additional_tests

test/custom_functions.jl

@@ -40,4 +40,95 @@ for factor in [1e-2, 1e-1, 1e0, 1e1, 1e2]
     @test all(abs.(f1(u0,p,t) .- f2(u0,p,t)) .< 10e-10)
     @test all(abs.(f1.jac(u0,p,t) .- f2.jac(u0,p,t)) .< 10e-10)
     @test all(abs.(g1(u0,p,t) .- g2(u0,p,t)) .< 10e-10)
-end
+end
+
+### Tests that the various notations gives identical results ###
+
+# Michaelis-Menten function.
+mm_network = @reaction_network begin
+    (1.,1.), 0 ↔ X
+    mm(X,v,K), 0 --> X1
+    Mm(X,v,K), 0 --> X2
+    MM(X,v,K), 0 --> X3
+end v K
+f_mm = ODEFunction(convert(ODESystem,mm_network),jac=true)
+
+u0 = 10*rand(rng,length(get_states(mm_network)))
+p = 10*rand(rng,length(get_ps(mm_network)))
+t = 10*rand(rng)
+
+f_mm_output = f_mm(u0,p,t)[2:end]
+f_mm_jac_output = f_mm.jac(u0,p,t)[2:end,1]
+@test (maximum(f_mm_output) - minimum(f_mm_output)) .< 100*eps()
+@test (maximum(f_mm_jac_output) - minimum(f_mm_jac_output)) .< 100*eps()
+
+# Repressing Michaelis-Menten function.
+mmR_network = @reaction_network begin
+    (1.,1.), 0 ↔ X
+    mmR(X,v,K), 0 --> X1
+    MmR(X,v,K), 0 --> X2
+    MMR(X,v,K), 0 --> X3
+end v K
+f_mmR = ODEFunction(convert(ODESystem,mmR_network),jac=true)
+
+u0 = 10*rand(rng,length(get_states(mmR_network)))
+p = 10*rand(rng,length(get_ps(mmR_network)))
+t = 10*rand(rng)
+
+f_mmR_output = f_mmR(u0,p,t)[2:end]
+f_mmR_jac_output = f_mmR.jac(u0,p,t)[2:end,1]
+@test (maximum(f_mmR_output) - minimum(f_mmR_output)) .< 100*eps()
+@test (maximum(f_mmR_jac_output) - minimum(f_mmR_jac_output)) .< 100*eps()
+
+# Hill function.
+hill_network = @reaction_network begin
+    (1.,1.), 0 ↔ X
+    hill(X,v,K,2), 0 --> X1
+    Hill(X,v,K,2), 0 --> X2
+end v K
+f_hill = ODEFunction(convert(ODESystem,hill_network),jac=true)
+
+u0 = 10*rand(rng,length(get_states(hill_network)))
+p = 10*rand(rng,length(get_ps(hill_network)))
+t = 10*rand(rng)
+
+f_hill_output = f_hill(u0,p,t)[2:end]
+f_hill_jac_output = f_hill.jac(u0,p,t)[2:end,1]
+@test (maximum(f_hill_output) - minimum(f_hill_output)) .< 100*eps()
+@test (maximum(f_hill_jac_output) - minimum(f_hill_jac_output)) .< 100*eps()
+
+# Repressing Hill function.
+hillR_network = @reaction_network begin
+    (1.,1.), 0 ↔ X
+    hillR(X,v,K,2), 0 --> X1
+    HillR(X,v,K,2), 0 --> X2
+end v K
+f_hillR = ODEFunction(convert(ODESystem,hillR_network),jac=true)
+
+u0 = 10*rand(rng,length(get_states(hillR_network)))
+p = 10*rand(rng,length(get_ps(hillR_network)))
+t = 10*rand(rng)
+
+f_hillR_output = f_hillR(u0,p,t)[2:end]
+f_hillR_jac_output = f_hillR.jac(u0,p,t)[2:end,1]
+@test (maximum(f_hillR_output) - minimum(f_hillR_output)) .< 100*eps()
+@test (maximum(f_hillR_jac_output) - minimum(f_hillR_jac_output)) .< 100*eps()
+
+# Activation/repressing Hill function.
+hillAR_network = @reaction_network begin
+    (1.,1.), 0 ↔ (X,Y)
+    hillC(X,Y,v,K,2), 0 --> X1
+    HillC(X,Y,v,K,2), 0 --> X2
+    hillAR(X,Y,v,K,2), 0 --> X3
+    HillAR(X,Y,v,K,2), 0 --> X4
+end v K
+f_hillAR = ODEFunction(convert(ODESystem,hillAR_network),jac=true)
+
+u0 = 10*rand(rng,length(get_states(hillAR_network)))
+p = 10*rand(rng,length(get_ps(hillAR_network)))
+t = 10*rand(rng)
+
+f_hillAR_output = f_hillAR(u0,p,t)[3:end]
+f_hillAR_jac_output = f_hillAR.jac(u0,p,t)[3:end,1]
+@test (maximum(f_hillAR_output) - minimum(f_hillAR_output)) .< 100*eps()
+@test (maximum(f_hillAR_jac_output) - minimum(f_hillAR_jac_output)) .< 100*eps()