Jc/standardize trajectories 2 (#316)

* suppress Control folder * standardize trajectories * format
dionysos-dev · Oct 19, 2023 · 7256c40 · 7256c40
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diff --git a/docs/src/examples/DC-DC converter.jl b/docs/src/examples/DC-DC converter.jl
@@ -47,7 +47,6 @@ const UT = DI.Utils
 const DO = DI.Domain
 const ST = DI.System
 const SY = DI.Symbolic
-const CO = DI.Control
 const OP = DI.Optim
 const AB = OP.Abstraction
 
@@ -82,12 +81,12 @@ concrete_controller = MOI.get(optimizer, MOI.RawOptimizerAttribute("concrete_con
 # as well as the true initial state `x0` which is contained in the initial state-space defined previously.
 nstep = 300
 x0 = SVector(1.2, 5.6)
-x_traj, u_traj =
-    CO.get_closed_loop_trajectory(concrete_system.f, concrete_controller, x0, nstep)
+control_trajectory =
+    ST.get_closed_loop_trajectory(concrete_system.f, concrete_controller, x0, nstep)
 
 fig = plot(; aspect_ratio = :equal);
 plot!(concrete_system.X);
-plot!(UT.DrawTrajectory(x_traj))
+plot!(control_trajectory)
 
 # ### References
 # 1. A. Girard, G. Pola and P. Tabuada, "Approximately Bisimilar Symbolic Models for Incrementally Stable Switched Systems," in IEEE Transactions on Automatic Control, vol. 55, no. 1, pp. 116-126, Jan. 2010.

diff --git a/docs/src/examples/Getting Started.jl b/docs/src/examples/Getting Started.jl
@@ -23,7 +23,6 @@ const DI = Dionysos
 const UT = DI.Utils
 const DO = DI.Domain
 const ST = DI.System
-const CO = DI.Control
 const SY = DI.Symbolic
 
 # Additionally, we will short the submodules accondingly 

diff --git a/docs/src/examples/Gol, Lazar & Belta (2013).jl b/docs/src/examples/Gol, Lazar & Belta (2013).jl
@@ -30,7 +30,7 @@ import Ipopt
 using Dionysos
 const DI = Dionysos
 const UT = DI.Utils
-const CO = DI.Control
+const ST = DI.System
 const OP = DI.Optim
 
 # And the file defining the hybrid system for this problem
@@ -86,7 +86,7 @@ objective_value = MOI.get(optimizer, MOI.ObjectiveValue())
 @test objective_value ≈ 11.38 atol = 1e-2     #src
 
 # and recover the corresponding continuous trajectory
-xu = MOI.get(optimizer, CO.ContinuousTrajectoryAttribute());
+xu = MOI.get(optimizer, ST.ContinuousTrajectoryAttribute());
 
 # ## A little bit of data visualization now:
 

diff --git a/docs/src/examples/Hierarchical-abstraction.jl b/docs/src/examples/Hierarchical-abstraction.jl
@@ -13,7 +13,6 @@ const UT = DI.Utils
 const DO = DI.Domain
 const ST = DI.System
 const SY = DI.Symbolic
-const CO = DI.Control
 const PR = DI.Problem
 const OP = DI.Optim
 const AB = OP.Abstraction
@@ -160,9 +159,12 @@ println("Solved : ", optimizer.solved)
 
 # ## Simulation
 x0 = UT.get_center(concrete_problem.initial_set)
-x_traj, u_traj, cost_traj = AB.HierarchicalAbstraction.simulate_trajectory(optimizer, x0)
-cost = sum(cost_traj);
-println("Goal set reached: $(x_traj[end]∈concrete_problem.target_set)")
+cost_control_trajectory =
+    AB.HierarchicalAbstraction.get_closed_loop_trajectory(optimizer, x0)
+cost = sum(cost_control_trajectory.costs.seq);
+println(
+    "Goal set reached: $(ST.get_state(cost_control_trajectory, ST.length(cost_control_trajectory))∈concrete_problem.target_set)",
+)
 println("Cost:\t $(cost)")
 
