docs: use @mtkbuild instead of manually calling complete

Author: AayushSabharwal

docs/src/basics/Events.md

@@ -72,8 +72,8 @@ function UnitMassWithFriction(k; name)
         D(v) ~ sin(t) - k * sign(v)]
     ODESystem(eqs, t; continuous_events = [v ~ 0], name) # when v = 0 there is a discontinuity
 end
-@named m = UnitMassWithFriction(0.7)
-prob = ODEProblem(complete(m), Pair[], (0, 10pi))
+@mtkbuild m = UnitMassWithFriction(0.7)
+prob = ODEProblem(m, Pair[], (0, 10pi))
 sol = solve(prob, Tsit5())
 plot(sol)
 ```
@@ -243,8 +243,8 @@ injection = (t == tinject) => [N ~ N + M]
 u0 = [N => 0.0]
 tspan = (0.0, 20.0)
 p = [α => 100.0, tinject => 10.0, M => 50]
-@named osys = ODESystem(eqs, t, [N], [α, M, tinject]; discrete_events = injection)
-oprob = ODEProblem(complete(osys), u0, tspan, p)
+@mtkbuild osys = ODESystem(eqs, t, [N], [α, M, tinject]; discrete_events = injection)
+oprob = ODEProblem(osys, u0, tspan, p)
 sol = solve(oprob, Tsit5(); tstops = 10.0)
 plot(sol)
 ```
@@ -262,7 +262,7 @@ to
 ```@example events
 injection = ((t == tinject) & (N < 50)) => [N ~ N + M]
 
-@named osys = ODESystem(eqs, t, [N], [M, tinject, α]; discrete_events = injection)
+@mtkbuild osys = ODESystem(eqs, t, [N], [M, tinject, α]; discrete_events = injection)
 oprob = ODEProblem(osys, u0, tspan, p)
 sol = solve(oprob, Tsit5(); tstops = 10.0)
 plot(sol)
@@ -287,7 +287,7 @@ killing = (t == tkill) => [α ~ 0.0]
 
 tspan = (0.0, 30.0)
 p = [α => 100.0, tinject => 10.0, M => 50, tkill => 20.0]
-@named osys = ODESystem(eqs, t, [N], [α, M, tinject, tkill];
+@mtkbuild osys = ODESystem(eqs, t, [N], [α, M, tinject, tkill];
     discrete_events = [injection, killing])
 oprob = ODEProblem(osys, u0, tspan, p)
 sol = solve(oprob, Tsit5(); tstops = [10.0, 20.0])
@@ -315,7 +315,7 @@ injection = [10.0] => [N ~ N + M]
 killing = [20.0] => [α ~ 0.0]
 
 p = [α => 100.0, M => 50]
-@named osys = ODESystem(eqs, t, [N], [α, M];
+@mtkbuild osys = ODESystem(eqs, t, [N], [α, M];
     discrete_events = [injection, killing])
 oprob = ODEProblem(osys, u0, tspan, p)
 sol = solve(oprob, Tsit5())

docs/src/examples/parsing.md

@@ -25,8 +25,8 @@ nonlinear solve:
 ```@example parsing
 using ModelingToolkit, NonlinearSolve
 vars = union(ModelingToolkit.vars.(eqs)...)
-@named ns = NonlinearSystem(eqs, vars, [])
+@mtkbuild ns = NonlinearSystem(eqs, vars, [])
 
-prob = NonlinearProblem(complete(ns), [1.0, 1.0, 1.0])
+prob = NonlinearProblem(ns, [1.0, 1.0, 1.0])
 sol = solve(prob, NewtonRaphson())
 ```

docs/src/examples/sparse_jacobians.md

@@ -54,8 +54,7 @@ prob = ODEProblem(brusselator_2d_loop, u0, (0.0, 11.5), p)
 Now let's use `modelingtoolkitize` to generate the symbolic version:
 
 ```@example sparsejac
-sys = modelingtoolkitize(prob);
-sys = complete(sys);
+@mtkbuild sys = modelingtoolkitize(prob);
 nothing # hide
 ```
 

docs/src/tutorials/bifurcation_diagram_computation.md

@@ -12,8 +12,7 @@ using ModelingToolkit
 @parameters μ α
 eqs = [0 ~ μ * x - x^3 + α * y,
     0 ~ -y]
-@named nsys = NonlinearSystem(eqs, [x, y], [μ, α])
-nsys = complete(nsys)
+@mtkbuild nsys = NonlinearSystem(eqs, [x, y], [μ, α])
 ```
 
 we wish to compute a bifurcation diagram for this system as we vary the parameter `μ`. For this, we need to provide the following information:
@@ -94,8 +93,7 @@ using BifurcationKit, ModelingToolkit, Plots
 D = Differential(t)
 eqs = [D(x) ~ μ * x - y - x * (x^2 + y^2),
     D(y) ~ x + μ * y - y * (x^2 + y^2)]
-@named osys = ODESystem(eqs, t)
-osys = complete(osys)
+@mtkbuild osys = ODESystem(eqs, t)
 
 bif_par = μ
 plot_var = x

docs/src/tutorials/domain_connections.md

@@ -135,7 +135,7 @@ To see how the domain works, we can examine the set parameter values for each of
 
 ```@repl domain
 sys = structural_simplify(odesys)
-ModelingToolkit.defaults(sys)[complete(odesys).vol.port.ρ]
+ModelingToolkit.defaults(sys)[odesys.vol.port.ρ]
 ```
 
 ## Multiple Domain Networks
@@ -280,8 +280,7 @@ Adding the `Restrictor` to the original system example will cause a break in the
     ODESystem(eqs, t, [], []; systems, name)
 end
 
-@named ressys = RestrictorSystem()
-sys = structural_simplify(ressys)
+@mtkbuild ressys = RestrictorSystem()
 nothing #hide
 ```
 
@@ -296,9 +295,9 @@ nothing #hide
 When `structural_simplify()` is applied to this system it can be seen that the defaults are missing for `res.port_b` and `vol.port`.
 
