Merge branch 'SciML:master' into testdocs

docs/src/model_creation/dsl_advanced.md

@@ -256,7 +256,7 @@ Sometimes, one wishes to declare a large number of similar parameters or species
 using Catalyst # hide
 two_state_model = @reaction_network begin
     @parameters k[1:2]
-    @species X(t)[1:2]
+    @species (X(t))[1:2]
     (k[1],k[2]), X[1] <--> X[2]
 end
 ```

docs/src/model_creation/examples/programmatic_generative_linear_pathway.md

@@ -84,7 +84,7 @@ t = default_t()
 @parameters τ
 function generate_lp(n)
     # Creates a vector `X` with n+1 species.
-    @species X(t)[1:n+1]
+    @species (X(t))[1:n+1]
     @species Xend(t)
 
     # Generate

docs/src/model_creation/examples/smoluchowski_coagulation_equation.md

@@ -98,7 +98,7 @@ for n = 1:nr
                            [1, 1], [1]))
     end
 end
-@named rs = ReactionSystem(rx, t, collect(X), collect(k))
+@named rs = ReactionSystem(rx, t, collect(X), [k])
 rs = complete(rs)
 ```
 We now convert the [`ReactionSystem`](@ref) into a `ModelingToolkit.JumpSystem`, and solve it using Gillespie's direct method. For details on other possible solvers (SSAs), see the [DifferentialEquations.jl](https://docs.sciml.ai/DiffEqDocs/stable/types/jump_types/) documentation

src/dsl.jl

@@ -369,10 +369,12 @@ function make_reaction_system(ex::Expr; name = :(gensym(:ReactionSystem)))
     sexprs = get_sexpr(species_extracted, options; iv_symbols = ivs)
     vexprs = get_sexpr(vars_extracted, options, :variables; iv_symbols = ivs)
     pexprs = get_pexpr(parameters_extracted, options)
-    ps, pssym = scalarize_macro(!isempty(parameters), pexprs, "ps")
-    vars, varssym = scalarize_macro(!isempty(variables), vexprs, "vars")
-    sps, spssym = scalarize_macro(!isempty(species), sexprs, "specs")
-    comps, compssym = scalarize_macro(!isempty(compound_species), compound_expr, "comps")
+    ps, pssym = assign_expr_to_var(!isempty(parameters), pexprs, "ps")
+    vars, varssym = assign_expr_to_var(!isempty(variables), vexprs, "vars";
+        scalarize = true)
+    sps, spssym = assign_expr_to_var(!isempty(species), sexprs, "specs"; scalarize = true)
+    comps, compssym = assign_expr_to_var(!isempty(compound_species), compound_expr,
+        "comps"; scalarize = true)
     rxexprs = :(CatalystEqType[])
     for reaction in reactions
         push!(rxexprs.args, get_rxexprs(reaction))
@@ -591,14 +593,21 @@ function get_rxexprs(rxstruct)
 end
 
 # takes a ModelingToolkit declaration macro like @parameters and returns an expression
-# that calls the macro and then scalarizes all the symbols created into a vector of Nums
-function scalarize_macro(nonempty, ex, name)
+# that calls the macro and saves it in a variable given by namesym based on name.
+# scalarizes if desired
+function assign_expr_to_var(nonempty, ex, name; scalarize = false)
     namesym = gensym(name)
     if nonempty
-        symvec = gensym()
-        ex = quote
-            $symvec = $ex
-            $namesym = reduce(vcat, Symbolics.scalarize($symvec))
+        if scalarize
+            symvec = gensym()
+            ex = quote
+                $symvec = $ex
+                $namesym = reduce(vcat, Symbolics.scalarize($symvec))
+            end
+        else
+            ex = quote
+                $namesym = $ex
+            end
         end
     else
         ex = :($namesym = Num[])

