Merge pull request #366 from control-toolbox/auto-juliaformatter-pr

[AUTO] JuliaFormatter.jl run

docs/make.jl

@@ -7,26 +7,26 @@ using CTDirect
 
 # to add docstrings from external packages
 Modules = [CTBase, CTFlows, CTDirect, OptimalControl]
-for Module ∈ Modules
+for Module in Modules
     isnothing(DocMeta.getdocmeta(Module, :DocTestSetup)) &&
-        DocMeta.setdocmeta!(Module, :DocTestSetup, :(using $Module); recursive = true)
+        DocMeta.setdocmeta!(Module, :DocTestSetup, :(using $Module); recursive=true)
 end
 
 repo_url = "github.com/control-toolbox/OptimalControl.jl"
 
-makedocs(
-    warnonly = [:cross_references, :autodocs_block],
-    sitename = "OptimalControl.jl",
-    format = Documenter.HTML(;
-        repolink = "https://" * repo_url,
-        prettyurls = false,
-        size_threshold_ignore = ["api-ctbase/types.md", "tutorial-plot.md"],
-        assets = [
+makedocs(;
+    warnonly=[:cross_references, :autodocs_block],
+    sitename="OptimalControl.jl",
+    format=Documenter.HTML(;
+        repolink="https://" * repo_url,
+        prettyurls=false,
+        size_threshold_ignore=["api-ctbase/types.md", "tutorial-plot.md"],
+        assets=[
             asset("https://control-toolbox.org/assets/css/documentation.css"),
             asset("https://control-toolbox.org/assets/js/documentation.js"),
         ],
     ),
-    pages = [
+    pages=[
         "Introduction" => "index.md",
         "Basic examples" => [
             "Energy minimisation" => "tutorial-double-integrator-energy.md",
@@ -64,4 +64,4 @@ makedocs(
     ],
 )
 
-deploydocs(; repo = repo_url * ".git", devbranch = "main")
+deploydocs(; repo=repo_url * ".git", devbranch="main")

src/OptimalControl.jl

@@ -53,7 +53,8 @@ export OptimalControlModel, OptimalControlSolution
 export Autonomous, NonAutonomous
 export NonFixed, Fixed
 export Model, __OCPModel
-export variable!, time!, constraint!, dynamics!, objective!, state!, control!, remove_constraint!
+export variable!,
+    time!, constraint!, dynamics!, objective!, state!, control!, remove_constraint!
 export constraint
 export time_grid, control, state, variable, costate, objective
 export iterations, stopping, message, infos
@@ -65,7 +66,7 @@ export ParsingError
 # repl
 function __init__()
     isdefined(Base, :active_repl) && ct_repl()
-    nothing
+    return nothing
 end
 
 end

src/solve.jl

@@ -6,7 +6,7 @@ Return the list of available methods that can be used to solve the optimal contr
 function available_methods()
     # by order of preference: from top to bottom
     methods = ()
-    for method ∈ CTDirect.available_methods()
+    for method in CTDirect.available_methods()
         methods = add(methods, (:direct, method...))
     end
     return methods
@@ -84,14 +84,15 @@ julia> sol = solve(ocp, init=(state=[-0.5, 0.2], control=0.5))
     For more information on how to provide the initial guess, see the [tutorial on the initial guess](@ref tutorial-init).
 
 """
-function CommonSolve.solve(ocp::OptimalControlModel, description::Symbol...; kwargs...)::OptimalControlSolution
+function CommonSolve.solve(
+    ocp::OptimalControlModel, description::Symbol...; kwargs...
+)::OptimalControlSolution
 
     # get the full description
     method = getFullDescription(description, available_methods())
 
     # solve the problem
-    if :direct ∈ method 
+    if :direct ∈ method
         return CTDirect.direct_solve(ocp, clean(description)...; kwargs...)
     end
-
 end

test/problems/beam.jl

@@ -13,5 +13,5 @@ function beam()
         ∫(u(t)^2) → min
     end
 
-    return ((ocp = beam, obj = 8.898598, name = "beam", init = nothing))
+    return ((ocp=beam, obj=8.898598, name="beam", init=nothing))
 end

test/problems/bioreactor.jl

@@ -55,7 +55,7 @@ function bioreactor_1day_periodic()
         ∫(mu2 * b(t) / (beta + c)) → max
     end
 
