Merge branch 'SciML:master' into dg/scp

Author: DhairyaLGandhi

.github/workflows/Documentation.yml

@@ -25,7 +25,7 @@ jobs:
           version: 'lts'
       - run: sudo apt-get update && sudo apt-get install -y xorg-dev mesa-utils xvfb libgl1 freeglut3-dev libxrandr-dev libxinerama-dev libxcursor-dev libxi-dev libxext-dev
       - name: Install dependencies
-        run: DISPLAY=:0 xvfb-run -s '-screen 0 1024x768x24' julia --project=docs/ -e 'using Pkg; Pkg.develop([PackageSpec(path=pwd()), PackageSpec(path=joinpath(pwd(), "lib", "ModelingToolkitBase"))]); Pkg.instantiate()'
+        run: DISPLAY=:0 xvfb-run -s '-screen 0 1024x768x24' julia --project=docs/ -e 'using Pkg; Pkg.develop([PackageSpec(path=pwd()), PackageSpec(path=joinpath(pwd(), "lib", "ModelingToolkitBase")), PackageSpec(path=joinpath(pwd(), "lib", "SciCompDSL"))]); Pkg.instantiate()'
       - name: Build and deploy
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # For authentication with GitHub Actions token

Project.toml

@@ -1,6 +1,6 @@
 name = "ModelingToolkit"
 uuid = "961ee093-0014-501f-94e3-6117800e7a78"
-version = "11.7.1"
+version = "11.8.0"
 authors = ["Yingbo Ma <[email protected]>", "Chris Rackauckas <[email protected]> and contributors"]
 
 [deps]
@@ -81,7 +81,7 @@ Libdl = "1"
 LinearAlgebra = "1"
 LinearSolve = "3.56"
 Logging = "1"
-ModelingToolkitBase = "1.5.3"
+ModelingToolkitBase = "1.7.0"
 ModelingToolkitStandardLibrary = "2.20"
 ModelingToolkitTearing = "1.2.1"
 Moshi = "0.3.6"

docs/Project.toml

@@ -26,6 +26,7 @@ OptimizationOptimJL = "36348300-93cb-4f02-beb5-3c3902f8871e"
 OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 PreallocationTools = "d236fae5-4411-538c-8e31-a6e3d9e00b46"
+SciCompDSL = "91a8cdf1-4ca6-467b-a780-87fda3fff15e"
 SciMLStructures = "53ae85a6-f571-4167-b2af-e1d143709226"
 Setfield = "efcf1570-3423-57d1-acb7-fd33fddbac46"
 StochasticDiffEq = "789caeaf-c7a9-5a7d-9973-96adeb23e2a0"
@@ -36,31 +37,34 @@ Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"
 [compat]
 Attractors = "1.24"
 BenchmarkTools = "1.3"
-BifurcationKit = "0.4, 0.5"
-CairoMakie = "0.13, 0.15"
+BifurcationKit = "0.5"
+CairoMakie = "0.15"
 CommonSolve = "0.2"
-DataInterpolations = "6.5, 8"
+ControlSystemsBase = "1.20"
+ControlSystemsMTK = "2.6"
+DataInterpolations = "8"
 DiffEqDevTools = "2"
 Distributions = "0.25"
 Documenter = "1"
-DynamicQuantities = "^0.11.2, 0.12, 1"
+DynamicQuantities = "1"
 FMI = "0.14"
 FMIZoo = "1"
-InfiniteOpt = "0.5"
+InfiniteOpt = "0.6"
 Ipopt = "1"
 JumpProcesses = "9"
 ModelingToolkit = "11"
-ModelingToolkitStandardLibrary = "2.19"
-NonlinearSolve = "3, 4"
-Optim = "1.7, 2"
-Optimization = "4, 5"
+ModelingToolkitStandardLibrary = "2.26"
+NonlinearSolve = "4"
+Optim = "1.9, 2"
+Optimization = "5"
 OptimizationOptimJL = "0.4"
 OrdinaryDiffEq = "6.31"
 Plots = "1.36"
 PreallocationTools = "0.4, 1"
+SciCompDSL = "1"
 SciMLStructures = "1.1"
 Setfield = "1"
 StochasticDiffEq = "6"
 SymbolicIndexingInterface = "0.3.1"
-SymbolicUtils = "3, 4"
-Symbolics = "6"
+SymbolicUtils = "4"
+Symbolics = "7"

docs/make.jl

@@ -41,6 +41,8 @@ makedocs(
         # this link tends to fail linkcheck stochastically and often takes much longer to succeed
         # even in the browser it takes ages
         "http://www.scholarpedia.org/article/Differential-algebraic_equations",
+        "https://link.springer.com/book/10.1007/978-3-030-91032-7",
+
     ],
     format = Documenter.HTML(;
         assets = ["assets/favicon.ico"],

docs/src/API/System.md

@@ -72,9 +72,8 @@ nameof
 ModelingToolkit.has_description
 ModelingToolkit.get_description
 ModelingToolkit.description
-ModelingToolkit.has_defaults
-ModelingToolkit.get_defaults
-defaults
+bindings
+initial_conditions
 ModelingToolkit.has_guesses
 ModelingToolkit.get_guesses
 guesses

docs/src/API/variables.md

@@ -20,8 +20,19 @@ additional types of metadata.
 
