repeat test for SDEProblem as well

Author: TorkelE

test/network_analysis/conservation_laws.jl

@@ -244,11 +244,10 @@ let
         Reaction(k2, [X2], [X1])
     ]
     @named rs = ReactionSystem(rxs, t)
-    osys = convert(ODESystem, complete(rs); remove_conserved = true)
-    osys = complete(osys)
+    osys = complete(convert(ODESystem, complete(rs); remove_conserved = true))
     @unpack Γ = osys
 
-    # Creates an `ODEProblem`.
+    # Creates the various problem types.
     u0 = [X1 => 1.0, X2 => 2.0]
     ps = [k1 => 0.1, k2 => 0.2]
     oprob = ODEProblem(osys, u0, (0.0, 1.0), ps)
@@ -294,65 +293,72 @@ let
     @unpack X1, X2, X3 = rn
     u0 = [X1 => 1.0, X2 => 1.0, X3 => 1.0]
     ps = [:k1 => 0.1, :k2 => 0.2, :k3 => 0.3, :k4 => 0.4]
-    prob_old = ODEProblem(rn, u0, 1.0, ps; remove_conserved = true)
     conserved_quantity = conservationlaw_constants(rn)[1].rhs
 
-    # For a couple of iterations, updates the problem, ensuring that when a species is updated:
-    # - Only that species and the conservation constant have their values updated.
-    # The `≈` is because sometimes the computed values will not be fully exact.
-    for _ = 1:3
-        # Updates X2, checks the values of all species and Γ, then sets which is the old problem.
-        X2_new = rand(rng, 1.0:10.0)
-        prob_new = remake(prob_old; u0 = [:X2 => X2_new])
-        @test prob_old[:X1] ≈ prob_new[:X1]
-        @test X2_new ≈ prob_new[:X2]
-        @test prob_old[:X3] ≈ prob_new[:X3]
-        @test substitute(conserved_quantity, Dict([X1 => prob_old[X1], X2 => X2_new, X3 => prob_old[X3]])) ≈ prob_new.ps[:Γ][1]
-        prob_old = prob_new
-
-        # Updates X3, checks the values of all species and Γ, then sets which is the old problem.
-        X3_new = rand(rng, 1.0:10.0)
-        prob_new = remake(prob_old; u0 = [:X3 => X3_new])
-        @test prob_old[:X1] ≈ prob_new[:X1]
-        @test prob_old[:X2] ≈ prob_new[:X2]
-        @test X3_new ≈ prob_new[:X3]
-        @test substitute(conserved_quantity, Dict([X1 => prob_old[X1], X2 => prob_old[X2], X3 => X3_new])) ≈ prob_new.ps[:Γ][1]
-        prob_old = prob_new
-    end
-
-    # Similarly, but now also updates the conservation constant. Here, once Γ has been updated:
-    # - The conservation law constant will be kept fixed, and secondary updates are made to the
-    # eliminated species.
-    # Assumes that X3 is the eliminated species.
-    # The random Γ is ensured to be large enough not to generate negative values in the eliminated species.
-    for _ in 1:3
-        # Updates Γ, checks the values of all species and Γ, then sets which is the old problem.
-        Γ_new = substitute(conserved_quantity, Dict([X1 => prob_old[X1], X2 => prob_old[X2], X3 => 0])) + rand(rng, 0.0:5.0)
-        prob_new = remake(prob_old; p = [:Γ => [Γ_new]], warn_initialize_determined = false)
-        @test prob_old[:X1] ≈ prob_new[:X1]
-        @test prob_old[:X2] ≈ prob_new[:X2]
-        @test Γ_new ≈ prob_new.ps[:Γ][1]
-        @test substitute(conserved_quantity, Dict([X1 => prob_old[X1], X2 => prob_old[X2], X3 => prob_new[X3]])) ≈ prob_new.ps[:Γ][1]
-        prob_old = prob_new
-
-        # Updates X1 (non-eliminated species), checks the values of all species and Γ, then sets which is the old problem.
-        # Note that now, `X3` will have its value modified (not and `Γ` remains unchanged).
-        X1_new = rand(rng, 1.0:10.0)
-        prob_new = remake(prob_old; u0 = [:X1 => X1_new])
-        @test X1_new ≈ prob_new[:X1]
-        @test prob_old[:X2] ≈ prob_new[:X2]
-        @test prob_old.ps[:Γ][1] ≈ prob_new.ps[:Γ][1]
-        @test substitute(conserved_quantity, Dict([X1 => X1_new, X2 => prob_old[X2], X3 => prob_new[X3]])) ≈ prob_new.ps[:Γ][1]
-        prob_old = prob_new
-
-        # Updates X3 (the eliminated species). Right now, this will have no effect on `X3` (or the system).
-        X3_new = rand(rng, 1.0:10.0)
-        prob_new = remake(prob_old; u0 = [:X3 => X3_new], warn_initialize_determined = false)
-        @test prob_old[:X1] ≈ prob_new[:X1]
