refactor: rename @mtkbuild to @mtkcompile

Author: AayushSabharwal

src/ModelingToolkit.jl

@@ -288,7 +288,7 @@ export alias_elimination, flatten
 export connect, domain_connect, @connector, Connection, AnalysisPoint, Flow, Stream,
        instream
 export initial_state, transition, activeState, entry, ticksInState, timeInState
-export @component, @mtkmodel, @mtkbuild
+export @component, @mtkmodel, @mtkcompile
 export isinput, isoutput, getbounds, hasbounds, getguess, hasguess, isdisturbance,
        istunable, getdist, hasdist,
        tunable_parameters, isirreducible, getdescription, hasdescription,

src/problems/bvproblem.jl

@@ -30,7 +30,7 @@ If a `System` without `constraints` is specified, it will be treated as an initi
            D(D(y)) ~ λ * y - g
            x(t)^2 + y^2 ~ 1]
     cstr = [x(0.5) ~ 1]
-    @mtkbuild pend = System(eqs, t; constraints = cstrs)
+    @mtkcompile pend = System(eqs, t; constraints = cstrs)
 
     tspan = (0.0, 1.5)
     u0map = [x(t) => 0.6, y => 0.8]

src/systems/abstractsystem.jl

@@ -146,7 +146,7 @@ may be subsetted using `dvs` and `ps`. All `kwargs` are passed to the internal
 [`build_function`](@ref) call. The returned function can be called as `f(u, p, t)` or
 `f(du, u, p, t)` for time-dependent systems and `f(u, p)` or `f(du, u, p)` for
 time-independent systems. If `split=true` (the default) was passed to [`complete`](@ref),
-[`structural_simplify`](@ref) or [`@mtkbuild`](@ref), `p` is expected to be an `MTKParameters`
+[`structural_simplify`](@ref) or [`@mtkcompile`](@ref), `p` is expected to be an `MTKParameters`
 object.
 """
 function generate_custom_function(sys::AbstractSystem, exprs, dvs = unknowns(sys),
@@ -2470,7 +2470,7 @@ macro component(expr)
     esc(component_post_processing(expr, false))
 end
 
-macro mtkbuild(exprs...)
+macro mtkcompile(exprs...)
     expr = exprs[1]
     named_expr = ModelingToolkit.named_expr(expr)
     name = named_expr.args[1]

src/systems/parameter_buffer.jl

@@ -23,7 +23,7 @@ dependent systems. It is only required if the symbolic expressions also use the
 variable of the system.
 
 This requires that `complete` has been called on the system (usually via
-`structural_simplify` or `@mtkbuild`) and the keyword `split = true` was passed (which is
+`structural_simplify` or `@mtkcompile`) and the keyword `split = true` was passed (which is
 the default behavior).
 """
 function MTKParameters(

test/bvproblem.jl

@@ -20,7 +20,7 @@ daesolvers = [Ascher2, Ascher4, Ascher6]
     parammap = [α => 7.5, β => 4, γ => 8.0, δ => 5.0]
     tspan = (0.0, 10.0)
 
-    @mtkbuild lotkavolterra = System(eqs, t)
+    @mtkcompile lotkavolterra = System(eqs, t)
     op = ODEProblem(lotkavolterra, u0map, tspan, parammap)
     osol = solve(op, Vern9())
 
@@ -52,7 +52,7 @@ end
     eqs = [D(θ) ~ θ_t
            D(θ_t) ~ -(g / L) * sin(θ)]
 
-    @mtkbuild pend = System(eqs, t)
+    @mtkcompile pend = System(eqs, t)
 
     u0map = [θ => π / 2, θ_t => π / 2]
     parammap = [:L => 1.0, :g => 9.81]
@@ -91,7 +91,7 @@ end
         D(y(t)) ~ -γ * y(t) + δ * x(t) * y(t)]
 
     tspan = (0.0, 1.0)
-    @mtkbuild lksys = System(eqs, t)
+    @mtkcompile lksys = System(eqs, t)
 
     function lotkavolterra!(du, u, p, t)
         du[1] = p[1] * u[1] - p[2] * u[1] * u[2]
@@ -104,7 +104,7 @@ end
 
     # Test with a constraint.
     constr = [y(0.5) ~ 2.0]
-    @mtkbuild lksys = System(eqs, t; constraints = constr)
+    @mtkcompile lksys = System(eqs, t; constraints = constr)
 
     function bc!(resid, u, p, t)
         resid[1] = u(0.0)[1] - 1.0
@@ -177,21 +177,21 @@ end
     tspan = (0.0, 1.0)
     guess = [x(t) => 4.0, y(t) => 2.0]
     constr = [x(0.6) ~ 3.5, x(0.3) ~ 7.0]
-    @mtkbuild lksys = System(eqs, t; constraints = constr)
+    @mtkcompile lksys = System(eqs, t; constraints = constr)
 
     bvp = SciMLBase.BVProblem{true, SciMLBase.AutoSpecialize}(
         lksys, u0map, tspan; guesses = guess)
     test_solvers(solvers, bvp, u0map, constr; dt = 0.05)
 
