Merge branch 'master' into remove_glmakie_in_docs

test/reactionsystem_core/coupled_equation_crn_systems.jl

@@ -228,7 +228,7 @@ let
         Reaction(d, [X], []),
         Reaction(d, [X], nothing, [2], nothing),
         D(V) ~ X - v*V,
-        W^2 ~ log(V) + X 
+        W^2 ~ log(V) + X
     ]
     @named coupled_rs = ReactionSystem(eqs, t)
 

test/upstream/mtk_problem_inputs.jl

@@ -3,7 +3,7 @@
 ### Prepares Tests ###
 
 # Fetch packages
-using Catalyst, JumpProcesses, NonlinearSolve, OrdinaryDiffEqTsit5, StaticArrays, SteadyStateDiffEq, 
+using Catalyst, JumpProcesses, NonlinearSolve, OrdinaryDiffEqTsit5, StaticArrays, SteadyStateDiffEq,
     StochasticDiffEq, Test
 
 # Sets rnd number.
@@ -15,7 +15,7 @@ seed = rand(rng, 1:100)
 ### Basic Tests ###
 
 # Prepares a models and initial conditions/parameters (of different forms) to be used as problem inputs.
-begin 
+begin
     model = @reaction_network begin
         @species Z(t) = Z0
         @parameters k2=0.5 Z0
@@ -97,7 +97,7 @@ begin
 end
 
 # Perform ODE simulations (singular and ensemble).
-let 
+let
     # Creates normal and ensemble problems.
     base_oprob = ODEProblem(model, u0_alts[1], tspan, p_alts[1])
     base_sol = solve(base_oprob, Tsit5(); saveat = 1.0)
@@ -114,7 +114,7 @@ let
 end
 
 # Perform SDE simulations (singular and ensemble).
-let 
+let
     # Creates normal and ensemble problems.
     base_sprob = SDEProblem(model, u0_alts[1], tspan, p_alts[1])
     base_sol = solve(base_sprob, ImplicitEM(); seed, saveat = 1.0)
@@ -131,7 +131,7 @@ let
 end
 
 # Perform jump simulations (singular and ensemble).
-let 
+let
     # Creates normal and ensemble problems.
     base_dprob = DiscreteProblem(model, u0_alts[1], tspan, p_alts[1])
     base_jprob = JumpProblem(model, base_dprob, Direct(); rng)
@@ -159,7 +159,7 @@ let
 end
 
 # Perform steady state simulations (singular and ensemble).
-let 
+let
     # Creates normal and ensemble problems.
     base_ssprob = SteadyStateProblem(model, u0_alts[1], p_alts[1])
     base_sol = solve(base_ssprob, DynamicSS(Tsit5()))
@@ -177,7 +177,7 @@ end
 