src/network_analysis.jl

@@ -657,8 +657,8 @@ function cache_conservationlaw_eqs!(rn::ReactionSystem, N::AbstractMatrix, col_o
     indepspecs = sts[indepidxs]
     depidxs = col_order[(r + 1):end]
     depspecs = sts[depidxs]
-    constants = MT.unwrap.(MT.scalarize(only(
-        @parameters $(CONSERVED_CONSTANT_SYMBOL)[1:nullity] [conserved = true])))
+    constants = MT.unwrap(only(
+        @parameters $(CONSERVED_CONSTANT_SYMBOL)[1:nullity] [conserved = true]))
 
     conservedeqs = Equation[]
     constantdefs = Equation[]

src/reactionsystem.jl

@@ -405,11 +405,11 @@ function ReactionSystem(eqs, iv, unknowns, ps;
     sivs′ = if spatial_ivs === nothing
         Vector{typeof(iv′)}()
     else
-        value.(MT.scalarize(spatial_ivs))
+        value.(spatial_ivs)
     end
-    unknowns′ = sort!(value.(MT.scalarize(unknowns)), by = !isspecies)
+    unknowns′ = sort!(value.(unknowns), by = !isspecies)
     spcs = filter(isspecies, unknowns′)
-    ps′ = value.(MT.scalarize(ps))
+    ps′ = value.(ps)
 
     # Checks that no (by Catalyst) forbidden symbols are used.
     allsyms = Iterators.flatten((ps′, unknowns′))
@@ -467,7 +467,7 @@ end
 # Two-argument constructor (reactions/equations and time variable).
 # Calls the `make_ReactionSystem_internal`, which in turn calls the four-argument constructor.
 function ReactionSystem(rxs::Vector, iv = Catalyst.DEFAULT_IV; kwargs...)
-    make_ReactionSystem_internal(rxs, iv, Vector{Num}(), Vector{Num}(); kwargs...)
+    make_ReactionSystem_internal(rxs, iv, [], []; kwargs...)
 end
 
 # One-argument constructor. Creates an emtoy `ReactionSystem` from a time independent variable only.
@@ -485,24 +485,25 @@ function make_ReactionSystem_internal(rxs_and_eqs::Vector, iv, us_in, ps_in;
         spatial_ivs = nothing, continuous_events = [], discrete_events = [],
         observed = [], kwargs...)
 
-    # Filters away any potential observables from `states` and `spcs`.
-    obs_vars = [obs_eq.lhs for obs_eq in observed]
-    us_in = filter(u -> !any(isequal(u, obs_var) for obs_var in obs_vars), us_in)
+    # Error if any observables have been declared a species or variable
+    obs_vars = Set(obs_eq.lhs for obs_eq in observed)
+    any(in(obs_vars), us_in) &&
+        error("Found an observable in the list of unknowns. This is not allowed.")
 
     # Creates a combined iv vector (iv and sivs). This is used later in the function (so that 
     # independent variables can be excluded when encountered quantities are added to `us` and `ps`).
     t = value(iv)
     ivs = Set([t])
     if (spatial_ivs !== nothing)
-        for siv in (MT.scalarize(spatial_ivs))
+        for siv in (spatial_ivs)
             push!(ivs, value(siv))
         end
     end
 
     # Initialises the new unknowns and parameter vectors.
     # Preallocates the `vars` set, which is used by `findvars!`
-    us = OrderedSet{eltype(us_in)}(us_in)
-    ps = OrderedSet{eltype(ps_in)}(ps_in)
+    us = OrderedSet{Any}(us_in)
+    ps = OrderedSet{Any}(ps_in)
     vars = OrderedSet()
 
     # Extracts the reactions and equations from the combined reactions + equations input vector.
@@ -548,7 +549,22 @@ function make_ReactionSystem_internal(rxs_and_eqs::Vector, iv, us_in, ps_in;
 
     # Converts the found unknowns and parameters to vectors.
     usv = collect(us)
-    psv = collect(ps)
+
+    new_ps = OrderedSet()
+    for p in ps
+        if iscall(p) && operation(p) === getindex
+            par = arguments(p)[begin]
+            if Symbolics.shape(Symbolics.unwrap(par)) !== Symbolics.Unknown() &&
+               all(par[i] in ps for i in eachindex(par))
+                push!(new_ps, par)
+            else
+                push!(new_ps, p)
+            end
+        else
+            push!(new_ps, p)
+        end
+    end
+    psv = collect(new_ps)
 
