fix project toml for OptimizationBase

docs/Project.toml

@@ -4,8 +4,6 @@ BifurcationKit = "0f109fa4-8a5d-4b75-95aa-f515264e7665"
 CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0"
 Catalyst = "479239e8-5488-4da2-87a7-35f2df7eef83"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
-DiffEqParamEstim = "1130ab10-4a5a-5621-a13d-e4788d82bd4c"
-DifferentialEquations = "0c46a032-eb83-5123-abaf-570d42b7fbaa"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 DynamicalSystems = "61744808-ddfa-5f27-97ff-6e42cc95d634"
@@ -15,21 +13,18 @@ IncompleteLU = "40713840-3770-5561-ab4c-a76e7d0d7895"
 JumpProcesses = "ccbc3e58-028d-4f4c-8cd5-9ae44345cda5"
 Latexify = "23fbe1c1-3f47-55db-b15f-69d7ec21a316"
 LinearSolve = "7ed4a6bd-45f5-4d41-b270-4a48e9bafcae"
-Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
 ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78"
 NonlinearSolve = "8913a72c-1f9b-4ce2-8d82-65094dcecaec"
 Optim = "429524aa-4258-5aef-a3af-852621145aeb"
 Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
 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"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 QuasiMonteCarlo = "8a4e6c94-4038-4cdc-81c3-7e6ffdb2a71b"
 SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
 SciMLSensitivity = "1ed8b502-d754-442c-8d5d-10ac956f44a1"
-Setfield = "efcf1570-3423-57d1-acb7-fd33fddbac46"
 SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
 StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
 SteadyStateDiffEq = "9672c7b4-1e72-59bd-8a11-6ac3964bc41f"
@@ -39,38 +34,34 @@ Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"
 
 [compat]
 BenchmarkTools = "1.5.0"
-BifurcationKit = "0.3"
+BifurcationKit = "0.3.3"
 CairoMakie = "0.12"
 Catalyst = "13"
 DataFrames = "1.6"
-DiffEqParamEstim = "2.2"
-DifferentialEquations = "7.13"
 Distributions = "0.25"
-DynamicalSystems = "3.3"
 Documenter = "1.4.1"
+DynamicalSystems = "3.3"
 GlobalSensitivity = "2.6"
 HomotopyContinuation = "2.9"
 IncompleteLU = "0.2"
 JumpProcesses = "9.11"
 Latexify = "0.16"
 LinearSolve = "2.30"
 ModelingToolkit = "9.15"
-NonlinearSolve = "3.11"
+NonlinearSolve = "3.12"
 Optim = "1.9"
-Optimization = "3.24"
+Optimization = "3.25"
 OptimizationBBO = "0.2"
 OptimizationNLopt = "0.2"
 OptimizationOptimJL = "0.3"
-OptimizationOptimisers = "0.2"
 OrdinaryDiffEq = "6.80"
 Plots = "1.40"
 QuasiMonteCarlo = "0.3"
-SciMLBase = "2.38"
-SciMLSensitivity = "7.56"
-Setfield = "1.1"
+SciMLBase = "2.39"
+SciMLSensitivity = "7.59"
 SpecialFunctions = "2.4"
 StaticArrays = "1.9"
 SteadyStateDiffEq = "2.2"
 StochasticDiffEq = "6.65"
-StructuralIdentifiability = "0.5"
-Symbolics = "5.28"
+StructuralIdentifiability = "0.5.7"
+Symbolics = "5.28"

docs/src/assets/Project.toml

@@ -9,7 +9,7 @@ DifferentialEquations = "0c46a032-eb83-5123-abaf-570d42b7fbaa"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 DynamicalSystems = "61744808-ddfa-5f27-97ff-6e42cc95d634"
-GlobalSensitivity = "af5da776-676b-467e-8baf-acd8249e4f0f"#
+GlobalSensitivity = "af5da776-676b-467e-8baf-acd8249e4f0f"
 HomotopyContinuation = "f213a82b-91d6-5c5d-acf7-10f1c761b327"
 IncompleteLU = "40713840-3770-5561-ab4c-a76e7d0d7895"
 JumpProcesses = "ccbc3e58-028d-4f4c-8cd5-9ae44345cda5"

