refactor: format

docs/src/API/variables.md

@@ -293,7 +293,7 @@ For systems that contain parameters with metadata like described above, have som
 In the example below, we define a system with tunable parameters and extract bounds vectors
 
 ```@example metadata
-@variables x(t)=0 u(t)=0 [input = true] y(t)=0 [output = true]
+@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 = [D(x) ~ (-x + k * u) / T # A first-order system with time constant T and gain k

docs/src/basics/Events.md

@@ -92,8 +92,8 @@ The basic purely symbolic continuous event interface to encode *one* continuous
 event is
 
 ```julia
-AbstractSystem(eqs, ...; continuous_events::Vector{Equation})
-AbstractSystem(eqs, ...; continuous_events::Pair{Vector{Equation}, Vector{Equation}})
+AbstractSystem(eqs, _...; continuous_events::Vector{Equation})
+AbstractSystem(eqs, _...; continuous_events::Pair{Vector{Equation}, Vector{Equation}})
 ```
 
 In the former, equations that evaluate to 0 will represent conditions that should
@@ -272,7 +272,7 @@ In addition to continuous events, discrete events are also supported. The
 general interface to represent a collection of discrete events is
 
 ```julia
-AbstractSystem(eqs, ...; discrete_events = [condition1 => affect1, condition2 => affect2])
+AbstractSystem(eqs, _...; discrete_events = [condition1 => affect1, condition2 => affect2])
 ```
 
 where conditions are symbolic expressions that should evaluate to `true` when an
@@ -497,7 +497,8 @@ so far we aren't using anything that's not possible with the implicit interface.
 You can also write
 
 ```julia
-[temp ~ furnace_off_threshold] => ModelingToolkit.ImperativeAffect(modified = (;
+[temp ~
+ furnace_off_threshold] => ModelingToolkit.ImperativeAffect(modified = (;
     furnace_on)) do x, o, i, c
     @set! x.furnace_on = false
 end

docs/src/basics/FAQ.md

@@ -63,7 +63,7 @@ The same principle applies to any parameter type that is not `Float64`.
 @parameters p1::Int # integer-valued
 @parameters p2::Bool # boolean-valued
 @parameters p3::MyCustomStructType # non-numeric
-@parameters p4::ComponentArray{...} # non-standard array
+@parameters p4::ComponentArray{_...} # non-standard array
 ```
 
 ## Getting the index for a symbol

docs/src/basics/MTKLanguage.md

@@ -381,7 +381,8 @@ Refer the following example for different ways to define symbolic arrays.
     @parameters begin
         p1[1:4]
         p2[1:N]
-        p3[1:N, 1:M] = 10,
+        p3[1:N,
+            1:M] = 10,
         [description = "A multi-dimensional array of arbitrary length with description"]
         (p4[1:N, 1:M] = 10),
         [description = "An alternate syntax for p3 to match the syntax of vanilla parameters macro"]

docs/src/basics/Validation.md

@@ -108,7 +108,7 @@ function ModelingToolkit.get_unit(op::typeof(dummycomplex), args)
 end
 
 sts = @variables a(t)=0 [unit = u"cm"]
-ps = @parameters s=-1 [unit = u"cm"] c=c [unit = u"cm"]
+ps = @parameters s=-1 [unit=u"cm"] c=c [unit=u"cm"]
 eqs = [D(a) ~ dummycomplex(c, s);]
 sys = System(
     eqs, t, [sts...;], [ps...;], name = :sys, checks = ~ModelingToolkit.CheckUnits)

docs/src/examples/sparse_jacobians.md

