Merge branch 'master' into myb/ss

Author: YingboMa

.gitignore

@@ -2,3 +2,5 @@
 *.jl.*.cov
 *.jl.mem
 Manifest.toml
+.vscode
+.vscode/*

Project.toml

@@ -1,9 +1,10 @@
 name = "ModelingToolkit"
 uuid = "961ee093-0014-501f-94e3-6117800e7a78"
 authors = ["Chris Rackauckas <[email protected]>"]
-version = "5.13.4"
+version = "5.13.6"
 
 [deps]
+AbstractTrees = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"
 ArrayInterface = "4fba245c-0d91-5ea0-9b3e-6abc04ee57a9"
 ConstructionBase = "187b0558-2788-49d3-abe0-74a17ed4e7c9"
 DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
@@ -35,11 +36,11 @@ SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
 StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
 SymbolicUtils = "d1185830-fcd6-423d-90d6-eec64667417b"
 Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"
-TreeViews = "a2a6695c-b41b-5b7d-aed9-dbfdeacea5d7"
 UnPack = "3a884ed6-31ef-47d7-9d2a-63182c4928ed"
 Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"
 
 [compat]
+AbstractTrees = "0.3"
 ArrayInterface = "2.8, 3.0"
 ConstructionBase = "1"
 DataStructures = "0.17, 0.18"
@@ -66,7 +67,6 @@ SpecialFunctions = "0.7, 0.8, 0.9, 0.10, 1.0"
 StaticArrays = "0.10, 0.11, 0.12, 1.0"
 SymbolicUtils = "0.8.3, 0.9"
 Symbolics = "0.1.8"
-TreeViews = "0.3"
 UnPack = "0.1, 1.0"
 Unitful = "1.1"
 julia = "1.2"

docs/src/basics/FAQ.md

@@ -21,20 +21,3 @@ lowered array? You can use the internal function `varmap_to_vars`. For example:
 ```julia
 pnew = varmap_to_vars([β=>3.0, c=>10.0, γ=>2.0],parameters(sys))
 ```
-
-## Embedding data into a symbolic model
-
-Let's say for example you want to embed data for the timeseries of some
-forcing equations into the right-hand side of and ODE, or data into a PDE. What
-you would do in these cases is use the `@register` function over an interpolator.
-For example, [DataInterpolations.jl](https://github.com/PumasAI/DataInterpolations.jl)
-is a good library for interpolating the data. Then you can do:
-
-```julia
-spline = CubicSpline(data,datat)
-f(t) = spline(t)
-@register f(t)
-```
-
-This will make `f(t)` be a function that Symbolics.jl will not attempt to trace.
-One should also consider defining the derivative to the function, if available.

docs/src/index.md

@@ -97,17 +97,21 @@ Because ModelingToolkit.jl is the core foundation of a equation-based modeling
 ecosystem, there is a large set of libraries adding features to this system.
 Below is an incomplete list of extension libraries one may want to be aware of:
 
-- [Catalyst.jl](https://github.com/SciML/Catalyst.jl): Symbolic representations of chemical reactions
+- [Catalyst.jl](https://github.com/SciML/Catalyst.jl): Symbolic representations
+  of chemical reactions
     - Symbolically build and represent large systems of chemical reactions
     - Generate code for ODEs, SDEs, continuous-time Markov Chains, and more
     - Simulate the models using the SciML ecosystem with O(1) Gillespie methods
-- [DataDrivenDiffEq.jl](https://github.com/SciML/DataDrivenDiffEq.jl): Automatic identification of equations from data
+- [DataDrivenDiffEq.jl](https://github.com/SciML/DataDrivenDiffEq.jl): Automatic
+  identification of equations from data
     - Automated construction of ODEs and DAEs from data
     - Representations of Koopman operators and Dynamic Mode Decomposition (DMD)
-- [MomentClosure.jl](https://github.com/augustinas1/MomentClosure.jl): Automatic transformation of ReactionSystems into deterministic systems
+- [MomentClosure.jl](https://github.com/augustinas1/MomentClosure.jl): Automatic
+  transformation of ReactionSystems into deterministic systems
     - Generates ODESystems for the moment closures
     - Allows for geometrically-distributed random reaction rates
-- [ReactionMechanismSimulator.jl](https://github.com/ReactionMechanismGenerator/ReactionMechanismSimulator.jl): simulating and analyzing large chemical reaction mechanisms
+- [ReactionMechanismSimulator.jl](https://github.com/ReactionMechanismGenerator/ReactionMechanismSimulator.jl):
+  simulating and analyzing large chemical reaction mechanisms
     - Ideal gas and dilute liquid phases.
     - Constant T and P and constant V adiabatic ideal gas reactors.
     - Constant T and V dilute liquid reactors.
@@ -124,7 +128,8 @@ the solver libraries which are the numerical targets of the ModelingToolkit
 system:
 
 - [DifferentialEquations.jl](https://diffeq.sciml.ai/stable/)
-    - Multi-package interface of high performance numerical solvers for `ODESystem`, `SDESystem`, and `JumpSystem`
+    - Multi-package interface of high performance numerical solvers for `ODESystem`,
+      `SDESystem`, and `JumpSystem`
 - [NonlinearSolve.jl](https://github.com/JuliaComputing/NonlinearSolve.jl)
     - High performance numerical solving of `NonlinearSystem`
 - [GalacticOptim.jl](https://github.com/SciML/GalacticOptim.jl)
@@ -136,7 +141,9 @@ system:
 
 ## Contributing
 
-- Please refer to the [SciML ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://github.com/SciML/ColPrac/blob/master/README.md) for guidance on PRs, issues, and other matters relating to contributing to ModelingToolkit.
+- Please refer to the
+  [SciML ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://github.com/SciML/ColPrac/blob/master/README.md)
+  for guidance on PRs, issues, and other matters relating to contributing to ModelingToolkit.
 - There are a few community forums:
     - The #diffeq-bridged channel in the [Julia Slack](https://julialang.org/slack/)
     - [JuliaDiffEq](https://gitter.im/JuliaDiffEq/Lobby) on Gitter