Merge remote-tracking branch 'origin/master' into pdmps

Author: isaacsas

Project.toml

@@ -41,11 +41,11 @@ CatalystGraphMakieExtension = "GraphMakie"
 CatalystHomotopyContinuationExtension = "HomotopyContinuation"
 
 [compat]
-BifurcationKit = "0.3"
+BifurcationKit = "0.4.4"
 CairoMakie = "0.12"
 Combinatorics = "1.0.2"
 DataStructures = "0.18"
-DiffEqBase = "6.83.0"
+DiffEqBase = "< 6.159.0"
 DocStringExtensions = "0.8, 0.9"
 DynamicPolynomials = "0.5, 0.6"
 DynamicQuantities = "0.13.2, 1"

docs/Project.toml

@@ -4,6 +4,7 @@ BifurcationKit = "0f109fa4-8a5d-4b75-95aa-f515264e7665"
 CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0"
 Catalyst = "479239e8-5488-4da2-87a7-35f2df7eef83"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
+DiffEqBase = "2b5f629d-d688-5b77-993f-72d75c75574e"
 DiffEqParamEstim = "1130ab10-4a5a-5621-a13d-e4788d82bd4c"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
@@ -26,7 +27,12 @@ OptimizationBBO = "3e6eede4-6085-4f62-9a71-46d9bc1eb92b"
 OptimizationNLopt = "4e6fcdb7-1186-4e1f-a706-475e75c168bb"
 OptimizationOptimJL = "36348300-93cb-4f02-beb5-3c3902f8871e"
 OptimizationOptimisers = "42dfb2eb-d2b4-4451-abcd-913932933ac1"
-OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
+OrdinaryDiffEqBDF = "6ad6398a-0878-4a85-9266-38940aa047c8"
+OrdinaryDiffEqDefault = "50262376-6c5a-4cf5-baba-aaf4f84d72d7"
+OrdinaryDiffEqRosenbrock = "43230ef6-c299-4910-a778-202eb28ce4ce"
+OrdinaryDiffEqSDIRK = "2d112036-d095-4a1e-ab9a-08536f3ecdbf"
+OrdinaryDiffEqTsit5 = "b1df2697-797e-41e3-8120-5422d3b24e4a"
+OrdinaryDiffEqVerner = "79d7bb75-1356-48c1-b8c0-6832512096c2"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 QuasiMonteCarlo = "8a4e6c94-4038-4cdc-81c3-7e6ffdb2a71b"
 SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
@@ -35,15 +41,15 @@ SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
 StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
 SteadyStateDiffEq = "9672c7b4-1e72-59bd-8a11-6ac3964bc41f"
 StochasticDiffEq = "789caeaf-c7a9-5a7d-9973-96adeb23e2a0"
-#StructuralIdentifiability = "220ca800-aa68-49bb-acd8-6037fa93a544"
 Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"
 
 [compat]
 BenchmarkTools = "1.5"
-BifurcationKit = "0.3.4"
+BifurcationKit = "0.4.4"
 CairoMakie = "0.12"
-Catalyst = "14.2"
+Catalyst = "14.4"
 DataFrames = "1.6"
+DiffEqBase = "< 6.159.0"
 DiffEqParamEstim = "2.2"
 Distributions = "0.25"
 Documenter = "1.4.1"
@@ -58,14 +64,19 @@ JumpProcesses = "9.13.2"
 Latexify = "0.16.5"
 LinearSolve = "2.30"
 ModelingToolkit = "9.32"
-NonlinearSolve = "3.12"
+NonlinearSolve = "3.12, 4"
 Optim = "1.9"
 Optimization = "4"
 OptimizationBBO = "0.4"
 OptimizationNLopt = "0.3"
 OptimizationOptimJL = "0.4"
 OptimizationOptimisers = "0.3"
-OrdinaryDiffEq = "6.80.1"
+OrdinaryDiffEqBDF = "1"
+OrdinaryDiffEqDefault = "1"
+OrdinaryDiffEqRosenbrock = "1"
+OrdinaryDiffEqSDIRK = "1"
+OrdinaryDiffEqTsit5 = "1"
+OrdinaryDiffEqVerner = "1"
 Plots = "1.40"
 QuasiMonteCarlo = "0.3"
 SciMLBase = "2.46"
@@ -74,5 +85,4 @@ SpecialFunctions = "2.4"
 StaticArrays = "1.9"
 SteadyStateDiffEq = "2.2"
 StochasticDiffEq = "6.65"
-#StructuralIdentifiability = "0.5.8"
 Symbolics = "5.30.1, 6"

docs/make.jl

@@ -34,13 +34,14 @@ makedocs(sitename = "Catalyst.jl",
     authors = "Samuel Isaacson",
     format = Documenter.HTML(; analytics = "UA-90474609-3",
         prettyurls = (get(ENV, "CI", nothing) == "true"),
+        collapselevel = 1,
         assets = ["assets/favicon.ico"],
         canonical = "https://docs.sciml.ai/Catalyst/stable/"),
     modules = [Catalyst, ModelingToolkit],
     doctest = false,
     clean = true,
     pages = pages,
-    pagesonly = false,
+    pagesonly = true,
     warnonly = [:missing_docs])
 
