Merge branch 'master' into myb/connector_type

Author: YingboMa

Project.toml

@@ -1,7 +1,7 @@
 name = "ModelingToolkit"
 uuid = "961ee093-0014-501f-94e3-6117800e7a78"
 authors = ["Yingbo Ma <[email protected]>", "Chris Rackauckas <[email protected]> and contributors"]
-version = "8.29.1"
+version = "8.30.0"
 
 [deps]
 AbstractTrees = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"

docs/make.jl

@@ -1,5 +1,9 @@
 using Documenter, ModelingToolkit
 
+# Make sure that plots don't throw a bunch of warnings / errors!
+ENV["GKSwstype"] = "100"
+using Plots
+
 include("pages.jl")
 
 mathengine = MathJax3(Dict(:loader => Dict("load" => ["[tex]/require", "[tex]/mathtools"]),

docs/src/basics/ContextualVariables.md

@@ -20,6 +20,17 @@ All modeling projects have some form of parameters. `@parameters` marks a variab
 as being the parameter of some system, which allows automatic detection algorithms
 to ignore such variables when attempting to find the states of a system.
 
+## Wildcard Variable Arguments
+
+```julia
+@variables u(..)
+```
+
+It is possible to define a dependent variable which is an open function as above,
+for which its arguments must be specified each time it is used. This is useful with
+PDEs for example, where one may need to use `u(t, x)` in the equations, but will 
+need to be able to write `u(t, 0.0)` to define a boundary condition at `x = 0`.
+
 ## Variable metadata [Experimental/TODO]
 
 In many engineering systems some variables act like "flows" while others do not.

src/systems/abstractsystem.jl

@@ -846,7 +846,7 @@ function _named(name, call, runtime = false)
         end
     end
 
-    is_sys_construction = Symbol("###__is_system_construction###")
+    is_sys_construction = gensym("###__is_system_construction###")
     kws = call.args[2].args
     for (i, kw) in enumerate(kws)
         if Meta.isexpr(kw, (:(=), :kw))

src/systems/alias_elimination.jl

@@ -708,17 +708,27 @@ function alias_eliminate_graph!(graph, var_to_diff, mm_orig::SparseMatrixCLIL)
         prev_r = -1
         for _ in 1:10_000 # just to make sure that we don't stuck in an infinite loop
             reach₌ = Pair{Int, Int}[]
+            # `r` is aliased to its equality aliases
             r === nothing || for n in neighbors(eqg, r)
                 (n == r || is_diff_edge(r, n)) && continue
                 c = get_weight(eqg, r, n)
                 push!(reach₌, c => n)
             end
+            # `r` is aliased to its previous differentiation level's aliases'
+            # derivative
             if (n = length(diff_aliases)) >= 1
                 as = diff_aliases[n]
                 for (c, a) in as
                     (da = var_to_diff[a]) === nothing && continue
                     da === r && continue
                     push!(reach₌, c => da)
+                    # `r` is aliased to its previous differentiation level's
+                    # aliases' derivative's equality aliases
+                    r === nothing || for n in neighbors(eqg, da)
+                        (n == da || n == prev_r || is_diff_edge(prev_r, n)) && continue
+                        c′ = get_weight(eqg, da, n)
+                        push!(reach₌, c * c′ => n)
+                    end
                 end
             end
             if r === nothing

src/systems/optimization/optimizationsystem.jl

@@ -329,7 +329,7 @@ function OptimizationProblemExpr(sys::OptimizationSystem, args...; kwargs...)
     OptimizationProblemExpr{true}(sys::OptimizationSystem, args...; kwargs...)
 end
 
-function OptimizationProblemExpr{iip}(sys::OptimizationSystem, u0,
+function OptimizationProblemExpr{iip}(sys::OptimizationSystem, u0map,
                                       parammap = DiffEqBase.NullParameters();
                                       lb = nothing, ub = nothing,
                                       grad = false,

test/reduction.jl

@@ -302,3 +302,25 @@ ss = alias_elimination(sys)
 @test length(equations(ss)) == length(states(ss)) == 1
 ss = structural_simplify(sys)
 @test length(equations(ss)) == length(states(ss)) == 2
+
+@variables t
+vars = @variables x(t) y(t) k(t) z(t) zₜ(t) ddx(t)
+D = Differential(t)
+eqs = [D(D(x)) ~ ddx
+       ddx ~ y
+       D(x) ~ z
+       D(z) ~ zₜ
+       D(zₜ) ~ sin(t)
+       D(x) ~ D(k)
+       D(D(D(x))) ~ sin(t)]
+@named sys = ODESystem(eqs, t, vars, [])
+state = TearingState(sys);
+ag, mm, complete_ag, complete_mm = ModelingToolkit.alias_eliminate_graph!(state)
+fullvars = state.fullvars
+aliases = []
+for (v, (c, a)) in complete_ag
+    push!(aliases, fullvars[v] => c == 0 ? 0 : c * fullvars[a])
+end
+ref_aliases = [D(k) => D(x); z => D(x); D(z) => D(D(x)); zₜ => D(D(x)); ddx => D(D(x));
+               y => D(D(x)); D(zₜ) => D(D(D(x)))]
+@test Set(aliases) == Set(ref_aliases)