-    return ((ocp = bioreactor_1, obj = 0.614134, name = "bioreactor_1day_periodic", init = nothing))
+    return ((ocp=bioreactor_1, obj=0.614134, name="bioreactor_1day_periodic", init=nothing))
 end
 
 # N days (non periodic)
@@ -96,9 +96,6 @@ function bioreactor_Ndays()
     end
 
     return ((
-        ocp = bioreactor_N,
-        obj = 19.0745,
-        init = (state = [50, 50, 50],),
-        name = "bioreactor_Ndays",
+        ocp=bioreactor_N, obj=19.0745, init=(state=[50, 50, 50],), name="bioreactor_Ndays"
     ))
 end

test/problems/fuller.jl

@@ -11,5 +11,5 @@ function fuller()
         ∫(x₁(t)^2) → min
     end
 
-    return ((ocp = fuller, obj = 2.683944e-1, name = "fuller", init = nothing))
+    return ((ocp=fuller, obj=2.683944e-1, name="fuller", init=nothing))
 end

test/problems/goddard.jl

@@ -13,7 +13,7 @@ function F1(x, Tmax, b)
     return [0, Tmax / m, -b * Tmax]
 end
 
-function goddard(; vmax = 0.1, Tmax = 3.5, functional_constraints = false)
+function goddard(; vmax=0.1, Tmax=3.5, functional_constraints=false)
     # constants
     Cd = 310
     beta = 500
@@ -25,31 +25,33 @@ function goddard(; vmax = 0.1, Tmax = 3.5, functional_constraints = false)
     x0 = [r0, v0, m0]
 
     #ocp
-    goddard = Model(variable = true)
+    goddard = Model(; variable=true)
     state!(goddard, 3)
     control!(goddard, 1)
     variable!(goddard, 1)
-    time!(goddard, t0 = 0, indf = 1)
-    constraint!(goddard, :initial, lb = x0, ub = x0)
-    constraint!(goddard, :final, rg = 3, lb = mf, ub = Inf)
+    time!(goddard; t0=0, indf=1)
+    constraint!(goddard, :initial; lb=x0, ub=x0)
+    constraint!(goddard, :final; rg=3, lb=mf, ub=Inf)
     if functional_constraints
         # note: the equations do not handle r<1 well
         # without the box constraint on x, the default init (0.1) is not suitable
-        constraint!(goddard, :state, f = (x, v) -> x, lb = [r0, v0, mf], ub = [r0 + 0.2, vmax, m0])
-        constraint!(goddard, :control, f = (u, v) -> u, lb = 0, ub = 1)
+        constraint!(
+            goddard, :state; f=(x, v) -> x, lb=[r0, v0, mf], ub=[r0 + 0.2, vmax, m0]
+        )
+        constraint!(goddard, :control; f=(u, v) -> u, lb=0, ub=1)
     else
-        constraint!(goddard, :state, lb = [r0, v0, mf], ub = [r0 + 0.2, vmax, m0])
-        constraint!(goddard, :control, lb = 0, ub = 1)
+        constraint!(goddard, :state; lb=[r0, v0, mf], ub=[r0 + 0.2, vmax, m0])
+        constraint!(goddard, :control; lb=0, ub=1)
     end
-    constraint!(goddard, :variable, lb = 0.01, ub = Inf)
+    constraint!(goddard, :variable; lb=0.01, ub=Inf)
     objective!(goddard, :mayer, (x0, xf, v) -> xf[1], :max)
     dynamics!(goddard, (x, u, v) -> F0(x, Cd, beta) + u * F1(x, Tmax, b))
 