 ### Vector Species/Parameters Tests ###
 
-begin 
+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]
@@ -235,11 +235,11 @@ begin
         Dict([model_vec.X[1] => 1.0, model_vec.X[2] => 2.0, model_vec.Y[1] => 10.0, model_vec.Y[2] => 20.0]),
         Dict([:X => [1.0, 2.0], :Y => [10.0, 20.0]]),
         # Tuples not providing default values.
-        (X => [1.0, 2.0]),
+        (X => [1.0, 2.0],),
         (X[1] => 1.0, X[2] => 2.0),
-        (model_vec.X => [1.0, 2.0]),
+        (model_vec.X => [1.0, 2.0],),
         (model_vec.X[1] => 1.0, model_vec.X[2] => 2.0),
-        (:X => [1.0, 2.0]),
+        (:X => [1.0, 2.0],),
         # Tuples providing default values.
         (X => [1.0, 2.0], Y => [10.0, 20.0]),
         (X[1] => 1.0, X[2] => 2.0, Y[1] => 10.0, Y[2] => 20.0),
@@ -251,33 +251,33 @@ begin
     # Declares various ps versions (vector forms only).
     p_alts_vec = [
         # Vectors not providing default values.
-        [p => [1.0, 2.0]],
-        [model_vec.p => [1.0, 2.0]],
-        [:p => [1.0, 2.0]],
+        [p => [4.0, 5.0]],
+        [model_vec.p => [4.0, 5.0]],
+        [:p => [4.0, 5.0]],
         # Vectors providing default values.
         [p => [4.0, 5.0], d => [0.2, 0.5]],
         [model_vec.p => [4.0, 5.0], model_vec.d => [0.2, 0.5]],
         [:p => [4.0, 5.0], :d => [0.2, 0.5]],
         # Static vectors not providing default values.
-        SA[p => [1.0, 2.0]],
-        SA[model_vec.p => [1.0, 2.0]],
-        SA[:p => [1.0, 2.0]],
+        SA[p => [4.0, 5.0]],
+        SA[model_vec.p => [4.0, 5.0]],
+        SA[:p => [4.0, 5.0]],
         # Static vectors providing default values.
         SA[p => [4.0, 5.0], d => [0.2, 0.5]],
         SA[model_vec.p => [4.0, 5.0], model_vec.d => [0.2, 0.5]],
         SA[:p => [4.0, 5.0], :d => [0.2, 0.5]],
         # Dicts not providing default values.
-        Dict([p => [1.0, 2.0]]),
-        Dict([model_vec.p => [1.0, 2.0]]),
-        Dict([:p => [1.0, 2.0]]),
+        Dict([p => [4.0, 5.0]]),
+        Dict([model_vec.p => [4.0, 5.0]]),
+        Dict([:p => [4.0, 5.0]]),
         # Dicts providing default values.
         Dict([p => [4.0, 5.0], d => [0.2, 0.5]]),
         Dict([model_vec.p => [4.0, 5.0], model_vec.d => [0.2, 0.5]]),
         Dict([:p => [4.0, 5.0], :d => [0.2, 0.5]]),
         # Tuples not providing default values.
-        (p => [1.0, 2.0]),
-        (model_vec.p => [1.0, 2.0]),
-        (:p => [1.0, 2.0]),
+        (p => [4.0, 5.0],),
+        (model_vec.p => [4.0, 5.0],),
+        (:p => [4.0, 5.0],),
         # Tuples providing default values.
         (p => [4.0, 5.0], d => [0.2, 0.5]),
         (model_vec.p => [4.0, 5.0], model_vec.d => [0.2, 0.5]),
@@ -289,7 +289,7 @@ begin
 end
 
 # Perform ODE simulations (singular and ensemble).
-let 
+let
     # Creates normal and ensemble problems.
     base_oprob = ODEProblem(model_vec, u0_alts_vec[1], tspan, p_alts_vec[1])
     base_sol = solve(base_oprob, Tsit5(); saveat = 1.0)
@@ -307,7 +307,7 @@ let
 end
 
 # Perform SDE simulations (singular and ensemble).
-let 
+let
     # Creates normal and ensemble problems.
     base_sprob = SDEProblem(model_vec, u0_alts_vec[1], tspan, p_alts_vec[1])
     base_sol = solve(base_sprob, ImplicitEM(); seed, saveat = 1.0)
@@ -325,7 +325,7 @@ let
 end
 
 # Perform jump simulations (singular and ensemble).
-let 
+let
     # Creates normal and ensemble problems.
     base_dprob = DiscreteProblem(model_vec, u0_alts_vec[1], tspan, p_alts_vec[1])
     base_jprob = JumpProblem(model_vec, base_dprob, Direct(); rng)
@@ -355,7 +355,7 @@ let
 end
 
 # Perform steady state simulations (singular and ensemble).
-let 
+let
     # Creates normal and ensemble problems.
     base_ssprob = SteadyStateProblem(model_vec, u0_alts_vec[1], p_alts_vec[1])
     base_sol = solve(base_ssprob, DynamicSS(Tsit5()))

test/upstream/mtk_structure_indexing.jl

@@ -52,7 +52,7 @@ begin
     nint = init(nprob, NewtonRaphson(); save_everystep = false)
     @test_broken ssint = init(ssprob, DynamicSS(Tsit5()); save_everystep = false) # https://github.com/SciML/SciMLBase.jl/issues/660
     integrators = [oint, sint, jint, nint]
-    
+
     # Creates solutions.
     osol = solve(oprob, Tsit5())
     ssol = solve(sprob, ImplicitEM(); seed)
@@ -63,7 +63,7 @@ begin
 end
 