@@ -23,15 +23,20 @@ function brusselator_2d_loop(du, u, p, t)
     @inbounds for I in CartesianIndices((N, N))
         i, j = Tuple(I)
         x, y = xyd_brusselator[I[1]], xyd_brusselator[I[2]]
-        ip1, im1, jp1, jm1 = limit(i + 1, N), limit(i - 1, N), limit(j + 1, N),
+        ip1, im1, jp1,
+        jm1 = limit(i + 1, N), limit(i - 1, N), limit(j + 1, N),
         limit(j - 1, N)
-        du[i, j, 1] = alpha * (u[im1, j, 1] + u[ip1, j, 1] + u[i, jp1, 1] + u[i, jm1, 1] -
-                       4u[i, j, 1]) +
-                      B + u[i, j, 1]^2 * u[i, j, 2] - (A + 1) * u[i, j, 1] +
-                      brusselator_f(x, y, t)
-        du[i, j, 2] = alpha * (u[im1, j, 2] + u[ip1, j, 2] + u[i, jp1, 2] + u[i, jm1, 2] -
-                       4u[i, j, 2]) +
-                      A * u[i, j, 1] - u[i, j, 1]^2 * u[i, j, 2]
+        du[i,
+            j,
+            1] = alpha * (u[im1, j, 1] + u[ip1, j, 1] + u[i, jp1, 1] + u[i, jm1, 1] -
+                  4u[i, j, 1]) +
+                 B + u[i, j, 1]^2 * u[i, j, 2] - (A + 1) * u[i, j, 1] +
+                 brusselator_f(x, y, t)
+        du[i,
+            j,
+            2] = alpha * (u[im1, j, 2] + u[ip1, j, 2] + u[i, jp1, 2] + u[i, jm1, 2] -
+                  4u[i, j, 2]) +
+                 A * u[i, j, 1] - u[i, j, 1]^2 * u[i, j, 2]
     end
 end
 p = (3.4, 1.0, 10.0, step(xyd_brusselator))

docs/src/examples/tearing_parallelism.md

@@ -15,7 +15,7 @@ using ModelingToolkit: t_nounits as t, D_nounits as D
 
 # Basic electric components
 @connector function Pin(; name)
-    @variables v(t)=1.0 i(t)=1.0 [connect = Flow]
+    @variables v(t)=1.0 i(t)=1.0 [connect=Flow]
     System(Equation[], t, [v, i], [], name = name)
 end
 
@@ -36,7 +36,7 @@ function ConstantVoltage(; name, V = 1.0)
 end
 
 @connector function HeatPort(; name)
-    @variables T(t)=293.15 Q_flow(t)=0.0 [connect = Flow]
+    @variables T(t)=293.15 Q_flow(t)=0.0 [connect=Flow]
     System(Equation[], t, [T, Q_flow], [], name = name)
 end
 

docs/src/tutorials/disturbance_modeling.md

@@ -188,7 +188,9 @@ disturbance_inputs = [ssys.d1, ssys.d2]
 P = ssys.system_model
 outputs = [P.inertia1.phi, P.inertia2.phi, P.inertia1.w, P.inertia2.w]
 
-(f_oop, f_ip), x_sym, p_sym, io_sys = ModelingToolkit.generate_control_function(
+(f_oop, f_ip), x_sym,
+p_sym,
+io_sys = ModelingToolkit.generate_control_function(
     model_with_disturbance, inputs, disturbance_inputs; disturbance_argument = true)
 
 g = ModelingToolkit.build_explicit_observed_function(

docs/src/tutorials/linear_analysis.md

@@ -39,7 +39,7 @@ This is signified by the name being the middle argument to `connect`.
 Of the above mentioned functions, all except for [`open_loop`](@ref) return the output of [`ModelingToolkit.linearize`](@ref), which is
 
 ```julia
-matrices, simplified_sys = linearize(...)
+matrices, simplified_sys = linearize(_...)
 # matrices = (; A, B, C, D)
 ```
 

ext/MTKCasADiDynamicOptExt.jl

@@ -17,7 +17,9 @@ struct MXLinearInterpolation
     t::Vector{Float64}
     dt::Float64
 end
-Base.getindex(m::MXLinearInterpolation, i...) = length(i) == length(size(m.u)) ? m.u[i...] : m.u[i..., :]
+function Base.getindex(m::MXLinearInterpolation, i...)
