docs: fix various example block errors

Author: AayushSabharwal

docs/Project.toml

@@ -14,6 +14,7 @@ OptimizationOptimJL = "36348300-93cb-4f02-beb5-3c3902f8871e"
 OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 StochasticDiffEq = "789caeaf-c7a9-5a7d-9973-96adeb23e2a0"
+SymbolicIndexingInterface = "2efcf032-c050-4f8e-a9bb-153293bab1f5"
 SymbolicUtils = "d1185830-fcd6-423d-90d6-eec64667417b"
 Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"
 Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"
@@ -33,6 +34,7 @@ OptimizationOptimJL = "0.1"
 OrdinaryDiffEq = "6.31"
 Plots = "1.36"
 StochasticDiffEq = "6"
+SymbolicIndexingInterface = "0.3.1"
 SymbolicUtils = "1"
 Symbolics = "5"
 Unitful = "1.12"

docs/src/basics/Validation.md

@@ -59,7 +59,11 @@ ModelingToolkit.validate(eqs[1])
 ```
 
 ```@example validation
-ModelingToolkit.get_unit(eqs[1].rhs)
+try
+    ModelingToolkit.get_unit(eqs[1].rhs)
+catch e
+    showerror(e)
+end
 ```
 
 An example of an inconsistent system: at present, `ModelingToolkit` requires that the units of all terms in an equation or sum to be equal-valued (`ModelingToolkit.equivalent(u1,u2)`), rather than simply dimensionally consistent. In the future, the validation stage may be upgraded to support the insertion of conversion factors into the equations.
@@ -100,7 +104,8 @@ end
 sts = @variables a(t)=0 [unit = u"cm"]
 ps = @parameters s=-1 [unit = u"cm"] c=c [unit = u"cm"]
 eqs = [D(a) ~ dummycomplex(c, s);]
-sys = ODESystem(eqs, t, [sts...;], [ps...;], name = :sys)
+sys = ODESystem(
+    eqs, t, [sts...;], [ps...;], name = :sys, checks = ~ModelingToolkit.CheckUnits)
 sys_simple = structural_simplify(sys)
 ```
 

docs/src/basics/Variable_metadata.md

@@ -142,7 +142,7 @@ In the example below, we define a system with tunable parameters and extract bou
 @variables x(t)=0 u(t)=0 [input = true] y(t)=0 [output = true]
 @parameters T [tunable = true, bounds = (0, Inf)]
 @parameters k [tunable = true, bounds = (0, Inf)]
-eqs = [Dₜ(x) ~ (-x + k * u) / T # A first-order system with time constant T and gain k
+eqs = [D(x) ~ (-x + k * u) / T # A first-order system with time constant T and gain k
        y ~ x]
 sys = ODESystem(eqs, t, name = :tunable_first_order)
 ```

docs/src/examples/parsing.md

@@ -23,10 +23,11 @@ From there, we can use ModelingToolkit to transform the symbolic equations into
 nonlinear solve:
 
 ```@example parsing
-using ModelingToolkit, NonlinearSolve
+using ModelingToolkit, SymbolicIndexingInterface, NonlinearSolve
 vars = union(ModelingToolkit.vars.(eqs)...)
 @mtkbuild ns = NonlinearSystem(eqs, vars, [])
 
-prob = NonlinearProblem(ns, [1.0, 1.0, 1.0])
+varmap = Dict(SymbolicIndexingInterface.getname.(vars) .=> vars)
+prob = NonlinearProblem(ns, [varmap[:x] => 1.0, varmap[:y] => 1.0, varmap[:z] => 1.0])
 sol = solve(prob, NewtonRaphson())
 ```

docs/src/tutorials/ode_modeling.md

@@ -338,7 +338,7 @@ While defining the model `UnitstepFOLFactory`, an initial guess of 0.0 is assign
 Additionally, these initial guesses can be modified while creating instances of `UnitstepFOLFactory` by passing arguments.
 
 ```@example ode2
-@named fol = UnitstepFOLFactory(; x = 0.1)
+@mtkbuild fol = UnitstepFOLFactory(; x = 0.1)
 sol = ODEProblem(fol, [], (0.0, 5.0), []) |> solve
 ```
 

docs/src/tutorials/programmatically_generating.md

@@ -41,7 +41,7 @@ using ModelingToolkit: t_nounits as t, D_nounits as D
 eqs = [D(x) ~ (h - x) / τ] # create an array of equations
 
 # your first ODE, consisting of a single equation, indicated by ~
-@named fol_model = ODESystem(eqs, t)
+@named model = ODESystem(eqs, t)
 
 # Perform the standard transformations and mark the model complete
 # Note: Complete models cannot be subsystems of other models!

docs/src/tutorials/stochastic_diffeq.md

@@ -34,5 +34,5 @@ parammap = [
 ]
 
 prob = SDEProblem(de, u0map, (0.0, 100.0), parammap)
-sol = solve(prob, LambdaEulerHeun())
+sol = solve(prob, LambaEulerHeun())
 ```