-    return ((ocp = goddard, obj = 1.01257, name = "goddard", init = (state = [1.01, 0.05, 0.8],)))
+    return ((ocp=goddard, obj=1.01257, name="goddard", init=(state=[1.01, 0.05, 0.8],)))
 end
 
 # abstratc definition
-function goddard_a(; vmax = 0.1, Tmax = 3.5)
+function goddard_a(; vmax=0.1, Tmax=3.5)
     # constants
     Cd = 310
     beta = 500
@@ -79,10 +81,5 @@ function goddard_a(; vmax = 0.1, Tmax = 3.5)
         r(tf) → max
     end
 
-    return ((
-        ocp = goddard_a,
-        obj = 1.01257,
-        name = "goddard_a",
-        init = (state = [1.01, 0.05, 0.8],),
-    ))
+    return ((ocp=goddard_a, obj=1.01257, name="goddard_a", init=(state=[1.01, 0.05, 0.8],)))
 end

test/problems/insurance.jl

@@ -53,5 +53,5 @@ function insurance()
         ∫(U(t) * fx) → max
     end
 
-    return ((ocp = insurance, obj = 2.059511, name = "insurance", init = nothing))
+    return ((ocp=insurance, obj=2.059511, name="insurance", init=nothing))
 end

test/problems/jackson.jl

@@ -22,5 +22,5 @@ function jackson()
         x[3](4) → max
     end
 
-    return ((ocp = jackson, obj = 0.192011, name = "jackson", init = nothing))
+    return ((ocp=jackson, obj=0.192011, name="jackson", init=nothing))
 end

test/problems/parametric.jl

@@ -25,4 +25,4 @@ function myocp(ρ)
 end
 
 # return problem and objective
-return ((ocp = myocp(1.0), obj = -0.335936, name = "param"))
+return ((ocp=myocp(1.0), obj=-0.335936, name="param"))

test/problems/robbins.jl

@@ -16,5 +16,5 @@ function robbins()
         ∫(alpha * x[1](t) + beta * x[1](t)^2 + gamma * u(t)^2) → min
     end
 
-    return ((ocp = robbins, obj = 20.0, name = "robbins", init = nothing))
+    return ((ocp=robbins, obj=20.0, name="robbins", init=nothing))
 end

test/problems/swimmer.jl

@@ -43,27 +43,29 @@ function swimmer()
             2 * cos(2 * (b1 - b3)) +
             12 * cos(2 * b1 - b3) +
             186 * cos(b3) +
-            37 * cos(2 * b3) - 6 * cos(b1 + b3) - 3 * cos(2 * (b1 + b3)) - 6 * cos(2 * b1 + b3) -
-            6 * cos(b1 + 2 * b3)
+            37 * cos(2 * b3) - 6 * cos(b1 + b3) - 3 * cos(2 * (b1 + b3)) -
+            6 * cos(2 * b1 + b3) - 6 * cos(b1 + 2 * b3)
 
         g11 =
             (
-                -42 * sin(b1 - th) - 2 * sin(2 * b1 - th) - 24 * sin(th) - 300 * sin(b1 + th) -
-                12 * sin(2 * b1 + th) - 6 * sin(b1 - th - 2 * b3) - sin(2 * b1 - th - 2 * b3) +
-                4 * sin(th - 2 * b3) - 12 * sin(b1 + th - 2 * b3) - sin(2 * b1 + th - 2 * b3) +
+                -42 * sin(b1 - th) - 2 * sin(2 * b1 - th) - 24 * sin(th) -
+                300 * sin(b1 + th) - 12 * sin(2 * b1 + th) - 6 * sin(b1 - th - 2 * b3) -
+                sin(2 * b1 - th - 2 * b3) + 4 * sin(th - 2 * b3) -
+                12 * sin(b1 + th - 2 * b3) - sin(2 * b1 + th - 2 * b3) +
                 18 * sin(b1 - th - b3) +
                 8 * sin(th - b3) - 54 * sin(b1 + th - b3) - 2 * sin(2 * b1 + th - b3) -
                 18 * sin(b1 - th + b3) - 38 * sin(th + b3) - 90 * sin(b1 + th + b3) -
-                6 * sin(b1 - th + 2 * b3) - 18 * sin(th + 2 * b3) - 30 * sin(b1 + th + 2 * b3)
+                6 * sin(b1 - th + 2 * b3) - 18 * sin(th + 2 * b3) -
+                30 * sin(b1 + th + 2 * b3)
             ) / (4 * aux)
 