+    length(i) == length(size(m.u)) ? m.u[i...] : m.u[i..., :]
+end
 
 mutable struct CasADiModel
     model::Opti
@@ -55,7 +57,7 @@ function (M::MXLinearInterpolation)(τ)
     (i > length(M.t) || i < 1) && error("Cannot extrapolate past the tspan.")
     colons = ntuple(_ -> (:), length(size(M.u)) - 1)
     if i < length(M.t)
-        M.u[colons..., i] + Δ*(M.u[colons..., i+1] - M.u[colons..., i])
+        M.u[colons..., i] + Δ * (M.u[colons..., i + 1] - M.u[colons..., i])
     else
         M.u[colons..., i]
     end
@@ -65,7 +67,9 @@ function MTK.CasADiDynamicOptProblem(sys::System, op, tspan;
         dt = nothing,
         steps = nothing,
         guesses = Dict(), kwargs...)
-    prob, _ = MTK.process_DynamicOptProblem(CasADiDynamicOptProblem, CasADiModel, sys, op, tspan; dt, steps, guesses, kwargs...)
+    prob,
+    _ = MTK.process_DynamicOptProblem(
+        CasADiDynamicOptProblem, CasADiModel, sys, op, tspan; dt, steps, guesses, kwargs...)
     prob
 end
 
@@ -127,10 +131,10 @@ function MTK.lowered_integral(model::CasADiModel, expr, lo, hi)
     for (i, t) in enumerate(model.U.t)
         if lo < t < hi
             Δt = min(dt, t - lo)
-            total += (0.5*Δt*(expr[i] + expr[i-1]))
+            total += (0.5 * Δt * (expr[i] + expr[i - 1]))
         elseif t >= hi && (t - dt < hi)
             Δt = hi - t + dt
-            total += (0.5*Δt*(expr[i] + expr[i-1]))
+            total += (0.5 * Δt * (expr[i] + expr[i - 1]))
         end
     end
     model.tₛ * total
@@ -186,9 +190,13 @@ struct CasADiCollocation <: AbstractCollocation
     tableau::DiffEqBase.ODERKTableau
 end
 
-MTK.CasADiCollocation(solver, tableau = MTK.constructDefault()) = CasADiCollocation(solver, tableau)
+function MTK.CasADiCollocation(solver, tableau = MTK.constructDefault())
+    CasADiCollocation(solver, tableau)
+end
 
-function MTK.prepare_and_optimize!(prob::CasADiDynamicOptProblem, solver::CasADiCollocation; verbose = false, solver_options = Dict(), plugin_options = Dict(), kwargs...)
+function MTK.prepare_and_optimize!(
+        prob::CasADiDynamicOptProblem, solver::CasADiCollocation; verbose = false,
+        solver_options = Dict(), plugin_options = Dict(), kwargs...)