 # Tests problem indexing and updating.
-let 
+let
     @test_broken false # A few cases fails for JumpProblem: https://github.com/SciML/ModelingToolkit.jl/issues/2838
     for prob in deepcopy([oprob, sprob, dprob, nprob, ssprob, eoprob, esprob, edprob, enprob, essprob])
         # Get u values (including observables).
@@ -72,8 +72,8 @@ let
         @test prob[[XY,Y]] == prob[[model.XY,model.Y]] == prob[[:XY,:Y]] == [9, 5]
         @test prob[(XY,Y)] == prob[(model.XY,model.Y)] == prob[(:XY,:Y)] == (9, 5)
         @test getu(prob, X)(prob) == getu(prob, model.X)(prob) == getu(prob, :X)(prob) == 4
-        @test getu(prob, XY)(prob) == getu(prob, model.XY)(prob) == getu(prob, :XY)(prob) == 9 
-        @test getu(prob, [XY,Y])(prob) == getu(prob, [model.XY,model.Y])(prob) == getu(prob, [:XY,:Y])(prob) == [9, 5]  
+        @test getu(prob, XY)(prob) == getu(prob, model.XY)(prob) == getu(prob, :XY)(prob) == 9
+        @test getu(prob, [XY,Y])(prob) == getu(prob, [model.XY,model.Y])(prob) == getu(prob, [:XY,:Y])(prob) == [9, 5]
         @test getu(prob, (XY,Y))(prob) == getu(prob, (model.XY,model.Y))(prob) == getu(prob, (:XY,:Y))(prob) == (9, 5)
 
         # Set u values.
@@ -91,13 +91,13 @@ let
         @test prob[X] == 70
 
         # Get p values.
-        @test prob.ps[kp] == prob.ps[model.kp] == prob.ps[:kp] == 1.0    
+        @test prob.ps[kp] == prob.ps[model.kp] == prob.ps[:kp] == 1.0
         @test prob.ps[[k1,k2]] == prob.ps[[model.k1,model.k2]] == prob.ps[[:k1,:k2]] == [0.25, 0.5]
         @test prob.ps[(k1,k2)] == prob.ps[(model.k1,model.k2)] == prob.ps[(:k1,:k2)] == (0.25, 0.5)
         @test getp(prob, kp)(prob) == getp(prob, model.kp)(prob) == getp(prob, :kp)(prob) == 1.0
         @test getp(prob, [k1,k2])(prob) == getp(prob, [model.k1,model.k2])(prob) == getp(prob, [:k1,:k2])(prob) == [0.25, 0.5]
         @test getp(prob, (k1,k2))(prob) == getp(prob, (model.k1,model.k2))(prob) == getp(prob, (:k1,:k2))(prob) == (0.25, 0.5)
-        
+
         # Set p values.
         prob.ps[kp] = 2.0
         @test prob.ps[kp] == 2.0
@@ -115,7 +115,7 @@ let
 end
 
 # Test remake function.
-let 
+let
     @test_broken false # Cannot check result for JumpProblem: https://github.com/SciML/ModelingToolkit.jl/issues/2838
     for prob in deepcopy([oprob, sprob, dprob, nprob, ssprob, eoprob, esprob, edprob, enprob, essprob])
         # Remake for all u0s.
@@ -153,7 +153,7 @@ let
 end
 
 # Test integrator indexing.
-let 
+let
     @test_broken false # NOTE: Cannot even create a `ssint` (https://github.com/SciML/SciMLBase.jl/issues/660).
     for int in deepcopy([oint, sint, jint, nint])
         # Get u values.
@@ -162,8 +162,8 @@ let
         @test int[[XY,Y]] == int[[model.XY,model.Y]] == int[[:XY,:Y]] == [9, 5]
         @test int[(XY,Y)] == int[(model.XY,model.Y)] == int[(:XY,:Y)] == (9, 5)
         @test getu(int, X)(int) == getu(int, model.X)(int) == getu(int, :X)(int) == 4
-        @test getu(int, XY)(int) == getu(int, model.XY)(int) == getu(int, :XY)(int) == 9 
-        @test getu(int, [XY,Y])(int) == getu(int, [model.XY,model.Y])(int) == getu(int, [:XY,:Y])(int) == [9, 5]  
+        @test getu(int, XY)(int) == getu(int, model.XY)(int) == getu(int, :XY)(int) == 9
+        @test getu(int, [XY,Y])(int) == getu(int, [model.XY,model.Y])(int) == getu(int, [:XY,:Y])(int) == [9, 5]
         @test getu(int, (XY,Y))(int) == getu(int, (model.XY,model.Y))(int) == getu(int, (:XY,:Y))(int) == (9, 5)
 