     # Passes the processed input into the next `ReactionSystem` call.    
     ReactionSystem(fulleqs, t, usv, psv; spatial_ivs, continuous_events,

test/dsl/dsl_advanced_model_construction.jl

@@ -340,10 +340,23 @@ let
         k[1]*a+k[2], X[1] + V[1]*X[2] --> V[2]*W*Y + B*C
     end
 
-    @parameters k[1:3] a B
+    @parameters k[1:2] a B
     @variables (V(t))[1:2] W(t)
     @species (X(t))[1:2] Y(t) C(t)
     rx = Reaction(k[1]*a+k[2], [X[1], X[2]], [Y, C], [1, V[1]], [V[2] * W, B])
     @named arrtest = ReactionSystem([rx], t)
-    arrtest == rn
+    @test arrtest == rn
+
+    rn = @reaction_network twostate begin
+        @parameters k[1:2]
+        @species (X(t))[1:2]
+        (k[1],k[2]), X[1] <--> X[2]
+    end
+
+    @parameters k[1:2]
+    @species (X(t))[1:2]
+    rx1 = Reaction(k[1], [X[1]], [X[2]])
+    rx2 = Reaction(k[2], [X[2]], [X[1]])
+    @named twostate = ReactionSystem([rx1, rx2], t)
+    @test twostate == rn
 end

test/dsl/dsl_options.jl

@@ -604,7 +604,7 @@ let
         k, 0 --> X1 + X2
     end
     @test isequal(observed(rn1)[1].rhs, observed(rn2)[1].rhs)
-    @test isequal(observed(rn1)[1].lhs.metadata, observed(rn2)[1].lhs.metadata)
+    @test_broken isequal(observed(rn1)[1].lhs.metadata, observed(rn2)[1].lhs.metadata)
     @test isequal(unknowns(rn1), unknowns(rn2))
 
     # Case with metadata.
@@ -618,7 +618,7 @@ let
         k, 0 --> X1 + X2
     end
     @test isequal(observed(rn3)[1].rhs, observed(rn4)[1].rhs)
-    @test isequal(observed(rn3)[1].lhs.metadata, observed(rn4)[1].lhs.metadata)
+    @test_broken isequal(observed(rn3)[1].lhs.metadata, observed(rn4)[1].lhs.metadata)
     @test isequal(unknowns(rn3), unknowns(rn4))
 end
 
@@ -822,10 +822,7 @@ let
         @variables X(t)
         @equations 2X ~ $c - X
     end)
-
-    u0 = [rn.X => 0.0]
-    ps = []
-    oprob = ODEProblem(rn, u0, (0.0, 100.0), ps; structural_simplify=true)
+    oprob = ODEProblem(rn, [], (0.0, 100.0); structural_simplify=true)
     sol = solve(oprob, Tsit5(); abstol=1e-9, reltol=1e-9)
     @test sol[rn.X][end] ≈ 2.0
 end

test/network_analysis/conservation_laws.jl

@@ -343,7 +343,7 @@ end
 let 
     # Prepares the model.
     t = default_t()
-    @species X(t)[1:2]
+    @species (X(t))[1:2]
     @parameters k[1:2]
     rxs = [
         Reaction(k[1], [X[1]], [X[2]]),

test/reactionsystem_core/reactionsystem.jl

@@ -40,17 +40,18 @@ rxs = [Reaction(k[1], nothing, [A]),            # 0 -> A
     Reaction(k[19] * t, [A], [B]),                                # A -> B with non constant rate.
     Reaction(k[20] * t * A, [B, C], [D], [2, 1], [2]),                  # 2A +B -> 2C with non constant rate.
 ]
-@named rs = ReactionSystem(rxs, t, [A, B, C, D], k)
+@named rs = ReactionSystem(rxs, t, [A, B, C, D], [k])
 rs = complete(rs)
 odesys = complete(convert(ODESystem, rs))
 sdesys = complete(convert(SDESystem, rs))
 
