Merge branch 'master' into test_expansion

Author: TorkelE

HISTORY.md

@@ -1,6 +1,6 @@
 name = "Catalyst"
 uuid = "479239e8-5488-4da2-87a7-35f2df7eef83"
-version = "14.0.0-DEV"
+version = "14.0.1"
 
 [deps]
 Combinatorics = "861a8166-3701-5b0c-9a16-15d98fcdc6aa"
@@ -76,6 +76,7 @@ SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
 SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
 SciMLNLSolve = "e9a6253c-8580-4d32-9898-8661bb511710"
 StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
+StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 SteadyStateDiffEq = "9672c7b4-1e72-59bd-8a11-6ac3964bc41f"
 StochasticDiffEq = "789caeaf-c7a9-5a7d-9973-96adeb23e2a0"
@@ -84,4 +85,4 @@ Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"
 
 [targets]
-test = ["BifurcationKit", "DiffEqCallbacks", "DomainSets", "Graphviz_jll", "HomotopyContinuation", "Logging", "NonlinearSolve", "OrdinaryDiffEq", "Plots", "Random", "SafeTestsets", "SciMLBase", "SciMLNLSolve", "StableRNGs", "Statistics", "SteadyStateDiffEq", "StochasticDiffEq", "StructuralIdentifiability", "Test", "Unitful"]
+test = ["BifurcationKit", "DiffEqCallbacks", "DomainSets", "Graphviz_jll", "HomotopyContinuation", "Logging", "NonlinearSolve", "OrdinaryDiffEq", "Plots", "Random", "SafeTestsets", "SciMLBase", "SciMLNLSolve", "StableRNGs", "StaticArrays", "Statistics", "SteadyStateDiffEq", "StochasticDiffEq", "StructuralIdentifiability", "Test", "Unitful"]

Project.toml

@@ -1,10 +1,10 @@
 # Catalyst.jl
 
-[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://docs.sciml.ai/Catalyst/stable/)
-[![API Stable](https://img.shields.io/badge/API-stable-blue.svg)](https://docs.sciml.ai/Catalyst/stable/api/catalyst_api/)
-[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://docs.sciml.ai/Catalyst/dev/)
-[![API Dev](https://img.shields.io/badge/API-dev-blue.svg)](https://docs.sciml.ai/Catalyst/dev/api/catalyst_api/)
-[![Join the chat at https://julialang.zulipchat.com #sciml-bridged](https://img.shields.io/static/v1?label=Zulip&message=chat&color=9558b2&labelColor=389826)](https://julialang.zulipchat.com/#narrow/stream/279055-sciml-bridged)
+[![Latest Release (for users)](https://img.shields.io/badge/docs-latest_release_(for_users)-blue.svg)](https://docs.sciml.ai/Catalyst/stable/)
+[![API Latest Release (for users)](https://img.shields.io/badge/API-latest_release_(for_users)-blue.svg)](https://docs.sciml.ai/Catalyst/stable/api/catalyst_api/)
+[![Master (for developers)](https://img.shields.io/badge/docs-master_branch_(for_devs)-blue.svg)](https://docs.sciml.ai/Catalyst/dev/)
+[![API Master (for developers](https://img.shields.io/badge/API-master_branch_(for_devs)-blue.svg)](https://docs.sciml.ai/Catalyst/dev/api/catalyst_api/)
+<!--[![Join the chat at https://julialang.zulipchat.com #sciml-bridged](https://img.shields.io/static/v1?label=Zulip&message=chat&color=9558b2&labelColor=389826)](https://julialang.zulipchat.com/#narrow/stream/279055-sciml-bridged)-->
 
 [![Build Status](https://github.com/SciML/Catalyst.jl/workflows/CI/badge.svg)](https://github.com/SciML/Catalyst.jl/actions?query=workflow%3ACI)
 [![codecov.io](https://codecov.io/gh/SciML/Catalyst.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/SciML/Catalyst.jl)
@@ -25,8 +25,8 @@ large-scale simulations through auto-vectorization and parallelism. Symbolic
 `ReactionSystem`s can be used to generate ModelingToolkit-based models, allowing
 the easy simulation and parameter estimation of mass action ODE models, Chemical
 Langevin SDE models, stochastic chemical kinetics jump process models, and more.
-Generated models can be used with solvers throughout the broader
-[SciML](https://sciml.ai) ecosystem, including higher-level SciML packages (e.g.
+Generated models can be used with solvers throughout the broader Julia and
+[SciML](https://sciml.ai) ecosystems, including higher-level SciML packages (e.g.
 for sensitivity analysis, parameter estimation, machine learning applications,
 etc).
 
