Merge branch 'master' into tests-in-reactionsystems-networkanalysis

HISTORY.md

@@ -41,7 +41,18 @@
     (k1, k2), A <--> B
   end
   ```
-
+- Catalyst's network visualization capability has shifted from using Graphviz to [GraphMakie.jl](https://graph.makie.org/stable/). To use this functionality, load the GraphMakie extension by installing `Catalyst` and `GraphMakie`, along with a Makie backend like `GLMakie`. There are two new methods for visualizing graphs: `plot_network` and `plot_complexes`, which respectively display the species-reaction graph and complex graph.
+  ```julia
+  using Catalyst, GraphMakie, GLMakie
+  brusselator = @reaction_network begin
+     A, ∅ --> X
+     1, 2X + Y --> 3X
+     B, X --> Y
+     1, X --> ∅
+  end
+  plot_network(brusselator)
+  ```
+
 ## Catalyst 14.4.1
 - Support for user-defined functions on the RHS when providing coupled equations 
   for CRNs using the @equations macro. For example, the following now works: 

Project.toml

@@ -75,7 +75,6 @@ julia = "1.10"
 [extras]
 DiffEqCallbacks = "459566f4-90b8-5000-8ac3-15dfb0a30def"
 DomainSets = "5b8099bc-c8ec-5219-889f-1d9e522a28bf"
-Graphviz_jll = "3c863552-8265-54e4-a6dc-903eb78fde85"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
 NonlinearSolve = "8913a72c-1f9b-4ce2-8d82-65094dcecaec"
@@ -99,4 +98,4 @@ Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"
 
 [targets]
-test = ["DiffEqCallbacks", "DomainSets", "Graphviz_jll", "Logging", "NonlinearSolve", "OrdinaryDiffEqBDF", "OrdinaryDiffEqDefault", "OrdinaryDiffEqRosenbrock", "OrdinaryDiffEqTsit5", "OrdinaryDiffEqVerner", "Pkg", "Plots", "Random", "SafeTestsets", "SciMLBase", "SciMLNLSolve", "StableRNGs", "StaticArrays", "Statistics", "SteadyStateDiffEq", "StochasticDiffEq", "Test", "Unitful"]
+test = ["DiffEqCallbacks", "DomainSets", "Logging", "NonlinearSolve", "OrdinaryDiffEqBDF", "OrdinaryDiffEqDefault", "OrdinaryDiffEqRosenbrock", "OrdinaryDiffEqTsit5", "OrdinaryDiffEqVerner", "Pkg", "Plots", "Random", "SafeTestsets", "SciMLBase", "SciMLNLSolve", "StableRNGs", "StaticArrays", "Statistics", "SteadyStateDiffEq", "StochasticDiffEq", "Test", "Unitful"]

README.md

@@ -72,10 +72,10 @@ be found in its corresponding research paper, [Catalyst: Fast and flexible model
 - 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), [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.
 - [SciMLSensitivity.jl](https://github.com/SciML/SciMLSensitivity.jl) can be used to compute local sensitivities of functions containing forward model simulations.
+<!-- - [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/). -->
 
 #### Features of packages built upon Catalyst
 - Catalyst [`ReactionSystem`](@ref)s can be [imported from SBML files](https://docs.sciml.ai/Catalyst/stable/model_creation/model_file_loading_and_export/#Loading-SBML-files-using-SBMLImporter.jl-and-SBMLToolkit.jl) via [SBMLImporter.jl](https://github.com/sebapersson/SBMLImporter.jl) and [SBMLToolkit.jl](https://github.com/SciML/SBMLToolkit.jl), and [from BioNetGen .net files](https://docs.sciml.ai/Catalyst/stable/model_creation/model_file_loading_and_export/#file_loading_rni_net) and various stoichiometric matrix network representations using [ReactionNetworkImporters.jl](https://github.com/SciML/ReactionNetworkImporters.jl).

docs/Project.toml

@@ -18,6 +18,7 @@ Latexify = "23fbe1c1-3f47-55db-b15f-69d7ec21a316"
 LinearSolve = "7ed4a6bd-45f5-4d41-b270-4a48e9bafcae"
 Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
 ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78"
+NetworkLayout = "46757867-2c16-5918-afeb-47bfcb05e46a"
 NonlinearSolve = "8913a72c-1f9b-4ce2-8d82-65094dcecaec"
 Optim = "429524aa-4258-5aef-a3af-852621145aeb"
 Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"

docs/make.jl

@@ -1,5 +1,7 @@
 using Documenter
 using Catalyst, ModelingToolkit
+# Add packages for plotting
+using GraphMakie, CairoMakie
 
 docpath = Base.source_dir()
 assetpath = joinpath(docpath, "src", "assets")
@@ -37,7 +39,9 @@ makedocs(sitename = "Catalyst.jl",
         collapselevel = 1,
         assets = ["assets/favicon.ico"],
         canonical = "https://docs.sciml.ai/Catalyst/stable/"),
-    modules = [Catalyst, ModelingToolkit],
+    modules = [Catalyst, ModelingToolkit, 
+               isdefined(Base, :get_extension) ? Base.get_extension(Catalyst, :CatalystGraphMakieExtension) :
+               Catalyst.CatalystGraphMakieExtension],
     doctest = false,
     clean = true,
     pages = pages,

docs/src/api.md

@@ -271,7 +271,7 @@ isequivalent
 ==(rn1::ReactionSystem, rn2::ReactionSystem)
 ```
 