 # Hard coded ODE rhs.
-function oderhs(u, k, t)
+function oderhs(u, kv, t)
     A = u[1]
     B = u[2]
     C = u[3]
     D = u[4]
+    k = kv[1]
     du = zeros(eltype(u), 4)
     du[1] = k[1] - k[3] * A + k[4] * C + 2 * k[5] * C - k[6] * A * B + k[7] * B^2 / 2 -
             k[9] * A * B - k[10] * A^2 - k[11] * A^2 / 2 - k[12] * A * B^3 * C^4 / 144 -
@@ -68,11 +69,12 @@ function oderhs(u, k, t)
 end
 
 # SDE noise coefs.
-function sdenoise(u, k, t)
+function sdenoise(u, kv, t)
     A = u[1]
     B = u[2]
     C = u[3]
     D = u[4]
+    k = kv[1]
     G = zeros(eltype(u), length(k), length(u))
     z = zero(eltype(u))
 
@@ -109,11 +111,12 @@ end
 
 # Defaults test.
 let
-    def_p = [ki => float(i) for (i, ki) in enumerate(k)]
+    kvals = Float64.(1:length(k))
+    def_p = [k => kvals]
     def_u0 = [A => 0.5, B => 1.0, C => 1.5, D => 2.0]
     defs = merge(Dict(def_p), Dict(def_u0))
 
-    @named rs = ReactionSystem(rxs, t, [A, B, C, D], k; defaults = defs)
+    @named rs = ReactionSystem(rxs, t, [A, B, C, D], [k]; defaults = defs)
     rs = complete(rs)
     odesys = complete(convert(ODESystem, rs))
     sdesys = complete(convert(SDESystem, rs))
@@ -126,15 +129,11 @@ let
           defs
 
     u0map = [A => 5.0]
-    pmap = [k[1] => 5.0]
+    kvals[1] = 5.0
+    pmap = [k => kvals]
     prob = ODEProblem(rs, u0map, (0, 10.0), pmap)
     @test prob.ps[k[1]] == 5.0
     @test prob.u0[1] == 5.0
-    u0 = [10.0, 11.0, 12.0, 13.0]
-    ps = [float(x) for x in 100:119]
-    prob = ODEProblem(rs, u0, (0, 10.0), ps)
-    @test  [prob.ps[k[i]] for i in 1:20] == ps
-    @test prob.u0 == u0
 end
 
 ### Check ODE, SDE, and Jump Functions ###
@@ -181,7 +180,7 @@ let
         Reaction(k[19] * t, [D], [E]),                                # D -> E with non constant rate.
         Reaction(k[20] * t * A, [D, E], [F], [2, 1], [2]),                  # 2D + E -> 2F with non constant rate.
     ]
-    @named rs = ReactionSystem(rxs, t, [A, B, C, D, E, F], k)
+    @named rs = ReactionSystem(rxs, t, [A, B, C, D, E, F], [k])
     rs = complete(rs)
     js = complete(convert(JumpSystem, rs))
 
@@ -193,7 +192,7 @@ let
     @test all(map(i -> typeof(equations(js)[i]) <: JumpProcesses.VariableRateJump, vidxs))
 
     p = rand(rng, length(k))
-    pmap = parameters(js) .=> p
+    pmap = [k => p]
     u0 = rand(rng, 2:10, 6)
     u0map = unknowns(js) .=> u0
     ttt = rand(rng)
@@ -392,7 +391,7 @@ end
 # Needs fix for https://github.com/JuliaSymbolics/Symbolics.jl/issues/842.
 let
     @parameters a
-    @species A(t) B(t) C(t)[1:2]
+    @species A(t) B(t) (C(t))[1:2]
     rx1 = Reaction(a, [A, C[1]], [C[2], B], [1, 2], [2, 3])
     io = IOBuffer()
     show(io, rx1)
@@ -868,11 +867,9 @@ end
 let
     @species (A(t))[1:20]
     using ModelingToolkit: value
-    @test isspecies(value(A))
-    @test isspecies(value(A[2]))
-    Av = value.(ModelingToolkit.scalarize(A))
-    @test isspecies(Av[2])
-    @test isequal(value(Av[2]), value(A[2]))
+    Av = value(A)
+    @test isspecies(Av)
+    @test all(i -> isspecies(Av[i]), 1:length(Av))
 end
 