-        @test prob_old[:X2] ≈ prob_new[:X2]
-        @test prob_old[:X3] ≈ prob_new[:X3]
-        @test prob_old.ps[:Γ][1] ≈ prob_new.ps[:Γ][1]
-        prob_old = prob_new
+    # Loops through the tests for different problem types.
+    oprob_old = ODEProblem(rn, u0, 1.0, ps; remove_conserved = true)
+    sprob_old = SDEProblem(rn, u0, 1.0, ps; remove_conserved = true)
+
+    for prob in [oprob_old, sprob_old]
+        prob_old = prob
+        
+        # For a couple of iterations, updates the problem, ensuring that when a species is updated:
+        # - Only that species and the conservation constant have their values updated.
+        # The `≈` is because sometimes the computed values will not be fully exact.
+        for _ = 1:3
+            # Updates X2, checks the values of all species and Γ, then sets which is the old problem.
+            X2_new = rand(rng, 1.0:10.0)
+            prob_new = remake(prob_old; u0 = [:X2 => X2_new])
+            @test prob_old[:X1] ≈ prob_new[:X1]
+            @test X2_new ≈ prob_new[:X2]
+            @test prob_old[:X3] ≈ prob_new[:X3]
+            @test substitute(conserved_quantity, Dict([X1 => prob_old[X1], X2 => X2_new, X3 => prob_old[X3]])) ≈ prob_new.ps[:Γ][1]
+            prob_old = prob_new
+
+            # Updates X3, checks the values of all species and Γ, then sets which is the old problem.
+            X3_new = rand(rng, 1.0:10.0)
+            prob_new = remake(prob_old; u0 = [:X3 => X3_new])
+            @test prob_old[:X1] ≈ prob_new[:X1]
+            @test prob_old[:X2] ≈ prob_new[:X2]
+            @test X3_new ≈ prob_new[:X3]
+            @test substitute(conserved_quantity, Dict([X1 => prob_old[X1], X2 => prob_old[X2], X3 => X3_new])) ≈ prob_new.ps[:Γ][1]
+            prob_old = prob_new
+        end
+
+        # Similarly, but now also updates the conservation constant. Here, once Γ has been updated:
+        # - The conservation law constant will be kept fixed, and secondary updates are made to the
+        # eliminated species.
+        # Assumes that X3 is the eliminated species.
+        # The random Γ is ensured to be large enough not to generate negative values in the eliminated species.
+        for _ in 1:3
+            # Updates Γ, checks the values of all species and Γ, then sets which is the old problem.
+            Γ_new = substitute(conserved_quantity, Dict([X1 => prob_old[X1], X2 => prob_old[X2], X3 => 0])) + rand(rng, 0.0:5.0)
+            prob_new = remake(prob_old; p = [:Γ => [Γ_new]], warn_initialize_determined = false)
+            @test prob_old[:X1] ≈ prob_new[:X1]
+            @test prob_old[:X2] ≈ prob_new[:X2]
+            @test Γ_new ≈ prob_new.ps[:Γ][1]
+            @test substitute(conserved_quantity, Dict([X1 => prob_old[X1], X2 => prob_old[X2], X3 => prob_new[X3]])) ≈ prob_new.ps[:Γ][1]
+            prob_old = prob_new
+
+            # Updates X1 (non-eliminated species), checks the values of all species and Γ, then sets which is the old problem.
+            # Note that now, `X3` will have its value modified (not and `Γ` remains unchanged).
+            X1_new = rand(rng, 1.0:10.0)
+            prob_new = remake(prob_old; u0 = [:X1 => X1_new])
+            @test X1_new ≈ prob_new[:X1]
+            @test prob_old[:X2] ≈ prob_new[:X2]
+            @test prob_old.ps[:Γ][1] ≈ prob_new.ps[:Γ][1]
+            @test substitute(conserved_quantity, Dict([X1 => X1_new, X2 => prob_old[X2], X3 => prob_new[X3]])) ≈ prob_new.ps[:Γ][1]
+            prob_old = prob_new
+
+            # Updates X3 (the eliminated species). Right now, this will have no effect on `X3` (or the system).
+            X3_new = rand(rng, 1.0:10.0)
+            prob_new = remake(prob_old; u0 = [:X3 => X3_new], warn_initialize_determined = false)
+            @test prob_old[:X1] ≈ prob_new[:X1]
+            @test prob_old[:X2] ≈ prob_new[:X2]
+            @test prob_old[:X3] ≈ prob_new[:X3]
+            @test prob_old.ps[:Γ][1] ≈ prob_new.ps[:Γ][1]
+            prob_old = prob_new
+        end
     end
 end