 ## Variable defaults
 
-Variables can be assigned default values to avoid having to specify defaults to the
-[`System`](@ref) constructor.
+Variables can be assigned default values during construction. For example:
+
+```julia
+@variables x(t) = 1 y(t) = x
+```
+
+Here `x` has a default of `1`, and `y` has a default of `x`. While both of these cases are
+stored under the same metadata key, ModelingToolkit treats them differently. Constant
+(non-symbolic) defaults (such as that of `x`) are translated to initial conditions
+([`initial_conditions`](@ref)). Symbolic defaults (such as that of `y`) are translated to
+bindings ([`bindings`](@ref)). For more information on the difference between bindings and
+initial conditions, please refer to the documentation on [initialization](@ref initialization)
+of systems, and specifically the section on [bindings and initial conditions](@ref bindings_and_ics).
 
 ```@docs
 ModelingToolkit.hasdefault

docs/src/basics/Composition.md

@@ -212,7 +212,7 @@ sir = compose(
         t,
         [S, I, R],
         [β, γ],
-        defaults = [seqn.β => β
+        bindings = [seqn.β => β
                     ieqn.β => β
                     ieqn.γ => γ
                     reqn.γ => γ], name = :sir),

docs/src/basics/Debugging.md

@@ -11,8 +11,8 @@ using ModelingToolkit: t_nounits as t, D_nounits as D
 
 @variables u1(t) u2(t) u3(t)
 eqs = [D(u1) ~ -√(u1), D(u2) ~ -√(u2), D(u3) ~ -√(u3)]
-defaults = [u1 => 1.0, u2 => 2.0, u3 => 3.0]
-@named sys = System(eqs, t; defaults)
+initial_conditions = [u1 => 1.0, u2 => 2.0, u3 => 3.0]
+@named sys = System(eqs, t; initial_conditions)
 sys = mtkcompile(sys)
 ```
 
@@ -38,7 +38,7 @@ We could have figured that out ourselves, but it is not always so obvious for mo
 Suppose we also want to validate that `u1 + u2 >= 2.0`. We can do this via the assertions functionality.
 
 ```@example debug
-@mtkcompile sys = System(eqs, t; defaults, assertions = [(u1 + u2 >= 2.0) => "Oh no!"])
+@mtkcompile sys = System(eqs, t; initial_conditions, assertions = [(u1 + u2 >= 2.0) => "Oh no!"])
 ```
 
 The assertions must be an iterable of pairs, where the first element is the symbolic condition and

docs/src/basics/Events.md

@@ -289,7 +289,8 @@ Suppose we have a population of `N(t)` cells that can grow and die, and at time
 `t1` we want to inject `M` more cells into the population. We can model this by
 
 ```@example events
-@parameters M tinject α(t)
+@parameters M tinject
+@discretes α(t)
 @variables N(t)
 Dₜ = Differential(t)
 eqs = [Dₜ(N) ~ α - N]
@@ -411,7 +412,7 @@ example:
 
 ```@example events
 @variables x(t)
-@parameters c(t)
+@discretes c(t)
 
 ev = ModelingToolkit.SymbolicDiscreteCallback(
     1.0 => [c ~ Pre(c) + 1], discrete_parameters = c, iv = t)
@@ -430,21 +431,9 @@ sol([1.0, 2.0], idxs = [c, c * cos(x)])
 ```
 
 Note that only time-dependent parameters that are explicitly passed as `discrete_parameters`
-will be saved. If we repeat the above example with `c` not a `discrete_parameter`:
-
-```@example events
-@variables x(t)
-@parameters c(t)
-
-@mtkcompile sys = System(
-    D(x) ~ c * cos(x), t, [x], [c]; discrete_events = [1.0 => [c ~ Pre(c) + 1]])
-
-prob = ODEProblem(sys, [x => 0.0, c => 1.0], (0.0, 2pi))
-sol = solve(prob, Tsit5())
-sol.ps[c] # sol[c] will error, since `c` is not a timeseries value
-```
-
-It can be seen that the timeseries for `c` is not saved.
+will be saved. The `discrete_parameters` argument is also required for the callback to compile
+successfully - omitting it will result in an error because the equation modifying the discrete
+would have no unknowns/observables.
 
 ## [(Experimental) Imperative affects](@id imp_affects)
 
@@ -464,7 +453,9 @@ Bang-bang control of a heater connected to a leaky plant requires hysteresis in
 
 ```@example events
 @variables temp(t)
-params = @parameters furnace_on_threshold=0.5 furnace_off_threshold=0.7 furnace_power=1.0 leakage=0.1 furnace_on(t)::Bool=false
+@parameters furnace_on_threshold=0.5 furnace_off_threshold=0.7 furnace_power=1.0 leakage=0.1
+@discretes furnace_on(t)::Bool = false
+params = [furnace_on_threshold, furnace_off_threshold, furnace_power, leakage, furnace_on]
 eqs = [
     D(temp) ~ furnace_on * furnace_power - temp^2 * leakage
 ]

docs/src/basics/Linearization.md

@@ -78,7 +78,7 @@ eqs = [D(x) ~ v
        D(v) ~ -k * x - k3 * x^3 - c * v + 10u.u
        y.u ~ x]
 
-@named duffing = System(eqs, t, systems = [y, u], defaults = [u.u => 0])
+@named duffing = System(eqs, t, systems = [y, u], initial_conditions = [u.u => 0])
 
 # pass a constant value for `x`, since it is the variable we will change in operating points
 linfun, simplified_sys = linearization_function(duffing, [u.u], [y.u]; op = Dict(x => NaN));