 deploydocs(repo = "github.com/SciML/Catalyst.jl.git";

docs/src/api.md

@@ -35,7 +35,7 @@ corresponding chemical reaction ODE models, chemical Langevin equation SDE
 models, and stochastic chemical kinetics jump process models.
 
 ```@example ex1
-using Catalyst, OrdinaryDiffEq, StochasticDiffEq, JumpProcesses, Plots
+using Catalyst, OrdinaryDiffEqTsit5, StochasticDiffEq, JumpProcesses, Plots
 t = default_t()
 @parameters β γ
 @species S(t) I(t) R(t)
@@ -393,4 +393,4 @@ Finally, we provide the following helper functions to plot and animate spatial l
 lattice_plot
 lattice_animation
 lattice_kymograph
-```
+```

docs/src/faqs.md

@@ -5,7 +5,7 @@ One can directly use symbolic variables to index into SciML solution objects.
 Moreover, observables can also be evaluated in this way. For example,
 consider the system
 ```@example faq1
-using Catalyst, OrdinaryDiffEq, Plots
+using Catalyst, OrdinaryDiffEqTsit5, Plots
 rn = @reaction_network ABtoC begin
   (k₊,k₋), A + B <--> C
 end
@@ -132,7 +132,7 @@ When directly constructing a `ReactionSystem`, we can set the symbolic values to
 have the desired default values, and this will automatically be propagated
 through to the equation solvers:
 ```@example faq3
-using Catalyst, Plots, OrdinaryDiffEq
+using Catalyst, Plots, OrdinaryDiffEqTsit5
 t = default_t()
 @parameters β=1e-4 ν=.01
 @species S(t)=999.0 I(t)=1.0 R(t)=0.0
@@ -175,7 +175,7 @@ Julia `Symbol`s corresponding to each variable/parameter to their values, or
 from ModelingToolkit symbolic variables/parameters to their values. Using
 `Symbol`s we have
 ```@example faq4
-using Catalyst, OrdinaryDiffEq
+using Catalyst, OrdinaryDiffEqTsit5
 rn = @reaction_network begin
     α, S + I --> 2I
     β, I --> R

docs/src/index.md

@@ -32,14 +32,14 @@ etc).
 - [Steady states](@ref homotopy_continuation) (and their [stabilities](@ref steady_state_stability)) can be computed for model ODE representations.
 
 #### [Features of Catalyst composing with other packages](@id doc_index_features_composed)
-- [OrdinaryDiffEq.jl](https://github.com/SciML/OrdinaryDiffEq.jl) Can be used to numerically solver generated reaction rate equation ODE models.
+- [OrdinaryDiffEq.jl](https://github.com/SciML/OrdinaryDiffEq.jl) Can be used to numerically solve generated reaction rate equation ODE models.
 - [StochasticDiffEq.jl](https://github.com/SciML/StochasticDiffEq.jl) can be used to numerically solve generated Chemical Langevin Equation SDE models.
 - [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/)).
 - 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](@ref bifurcation_diagrams) of model steady states (including finding periodic orbits).
+[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/).
@@ -92,7 +92,7 @@ Pkg.add("Catalyst")
 
 Many Catalyst features require the installation of additional packages. E.g. for ODE-solving and simulation plotting
 ```julia
-Pkg.add("OrdinaryDiffEq")
+Pkg.add("OrdinaryDiffEqDefault")
 Pkg.add("Plots")
 ```
 is also needed.
@@ -124,7 +124,7 @@ an ordinary differential equation.
 
 ```@example home_simple_example
 # Fetch required packages.
-using Catalyst, OrdinaryDiffEq, Plots
+using Catalyst, OrdinaryDiffEqDefault, Plots
 
 # Create model.
 model = @reaction_network begin

docs/src/introduction_to_catalyst/catalyst_for_new_julia_users.md

@@ -55,15 +55,15 @@ To import a Julia package into a session, you can use the `using PackageName` co
 using Pkg
 Pkg.add("Catalyst")
 ```
-Here, the Julia package manager package (`Pkg`) is by default installed on your computer when Julia is installed, and can be activated directly. Next, we also wish to install the `OrdinaryDiffEq` and `Plots` packages (for numeric simulation of models, and plotting, respectively).
+Here, the Julia package manager package (`Pkg`) is by default installed on your computer when Julia is installed, and can be activated directly. Next, we install an ODE solver from a sub-library of the larger `OrdinaryDiffEq` package, and install the `Plots` package for making graphs. We will import the recommended default solver from the `OrdinaryDiffEqDefault` sub-library. A full list of `OrdinaryDiffEq` solver sublibraries can be found on the sidebar of [this page](https://docs.sciml.ai/OrdinaryDiffEq/stable/).
 ```julia
-Pkg.add("OrdinaryDiffEq")
+Pkg.add("OrdinaryDiffEqDefault")
 Pkg.add("Plots")
 ```
 Once a package has been installed through the `Pkg.add` command, this command does not have to be repeated if we restart our Julia session. We can now import all three packages into our current session with:
 ```@example ex2
 using Catalyst
-using OrdinaryDiffEq
+using OrdinaryDiffEqDefault
 using Plots
 ```
 Here, if we restart Julia, these `using` commands *must be rerun*.
@@ -253,4 +253,4 @@ If you are a new Julia user who has used this tutorial, and there was something
 ---
 ## References
 [^1]: [Torkel E. Loman, Yingbo Ma, Vasily Ilin, Shashi Gowda, Niklas Korsbo, Nikhil Yewale, Chris Rackauckas, Samuel A. Isaacson, *Catalyst: Fast and flexible modeling of reaction networks*, PLOS Computational Biology (2023).](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011530)
-[^2]: [Jeff Bezanson, Alan Edelman, Stefan Karpinski, Viral B. Shah, *Julia: A Fresh Approach to Numerical Computing*, SIAM Review (2017).](https://epubs.siam.org/doi/abs/10.1137/141000671)
+[^2]: [Jeff Bezanson, Alan Edelman, Stefan Karpinski, Viral B. Shah, *Julia: A Fresh Approach to Numerical Computing*, SIAM Review (2017).](https://epubs.siam.org/doi/abs/10.1137/141000671)

