-to-value mappings, e.g. [k1 => 1.0, k2 => 2.0,...]. 
+
+    Equivalent to the adjacency matrix of the weighted graph whose weights are the 
+    reaction rates. 
+    Returns a symbolic matrix by default, but will return a numerical matrix if parameter values are specified via pmap. 
+
+    The difference between this matrix and [`fluxmat`](@ref) is quite subtle. The non-zero entries to both matrices are the rate constants. However:
+        - In `fluxmat`, the rows represent complexes and columns represent reactions, and an entry (c, r) is non-zero if reaction r's source complex is c. 
+        - In `adjacencymat`, the rows and columns both represent complexes, and an entry (c1, c2) is non-zero if there is a reaction c1 --> c2.
 """
-function ratematrix(rs::ReactionSystem, rates::Vector{Float64})
-    complexes, D = reactioncomplexes(rs)
-    n = length(complexes)
-    rxns = reactions(rs)
-    ratematrix = zeros(n, n)
+function adjacencymat(rn::ReactionSystem, pmap::Dict = Dict(); sparse = false)
+    deps = Set()
+    for (i, rx) in enumerate(reactions(rn))
+        empty!(deps)
+        get_variables!(deps, rx.rate, species(rn))
+        (!isempty(deps)) && (error("Reaction $rx's rate constant depends on species $(join(deps, ", ")). `adjacencymat` cannot support rate constants of this form."))
+    end
 
-    for r in 1:length(rxns)
-        rxn = rxns[r]
-        s = findfirst(==(-1), @view D[:, r])
-        p = findfirst(==(1), @view D[:, r])
-        ratematrix[s, p] = rates[r]
+    rates = if isempty(pmap)
+        reactionrates(rn)
+    else
+        substitutevals(rn, pmap, parameters(rn), reactionrates(rn))
+    end
+    mtype = eltype(rates) <: Symbolics.BasicSymbolic ? Num : eltype(rates)
+
+    if sparse
+        return adjacencymat(SparseMatrixCSC{mtype, Int}, incidencemat(rn), rates)
+    else
+        return adjacencymat(Matrix{mtype}, incidencemat(rn), rates)
     end
-    ratematrix
 end
 
-function ratematrix(rs::ReactionSystem, parametermap::Dict)
-    if length(parametermap) != numparams(rs)
-        error("Incorrect number of parameters specified.")
+function adjacencymat(::Type{SparseMatrixCSC{T, Int}}, D, rates) where T
+    Is = Int[]
+    Js = Int[]
+    Vs = T[]
+    nc = size(D, 1)
+
+    for r in 1:length(rates)
+        s = findfirst(==(-1), @view D[:, r])
+        p = findfirst(==(1), @view D[:, r])
+        push!(Is, s)
+        push!(Js, p)
+        push!(Vs, rates[r])
     end
+    A = sparse(Is, Js, Vs, nc, nc)
+end
 
-    pmap = symmap_to_varmap(rs, parametermap)
-    pmap = Dict(ModelingToolkit.value(k) => v for (k, v) in pmap)
+function adjacencymat(::Type{Matrix{T}}, D, rates) where T
+    nc = size(D, 1)
+    A = zeros(T, nc, nc)
 
-    rates = [substitute(rate, pmap) for rate in reactionrates(rs)]
-    ratematrix(rs, rates)
+    for r in 1:length(rates)
+        s = findfirst(==(-1), @view D[:, r])
+        p = findfirst(==(1), @view D[:, r])
+        A[s, p] = rates[r]
+    end
+    A
 end
 
-function ratematrix(rs::ReactionSystem, parametermap::Vector{<:Pair})
-    pdict = Dict(parametermap)
-    ratematrix(rs, pdict)
+function adjacencymat(rn::ReactionSystem, pmap::Vector{<:Pair}; sparse=false)
+    pdict = Dict(pmap)
+    adjacencymat(rn, pdict; sparse)
 end
 
-function ratematrix(rs::ReactionSystem, parametermap::Tuple)
-    pdict = Dict(parametermap)
-    ratematrix(rs, pdict)
+function adjacencymat(rn::ReactionSystem, pmap::Tuple; sparse=false)
+    pdict = Dict(pmap)
+    adjacencymat(rn, pdict; sparse)
 end
 
-function ratematrix(rs::ReactionSystem, parametermap)
+function adjacencymat(rn::ReactionSystem, pmap)
     error("Parameter map must be a dictionary, tuple, or vector of symbol/value pairs.")
 end
 

test/network_analysis/crn_theory.jl

@@ -1,7 +1,6 @@
 # Tests for properties from chemical reaction network theory: deficiency theorems, complex/detailed balance, etc.
 using Catalyst, StableRNGs, LinearAlgebra, Test
 rng = StableRNG(514)
-
 # Tests that `iscomplexbalanced` works for different rate inputs.
 # Tests that non-valid rate input yields and error
 let
@@ -69,18 +68,21 @@ let
     rates_invalid = reshape(rate_vals, 1, 8)
 
     # Tests that all input types generates the correct rate matrix.
-    Catalyst.ratematrix(rn, rate_vals) == rate_mat
-    Catalyst.ratematrix(rn, rates_vec) == rate_mat
-    Catalyst.ratematrix(rn, rates_tup) == rate_mat
-    Catalyst.ratematrix(rn, rates_dict) == rate_mat
+    @test Catalyst.adjacencymat(rn, rates_vec) == rate_mat
+    @test Catalyst.adjacencymat(rn, rates_tup) == rate_mat
+    @test Catalyst.adjacencymat(rn, rates_dict) == rate_mat
+    @test_throws Exception Catalyst.adjacencymat(rn, rate_vals)
 
     # Tests that throws error in rate matrix.
     incorrect_param_dict = Dict(:k1 => 1.0)
 
-    @test_throws ErrorException Catalyst.ratematrix(rn, 123)
-    @test_throws ErrorException Catalyst.ratematrix(rn, incorrect_param_dict)
+    @test_throws ErrorException Catalyst.adjacencymat(rn, 123)
+    @test_throws ErrorException Catalyst.adjacencymat(rn, incorrect_param_dict)
 
     @test_throws Exception Catalyst.iscomplexbalanced(rn, rates_invalid)
+
+    # Test sparse matrix
+    @test Catalyst.adjacencymat(rn, rates_vec; sparse = true) == rate_mat
 end
 
 ### CONCENTRATION ROBUSTNESS TESTS