set the built module for the functions to fix precompilation

ChrisRackauckas · ChrisRackauckas · commit 05483eb863d3 · 2022-08-20T18:43:45.000-04:00
diff --git a/lib/JumpProblemLibrary/Project.toml b/lib/JumpProblemLibrary/Project.toml
@@ -1,6 +1,6 @@
 name = "JumpProblemLibrary"
 uuid = "faf0f6d7-8cee-47cb-b27c-1eb80cef534e"
-version = "0.1.1"
+version = "0.1.2"
 
 [deps]
 Catalyst = "479239e8-5488-4da2-87a7-35f2df7eef83"
diff --git a/lib/JumpProblemLibrary/src/JumpProblemLibrary.jl b/lib/JumpProblemLibrary/src/JumpProblemLibrary.jl
@@ -40,14 +40,15 @@ end k1 k2 k3 k4 k5 k6
 rates = [0.5, (20 * log(2.0) / 120.0), (log(2.0) / 120.0), (log(2.0) / 600.0), 0.025, 1.0]
 tf = 1000.0
 u0 = [1, 0, 0, 0]
-prob = DiscreteProblem(dna_rs, u0, (0.0, tf), rates)
+prob = DiscreteProblem(dna_rs, u0, (0.0, tf), rates, eval_module = @__MODULE__)
 Nsims = 8000
 expected_avg = 5.926553750000000e+02
 prob_data = Dict("num_sims_for_mean" => Nsims, "expected_mean" => expected_avg)
 """
     DNA negative feedback autoregulatory model. Protein acts as repressor.
 """
-prob_jump_dnarepressor = JumpProblemNetwork(dna_rs, rates, tf, u0, prob, prob_data)
+prob_jump_dnarepressor = JumpProblemNetwork(dna_rs, rates, tf, u0, prob, prob_data,
+                                            eval_module = @__MODULE__)
 
 bd_rs = @reaction_network begin
     k1, 0 --> A
@@ -56,14 +57,15 @@ end k1 k2
 rates = [1000.0, 10.0]
 tf = 1.0
 u0 = [0]
-prob = DiscreteProblem(bd_rs, u0, (0.0, tf), rates)
+prob = DiscreteProblem(bd_rs, u0, (0.0, tf), rates, eval_module = @__MODULE__)
 Nsims = 16000
 expected_avg = t -> rates[1] / rates[2] .* (1.0 - exp.(-rates[2] * t))
 prob_data = Dict("num_sims_for_mean" => Nsims, "expected_mean_at_t" => expected_avg)
 """
     Simple birth-death process with constant production and degradation.
 """
-prob_jump_constproduct = JumpProblemNetwork(bd_rs, rates, tf, u0, prob, prob_data)
+prob_jump_constproduct = JumpProblemNetwork(bd_rs, rates, tf, u0, prob, prob_data,
+                                            eval_module = @__MODULE__)
 
 nonlin_rs = @reaction_network begin
     k1, 2A --> B
@@ -75,14 +77,15 @@ end k1 k2 k3 k4 k5
 rates = [1.0, 2.0, 0.5, 0.75, 0.25]
 tf = 0.01
 u0 = [200, 100, 150]
-prob = DiscreteProblem(nonlin_rs, u0, (0.0, tf), rates)
+prob = DiscreteProblem(nonlin_rs, u0, (0.0, tf), rates, eval_module = @__MODULE__)
 Nsims = 32000
 expected_avg = 84.876015624999994
 prob_data = Dict("num_sims_for_mean" => Nsims, "expected_mean" => expected_avg)
 """
     Example with a mix of nonlinear reactions, including third order
 """
-prob_jump_nonlinrxs = JumpProblemNetwork(nonlin_rs, rates, tf, u0, prob, prob_data)
+prob_jump_nonlinrxs = JumpProblemNetwork(nonlin_rs, rates, tf, u0, prob, prob_data,
+                                         eval_module = @__MODULE__)
 
 oscil_rs = @reaction_network begin
     0.01, (X, Y, Z) --> 0
@@ -98,11 +101,12 @@ oscil_rs = @reaction_network begin
 end
 u0 = [200.0, 60.0, 120.0, 100.0, 50.0, 50.0, 50.0]  # Hill equations force use of floats!
 tf = 4000.0
-prob = DiscreteProblem(oscil_rs, u0, (0.0, tf))
+prob = DiscreteProblem(oscil_rs, u0, (0.0, tf), eval_module = @__MODULE__)
 """
     Oscillatory system, uses a mixture of jump types.
 """
-prob_jump_osc_mixed_jumptypes = JumpProblemNetwork(oscil_rs, nothing, tf, u0, prob, nothing)
+prob_jump_osc_mixed_jumptypes = JumpProblemNetwork(oscil_rs, nothing, tf, u0, prob, nothing,
+                                                   eval_module = @__MODULE__)
 