 # Test mixed models are formulated correctly.

test/simulation_and_solving/simulate_SDEs.jl

@@ -204,9 +204,9 @@ let
         (k1, k2), X1 ↔ X2, ([noise_scaling=η[1]],[noise_scaling=η[2]])
     end
     @unpack k1, k2, η = noise_scaling_network_2
-    sprob_2_1 = SDEProblem(noise_scaling_network_2, u0, (0.0, 1000.0), [k1 => 2.0, k2 => 0.66, η[1] => 2.0, η[2] => 2.0])
-    sprob_2_2 = SDEProblem(noise_scaling_network_2, u0, (0.0, 1000.0), [k1 => 2.0, k2 => 0.66, η[1] => 2.0, η[2] => 0.2])
-    sprob_2_3 = SDEProblem(noise_scaling_network_2, u0, (0.0, 1000.0), [k1 => 2.0, k2 => 0.66, η[1] => 0.2, η[2] => 0.2])
+    sprob_2_1 = SDEProblem(noise_scaling_network_2, u0, (0.0, 1000.0), [k1 => 2.0, k2 => 0.66, η => [2.0, 2.0]])
+    sprob_2_2 = SDEProblem(noise_scaling_network_2, u0, (0.0, 1000.0), [k1 => 2.0, k2 => 0.66, η => [2.0, 0.2]])
+    sprob_2_3 = SDEProblem(noise_scaling_network_2, u0, (0.0, 1000.0), [k1 => 2.0, k2 => 0.66, η => [0.2, 0.2]])
     for repeat in 1:5
         sol_2_1 = solve(sprob_2_1, ImplicitEM(); saveat = 1.0, seed = rand(rng, 1:100))
         sol_2_2 = solve(sprob_2_2, ImplicitEM(); saveat = 1.0, seed = rand(rng, 1:100))

test/test_functions.jl

@@ -7,22 +7,22 @@ using Random, Test
 
 # Generates a random initial condition (in the form of a map). Each value is a Float64.
 function rnd_u0(sys, rng; factor = 1.0, min = 0.0)
-    return [u => min + factor * rand(rng) for u in unknowns(sys)]
+    return [u => (min .+ factor .* rand(rng, size(u)...)) for u in unknowns(sys)]
 end
 
 # Generates a random initial condition (in the form of a map). Each value is a Int64.
 function rnd_u0_Int64(sys, rng; n = 5, min = 0)
-    return [u => min + rand(rng, 1:n) for u in unknowns(sys)]
+    return [u => (min .+ rand(rng, 1:n, size(u)...)) for u in unknowns(sys)]
 end
 
 # Generates a random parameter set (in the form of a map). Each value is a Float64.
 function rnd_ps(sys, rng; factor = 1.0, min = 0.0)
-    return [p => min + factor * rand(rng) for p in parameters(sys)]
+    return [p => ( min .+ factor .* rand(rng, size(p)...)) for p in parameters(sys)]
 end
 
 # Generates a random parameter set (in the form of a map). Each value is a Float64.
 function rnd_ps_Int64(sys, rng; n = 5, min = 0)
-    return [p => min + rand(rng, 1:n) for p in parameters(sys)]
+    return [p => (min .+ rand(rng, 1:n, size(p)...)) for p in parameters(sys)]
 end
 
 # Used to convert a generated initial condition/parameter set to a vector that can be used for normal

test/upstream/mtk_problem_inputs.jl

@@ -180,7 +180,7 @@ end
 begin 
     # Declares the model (with vector species/parameters, with/without default values, and observables).
     t = default_t()
-    @species X(t)[1:2] Y(t)[1:2] = [10.0, 20.0] XY(t)[1:2]
+    @species (X(t))[1:2] (Y(t))[1:2] = [10.0, 20.0] (XY(t))[1:2]
     @parameters p[1:2] d[1:2] = [0.2, 0.5]
     rxs = [
         Reaction(p[1], [], [X[1]]),