         g12 =
             (
                 -42 * cos(b1 - th) - 2 * cos(2 * b1 - th) +
                 24 * cos(th) +
                 300 * cos(b1 + th) +
-                12 * cos(2 * b1 + th) - 6 * cos(b1 - th - 2 * b3) - cos(2 * b1 - th - 2 * b3) -
-                4 * cos(th - 2 * b3) +
+                12 * cos(2 * b1 + th) - 6 * cos(b1 - th - 2 * b3) -
+                cos(2 * b1 - th - 2 * b3) - 4 * cos(th - 2 * b3) +
                 12 * cos(b1 + th - 2 * b3) +
                 cos(2 * b1 + th - 2 * b3) +
                 18 * cos(b1 - th - b3) - 8 * cos(th - b3) +
@@ -82,7 +84,8 @@ function swimmer()
                 2 * cos(2 * b1) +
                 12 * cos(b1 - 2 * b3) +
                 30 * cos(b1 - b3) +
-                cos(2 * (b1 - b3)) - 4 * cos(2 * b3) - 6 * cos(b1 + b3) - 6 * cos(b1 + 2 * b3)
+                cos(2 * (b1 - b3)) - 4 * cos(2 * b3) - 6 * cos(b1 + b3) -
+                6 * cos(b1 + 2 * b3)
             ) / (2 * aux)
 
         g21 =
@@ -91,8 +94,9 @@ function swimmer()
                 4 * sin(2 * b1 - th) +
                 24 * sin(th) +
                 38 * sin(b1 + th) +
-                18 * sin(2 * b1 + th) - 2 * sin(b1 - th - 2 * b3) - sin(2 * b1 - th - 2 * b3) -
-                2 * sin(th - 2 * b3) - sin(2 * b1 + th - 2 * b3) - 54 * sin(b1 - th - b3) -
+                18 * sin(2 * b1 + th) - 2 * sin(b1 - th - 2 * b3) -
+                sin(2 * b1 - th - 2 * b3) - 2 * sin(th - 2 * b3) -
+                sin(2 * b1 + th - 2 * b3) - 54 * sin(b1 - th - b3) -
                 12 * sin(2 * b1 - th - b3) - 42 * sin(th - b3) + 18 * sin(b1 + th - b3) -
                 6 * sin(2 * b1 + th - b3) +
                 18 * sin(b1 - th + b3) +
@@ -106,10 +110,11 @@ function swimmer()
         g22 =
             (
                 8 * cos(b1 - th) + 4 * cos(2 * b1 - th) - 24 * cos(th) - 38 * cos(b1 + th) -
-                18 * cos(2 * b1 + th) - 2 * cos(b1 - th - 2 * b3) - cos(2 * b1 - th - 2 * b3) +
+                18 * cos(2 * b1 + th) - 2 * cos(b1 - th - 2 * b3) -
+                cos(2 * b1 - th - 2 * b3) +
                 2 * cos(th - 2 * b3) +
-                cos(2 * b1 + th - 2 * b3) - 54 * cos(b1 - th - b3) - 12 * cos(2 * b1 - th - b3) +
-                42 * cos(th - b3) - 18 * cos(b1 + th - b3) +
+                cos(2 * b1 + th - 2 * b3) - 54 * cos(b1 - th - b3) -
+                12 * cos(2 * b1 - th - b3) + 42 * cos(th - b3) - 18 * cos(b1 + th - b3) +
                 6 * cos(2 * b1 + th - b3) +
                 18 * cos(b1 - th + b3) +
                 6 * cos(2 * b1 - th + b3) - 300 * cos(th + b3) - 90 * cos(b1 + th + b3) -
@@ -131,5 +136,5 @@ function swimmer()
         #∫(u1^2 + u2^2) → min
     end
 
