From 203a7eb84dfc0d892e1e4f32386ff9734370ac88 Mon Sep 17 00:00:00 2001 From: Torkel Date: Wed, 22 May 2024 17:58:36 -0400 Subject: [PATCH 1/2] init --- docs/src/model_creation/parametric_stoichiometry.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/docs/src/model_creation/parametric_stoichiometry.md b/docs/src/model_creation/parametric_stoichiometry.md index 434002f72c..0788f01125 100644 --- a/docs/src/model_creation/parametric_stoichiometry.md +++ b/docs/src/model_creation/parametric_stoichiometry.md @@ -58,6 +58,7 @@ stoichiometries `(F,2*H,2)`. Let's now convert `revsys` to ODEs and look at the resulting equations: ```@example s1 osys = convert(ODESystem, revsys) +osys = complete(osys) equations(osys) show(stdout, MIME"text/plain"(), equations(osys)) # hide ``` @@ -88,6 +89,7 @@ converting to an `ODESystem`). For the previous example this gives the following (different) system of ODEs ```@example s1 osys = convert(ODESystem, revsys; combinatoric_ratelaws = false) +osys = complete(osys) equations(osys) show(stdout, MIME"text/plain"(), equations(osys)) # hide ``` @@ -140,6 +142,7 @@ The parameter `b` does not need to be explicitly declared in the We next convert our network to a jump process representation ```@example s1 jsys = convert(JumpSystem, burstyrn; combinatoric_ratelaws = false) +jsys = complete(jsys) equations(jsys) show(stdout, MIME"text/plain"(), equations(jsys)) # hide ``` From 56acc1f21f3ed69cc0acefee50474da67cba9b57 Mon Sep 17 00:00:00 2001 From: Torkel Date: Wed, 29 May 2024 18:16:59 -0400 Subject: [PATCH 2/2] up --- docs/pages.jl | 2 +- docs/src/model_creation/parametric_stoichiometry.md | 3 ++- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/docs/pages.jl b/docs/pages.jl index fd9cfa18ab..b4daba47bd 100644 --- a/docs/pages.jl +++ b/docs/pages.jl @@ -12,7 +12,7 @@ pages = Any[ #"model_creation/compositional_modeling.md", #"model_creation/constraint_equations.md", # Events. - #"model_creation/parametric_stoichiometry.md",# Distributed parameters, rates, and initial conditions. + "model_creation/parametric_stoichiometry.md",# Distributed parameters, rates, and initial conditions. # Loading and writing models to files. "model_creation/model_visualisation.md", #"model_creation/network_analysis.md", diff --git a/docs/src/model_creation/parametric_stoichiometry.md b/docs/src/model_creation/parametric_stoichiometry.md index 0788f01125..a0d370ef0d 100644 --- a/docs/src/model_creation/parametric_stoichiometry.md +++ b/docs/src/model_creation/parametric_stoichiometry.md @@ -7,7 +7,7 @@ use symbolic stoichiometries, and discuss several caveats to be aware of. Let's first consider a simple reversible reaction where the number of reactants is a parameter, and the number of products is the product of two parameters. ```@example s1 -using Catalyst, Latexify, DifferentialEquations, ModelingToolkit, Plots +using Catalyst, Latexify, OrdinaryDiffEq, ModelingToolkit, Plots revsys = @reaction_network revsys begin k₊, m*A --> (m*n)*B k₋, B --> A @@ -141,6 +141,7 @@ The parameter `b` does not need to be explicitly declared in the We next convert our network to a jump process representation ```@example s1 +using JumpProcesses jsys = convert(JumpSystem, burstyrn; combinatoric_ratelaws = false) jsys = complete(jsys) equations(jsys)