     # Testing that more complicated constraints give correct solutions.
     constr = [y(0.2) + x(0.8) ~ 3.0, y(0.3) ~ 2.0]
-    @mtkbuild lksys = System(eqs, t; constraints = constr)
+    @mtkcompile lksys = System(eqs, t; constraints = constr)
     bvp = SciMLBase.BVProblem{false, SciMLBase.FullSpecialize}(
         lksys, u0map, tspan; guesses = guess)
     test_solvers(solvers, bvp, u0map, constr; dt = 0.05)
 
     constr = [α * β - x(0.6) ~ 0.0, y(0.2) ~ 3.0]
-    @mtkbuild lksys = System(eqs, t; constraints = constr)
+    @mtkcompile lksys = System(eqs, t; constraints = constr)
     bvp = SciMLBase.BVProblem{true, SciMLBase.AutoSpecialize}(
         lksys, u0map, tspan; guesses = guess)
     test_solvers(solvers, bvp, u0map, constr)
@@ -205,7 +205,7 @@ end
 #     eqs = [D(D(x)) ~ λ * x
 #            D(D(y)) ~ λ * y - g
 #            x^2 + y^2 ~ 1]
-#     @mtkbuild pend = System(eqs, t)
+#     @mtkcompile pend = System(eqs, t)
 # 
 #     tspan = (0.0, 1.5)
 #     u0map = [x => 1, y => 0]
@@ -243,7 +243,7 @@ end
 #            D(D(y)) ~ λ * y - g
 #            x(t)^2 + y^2 ~ 1]
 #     constr = [x(0.5) ~ 1]
-#     @mtkbuild pend = System(eqs, t; constr)
+#     @mtkcompile pend = System(eqs, t; constr)
 # 
 #     tspan = (0.0, 1.5)
 #     u0map = [x(t) => 0.6, y => 0.8]
@@ -262,13 +262,13 @@ end
 # 
 #     constr = [x(0.5) ~ 1, 
 #                    x(0.3)^3 + y(0.6)^2 ~ 0.5]
-#     @mtkbuild pend = System(eqs, t; constr)
+#     @mtkcompile pend = System(eqs, t; constr)
 #     bvp = SciMLBase.BVProblem{true, SciMLBase.AutoSpecialize}(pend, u0map, tspan, parammap; guesses, check_length = false)
 #     test_solvers(daesolvers, bvp, u0map, constr, get_alg_eqs(pend))
 # 
 #     constr = [x(0.4) * g ~ y(0.2),
 #                    y(0.7) ~ 0.3]
-#     @mtkbuild pend = System(eqs, t; constr)
+#     @mtkcompile pend = System(eqs, t; constr)
 #     bvp = SciMLBase.BVProblem{true, SciMLBase.AutoSpecialize}(pend, u0map, tspan, parammap; guesses, check_length = false)
 #     test_solvers(daesolvers, bvp, u0map, constr, get_alg_eqs(pend))
 # end
@@ -286,7 +286,7 @@ end
     parammap = [α => 7.5, β => 4, γ => 8.0, δ => 5.0]
     costs = [x(0.6), x(0.3)^2]
     consolidate(u, sub) = (u[1] + 3)^2 + u[2] + sum(sub; init = 0)
-    @mtkbuild lksys = System(eqs, t; costs, consolidate)
+    @mtkcompile lksys = System(eqs, t; costs, consolidate)
 
     @test_throws ModelingToolkit.SystemCompatibilityError ODEProblem(
         lksys, u0map, tspan, parammap)
@@ -301,7 +301,7 @@ end
     @parameters t_c
     costs = [y(t_c) + x(0.0), x(0.4)^2]
     consolidate(u, sub) = log(u[1]) - u[2] + sum(sub; init = 0)
-    @mtkbuild lksys = System(eqs, t; costs, consolidate)
+    @mtkcompile lksys = System(eqs, t; costs, consolidate)
     @test t_c ∈ Set(parameters(lksys))
     push!(parammap, t_c => 0.56)
     prob = ODEProblem(lksys, u0map, tspan, parammap; check_compatibility = false)

test/clock.jl

@@ -518,7 +518,7 @@ eqs = [yd ~ Sample(dt)(y)
         end
     end
 
-    @mtkbuild model = FirstOrderWithStepCounter()
+    @mtkcompile model = FirstOrderWithStepCounter()
     prob = ODEProblem(model, [], (0.0, 10.0))
     sol = solve(prob, Tsit5(), kwargshandle = KeywordArgSilent)
 