@@ -43,7 +43,7 @@ documentation](https://docs.sciml.ai/Catalyst/stable/). The [in-development
 documentation](https://docs.sciml.ai/Catalyst/dev/) describes unreleased features in
 the current master branch.
 
-An overview of the package, its features, and comparative benchmarking (as of version 13) can also 
+An overview of the package, its features, and comparative benchmarking (as of version 13) can also
 be found in its corresponding research paper, [Catalyst: Fast and flexible modeling of reaction networks](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011530).
 
 ## Features
@@ -69,9 +69,9 @@ be found in its corresponding research paper, [Catalyst: Fast and flexible model
 - Support for [parallelization of all simulations](https://docs.sciml.ai/Catalyst/stable/model_simulation/ode_simulation_performance/#ode_simulation_performance_parallelisation), including parallelization of [ODE simulations on GPUs](https://docs.sciml.ai/Catalyst/stable/model_simulation/ode_simulation_performance/#ode_simulation_performance_parallelisation_GPU) using [DiffEqGPU.jl](https://github.com/SciML/DiffEqGPU.jl).
 - [Latexify](https://korsbo.github.io/Latexify.jl/stable/) can be used to [generate LaTeX expressions](https://docs.sciml.ai/Catalyst/stable/model_creation/model_visualisation/#visualisation_latex) corresponding to generated mathematical models or the underlying set of reactions.
 - [Graphviz](https://graphviz.org/) can be used to generate and [visualize reaction network graphs](https://docs.sciml.ai/Catalyst/stable/model_creation/model_visualisation/#visualisation_graphs) (reusing the Graphviz interface created in [Catlab.jl](https://algebraicjulia.github.io/Catlab.jl/stable/)).
-- Model steady states can be computed through homotopy continuation using [HomotopyContinuation.jl](https://github.com/JuliaHomotopyContinuation/HomotopyContinuation.jl) (which can find *all* steady states of systems with multiple ones), by forward ODE simulations using [SteadyStateDiffEq.jl](https://github.com/SciML/SteadyStateDiffEq.jl), or by numerically solving steady-state nonlinear equations using [NonlinearSolve.jl](https://github.com/SciML/NonlinearSolve.jl).
+- Model steady states can be [computed through homotopy continuation](https://docs.sciml.ai/Catalyst/stable/steady_state_functionality/homotopy_continuation/) using [HomotopyContinuation.jl](https://github.com/JuliaHomotopyContinuation/HomotopyContinuation.jl) (which can find *all* steady states of systems with multiple ones), by [forward ODE simulations](https://docs.sciml.ai/Catalyst/stable/steady_state_functionality/nonlinear_solve/#steady_state_solving_simulation) using [SteadyStateDiffEq.jl](https://github.com/SciML/SteadyStateDiffEq.jl), or by [numerically solving steady-state nonlinear equations](https://docs.sciml.ai/Catalyst/stable/steady_state_functionality/nonlinear_solve/#steady_state_solving_nonlinear) using [NonlinearSolve.jl](https://github.com/SciML/NonlinearSolve.jl).
 - [BifurcationKit.jl](https://github.com/bifurcationkit/BifurcationKit.jl) can be used to [compute bifurcation diagrams](https://docs.sciml.ai/Catalyst/stable/steady_state_functionality/bifurcation_diagrams/) of model steady states (including finding periodic orbits).
-- [DynamicalSystems.jl](https://github.com/JuliaDynamics/DynamicalSystems.jl) can be used to compute model [basins of attraction](https://docs.sciml.ai/Catalyst/stable/steady_state_functionality/dynamical_systems/#dynamical_systems_basins_of_attraction) and [Lyapunov spectrums](https://docs.sciml.ai/Catalyst/stable/steady_state_functionality/dynamical_systems/#dynamical_systems_lyapunov_exponents).
+- [DynamicalSystems.jl](https://github.com/JuliaDynamics/DynamicalSystems.jl) can be used to compute model [basins of attraction](https://docs.sciml.ai/Catalyst/stable/steady_state_functionality/dynamical_systems/#dynamical_systems_basins_of_attraction), [Lyapunov spectrums](https://docs.sciml.ai/Catalyst/stable/steady_state_functionality/dynamical_systems/#dynamical_systems_lyapunov_exponents), and other dynamical system properties.
 - [StructuralIdentifiability.jl](https://github.com/SciML/StructuralIdentifiability.jl) can be used to [perform structural identifiability analysis](https://docs.sciml.ai/Catalyst/stable/inverse_problems/structural_identifiability/).
 - [Optimization.jl](https://github.com/SciML/Optimization.jl), [DiffEqParamEstim.jl](https://github.com/SciML/DiffEqParamEstim.jl), and [PEtab.jl](https://github.com/sebapersson/PEtab.jl) can all be used to [fit model parameters to data](https://sebapersson.github.io/PEtab.jl/stable/Define_in_julia/).
 - [GlobalSensitivity.jl](https://github.com/SciML/GlobalSensitivity.jl) can be used to perform [global sensitivity analysis](https://docs.sciml.ai/Catalyst/stable/inverse_problems/global_sensitivity_analysis/) of model behaviors.
@@ -88,7 +88,7 @@ be found in its corresponding research paper, [Catalyst: Fast and flexible model
 ## Illustrative example
 
