tests ok

Project.toml

@@ -22,20 +22,22 @@ StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
 Unicode = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"
 
 [weakdeps]
-Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819"
 JSON3 = "0f8b85d8-7281-11e9-16c2-39a750bddbf1"
+Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 
 [extensions]
-CTBasePlots = "Plots"
 CTBaseLoadSave = ["JLD2", "JSON3"]
+CTBasePlots = "Plots"
 
 [compat]
 DataStructures = "0.18"
 DifferentiationInterface = "0.5"
 DocStringExtensions = "0.9"
 ForwardDiff = "0.10"
 Interpolations = "0.15"
+JLD2 = "0.5"
+JSON3 = "1"
 MLStyle = "0.4"
 MacroTools = "0.5"
 Parameters = "0.12"

src/optimal_control_solution-setters.jl

@@ -224,6 +224,142 @@ function OptimalControlSolution(
     )
 end
 
+
+"""
+$(TYPEDSIGNATURES)
+    
+Build OCP functional solution from discrete solution (given as raw variables and multipliers plus some optional infos)
+"""
+function OptimalControlSolution(
+    ocp::OptimalControlModel,
+    T,
+    X,
+    U,
+    v,
+    P;
+    objective = 0,
+    iterations = 0,
+    constraints_violation = 0,
+    message = "No msg",
+    stopping = nothing,
+    success = nothing,
+    constraints_types = (nothing, nothing, nothing, nothing, nothing),
+    constraints_mult = (nothing, nothing, nothing, nothing, nothing),
+    box_multipliers = (nothing, nothing, nothing, nothing, nothing, nothing),
+)
+    dim_x = state_dimension(ocp)
+    dim_u = control_dimension(ocp)
+    dim_v = variable_dimension(ocp)
+
+    # check that time grid is strictly increasing
+    # if not proceed with list of indexes as time grid
+    if !issorted(T, lt = <=)
+        println(
+            "WARNING: time grid at solution is not strictly increasing, replacing with list of indices...",
+        )
+        println(T)
+        dim_NLP_steps = length(T) - 1
+        T = LinRange(0, dim_NLP_steps, dim_NLP_steps + 1)
+    end
+
+    # variables: remove additional state for lagrange cost
+    x = ctinterpolate(T, matrix2vec(X[:, 1:dim_x], 1))
+    p = ctinterpolate(T[1:(end - 1)], matrix2vec(P[:, 1:dim_x], 1))
+    u = ctinterpolate(T, matrix2vec(U[:, 1:dim_u], 1))
+
+    # force scalar output when dimension is 1
+    fx = (dim_x == 1) ? deepcopy(t -> x(t)[1]) : deepcopy(t -> x(t))
+    fu = (dim_u == 1) ? deepcopy(t -> u(t)[1]) : deepcopy(t -> u(t))
+    fp = (dim_x == 1) ? deepcopy(t -> p(t)[1]) : deepcopy(t -> p(t))
+    var = (dim_v == 1) ? v[1] : v
+
+    # misc infos
+    infos = Dict{Symbol, Any}()
+    infos[:constraints_violation] = constraints_violation
+
+    # nonlinear constraints and multipliers
+    control_constraints = t -> ctinterpolate(T, matrix2vec(constraints_types[1], 1))(t)
+    mult_control_constraints = t -> ctinterpolate(T, matrix2vec(constraints_mult[1], 1))(t)
+    state_constraints = t -> ctinterpolate(T, matrix2vec(constraints_types[2], 1))(t)
+    mult_state_constraints = t -> ctinterpolate(T, matrix2vec(constraints_mult[2], 1))(t)
+    mixed_constraints = t -> ctinterpolate(T, matrix2vec(constraints_types[3], 1))(t)
+    mult_mixed_constraints = t -> ctinterpolate(T, matrix2vec(constraints_mult[3], 1))(t)
+
+    # boundary and variable constraints
+    boundary_constraints = constraints_types[4]
+    mult_boundary_constraints = constraints_mult[4]
+    variable_constraints = constraints_types[5]