@@ -529,7 +529,7 @@ eqs = [yd ~ Sample(dt)(y)
     @variables x(t)=1.0 y(t)=1.0
     eqs = [D(y) ~ Hold(x)
            x ~ x(k - 1) + x(k - 2)]
-    @mtkbuild sys = System(eqs, t)
+    @mtkcompile sys = System(eqs, t)
     prob = ODEProblem(sys, [], (0.0, 10.0))
     int = init(prob, Tsit5(); kwargshandle = KeywordArgSilent)
     @test int.ps[x] == 2.0

test/code_generation.jl

@@ -58,7 +58,7 @@ end
 @testset "Non-standard array variables" begin
     @variables x(t)
     @parameters p[0:2] (f::Function)(..)
-    @mtkbuild sys = System(D(x) ~ p[0] * x + p[1] * t + p[2] + f(p), t)
+    @mtkcompile sys = System(D(x) ~ p[0] * x + p[1] * t + p[2] + f(p), t)
     prob = ODEProblem(sys, [x => 1.0], (0.0, 1.0), [p => [1.0, 2.0, 3.0], f => sum])
     @test prob.ps[p] == [1.0, 2.0, 3.0]
     @test prob.ps[p[0]] == 1.0

test/constants.jl

@@ -41,7 +41,7 @@ simp = structural_simplify(sys)
     @variables x(MT.t_nounits) = h
     eqs = [MT.D_nounits(x) ~ (h - x) / τ]
 
-    @mtkbuild fol_model = System(eqs, MT.t_nounits)
+    @mtkcompile fol_model = System(eqs, MT.t_nounits)
 
     prob = ODEProblem(fol_model, [], (0.0, 10.0), [h => 1])
     @test prob[x] ≈ 1

test/dde.jl

@@ -38,7 +38,7 @@ eqs = [D(x₀) ~ (v0 / (1 + beta0 * (x₂(t - tau)^2))) * (p0 - q0) * x₀ - d0
        D(x₁) ~ (v0 / (1 + beta0 * (x₂(t - tau)^2))) * (1 - p0 + q0) * x₀ +
                (v1 / (1 + beta1 * (x₂(t - tau)^2))) * (p1 - q1) * x₁ - d1 * x₁
        D(x₂(t)) ~ (v1 / (1 + beta1 * (x₂(t - tau)^2))) * (1 - p1 + q1) * x₁ - d2 * x₂(t)]
-@mtkbuild sys = System(eqs, t)
+@mtkcompile sys = System(eqs, t)
 @test ModelingToolkit.is_dde(sys)
 @test !is_markovian(sys)
 prob = DDEProblem(sys,
@@ -81,7 +81,7 @@ sol = solve(prob, RKMil(), seed = 100)
 @brownian η
 τ = 1.0
 eqs = [D(x(t)) ~ a * x(t) + b * x(t - τ) + c + (α * x(t) + γ) * η, delx ~ x(t - τ)]
-@mtkbuild sys = System(eqs, t)
+@mtkcompile sys = System(eqs, t)
 @test ModelingToolkit.has_observed_with_lhs(sys, delx)
 @test ModelingToolkit.is_dde(sys)
 @test !is_markovian(sys)
@@ -162,7 +162,7 @@ prob_sa = DDEProblem(sys, [], (0.0, 10.0); constant_lags = [sys.osc1.τ, sys.osc
         return System([D(x) ~ dx], t; name = name)
     end
 
-    @mtkbuild ssys = System(
+    @mtkcompile ssys = System(
         Equation[], t; systems = [valve(name = :valve), veccy(name = :vvecs)])
     prob = DDEProblem(ssys, [ssys.valve.opening => 1.0], (0.0, 1.0))
     sol = solve(prob, MethodOfSteps(Tsit5()))

test/debugging.jl

@@ -42,7 +42,7 @@ end
         (System, ODEProblem, inner_ode, Tsit5()),
         (System, SDEProblem, inner_sde, ImplicitEM())]
         kwargs = Problem == SDEProblem ? (; seed = SEED) : (;)
-        @mtkbuild outer = ctor(Equation[], t; systems = [inner])
+        @mtkcompile outer = ctor(Equation[], t; systems = [inner])
         dsys = debug_system(outer; functions = [])
         @test is_parameter(dsys, ASSERTION_LOG_VARIABLE)
         prob = Problem(dsys, [inner.x => 0.1], (0.0, 5.0); kwargs...)