         # Set u values.
@@ -181,13 +181,13 @@ let
         @test int[X] == 70
 
         # Get p values.
-        @test int.ps[kp] == int.ps[model.kp] == int.ps[:kp] == 1.0    
+        @test int.ps[kp] == int.ps[model.kp] == int.ps[:kp] == 1.0
         @test int.ps[[k1,k2]] == int.ps[[model.k1,model.k2]] == int.ps[[:k1,:k2]] == [0.25, 0.5]
         @test int.ps[(k1,k2)] == int.ps[(model.k1,model.k2)] == int.ps[(:k1,:k2)] == (0.25, 0.5)
         @test getp(int, kp)(int) == getp(int, model.kp)(int) == getp(int, :kp)(int) == 1.0
         @test getp(int, [k1,k2])(int) == getp(int, [model.k1,model.k2])(int) == getp(int, [:k1,:k2])(int) == [0.25, 0.5]
         @test getp(int, (k1,k2))(int) == getp(int, (model.k1,model.k2))(int) == getp(int, (:k1,:k2))(int) == (0.25, 0.5)
-        
+
         # Set p values.
         int.ps[kp] = 2.0
         @test int.ps[kp] == 2.0
@@ -206,7 +206,7 @@ end
 
 # Test solve's save_idxs argument.
 # Currently, `save_idxs` is broken with symbolic stuff (https://github.com/SciML/ModelingToolkit.jl/issues/1761).
-let 
+let
     for (prob, solver) in zip(deepcopy([oprob, sprob, jprob]), [Tsit5(), ImplicitEM(), SSAStepper()])
 
         # Save single variable
@@ -218,7 +218,7 @@ let
             @test_broken solve(prob, solver; seed, save_idxs=X)[X][1] == 4
             @test_broken solve(prob, solver; seed, save_idxs=model.X)[X][1] == 4
             @test_broken solve(prob, solver; seed, save_idxs=:X)[X][1] == 4
-        end    
+        end
 
         # Save observable.
         @test_broken solve(prob, solver; seed, save_idxs=XY)[XY][1] == 9
@@ -233,17 +233,17 @@ let
 end
 
 # Tests solution indexing.
-let 
+let
     for sol in deepcopy([osol, ssol, jsol])
         # Get u values.
         @test sol[X][1] == sol[model.X][1] == sol[:X][1] == 4
         @test sol[XY][1] == sol[model.XY][1] == sol[:XY][1] == 9
         @test sol[[XY,Y]][1] == sol[[model.XY,model.Y]][1] == sol[[:XY,:Y]][1] == [9, 5]
         @test sol[(XY,Y)][1] == sol[(model.XY,model.Y)][1] == sol[(:XY,:Y)][1] == (9, 5)
         @test getu(sol, X)(sol)[1] == getu(sol, model.X)(sol)[1] == getu(sol, :X)(sol)[1] == 4
-        @test getu(sol, XY)(sol)[1] == getu(sol, model.XY)(sol)[1] == getu(sol, :XY)(sol)[1] == 9 
-        @test getu(sol, [XY,Y])(sol)[1] == getu(sol, [model.XY,model.Y])(sol)[1] == getu(sol, [:XY,:Y])(sol)[1] == [9, 5]  
-        @test getu(sol, (XY,Y))(sol)[1] == getu(sol, (model.XY,model.Y))(sol)[1] == getu(sol, (:XY,:Y))(sol)[1] == (9, 5)       
+        @test getu(sol, XY)(sol)[1] == getu(sol, model.XY)(sol)[1] == getu(sol, :XY)(sol)[1] == 9
+        @test getu(sol, [XY,Y])(sol)[1] == getu(sol, [model.XY,model.Y])(sol)[1] == getu(sol, [:XY,:Y])(sol)[1] == [9, 5]
+        @test getu(sol, (XY,Y))(sol)[1] == getu(sol, (model.XY,model.Y))(sol)[1] == getu(sol, (:XY,:Y))(sol)[1] == (9, 5)
 