 # ## Display the results
@@ -180,7 +182,7 @@ plot!(concrete_problem.initial_set; color = :green, opacity = 0.8);
 plot!(concrete_problem.target_set; dims = [1, 2], color = :red, opacity = 0.8);
 
 #We display the concrete trajectory
-plot!(UT.DrawTrajectory(x_traj); ms = 0.5)
+plot!(cost_control_trajectory; ms = 0.5)
 
 # # Display the lazy abstraction 
 fig = plot(; aspect_ratio = :equal);
@@ -191,4 +193,4 @@ plot!(
     heuristic = false,
     fine = true,
 )
-plot!(UT.DrawTrajectory(x_traj); ms = 0.5)
+plot!(cost_control_trajectory; ms = 0.5)
diff --git a/docs/src/examples/Lazy-Ellipsoids-Abstraction.jl b/docs/src/examples/Lazy-Ellipsoids-Abstraction.jl
@@ -11,7 +11,6 @@ const UT = DI.Utils
 const DO = DI.Domain
 const ST = DI.System
 const SY = DI.Symbolic
-const CO = DI.Control
 const PR = DI.Problem
 const OP = DI.Optim
 const AB = OP.Abstraction
@@ -73,7 +72,7 @@ reached(x) = x ∈ concrete_problem.target_set
 nstep = typeof(concrete_problem.time) == PR.Infinity ? 100 : concrete_problem.time; # max num of steps
 # We simulate the closed loop trajectory
 x0 = concrete_problem.initial_set.c
-x_traj, u_traj, cost_traj = CO.get_closed_loop_trajectory(
+cost_control_trajectory = ST.get_closed_loop_trajectory(
     concrete_system.f_eval,
     concrete_controller,
     cost_eval,
@@ -83,7 +82,7 @@ x_traj, u_traj, cost_traj = CO.get_closed_loop_trajectory(
     noise = true,
 )
 cost_bound = concrete_lyap_fun(x0)
-cost_true = sum(cost_traj);
+cost_true = sum(cost_control_trajectory.costs.seq);
 println("Goal set reached")
 println("Guaranteed cost:\t $(cost_bound)")
 println("True cost:\t\t $(cost_true)")
@@ -141,6 +140,6 @@ for obs in concrete_system.obstacles
     plot!(obs; color = :black)
 end
 plot!(abstract_system; arrowsB = false, cost = true);
-plot!(UT.DrawTrajectory(x_traj); color = :black)
+plot!(cost_control_trajectory; color = :black)
 
 @test cost_true <= cost_bound             #src
diff --git a/docs/src/examples/Lazy-abstraction-reachability.jl b/docs/src/examples/Lazy-abstraction-reachability.jl
@@ -30,7 +30,6 @@ const UT = DI.Utils
 const DO = DI.Domain
 const ST = DI.System
 const SY = DI.Symbolic
-const CO = DI.Control
 const PR = DI.Problem
 const OP = DI.Optim
 const AB = OP.Abstraction
@@ -148,7 +147,7 @@ reached(x) = x ∈ concrete_problem.target_set
 nstep = typeof(concrete_problem.time) == PR.Infinity ? 100 : concrete_problem.time; # max num of steps
 # We simulate the closed loop trajectory
 x0 = UT.get_center(concrete_problem.initial_set)
-x_traj, u_traj, cost_traj = CO.get_closed_loop_trajectory(
+cost_control_trajectory = ST.get_closed_loop_trajectory(
     concrete_system.f_eval,
     concrete_controller,
     cost_eval,
@@ -159,7 +158,7 @@ x_traj, u_traj, cost_traj = CO.get_closed_loop_trajectory(
 )
 
 cost_bound = concrete_lyap_fun(x0)
-cost_true = sum(cost_traj);
+cost_true = sum(cost_control_trajectory.costs.seq);
 println("Goal set reached")
 println("Guaranteed cost:\t $(cost_bound)")
 println("True cost:\t\t $(cost_true)")
@@ -178,7 +177,7 @@ plot!(concrete_problem.initial_set; color = :green, opacity = 0.8);
 plot!(concrete_problem.target_set; dims = [1, 2], color = :red, opacity = 0.8);
 