docs/src/inverse_problems/behaviour_optimisation.md

@@ -8,7 +8,7 @@ Our model consists of 3 species: $X$ (the input node), $Y$ (an intermediary), an
 ```@example behaviour_optimization
 using Catalyst
 incoherent_feed_forward = @reaction_network begin
-    @discrete_events [10.0] ~ [p ~ 10*p]
+    @discrete_events [10.0] => [pX ~ 10*pX]
     pX, 0 --> X
     pY*X, 0 --> Y
     pZ*X/Y, 0 --> Z

docs/src/model_creation/dsl_advanced.md

@@ -182,7 +182,7 @@ end
 It is possible to add both default values and metadata to a parameter/species. In this case, first provide the default value, and next the metadata. I.e. to in the above example set $kA$'s default value to $1.0$ we use
 ```@example dsl_advanced_metadata
 two_state_system = @reaction_network begin
-  @parameters kA=1.0 [description="X's activation rate", bounsd=(0.01,10.0)]
+  @parameters kA=1.0 [description="X's activation rate", bounds=(0.01,10.0)]
   (ka,kD), Xi <--> Xa
 end
 ```
@@ -191,10 +191,10 @@ When designating metadata for species/parameters in `begin ... end` blocks the s
 ```@example dsl_advanced_metadata
 two_state_system = @reaction_network begin
   @parameters begin
-    kA, [description="X's activation rate", bounds=(0.01,10.0)]
-    kD = 1.0, [description="X's deactivation rate"]
+      kA, [description="X's activation rate", bounds=(0.01,10.0)]
+      kD = 1.0, [description="X's deactivation rate"]
   end
-  (ka,kD), Xi <--> Xa
+  (kA,kD), Xi <--> Xa
 end
 ```
 
@@ -350,7 +350,7 @@ sol[:Xtot]
 to get a vector with `Xtot`'s value throughout the simulation. We can also use
 ```@example dsl_advanced_observables
 using Plots
-plot(sol; idxs = [:Xtot, :Ytot])
+plot(sol; idxs = :Xtot)
 ```
 to plot the observables (rather than the species).
 
@@ -378,7 +378,7 @@ Observables are by default considered [variables](@ref ref) (not species). To de
 ```@example dsl_advanced_observables
 rn = @reaction_network begin
   @species Xtot(t)
-  @observables Xtot ~ X + n*XnXY  
+  @observables Xtot ~ X + n*Xn  
   (kB,kD), n*X <--> Xn
 end
 nothing # hide

docs/src/model_creation/examples/programmatic_generative_linear_pathway.md

@@ -117,6 +117,7 @@ nothing # hide
 ```
 We can now simulate linear pathways of arbitrary lengths using a simple syntax. We use this to recreate our previous result from the DSL:
 ```@example programmatic_generative_linear_pathway_generative
+using OrdinaryDiffEq, Plots # hide
 sol_n3 = solve(generate_oprob(3))
 sol_n10 = solve(generate_oprob(10))
 plot(sol_n3; idxs = :Xend, label = "n = 3")

docs/src/model_simulation/ode_simulation_performance.md

@@ -235,7 +235,7 @@ plot(0.01:0.01:1.0, map(sol -> sol[:P][end], esol.u), xguide = "kP", yguide = "P
 ```
 
 Above, we have simply used `EnsembleProblem` as a convenient interface to run a large number of similar simulations. However, these problems have the advantage that they allow the passing of an *ensemble algorithm* to the `solve` command, which describes a strategy for parallelising the simulations. By default, `EnsembleThreads` is used. This parallelises the simulations using [multithreading](https://en.wikipedia.org/wiki/Multithreading_(computer_architecture)) (parallelisation within a single process), which is typically advantageous for small problems on shared memory devices. An alternative is `EnsembleDistributed` which instead parallelises the simulations using [multiprocessing](https://en.wikipedia.org/wiki/Multiprocessing) (parallelisation across multiple processes). To do this, we simply supply this additional solver to the solve command:
-```@example ode_simulation_performance_4
+```julia
 esol = solve(eprob, Tsit5(), EnsembleDistributed(); trajectories=100)
 nothing # hide
 ```

src/Catalyst.jl

@@ -31,7 +31,7 @@ using ModelingToolkit: Symbolic, value, istree, get_unknowns, get_ps, get_iv, ge
 import ModelingToolkit: get_variables, namespace_expr, namespace_equation, get_variables!,
                         modified_unknowns!, validate, namespace_variables,
                         namespace_parameters, rename, renamespace, getname, flatten,
-                        is_alg_equation, is_diff_equation
+                        is_alg_equation, is_diff_equation, getdescription, hasdescription
 
 # internal but needed ModelingToolkit functions
 import ModelingToolkit: check_variables,