-    return ((ocp = swimmer, obj = 0.984273, name = "swimmer", init = nothing))
+    return ((ocp=swimmer, obj=0.984273, name="swimmer", init=nothing))
 end

test/problems/vanderpol.jl

@@ -9,9 +9,10 @@ function vanderpol()
         x ∈ R², state
         u ∈ R, control
         x(0) == [1, 0]
-        ẋ(t) == [x[2](t), epsilon * omega * (1 - x[1](t)^2) * x[2](t) - omega^2 * x[1](t) + u(t)]
+        ẋ(t) ==
+        [x[2](t), epsilon * omega * (1 - x[1](t)^2) * x[2](t) - omega^2 * x[1](t) + u(t)]
         ∫(0.5 * (x[1](t)^2 + x[2](t)^2 + u(t)^2)) → min
     end
 
-    return ((ocp = vanderpol, obj = 1.047921, name = "vanderpol", init = nothing))
+    return ((ocp=vanderpol, obj=1.047921, name="vanderpol", init=nothing))
 end

test/runtests.jl

@@ -12,7 +12,7 @@ include("problems/goddard.jl")
 
 #
 @testset verbose = true showtiming = true "Optimal control tests" begin
-    for name ∈ (
+    for name in (
         :abstract_ocp,
         :basic,
         :continuation,

test/test-nlp.jl

@@ -19,8 +19,8 @@ docp, nlp = direct_transcription(ocp)
 #println(nlp.meta.x0)
 
 using NLPModelsIpopt
-nlp_sol = ipopt(nlp; print_level = 5, mu_strategy = "adaptive", tol = 1e-8, sb = "yes")
-sol = OptimalControlSolution(docp, primal = nlp_sol.solution, dual = nlp_sol.multipliers)
+nlp_sol = ipopt(nlp; print_level=5, mu_strategy="adaptive", tol=1e-8, sb="yes")
+sol = OptimalControlSolution(docp; primal=nlp_sol.solution, dual=nlp_sol.multipliers)
 plot(sol)
 
 using MadNLP
@@ -30,10 +30,12 @@ set_initial_guess(docp, nlp, sol)
 #println(nlp.meta.x0)
 mad_nlp_sol = madnlp(nlp)
 
-docp, nlp = direct_transcription(ocp, init = sol)
+docp, nlp = direct_transcription(ocp; init=sol)
 mad_nlp_sol = madnlp(nlp)
 
-mad_sol = OptimalControlSolution(docp, primal = madnlp_sol.solution, dual = madnlp_sol.multipliers)
+mad_sol = OptimalControlSolution(
+    docp; primal=madnlp_sol.solution, dual=madnlp_sol.multipliers
+)
 plot(mad_sol)
 
 #=

test/test_abstract_ocp.jl

@@ -16,13 +16,13 @@ function test_abstract_ocp()
     end
 
     @testset verbose = true showtiming = true ":double_integrator :min_tf :abstract" begin
-        sol1 = solve(ocp1, print_level = 0, tol = 1e-12)
+        sol1 = solve(ocp1; print_level=0, tol=1e-12)
         @test sol1.objective ≈ 2.0 rtol = 1e-2
     end
 
     @testset verbose = true showtiming = true ":goddard :max_rf :abstract :constr" begin
         ocp3 = goddard_a().ocp
-        sol3 = solve(ocp3, print_level = 0, tol = 1e-12)
+        sol3 = solve(ocp3; print_level=0, tol=1e-12)
         @test sol3.objective ≈ 1.0125 rtol = 1e-2
     end
 end

test/test_basic.jl

@@ -9,6 +9,6 @@ function test_basic()
         ∫(0.5u(t)^2) → min
     end
 
-    sol = solve(ocp; display = false)
+    sol = solve(ocp; display=false)
     @test objective(sol) ≈ 6 atol = 1e-2
 end