 specs_sym_to_name = Dict(:S1 => "R(a,l)",
                          :S2 => "L(r)",
@@ -144,7 +148,7 @@ u0[findfirst(isequal(:S1), statesyms)] = params[1]
 u0[findfirst(isequal(:S2), statesyms)] = params[2]
 u0[findfirst(isequal(:S3), statesyms)] = params[3]
 tf = 100.0
-prob = DiscreteProblem(rs, u0, (0.0, tf), rates)
+prob = DiscreteProblem(rs, u0, (0.0, tf), rates, eval_module = @__MODULE__)
 """
     Multistate model from Gupta and Mendes,
     "An Overview of Network-Based and -Free Approaches for Stochastic Simulation of Biochemical Systems",
@@ -154,7 +158,8 @@ prob = DiscreteProblem(rs, u0, (0.0, tf), rates)
 prob_jump_multistate = JumpProblemNetwork(rs, rates, tf, u0, prob,
                                           Dict("specs_to_sym_name" => specs_sym_to_name,
                                                "rates_sym_to_idx" => rates_sym_to_idx,
-                                               "params" => params))
+                                               "params" => params),
+                                               eval_module = @__MODULE__)
 
 # generate the network
 N = 10  # number of genes
@@ -206,13 +211,14 @@ for i in 1:(2 * N)
     u0[findfirst(isequal(G[i]), states(rs))] = 1
 end
 tf = 2000.0
-prob = DiscreteProblem(rs, u0, (0.0, tf))
+prob = DiscreteProblem(rs, u0, (0.0, tf), eval_module = @__MODULE__)
 """
     Twenty-gene model from McCollum et al,
     "The sorting direct method for stochastic simulation of biochemical systems with varying reaction execution behavior"
     Comp. Bio. and Chem., 30, pg. 39-49 (2006).
 """
-prob_jump_twentygenes = JumpProblemNetwork(rs, nothing, tf, u0, prob, nothing)
+prob_jump_twentygenes = JumpProblemNetwork(rs, nothing, tf, u0, prob, nothing,
+                                           eval_module = @__MODULE__)
 
 rn = @reaction_network begin
     c1, G --> G + M
@@ -228,15 +234,16 @@ rnpar = [0.09, 0.05, 0.001, 0.0009, 0.00001, 0.0005, 0.005, 0.9]
 varlabels = ["G", "M", "P", "P2", "P2G"]
 u0 = [1000, 0, 0, 0, 0]
 tf = 4000.0
-prob = DiscreteProblem(rn, u0, (0.0, tf), rnpar)
+prob = DiscreteProblem(rn, u0, (0.0, tf), rnpar, eval_module = @__MODULE__)
 """
     Negative feedback autoregulatory gene expression model. Dimer is the repressor.
     Taken from Marchetti, Priami and Thanh,
     "Simulation Algorithms for Comptuational Systems Biology",
     Springer (2017).
 """
 prob_jump_dnadimer_repressor = JumpProblemNetwork(rn, rnpar, tf, u0, prob,
-                                                  Dict("specs_names" => varlabels))
+                                                  Dict("specs_names" => varlabels),
+                                                  eval_module = @__MODULE__)
 
 # diffusion model
 function getDiffNetwork(N)
@@ -265,6 +272,6 @@ tf = 10.0
     u0 is a similar function that returns the initial condition vector.
 """
 prob_jump_diffnetwork = JumpProblemNetwork(getDiffNetwork, params, tf, getDiffu0, nothing,
-                                           nothing)
+                                           nothing, eval_module = @__MODULE__)
 
 end # module
diff --git a/lib/ODEProblemLibrary/Project.toml b/lib/ODEProblemLibrary/Project.toml
@@ -1,6 +1,6 @@
 name = "ODEProblemLibrary"
 uuid = "fdc4e326-1af4-4b90-96e7-779fcce2daa5"
-version = "0.1.1"
+version = "0.1.2"
 
 [deps]
 DiffEqBase = "2b5f629d-d688-5b77-993f-72d75c75574e"
diff --git a/lib/ODEProblemLibrary/src/ode_simple_nonlinear_prob.jl b/lib/ODEProblemLibrary/src/ode_simple_nonlinear_prob.jl
@@ -55,7 +55,7 @@ D = Differential(t)
 eqs = [D(y) ~ μ * ((1 - x^2) * y - x),
     D(x) ~ y]
 de = ODESystem(eqs; name = :van_der_pol)
-van = ODEFunction(de, [y, x], [μ], jac = true)
+van = ODEFunction(de, [y, x], [μ], jac = true, eval_module = @__MODULE__)
 