 ```@repl domain
-ModelingToolkit.defaults(sys)[complete(ressys).res.port_a.ρ]
-ModelingToolkit.defaults(sys)[complete(ressys).res.port_b.ρ]
-ModelingToolkit.defaults(sys)[complete(ressys).vol.port.ρ]
+ModelingToolkit.defaults(ressys)[ressys.res.port_a.ρ]
+ModelingToolkit.defaults(ressys)[ressys.res.port_b.ρ]
+ModelingToolkit.defaults(ressys)[ressys.vol.port.ρ]
 ```
 
 To ensure that the `Restrictor` component does not disrupt the domain network, the [`domain_connect()`](@ref) function can be used, which explicitly only connects the domain network and not the unknown variables.
@@ -322,15 +321,14 @@ To ensure that the `Restrictor` component does not disrupt the domain network, t
     ODESystem(eqs, t, [], pars; systems, name)
 end
 
-@named ressys = RestrictorSystem()
-sys = structural_simplify(ressys)
+@mtkbuild ressys = RestrictorSystem()
 nothing #hide
 ```
 
 Now that the `Restrictor` component is properly defined using `domain_connect()`, the defaults for `res.port_b` and `vol.port` are properly defined.
 
 ```@repl domain
-ModelingToolkit.defaults(sys)[complete(ressys).res.port_a.ρ]
-ModelingToolkit.defaults(sys)[complete(ressys).res.port_b.ρ]
-ModelingToolkit.defaults(sys)[complete(ressys).vol.port.ρ]
+ModelingToolkit.defaults(ressys)[ressys.res.port_a.ρ]
+ModelingToolkit.defaults(ressys)[ressys.res.port_b.ρ]
+ModelingToolkit.defaults(ressys)[ressys.vol.port.ρ]
 ```

docs/src/tutorials/modelingtoolkitize.md

@@ -52,11 +52,11 @@ If we want to get a symbolic representation, we can simply call `modelingtoolkit
 on the `prob`, which will return an `ODESystem`:
 
 ```@example mtkize
-sys = modelingtoolkitize(prob)
+@mtkbuild sys = modelingtoolkitize(prob)
 ```
 
 Using this, we can symbolically build the Jacobian and then rebuild the ODEProblem:
 
 ```@example mtkize
-prob_jac = ODEProblem(complete(sys), [], (0.0, 1e5), jac = true)
+prob_jac = ODEProblem(sys, [], (0.0, 1e5), jac = true)
 ```

docs/src/tutorials/nonlinear.md

@@ -16,8 +16,7 @@ using ModelingToolkit, NonlinearSolve
 eqs = [0 ~ σ * (y - x),
     0 ~ x * (ρ - z) - y,
     0 ~ x * y - β * z]
-@named ns = NonlinearSystem(eqs, [x, y, z], [σ, ρ, β])
-ns = complete(ns)
+@mtkbuild ns = NonlinearSystem(eqs, [x, y, z], [σ, ρ, β])
 
 guess = [x => 1.0,
     y => 0.0,

docs/src/tutorials/optimization.md

@@ -19,7 +19,7 @@ Now we can define our optimization problem.
 end
 @parameters a=1 b=1
 loss = (a - x)^2 + b * (y - x^2)^2
-@named sys = OptimizationSystem(loss, [x, y], [a, b])
+@mtkbuild sys = OptimizationSystem(loss, [x, y], [a, b])
 ```
 
 A visualization of the objective function is depicted below.
@@ -50,7 +50,7 @@ u0 = [x => 1.0
 p = [a => 1.0
     b => 100.0]
 
-prob = OptimizationProblem(complete(sys), u0, p, grad = true, hess = true)
+prob = OptimizationProblem(sys, u0, p, grad = true, hess = true)
 solve(prob, GradientDescent())
 ```
 
@@ -68,10 +68,10 @@ loss = (a - x)^2 + b * (y - x^2)^2
 cons = [
     x^2 + y^2 ≲ 1,
 ]
-@named sys = OptimizationSystem(loss, [x, y], [a, b], constraints = cons)
+@mtkbuild sys = OptimizationSystem(loss, [x, y], [a, b], constraints = cons)
 u0 = [x => 0.14
     y => 0.14]
-prob = OptimizationProblem(complete(sys),
+prob = OptimizationProblem(sys,
     u0,
     grad = true,
     hess = true,

docs/src/tutorials/parameter_identifiability.md

@@ -63,8 +63,7 @@ D = Differential(t)
 end
 
 # define the system
-@named de = Biohydrogenation()
-de = complete(de)
+@mtkbuild de = Biohydrogenation()
 ```
 
 After that, we are ready to check the system for local identifiability:
@@ -185,8 +184,7 @@ D = Differential(t)
     end
 end
 
-@named ode = GoodwinOscillator()
-ode = complete(ode)
+@mtkbuild ode = GoodwinOscillator()
 
 # check only 2 parameters
 to_check = [ode.b, ode.c]

docs/src/tutorials/programmatically_generating.md

@@ -44,7 +44,7 @@ eqs = [D(x) ~ (h - x) / τ] # create an array of equations
 
 # Perform the standard transformations and mark the model complete
 # Note: Complete models cannot be subsystems of other models!
-fol_model = complete(structural_simplify(fol_model))
+fol_model = structural_simplify(model)
 ```
 
 As you can see, generating an ODESystem is as simple as creating the array of equations