+    mult_variable_constraints = constraints_mult[5]
+
+    # box constraints multipliers
+    mult_state_box_lower = t -> ctinterpolate(T, matrix2vec(box_multipliers[1][:, 1:dim_x], 1))(t)
+    mult_state_box_upper = t -> ctinterpolate(T, matrix2vec(box_multipliers[2][:, 1:dim_x], 1))
+    mult_control_box_lower = t -> ctinterpolate(T, matrix2vec(box_multipliers[3][:, 1:dim_u], 1))(t)
+    mult_control_box_upper = t -> ctinterpolate(T, matrix2vec(box_multipliers[4][:, 1:dim_u], 1))
+    mult_variable_box_lower, mult_variable_box_upper = box_multipliers[5], box_multipliers[6]
+
+    # build and return solution
+    if is_variable_dependent(ocp)
+        return OptimalControlSolution(
+            ocp;
+            state = fx,
+            control = fu,
+            objective = objective,
+            costate = fp,
+            time_grid = T,
+            variable = var,
+            iterations = iterations,
+            stopping = stopping,
+            message = message,
+            success = success,
+            infos = infos,
+            control_constraints = control_constraints,
+            state_constraints = state_constraints,
+            mixed_constraints = mixed_constraints,
+            boundary_constraints = boundary_constraints,
+            variable_constraints = variable_constraints,
+            mult_control_constraints = mult_control_constraints,
+            mult_state_constraints = mult_state_constraints,
+            mult_mixed_constraints = mult_mixed_constraints,
+            mult_boundary_constraints = mult_boundary_constraints,
+            mult_variable_constraints = mult_variable_constraints,
+            mult_state_box_lower = mult_state_box_lower,
+            mult_state_box_upper = mult_state_box_upper,
+            mult_control_box_lower = mult_control_box_lower,
+            mult_control_box_upper = mult_control_box_upper,
+            mult_variable_box_lower = mult_variable_box_lower,
+            mult_variable_box_upper = mult_variable_box_upper,
+        )
+    else
+        return OptimalControlSolution(
+            ocp;
+            state = fx,
+            control = fu,
+            objective = objective,
+            costate = fp,
+            time_grid = T,
+            iterations = iterations,
+            stopping = stopping,
+            message = message,
+            success = success,
+            infos = infos,
+            control_constraints = control_constraints,
+            state_constraints = state_constraints,
+            mixed_constraints = mixed_constraints,
+            boundary_constraints = boundary_constraints,
+            mult_control_constraints = mult_control_constraints,
+            mult_state_constraints = mult_state_constraints,
+            mult_mixed_constraints = mult_mixed_constraints,
+            mult_boundary_constraints = mult_boundary_constraints,
+            mult_state_box_lower = mult_state_box_lower,
+            mult_state_box_upper = mult_state_box_upper,
+            mult_control_box_lower = mult_control_box_lower,
+            mult_control_box_upper = mult_control_box_upper,
+        )
+    end
+end
+
+
 # setters
 #state!(sol::OptimalControlSolution, state::Function) = (sol.state = state; nothing)
 #control!(sol::OptimalControlSolution, control::Function) = (sol.control = control; nothing)