         # Get u values via idxs and functional call.
         @test sol(0.0; idxs=X) == sol(0.0; idxs=model.X) == sol(0.0; idxs=:X) == 4
@@ -252,7 +252,7 @@ let
         @test_broken sol(0.0; idxs = (XY,Y)) == sol(0.0; idxs = (model.XY,model.Y)) == sol(0.0; idxs = (:XY,:Y)) == (9, 5) # https://github.com/SciML/SciMLBase.jl/issues/711
 
         # Get p values.
-        @test sol.ps[kp] == sol.ps[model.kp] == sol.ps[:kp] == 1.0    
+        @test sol.ps[kp] == sol.ps[model.kp] == sol.ps[:kp] == 1.0
         @test sol.ps[[k1,k2]] == sol.ps[[model.k1,model.k2]] == sol.ps[[:k1,:k2]] == [0.25, 0.5]
         @test sol.ps[(k1,k2)] == sol.ps[(model.k1,model.k2)] == sol.ps[(:k1,:k2)] == (0.25, 0.5)
         @test getp(sol, kp)(sol) == getp(sol, model.kp)(sol) == getp(sol, :kp)(sol) == 1.0
@@ -271,7 +271,7 @@ let
             @test getu(sol, X)(sol) == getu(sol, model.X)(sol)[1] == getu(sol, :X)(sol)
             @test getu(sol, XY)(sol) == getu(sol, model.XY)(sol)[1] == getu(sol, :XY)(sol)
             @test getu(sol, [XY,Y])(sol) == getu(sol, [model.XY,model.Y])(sol) == getu(sol, [:XY,:Y])(sol)
-            @test_broken getu(sol, (XY,Y))(sol) == getu(sol, (model.XY,model.Y))(sol) == getu(sol, (:XY,:Y))(sol)[1] # https://github.com/SciML/SciMLBase.jl/issues/710
+            @test getu(sol, (XY,Y))(sol) == getu(sol, (model.XY,model.Y))(sol) == getu(sol, (:XY,:Y))(sol)
 
             # Get p values.
             @test sol.ps[kp] == sol.ps[model.kp] == sol.ps[:kp]
@@ -285,7 +285,7 @@ let
 end
 
 # Tests plotting.
-let 
+let
     for sol in deepcopy([osol, jsol, ssol])
         # Single variable.
         @test length(plot(sol; idxs = X).series_list) == 1
@@ -310,7 +310,7 @@ let
         @test length(plot(sol; idxs = (model.XY, model.Y)).series_list) == 1
         @test length(plot(sol; idxs = (:X, :Y)).series_list) == 1
         @test length(plot(sol; idxs = (:XY, :Y)).series_list) == 1
-    end     
+    end
 end
 
 
@@ -365,27 +365,27 @@ let
     @test jint.cb.condition.ma_jumps.scaled_rates[1] == 30.0
     reset_aggregated_jumps!(jint)
     @test jint.cb.condition.ma_jumps.scaled_rates[1] == 8.0
-    
+
     jint.ps[rn.p1] = 5.0
     @test jint.cb.condition.ma_jumps.scaled_rates[1] == 8.0
     reset_aggregated_jumps!(jint)
     @test jint.cb.condition.ma_jumps.scaled_rates[1] == 10.0
-    
+
     jint.ps[:p1] = 6.0
     @test jint.cb.condition.ma_jumps.scaled_rates[1] == 10.0
     reset_aggregated_jumps!(jint)
     @test jint.cb.condition.ma_jumps.scaled_rates[1] == 12.0
-    
+
     setp(jint, p1)(jint, 7.0)
     @test jint.cb.condition.ma_jumps.scaled_rates[1] == 12.0
     reset_aggregated_jumps!(jint)
     @test jint.cb.condition.ma_jumps.scaled_rates[1] == 14.0
-    
+
     setp(jint, rn.p1)(jint, 8.0)
     @test jint.cb.condition.ma_jumps.scaled_rates[1] == 14.0
     reset_aggregated_jumps!(jint)
     @test jint.cb.condition.ma_jumps.scaled_rates[1] == 16.0
-    
+
     setp(jint, :p1)(jint, 3.0)
     @test jint.cb.condition.ma_jumps.scaled_rates[1] == 16.0
     reset_aggregated_jumps!(jint)