 """
 Van der Pol Equations
@@ -97,7 +97,7 @@ eqs = [D(y₁) ~ -k₁ * y₁ + k₃ * y₂ * y₃,
     D(y₂) ~ k₁ * y₁ - k₂ * y₂^2 - k₃ * y₂ * y₃,
     D(y₃) ~ k₂ * y₂^2]
 de = ODESystem(eqs; name = :rober)
-rober = ODEFunction(de, [y₁, y₂, y₃], [k₁, k₂, k₃], jac = true)
+rober = ODEFunction(de, [y₁, y₂, y₃], [k₁, k₂, k₃], jac = true, eval_module = @__MODULE__)
 
 """
 The Robertson biochemical reactions: (Stiff)
@@ -178,7 +178,7 @@ eqs = [D(y₁) ~ I₁ * y₂ * y₃,
     D(y₂) ~ I₂ * y₁ * y₃,
     D(y₃) ~ I₃ * y₁ * y₂]
 de = ODESystem(eqs; name = :rigid_body)
-rigid = ODEFunction(de, [y₁, y₂, y₃], [I₁, I₂, I₃], jac = true)
+rigid = ODEFunction(de, [y₁, y₂, y₃], [I₁, I₂, I₃], jac = true, eval_module = @__MODULE__)
 
 """
 Rigid Body Equations (Non-stiff)
@@ -372,7 +372,7 @@ u0[8] = 0.0057
 """
 Hires Problem (Stiff)
 
-It is in the form of 
+It is in the form of
 
 ```math
 \\frac{dy}{dt} = f(y)
@@ -388,7 +388,7 @@ where ``f`` is defined by
 
 ``f(y) = \\begin{pmatrix} −1.71y_1 & +0.43y_2 & +8.32y_3 & +0.0007y_4 & \\\\ 1.71y_1 & −8.75y_2 & & & \\\\ −10.03y_3 & +0.43y_4 & +0.035y_5 & & \\\\ 8.32y_2 & +1.71y_3 & −1.12y_4 & & \\\\ −1.745y_5 & +0.43y_6 & +0.43y_7 & & \\\\ −280y_6y_8 & +0.69y_4 & +1.71y_5 & −0.43y_6 & +0.69y_7 \\\\ 280y_6y_8 & −1.81y_7 & & & \\\\ −280y_6y_8 & +1.81y_7 & & & \\end{pmatrix}``
 
-Reference: [demohires.pdf](http://www.radford.edu/~thompson/vodef90web/problems/demosnodislin/Demos_Pitagora/DemoHires/demohires.pdf)  
+Reference: [demohires.pdf](http://www.radford.edu/~thompson/vodef90web/problems/demosnodislin/Demos_Pitagora/DemoHires/demohires.pdf)
 Notebook: [Hires.ipynb](http://nbviewer.jupyter.org/github/JuliaDiffEq/DiffEqBenchmarks.jl/blob/master/StiffODE/Hires.ipynb)
 """
 prob_ode_hires = ODEProblem(hires, u0, (0.0, 321.8122),
@@ -404,7 +404,7 @@ eqs = [D(y1) ~ p1 * (y2 + y1 * (1 - p2 * y1 - y2)),
     D(y3) ~ p3 * (y1 - y3)]
 de = ODESystem(eqs; name = :orego)
 jac = calculate_jacobian(de)
-orego = ODEFunction(de, [y1, y2, y3], [p1, p2, p3], jac = true)
+orego = ODEFunction(de, [y1, y2, y3], [p1, p2, p3], jac = true, eval_module = @__MODULE__)
 
 """
 Orego Problem (Stiff)
diff --git a/lib/SDEProblemLibrary/Project.toml b/lib/SDEProblemLibrary/Project.toml
@@ -1,6 +1,6 @@
 name = "SDEProblemLibrary"
 uuid = "c72e72a9-a271-4b2b-8966-303ed956772e"
-version = "0.1.1"
+version = "0.1.2"
 
 [deps]
 Catalyst = "479239e8-5488-4da2-87a7-35f2df7eef83"
diff --git a/lib/SDEProblemLibrary/src/SDEProblemLibrary.jl b/lib/SDEProblemLibrary/src/SDEProblemLibrary.jl
@@ -506,6 +506,6 @@ p = (0.01, 3.0, 3.0, 4.5, 2.0, 15.0, 20.0, 0.005, 0.01, 0.05)
 An oscillatory chemical reaction system
 """
 prob_sde_oscilreact = SDEProblem(network, [200.0, 60.0, 120.0, 100.0, 50.0, 50.0, 50.0],
-                                 (0.0, 4000.0), p)
+                                 (0.0, 4000.0), p, eval_module = @__MODULE__)
 
 end # module