src/optimal_control_solution-type.jl

@@ -84,5 +84,9 @@ export export_ocp_solution
 export import_ocp_solution
 
 # placeholders (see extension CTBaseLoadSave)
-function export_ocp_solution end
-function import_ocp_solution end
+function export_ocp_solution(args...; kwargs...) 
+    error("Requires JLD2 and JSON3 packages")
+end
+function import_ocp_solution(args...; kwargs...)
+    error("Requires JLD2 and JSON3 packages")
+end

test/Project.toml

@@ -1,6 +1,8 @@
 [deps]
 Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
 DifferentiationInterface = "a0c0ee7d-e4b9-4e03-894e-1c5f64a51d63"
+JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819"
+JSON3 = "0f8b85d8-7281-11e9-16c2-39a750bddbf1"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 

test/runtests.jl

@@ -5,6 +5,7 @@ using Aqua
 using CTBase
 using DifferentiationInterface: AutoForwardDiff
 using Plots
+using JLD2, JSON3
 using Test
 
 # functions and types that are not exported

test/solution_test.json

@@ -0,0 +1,108 @@
+{
+    "time_grid": [
+        0,
+        0.1111111111111111,
+        0.2222222222222222,
+        0.3333333333333333,
+        0.4444444444444444,
+        0.5555555555555556,
+        0.6666666666666666,
+        0.7777777777777778,
+        0.8888888888888888,
+        1
+    ],
+    "objective": 1,
+    "control": [
+        0,
+        0.2222222222222222,
+        0.4444444444444444,
+        0.6666666666666666,
+        0.8888888888888888,
+        1.1111111111111112,
+        1.3333333333333333,
+        1.5555555555555556,
+        1.7777777777777777,
+        2
+    ],
+    "costate": [
+        [
+            0,
+            -1
+        ],
+        [
+            0.1111111111111111,
+            -0.8888888888888888
+        ],
+        [
+            0.2222222222222222,
+            -0.7777777777777778
+        ],
+        [
+            0.3333333333333333,
+            -0.6666666666666667
+        ],
+        [
+            0.4444444444444444,
+            -0.5555555555555556
+        ],
+        [
+            0.5555555555555556,
+            -0.4444444444444444
+        ],
+        [
+            0.6666666666666666,
+            -0.33333333333333337
+        ],
+        [
+            0.7777777777777778,
+            -0.2222222222222222
+        ],
+        [
+            0.8888888888888888,
+            -0.11111111111111116
+        ]
+    ],
+    "variable": null,
+    "state": [
+        [
+            0,
+            1
+        ],
+        [
+            0.1111111111111111,
+            1.1111111111111112
+        ],
+        [
+            0.2222222222222222,
+            1.2222222222222223
+        ],
+        [
+            0.3333333333333333,
+            1.3333333333333333
+        ],
+        [
+            0.4444444444444444,
+            1.4444444444444444
+        ],
+        [
+            0.5555555555555556,
+            1.5555555555555556
+        ],
+        [
+            0.6666666666666666,
+            1.6666666666666665
+        ],
+        [
+            0.7777777777777778,
+            1.7777777777777777
+        ],
+        [
+            0.8888888888888888,
+            1.8888888888888888
+        ],
+        [
+            1,
+            2
+        ]
+    ]
+}

test/test_solution.jl

@@ -12,9 +12,9 @@ function test_solution()
     end
 
     times = range(0, 1, 10)
-    x = t -> t
+    x = t -> [t, t+1]
     u = t -> 2t
-    p = t -> t
+    p = t -> [t, t-1]
     obj = 1
     sol = OptimalControlSolution(
         ocp;
@@ -33,6 +33,12 @@ function test_solution()
     @test all(control_discretized(sol) .== u.(times))
     @test all(costate_discretized(sol) .== p.(times))
 
+    # test export / read solution in JSON format (NB. requires time grid in solution !)
+    println(sol.time_grid)
+    export_ocp_solution(sol; filename_prefix = "solution_test", format = :JSON)
+    sol_reloaded = import_ocp_solution(ocp; filename_prefix = "solution_test", format = :JSON)
+    @test sol.objective == sol_reloaded.objective
+
     # NonFixed ocp
     @def ocp begin
         v ∈ R, variable
@@ -45,7 +51,7 @@ function test_solution()
         ∫(0.5u(t)^2) → min
     end
 
-    x = t -> t
+    x = t -> [t, t+1]
     u = t -> 2t
     obj = 1
     v = 1
@@ -55,19 +61,9 @@ function test_solution()
     @test typeof(sol) == OptimalControlSolution
     @test_throws UndefKeywordError OptimalControlSolution(ocp; x, u, obj)
 
-
     # test save / load solution in JLD2 format
-    @testset verbose = true showtiming = true ":save_load :JLD2" begin
-        export_ocp_solution(sol; filename_prefix = "solution_test")
-        sol_reloaded = import_ocp_solution(ocp; filename_prefix = "solution_test")
-        @test sol.objective == sol_reloaded.objective
-    end
-
-    # test export / read solution in JSON format
-    @testset verbose = true showtiming = true ":export_read :JSON" begin
-        export_ocp_solution(sol; filename_prefix = "solution_test", format = :JSON)
-        sol_reloaded = import_ocp_solution(ocp; filename_prefix = "solution_test", format = :JSON)
-        @test sol.objective == sol_reloaded.objective
-    end
+    export_ocp_solution(sol; filename_prefix = "solution_test")
+    sol_reloaded = import_ocp_solution(ocp; filename_prefix = "solution_test")
+    @test sol.objective == sol_reloaded.objective
 
 end