     solver_opti = add_solve_constraints!(prob, solver.tableau)
     verbose || (solver_options["print_level"] = 0)
     solver!(solver_opti, "$(solver.solver)", plugin_options, solver_options)
@@ -211,7 +219,7 @@ end
 function MTK.get_V_values(model::CasADiModel)
     value_getter = MTK.successful_solve(model) ? CasADi.debug_value : CasADi.value
     (nu, nt) = size(model.V.u)
-    if nu*nt != 0
+    if nu * nt != 0
         V_vals = value_getter(model.solver_opti, model.V.u)
         size(V_vals, 2) == 1 && (V_vals = V_vals')
         V_vals = [[V_vals[i, j] for i in 1:nu] for j in 1:nt]
@@ -224,9 +232,11 @@ function MTK.get_t_values(model::CasADiModel)
     value_getter = MTK.successful_solve(model) ? CasADi.debug_value : CasADi.value
     ts = value_getter(model.solver_opti, model.tₛ) .* model.U.t
 end
-MTK.objective_value(model::CasADiModel) = CasADi.pyconvert(Float64, model.solver_opti.py.value(model.solver_opti.py.f))
+function MTK.objective_value(model::CasADiModel)
+    CasADi.pyconvert(Float64, model.solver_opti.py.value(model.solver_opti.py.f))
+end
 
-function MTK.successful_solve(m::CasADiModel) 
+function MTK.successful_solve(m::CasADiModel)
     isnothing(m.solver_opti) && return false
     retcode = CasADi.return_status(m.solver_opti)
     retcode == "Solve_Succeeded" || retcode == "Solved_To_Acceptable_Level"

ext/MTKInfiniteOptExt.jl

@@ -48,26 +48,32 @@ struct InfiniteOptDynamicOptProblem{uType, tType, isinplace, P, F, K} <:
 end
 
 MTK.generate_internal_model(m::Type{InfiniteOptModel}) = InfiniteModel()
-MTK.generate_time_variable!(m::InfiniteModel, tspan, tsteps) = @infinite_parameter(m, t in [tspan[1], tspan[2]], num_supports = length(tsteps))
-MTK.generate_state_variable!(m::InfiniteModel, u0::Vector, ns, ts) = @variable(m, U[i = 1:ns], Infinite(m[:t]), start=u0[i])
-MTK.generate_input_variable!(m::InfiniteModel, c0, nc, ts) = @variable(m, V[i = 1:nc], Infinite(m[:t]), start=c0[i])
+function MTK.generate_time_variable!(m::InfiniteModel, tspan, tsteps)
+    @infinite_parameter(m, t in [tspan[1], tspan[2]], num_supports=length(tsteps))
+end
+function MTK.generate_state_variable!(m::InfiniteModel, u0::Vector, ns, ts)
+    @variable(m, U[i = 1:ns], Infinite(m[:t]), start=u0[i])
+end
+function MTK.generate_input_variable!(m::InfiniteModel, c0, nc, ts)
+    @variable(m, V[i = 1:nc], Infinite(m[:t]), start=c0[i])
+end
 
 function MTK.generate_timescale!(m::InfiniteModel, guess, is_free_t)
-    @variable(m, tₛ ≥ 0, start = guess)
+    @variable(m, tₛ≥0, start=guess)
     if !is_free_t
-        fix(tₛ, 1, force=true)
+        fix(tₛ, 1, force = true)
         set_start_value(tₛ, 1)
     end
     tₛ
 end
 
-function MTK.add_constraint!(m::InfiniteOptModel, expr::Union{Equation, Inequality}) 
+function MTK.add_constraint!(m::InfiniteOptModel, expr::Union{Equation, Inequality})
     if expr isa Equation
-        @constraint(m.model, expr.lhs - expr.rhs == 0)
+        @constraint(m.model, expr.lhs - expr.rhs==0)
     elseif expr.relational_op === Symbolics.geq
-        @constraint(m.model, expr.lhs - expr.rhs ≥ 0)
+        @constraint(m.model, expr.lhs - expr.rhs≥0)
     else
-        @constraint(m.model, expr.lhs - expr.rhs ≤ 0)
+        @constraint(m.model, expr.lhs - expr.rhs≤0)
     end
 end
 MTK.set_objective!(m::InfiniteOptModel, expr) = @objective(m.model, Min, expr)
@@ -76,20 +82,27 @@ function MTK.JuMPDynamicOptProblem(sys::System, op, tspan;
         dt = nothing,
         steps = nothing,
         guesses = Dict(), kwargs...)
-    prob, _ = MTK.process_DynamicOptProblem(JuMPDynamicOptProblem, InfiniteOptModel, sys, op, tspan; dt, steps, guesses, kwargs...)
+    prob,
+    _ = MTK.process_DynamicOptProblem(JuMPDynamicOptProblem, InfiniteOptModel, sys,
+        op, tspan; dt, steps, guesses, kwargs...)
     prob
 end
 
 function MTK.InfiniteOptDynamicOptProblem(sys::System, op, tspan;
         dt = nothing,
         steps = nothing,
         guesses = Dict(), kwargs...)
