pkg

Manifest.toml

@@ -2,7 +2,7 @@
 
 julia_version = "1.11.2"
 manifest_format = "2.0"
-project_hash = "bae0226fd7bb98a46eb96d2e9ced0f3a76d44c4a"
+project_hash = "7f95fd10ab0dcaae8174877b8b813d1c583b0887"
 
 [[deps.ADTypes]]
 git-tree-sha1 = "72af59f5b8f09faee36b4ec48e014a79210f2f4f"
@@ -577,6 +577,11 @@ weakdeps = ["ChainRulesCore", "EnzymeCore"]
     DispatchDoctorChainRulesCoreExt = "ChainRulesCore"
     DispatchDoctorEnzymeCoreExt = "EnzymeCore"
 
+[[deps.DisplayAs]]
+git-tree-sha1 = "43c017d5dd3a48d56486055973f443f8a39bb6d9"
+uuid = "0b91fe84-8a4c-11e9-3e1d-67c38462b6d6"
+version = "0.1.6"
+
 [[deps.Distances]]
 deps = ["LinearAlgebra", "Statistics", "StatsAPI"]
 git-tree-sha1 = "c7e3a542b999843086e2f29dac96a618c105be1d"

Project.toml

@@ -4,6 +4,7 @@ Catalyst = "479239e8-5488-4da2-87a7-35f2df7eef83"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 DiffEqCallbacks = "459566f4-90b8-5000-8ac3-15dfb0a30def"
 DifferentialEquations = "0c46a032-eb83-5123-abaf-570d42b7fbaa"
+DisplayAs = "0b91fe84-8a4c-11e9-3e1d-67c38462b6d6"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 Interpolations = "a98d9a8b-a2ab-59e6-89dd-64a1c18fca59"

docs/intro-04-gillespie.jl

@@ -8,7 +8,7 @@ using Plots
 using Interpolations
 using Statistics        ## mean()
 using Random            ## randexp()
-Random.seed!(2022)
+Random.seed!(2024)
 
 #===
 Stochastic chemical reaction: Gillespie Algorithm (direct method)
@@ -77,8 +77,8 @@ u0 = [200, 0]
 tend = 10.0
 
 # Solve the problem using both direct and first reaction method
-soldirect = ssa_direct(model, u0, tend, parameters, stoich)
-solfirst = ssa_first(model, u0, tend, parameters, stoich)
+@time soldirect = ssa_direct(model, u0, tend, parameters, stoich)
+@time solfirst = ssa_first(model, u0, tend, parameters, stoich)
 
 # Plot the solution from the direct method
 plot(soldirect.t, soldirect.u,
@@ -126,7 +126,7 @@ plot!(fig1, b_avg, 0.0, tend, linecolor=:black, linewidth=3, linestyle = :dash,
 fig1 |> display
 
 #===
-## Using Catalyst
+## Using Catalyst (recommended)
 
 [Catalyst.jl](https://github.com/SciML/Catalyst.jl) is a domain-specific language (DSL) package to solve law of mass action problems.
 ===#
@@ -156,9 +156,11 @@ plot(sol)
 # Parallel ensemble simulation
 ensprob = EnsembleProblem(jumpProb)
 sim = solve(ensprob, SSAStepper(), EnsembleThreads(); trajectories=50)
-plot(sim, alpha=0.1, color=[:blue :red])
+
 #---
+plot(sim, alpha=0.1, color=[:blue :red], fmt=:png)
 
+#---
 summ = EnsembleSummary(sim, 0:0.1:10)
 plot(summ,fillalpha=0.5)