-## Network visualization
+## [Network visualization](@id network_visualization)
 [Latexify](https://korsbo.github.io/Latexify.jl/stable/) can be used to convert
 networks to LaTeX equations by
 ```julia
@@ -292,13 +292,10 @@ displayed as the ODE form)
 
 Finally, another optional argument (`expand_functions=true`) automatically expands functions defined by Catalyst (such as `mm`). To disable this, set `expand_functions=false`.
 
-If [Graphviz](https://graphviz.org/) is installed and commandline accessible, it
-can be used to create and save network diagrams using [`Graph`](@ref) and
-[`savegraph`](@ref).
+Reaction networks can be plotted using the `GraphMakie` extension, which is loaded whenever all of `Catalyst`, `GraphMakie`, and `NetworkLayout` are loaded (note that a Makie backend, like `CairoMakie`, must be loaded as well). The two functions for plotting networks are `plot_network` and `plot_complexes`, which are two distinct representations. 
 ```@docs
-Graph
-complexgraph
-savegraph
+plot_network(::ReactionSystem)
+plot_complexes(::ReactionSystem)
 ```
 
 ## [Rate laws](@id api_rate_laws)

docs/src/index.md

@@ -37,14 +37,14 @@ etc).
 - [JumpProcesses.jl](https://github.com/SciML/JumpProcesses.jl) can be used to numerically sample generated Stochastic Chemical Kinetics Jump Process models.
 - Support for [parallelization of all simulations](@ref ode_simulation_performance_parallelisation), including parallelization of [ODE simulations on GPUs](@ref 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](@ref 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](@ref visualisation_graphs) (reusing the Graphviz interface created in [Catlab.jl](https://algebraicjulia.github.io/Catlab.jl/stable/)).
+- [GraphMakie](https://docs.makie.org/stable/) recipes are provided that can be used to generate and [visualize reaction network graphs](@ref visualisation_graphs) 
 - Model steady states can be [computed through homotopy continuation](@ref 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](@ref steady_state_solving_simulation) using [SteadyStateDiffEq.jl)](https://github.com/SciML/SteadyStateDiffEq.jl), or by [numerically solving steady-state nonlinear equations](@ref 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 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](@ref dynamical_systems_basins_of_attraction), [Lyapunov spectrums](@ref 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.-->
 - [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](@ref global_sensitivity_analysis) of model behaviors.
 - [SciMLSensitivity.jl](https://github.com/SciML/SciMLSensitivity.jl) can be used to compute local sensitivities of functions containing forward model simulations.
+<!--- [StructuralIdentifiability.jl](https://github.com/SciML/StructuralIdentifiability.jl) can be used to perform structural identifiability analysis.-->
 
 #### [Features of packages built upon Catalyst](@id doc_index_features_other_packages)
 - Catalyst [`ReactionSystem`](@ref)s can be [imported from SBML files](@ref model_file_import_export_sbml) via [SBMLImporter.jl](https://github.com/sebapersson/SBMLImporter.jl) and [SBMLToolkit.jl](https://github.com/SciML/SBMLToolkit.jl), and [from BioNetGen .net files](@ref model_file_import_export_sbml_rni_net) and various stoichiometric matrix network representations using [ReactionNetworkImporters.jl](https://github.com/SciML/ReactionNetworkImporters.jl).

docs/src/introduction_to_catalyst/introduction_to_catalyst.md

@@ -81,17 +81,18 @@ latexify(rn)
 ```@example tut1
 rn #hide
 ```
-Assuming [Graphviz](https://graphviz.org/) is installed and command line
-accessible, within a Jupyter notebook we can also graph the reaction network by
-```julia
-g = Graph(rn)
+Catalyst also has functionality for visualizing networks using the [Makie](https://docs.makie.org/stable/)
+plotting ecosystem. The relevant packages to load are Catalyst, GraphMakie, NetworkLayout, and a Makie backend
+such as CairoMakie. Doing so and then using the `plot_network` function allows us to 
+visualize the network: 
+```@example tut1
+using Catalyst
+import CairoMakie, GraphMakie, NetworkLayout
+g = plot_network(rn)
 ```
-giving
-
-![Repressilator solution](../assets/repressilator.svg)
 
 The network graph shows a variety of information, representing each species as a
-blue node, and each reaction as an orange dot. Black arrows from species to
+blue node, and each reaction as an green node. Black arrows from species to
 reactions indicate reactants, and are labelled with their input stoichiometry.
 Similarly, black arrows from reactions to species indicate products, and are
 labelled with their output stoichiometry. In contrast, red arrows from a species
@@ -105,8 +106,11 @@ hillr(P₂,α,K,n), ∅ --> m₃
 have rates that depend on the proteins, and hence lead to red arrows from each
 `Pᵢ`.
 
-Note, from the REPL or scripts one can always use [`savegraph`](@ref) to save
-the graph (assuming `Graphviz` is installed).
+Note, from the REPL or scripts one can always use Makie's `save` function to save
+the graph.
+```julia
+save("repressilator_graph.png", g)
+```
 
 ## Mass action ODE models
 Let's now use our `ReactionSystem` to generate and solve a corresponding mass
@@ -176,7 +180,7 @@ At this point we are all set to solve the ODEs. We can now use any ODE solver
 from within the
 [OrdinaryDiffEq.jl](https://docs.sciml.ai/DiffEqDocs/stable/solvers/ode_solve/)
 package. We'll use the recommended default explicit solver, `Tsit5()`, and then
-plot the solutions:
+plot the solutions: 
 
 ```@example tut1
 sol = solve(oprob, Tsit5(), saveat=10.0)