docs/src/introduction_to_catalyst/introduction_to_catalyst.md

@@ -20,7 +20,7 @@ Pkg.activate("catalyst_introduction")
 
 # packages we will use in this tutorial
 Pkg.add("Catalyst")
-Pkg.add("OrdinaryDiffEq")
+Pkg.add("OrdinaryDiffEqTsit5")
 Pkg.add("Plots")
 Pkg.add("Latexify")
 Pkg.add("JumpProcesses")
@@ -29,7 +29,7 @@ Pkg.add("StochasticDiffEq")
 
 We next load the basic packages we'll need for our first example:
 ```@example tut1
-using Catalyst, OrdinaryDiffEq, Plots, Latexify
+using Catalyst, OrdinaryDiffEqTsit5, Plots, Latexify
 ```
 
 Let's start by using the Catalyst [`@reaction_network`](@ref) macro to specify a
@@ -386,4 +386,4 @@ A more detailed summary of the precise mathematical equations Catalyst can gener
 
 ---
 ## References
-1. [Torkel E. Loman, Yingbo Ma, Vasily Ilin, Shashi Gowda, Niklas Korsbo, Nikhil Yewale, Chris Rackauckas, Samuel A. Isaacson, *Catalyst: Fast and flexible modeling of reaction networks*, PLOS Computational Biology (2023).](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011530)
+1. [Torkel E. Loman, Yingbo Ma, Vasily Ilin, Shashi Gowda, Niklas Korsbo, Nikhil Yewale, Chris Rackauckas, Samuel A. Isaacson, *Catalyst: Fast and flexible modeling of reaction networks*, PLOS Computational Biology (2023).](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011530)

docs/src/inverse_problems/behaviour_optimisation.md

@@ -17,7 +17,7 @@ end
 ```
 To demonstrate this pulsing behaviour we will simulate the system for an example parameter set. We select an initial condition (`u0`) so the system begins in a steady state.
 ```@example behaviour_optimization
-using OrdinaryDiffEq, Plots
+using OrdinaryDiffEqTsit5, Plots
 example_p = [:pX => 0.1, :pY => 1.0, :pZ => 1.0]
 tspan = (0.0, 50.0)
 example_u0 = [:X => 0.1, :Y => 0.1, :Z => 1.0]
@@ -107,4 +107,4 @@ If you use this functionality in your research, please cite the following paper
 ---
 ## References
 [^1]: [Mykel J. Kochenderfer, Tim A. Wheeler *Algorithms for Optimization*, The MIT Press (2019).](https://algorithmsbook.com/optimization/files/optimization.pdf)
-[^2]: [Lea Goentoro, Oren Shoval, Marc W Kirschner, Uri Alon *The incoherent feedforward loop can provide fold-change detection in gene regulation*, Molecular Cell (2009).](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2896310/)
+[^2]: [Lea Goentoro, Oren Shoval, Marc W Kirschner, Uri Alon *The incoherent feedforward loop can provide fold-change detection in gene regulation*, Molecular Cell (2009).](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2896310/)

docs/src/inverse_problems/examples/ode_fitting_oscillation.md

@@ -4,7 +4,7 @@ In this example we will use [Optimization.jl](https://github.com/SciML/Optimizat
 First, we fetch the required packages.
 ```@example pe_osc_example
 using Catalyst
-using OrdinaryDiffEq
+using OrdinaryDiffEqRosenbrock
 using Optimization
 using OptimizationOptimisers # Required for the ADAM optimizer.
 using SciMLSensitivity # Required for `Optimization.AutoZygote()` automatic differentiation option.