Merge pull request #229 from dionysos-dev/clean_problem_optim_adrien

`src/problem` and `src/optim` organization improvements

docs/src/examples/DC-DC converter.jl

@@ -49,6 +49,7 @@ const ST = DI.System
 const SY = DI.Symbolic
 const CO = DI.Control
 const OP = DI.Optim
+const AB = OP.Abstraction
 
 # ### Definition of the system
 # we can import the module containing the DCDC problem like this 
@@ -65,7 +66,7 @@ hu = SVector(1)
 input_grid = DO.GridFree(u0, hu)
 
 using JuMP
-optimizer = MOI.instantiate(OP.Abstraction.Optimizer)
+optimizer = MOI.instantiate(AB.SCOTSAbstraction.Optimizer)
 MOI.set(optimizer, MOI.RawOptimizerAttribute("problem"), problem)
 MOI.set(optimizer, MOI.RawOptimizerAttribute("state_grid"), state_grid)
 MOI.set(optimizer, MOI.RawOptimizerAttribute("input_grid"), input_grid)

docs/src/examples/Path planning.jl

@@ -44,9 +44,10 @@ using StaticArrays, Plots
 using Dionysos
 const DI = Dionysos
 const DO = DI.Domain
-const OP = DI.Optim
 const CO = DI.Control
 const UT = DI.Utils
+const OP = DI.Optim
+const AB = OP.Abstraction
 
 # And the file defining the hybrid system for this problem
 include(joinpath(dirname(dirname(pathof(Dionysos))), "problems", "PathPlanning.jl"))
@@ -73,10 +74,10 @@ u0 = SVector(0.0, 0.0);
 h = SVector(0.3, 0.3);
 input_grid = DO.GridFree(u0, h);
 
-# We now solve the optimal control problem with the `Abstraction.Optimizer`.
+# We now solve the optimal control problem with the `Abstraction.SCOTSAbstraction.Optimizer`.
 
 using JuMP
-optimizer = MOI.instantiate(OP.Abstraction.Optimizer)
+optimizer = MOI.instantiate(AB.SCOTSAbstraction.Optimizer)
 MOI.set(optimizer, MOI.RawOptimizerAttribute("problem"), problem)
 MOI.set(optimizer, MOI.RawOptimizerAttribute("state_grid"), state_grid)
 MOI.set(optimizer, MOI.RawOptimizerAttribute("input_grid"), input_grid)
@@ -105,18 +106,18 @@ fig = plot(aspect_ratio=:equal);
 ##We display the concrete domain
 plot!(problem.system.X, color=:yellow, opacity=0.5);
 
-##We display the abstract domain
-abstract_system = OP.Abstraction.get_abstract_system(optimizer)
-plot!(abstract_system.Xdom, color=:blue, opacity=0.5);
+# We display the abstract domain
+abstract_system = AB.SCOTSAbstraction.get_abstract_system(optimizer)
+plot!(abstract_system.Xdom, color=:blue, opacity=0.5)
 
 ##We display the concrete specifications
 plot!(problem.initial_set, color=:green, opacity=0.2);
 plot!(problem.target_set; dims=[1,2], color=:red, opacity=0.2);
 
-##We display the abstract specifications
-abstract_problem = OP.Abstraction.get_abstract_problem(optimizer)
-plot!(abstract_problem.initial_set, color=:green);
-plot!(abstract_problem.target_set, color=:red);
+# We display the abstract specifications
+abstract_problem = AB.SCOTSAbstraction.get_abstract_problem(optimizer)
+plot!(abstract_problem.initial_set, color=:green)
+plot!(abstract_problem.target_set, color=:red)
 
 ##We display the concrete trajectory
 plot!(fig, UT.DrawTrajectory(x_traj), ms=0.5)

docs/src/examples/State-feedback Abstraction PWA System.jl

@@ -82,6 +82,7 @@ const SY = DI.Symbolic
 const CO = DI.Control
 const PR = DI.Problem
 const OP = DI.Optim
+const AB = OP.Abstraction
 
 # Let us now define the state space $X$ within which we are searching for a optimal solution.
 max_x = 2 # bound on |X|_∞
@@ -100,10 +101,10 @@ P = (1/n_x)*diagm((X_step./2).^(-2))
 state_grid = DO.GridEllipsoidalRectangular(X_origin, X_step, P, rectX) 
 
 # At this point, we instantiate the optimizer provided in Dionysos that creates ellipsoidal-based 
-# abstractions `OptimizerEllipsoids`
+# abstractions `EllipsoidsAbstraction.Optimizer`
 
 using JuMP
-optimizer = MOI.instantiate(OP.Abstraction.OptimizerEllipsoids) # 
+optimizer = MOI.instantiate(AB.EllipsoidsAbstraction.Optimizer) # 
 
 MOI.set(optimizer, MOI.RawOptimizerAttribute("problem"), problem)
 MOI.set(optimizer, MOI.RawOptimizerAttribute("state_grid"), state_grid)