drop scalarizing

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/reactionsystem.jl

@@ -502,8 +502,8 @@ function make_ReactionSystem_internal(rxs_and_eqs::Vector, iv, us_in, ps_in;
 
     # 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.

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/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

@@ -391,7 +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)

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]]),