-    prob, pmap = MTK.process_DynamicOptProblem(InfiniteOptDynamicOptProblem, InfiniteOptModel, sys, op, tspan; dt, steps, guesses, kwargs...)
+    prob,
+    pmap = MTK.process_DynamicOptProblem(
+        InfiniteOptDynamicOptProblem, InfiniteOptModel,
+        sys, op, tspan; dt, steps, guesses, kwargs...)
     MTK.add_equational_constraints!(prob.wrapped_model, sys, pmap, tspan)
     prob
 end
 
-MTK.lowered_integral(model::InfiniteOptModel, expr, lo, hi) = model.tₛ * InfiniteOpt.∫(expr, model.model[:t], lo, hi)
+function MTK.lowered_integral(model::InfiniteOptModel, expr, lo, hi)
+    model.tₛ * InfiniteOpt.∫(expr, model.model[:t], lo, hi)
+end
 MTK.lowered_derivative(model::InfiniteOptModel, i) = ∂(model.U[i], model.model[:t])
 
 function MTK.process_integral_bounds(model::InfiniteOptModel, integral_span, tspan)
@@ -125,7 +138,7 @@ function add_solve_constraints!(prob::JuMPDynamicOptProblem, tableau)
     nᵥ = length(V)
     if MTK.is_explicit(tableau)
         K = Any[]
-        for τ in tsteps[1:end-1]
+        for τ in tsteps[1:(end - 1)]
             for (i, h) in enumerate(c)
                 ΔU = sum([A[i, j] * K[j] for j in 1:(i - 1)], init = zeros(nᵤ))
                 Uₙ = [U[i](τ) + ΔU[i] * dt for i in 1:nᵤ]
@@ -142,14 +155,15 @@ function add_solve_constraints!(prob::JuMPDynamicOptProblem, tableau)
         K = @variable(model, K[1:length(α), 1:nᵤ], Infinite(model[:t]))
         ΔUs = A * K
         ΔU_tot = dt * (K' * α)
-        for τ in tsteps[1:end-1]
+        for τ in tsteps[1:(end - 1)]
             for (i, h) in enumerate(c)
                 ΔU = @view ΔUs[i, :]
                 Uₙ = U + ΔU * dt
                 @constraint(model, [j = 1:nᵤ], K[i, j]==(tₛ * f(Uₙ, V, p, τ + h * dt)[j]),
                     DomainRestrictions(t => τ), base_name="solve_K$i($τ)")
             end
-            @constraint(model, [n = 1:nᵤ], U[n](τ) + ΔU_tot[n]==U[n](min(τ + dt, tsteps[end])),
+            @constraint(model,
+                [n = 1:nᵤ], U[n](τ) + ΔU_tot[n]==U[n](min(τ + dt, tsteps[end])),
                 DomainRestrictions(t => τ), base_name="solve_U($τ)")
         end
     end
@@ -159,15 +173,21 @@ struct JuMPCollocation <: AbstractCollocation
     solver::Any
     tableau::DiffEqBase.ODERKTableau
 end
-MTK.JuMPCollocation(solver, tableau = MTK.constructDefault()) = JuMPCollocation(solver, tableau)
+function MTK.JuMPCollocation(solver, tableau = MTK.constructDefault())
+    JuMPCollocation(solver, tableau)
+end
 
 struct InfiniteOptCollocation <: AbstractCollocation
     solver::Any
     derivative_method::InfiniteOpt.AbstractDerivativeMethod
 end
-MTK.InfiniteOptCollocation(solver, derivative_method = InfiniteOpt.FiniteDifference(InfiniteOpt.Backward())) = InfiniteOptCollocation(solver, derivative_method)
+function MTK.InfiniteOptCollocation(
+        solver, derivative_method = InfiniteOpt.FiniteDifference(InfiniteOpt.Backward()))
+    InfiniteOptCollocation(solver, derivative_method)
+end
 
-function MTK.prepare_and_optimize!(prob::JuMPDynamicOptProblem, solver::JuMPCollocation; verbose = false, kwargs...)