 #### Deterministic ODE simulation of Michaelis-Menten enzyme kinetics
-Here we show a simple example where a model is created using the Catalyst DSL, and then simulated as 
+Here we show a simple example where a model is created using the Catalyst DSL, and then simulated as
 an ordinary differential equation.
 
 ```julia
@@ -129,14 +129,15 @@ plot(jump_sol; lw = 2)
 ![Jump simulation](docs/src/assets/readme_jump_plot.svg)
 
 
-## Elaborate example
-In the above example, we used basic Catalyst-based workflows to simulate a simple model. Here we 
-instead show how various Catalyst features can compose to create a much more advanced model. Our 
-model describes how the volume of a cell ($V$) is affected by a growth factor ($G$). The growth 
-factor only promotes growth while in its phosphorylated form ($Gᴾ$). The phosphorylation of $G$ 
-($G \to Gᴾ$) is promoted by sunlight (modeled as the cyclic sinusoid $kₐ (sin(t) + 1)$), which
-phosphorylates the growth factor (producing $Gᴾ$). When the cell reaches a critical volume ($Vₘ$)
-it undergoes cell division. First, we declare our model:
+## More elaborate example
+In the above example, we used basic Catalyst workflows to simulate a simple
+model. Here we instead show how various Catalyst features can compose to create
+a much more advanced model. Our model describes how the volume of a cell ($V$)
+is affected by a growth factor ($G$). The growth factor only promotes growth
+while in its phosphorylated form ($G^P$). The phosphorylation of $G$ ($G \to G^P$)
+is promoted by sunlight (modeled as the cyclic sinusoid $k_a (\sin(t) + 1)$),
+which phosphorylates the growth factor (producing $G^P$). When the cell reaches a
+critical volume ($V_m$) it undergoes cell division. First, we declare our model:
 ```julia
 using Catalyst
 cell_model = @reaction_network begin
@@ -151,22 +152,22 @@ cell_model = @reaction_network begin
     kᵢ/V, Gᴾ --> G
 end
 ```
-We now study the system as a Chemical Langevin Dynamics SDE model, which can be generated as follows 
+We now study the system as a Chemical Langevin Dynamics SDE model, which can be generated as follows
 ```julia
 u0 = [:V => 25.0, :G => 50.0, :Gᴾ => 0.0]
 tspan = (0.0, 20.0)
 ps = [:Vₘ => 50.0, :g => 0.3, :kₚ => 100.0, :kᵢ => 60.0]
 sprob = SDEProblem(cell_model, u0, tspan, ps)
 ```
-This produces the following equations:
+This problem encodes the following stochastic differential equation model:
 ```math
 \begin{align*}
-dG(t) &=  - \left( \frac{kₚ*(sin(t)+1)}{V(t)} G(t) + \frac{kᵢ}{V(t)} Gᴾ(t) \right) dt - \sqrt{\frac{kₚ*(sin(t)+1)}{V(t)} G(t)} dW_1(t) + \sqrt{\frac{kᵢ}{V(t)} Gᴾ(t)} dW_2(t) \\
-dGᴾ(t) &= \left( \frac{kₚ*(sin(t)+1)}{V(t)} G(t) - \frac{kᵢ}{V(t)} Gᴾ(t) \right) dt + \sqrt{\frac{kₚ*(sin(t)+1)}{V(t)} G(t)} dW_1(t) - \sqrt{\frac{kᵢ}{V(t)} Gᴾ(t)} dW_2(t) \\
-dV(t) &= \left(g \cdot Gᴾ(t)\right) dt
+dG(t) &=  - \left( \frac{k_p(\sin(t)+1)}{V(t)} G(t) + \frac{k_i}{V(t)} G^P(t) \right) dt - \sqrt{\frac{k_p (\sin(t)+1)}{V(t)} G(t)} \, dW_1(t) + \sqrt{\frac{k_i}{V(t)} G^P(t)} \, dW_2(t) \\
+dG^P(t) &= \left( \frac{k_p(\sin(t)+1)}{V(t)} G(t) - \frac{k_i}{V(t)} G^P(t) \right) dt + \sqrt{\frac{k_p (\sin(t)+1)}{V(t)} G(t)} \, dW_1(t) - \sqrt{\frac{k_i}{V(t)} G^P(t)} \, dW_2(t) \\
+dV(t) &= \left(g \, G^P(t)\right) dt
 \end{align*}
 ```
-where the $dW_1(t)$ and $dW_2(t)$ terms represent independent Brownian Motions,  encoding the noise added by the Chemical Langevin Equation. Finally, we can simulate and plot the results.
+where the $dW_1(t)$ and $dW_2(t)$ terms represent independent Brownian Motions, encoding the noise added by the Chemical Langevin Equation. Finally, we can simulate and plot the results.
 ```julia
 using StochasticDiffEq, Plots
 sol = solve(sprob, EM(); dt = 0.05)
@@ -181,15 +182,14 @@ Some features we used here:
 - The model simulation was [plotted using Plots.jl](https://docs.sciml.ai/Catalyst/stable/model_simulation/simulation_plotting/).
 
 ## Getting help or getting involved
-Catalyst developers are active on the [Julia Discourse](https://discourse.julialang.org/), 
-the [Julia Slack](https://julialang.slack.com) channels \#sciml-bridged and \#sciml-sysbio, and the 
-[Julia Zulip sciml-bridged channel](https://julialang.zulipchat.com/#narrow/stream/279055-sciml-bridged). 
+Catalyst developers are active on the [Julia Discourse](https://discourse.julialang.org/) and
+the [Julia Slack](https://julialang.slack.com) channels \#sciml-bridged and \#sciml-sysbio.
 For bugs or feature requests, [open an issue](https://github.com/SciML/Catalyst.jl/issues).
 
 ## Supporting and citing Catalyst.jl
-The software in this ecosystem was developed as part of academic research. If you would like to help 
-support it, please star the repository as such metrics may help us secure funding in the future. If 
-you use Catalyst as part of your research, teaching, or other activities, we would be grateful if you 
+The software in this ecosystem was developed as part of academic research. If you would like to help
+support it, please star the repository as such metrics may help us secure funding in the future. If
+you use Catalyst as part of your research, teaching, or other activities, we would be grateful if you
 could cite our work:
 ```
 @article{CatalystPLOSCompBio2023,
@@ -205,4 +205,4 @@ could cite our work:
  pages = {1-19},
  number = {10},
 }
-```
+```

README.md

@@ -55,7 +55,7 @@ ModelingToolkit = "9.16.0"
 NonlinearSolve = "3.12"
 Optim = "1.9"
 Optimization = "3.25"
-OptimizationBBO = "0.2.1"
+OptimizationBBO = "0.3"
 OptimizationNLopt = "0.2.1"
 OptimizationOptimJL = "0.3.1"
 OptimizationOptimisers = "0.2.1"

docs/Project.toml

@@ -2,7 +2,8 @@ pages = Any[
     "Home" => "index.md",
     "Introduction to Catalyst" => Any[
         "introduction_to_catalyst/catalyst_for_new_julia_users.md",
-        "introduction_to_catalyst/introduction_to_catalyst.md"
+        "introduction_to_catalyst/introduction_to_catalyst.md",
+        "introduction_to_catalyst/math_models_intro.md"
     ],
     "Model Creation and Properties" => Any[
         "model_creation/dsl_basics.md",
@@ -27,7 +28,8 @@ pages = Any[
         "model_simulation/simulation_plotting.md",
         "model_simulation/simulation_structure_interfacing.md",
         "model_simulation/ensemble_simulations.md",
-        "model_simulation/ode_simulation_performance.md"
+        "model_simulation/ode_simulation_performance.md",
+        "model_simulation/sde_simulation_performance.md"
     ],
     "Steady state analysis" => Any[
         "steady_state_functionality/homotopy_continuation.md",

docs/pages.jl

@@ -77,6 +77,7 @@ plot(p1, p2, p3; layout = (3,1))
 
 ```@docs
 @reaction_network
+@network_component
 make_empty_network
 @reaction
 Reaction