+function MTK.prepare_and_optimize!(
+        prob::JuMPDynamicOptProblem, solver::JuMPCollocation; verbose = false, kwargs...)
     model = prob.wrapped_model.model
     verbose || set_silent(model)
     # Unregister current solver constraints
@@ -190,7 +210,8 @@ function MTK.prepare_and_optimize!(prob::JuMPDynamicOptProblem, solver::JuMPColl
     model
 end
 
-function MTK.prepare_and_optimize!(prob::InfiniteOptDynamicOptProblem, solver::InfiniteOptCollocation; verbose = false, kwargs...)
+function MTK.prepare_and_optimize!(prob::InfiniteOptDynamicOptProblem,
+        solver::InfiniteOptCollocation; verbose = false, kwargs...)
     model = prob.wrapped_model.model
     verbose || set_silent(model)
     set_derivative_method(model[:t], solver.derivative_method)
@@ -223,8 +244,8 @@ function MTK.successful_solve(model::InfiniteModel)
         error("Model not solvable; please report this to github.com/SciML/ModelingToolkit.jl with a MWE.")
 
     pstatus === FEASIBLE_POINT &&
-         (tstatus === OPTIMAL || tstatus === LOCALLY_SOLVED || tstatus === ALMOST_OPTIMAL ||
-          tstatus === ALMOST_LOCALLY_SOLVED)
+        (tstatus === OPTIMAL || tstatus === LOCALLY_SOLVED || tstatus === ALMOST_OPTIMAL ||
+         tstatus === ALMOST_LOCALLY_SOLVED)
 end
 
 import InfiniteOpt: JuMP, GeneralVariableRef

ext/MTKPyomoDynamicOptExt.jl

@@ -108,7 +108,8 @@ function MTK.add_constraint!(pmodel::PyomoDynamicOptModel, cons; n_idxs = 1)
     else
         cons.lhs - cons.rhs ≤ 0
     end
-    expr = Symbolics.substitute(Symbolics.unwrap(expr), SPECIAL_FUNCTIONS_DICT, fold = false)
+    expr = Symbolics.substitute(
+        Symbolics.unwrap(expr), SPECIAL_FUNCTIONS_DICT, fold = false)
 
     cons_sym = Symbol("cons", hash(cons))
     if occursin(Symbolics.unwrap(t_sym), expr)
@@ -141,17 +142,17 @@ end
 function MTK.lowered_integral(m::PyomoDynamicOptModel, arg, lo, hi)
     @unpack model, model_sym, t_sym, dummy_sym = m
     total = 0
-    dt = Pyomo.pyconvert(Float64, (model.t.at(-1) - model.t.at(1))/(model.steps - 1))
+    dt = Pyomo.pyconvert(Float64, (model.t.at(-1) - model.t.at(1)) / (model.steps - 1))
     f = Symbolics.build_function(arg, model_sym, t_sym, expression = false)
     for (i, t) in enumerate(model.t)
         if Bool(lo < t) && Bool(t < hi)
-            t_p = model.t.at(i-1)
+            t_p = model.t.at(i - 1)
             Δt = min(t - lo, t - t_p)
-            total += 0.5*Δt*(f(model, t) + f(model, t_p))
+            total += 0.5 * Δt * (f(model, t) + f(model, t_p))
         elseif Bool(t >= hi) && Bool(t - dt < hi)
-            t_p = model.t.at(i-1)
+            t_p = model.t.at(i - 1)
             Δt = hi - t + dt
-            total += 0.5*Δt*(f(model, t) + f(model, t_p))
+            total += 0.5 * Δt * (f(model, t) + f(model, t_p))
         end
     end
     PyomoVar(model.tₛ * total)