 #We display the concrete trajectory
-plot!(UT.DrawTrajectory(x_traj); ms = 0.5)
+plot!(cost_control_trajectory; ms = 0.5)
 
 # # Display the abstraction and Lyapunov-like function
 fig = plot(; aspect_ratio = :equal);

diff --git a/docs/src/examples/Path planning.jl b/docs/src/examples/Path planning.jl
@@ -47,7 +47,6 @@ const UT = DI.Utils
 const DO = DI.Domain
 const ST = DI.System
 const SY = DI.Symbolic
-const CO = DI.Control
 const PR = DI.Problem
 const OP = DI.Optim
 const AB = OP.Abstraction
@@ -102,7 +101,7 @@ function reached(x)
     end
 end
 x0 = SVector(0.4, 0.4, 0.0)
-x_traj, u_traj = CO.get_closed_loop_trajectory(
+control_trajectory = ST.get_closed_loop_trajectory(
     concrete_system.f,
     concrete_controller,
     x0,
@@ -133,7 +132,7 @@ plot!(
 );
 
 # We display the concrete trajectory
-plot!(UT.DrawTrajectory(x_traj); ms = 0.5)
+plot!(control_trajectory; ms = 0.5)
 
 # ### References
 # 1. G. Reissig, A. Weber and M. Rungger, "Feedback Refinement Relations for the Synthesis of Symbolic Controllers," in IEEE Transactions on Automatic Control, vol. 62, no. 4, pp. 1781-1796.

diff --git a/docs/src/examples/State-feedback Abstraction PWA System.jl b/docs/src/examples/State-feedback Abstraction PWA System.jl
@@ -48,7 +48,6 @@ const UT = DI.Utils
 const DO = DI.Domain
 const ST = DI.System
 const SY = DI.Symbolic
-const CO = DI.Control
 const PR = DI.Problem
 const OP = DI.Optim
 const AB = OP.Abstraction
@@ -148,7 +147,7 @@ end
 
 # We simulate the closed loop trajectory
 x0 = concrete_problem.initial_set
-x_traj, u_traj, cost_traj = CO.get_closed_loop_trajectory(
+cost_control_trajectory = ST.get_closed_loop_trajectory(
     f_eval1,
     concrete_controller,
     cost_eval,
@@ -157,7 +156,7 @@ x_traj, u_traj, cost_traj = CO.get_closed_loop_trajectory(
     stopping = reached,
 )
 cost_bound = concrete_lyap_fun(x0)
-cost_true = sum(cost_traj);
+cost_true = sum(cost_control_trajectory.costs.seq);
 println("Goal set reached")
 println("Guaranteed cost:\t $(cost_bound)")
 println("True cost:\t\t $(cost_true)")
@@ -224,7 +223,7 @@ xlabel!("\$x_1\$");
 ylabel!("\$x_2\$");
 title!("Trajectory and Lyapunov-like Fun.");
 plot!(abstract_system; arrowsB = false, cost = true, lyap_fun = optimizer.lyap);
-plot!(UT.DrawTrajectory(x_traj); color = :black)
+plot!(cost_control_trajectory; color = :black)
 
 @test cost_bound ≈ 2.35825 rtol = 1e-3    #src
 @test cost_true <= cost_bound             #src

diff --git a/docs/src/reference/System.md b/docs/src/reference/System.md
@@ -20,6 +20,6 @@ Dionysos.System.AffineController
 
 ## SHOULD BE REMOVED 
 ```@docs
-Dionysos.Control.DiscreteTrajectory
-Dionysos.Control.ContinuousTrajectory
+Dionysos.System.DiscreteTrajectory
+Dionysos.System.ContinuousTrajectory
 ```
diff --git a/problems/dc_dc.jl b/problems/dc_dc.jl
@@ -12,7 +12,6 @@ const UT = DI.Utils
 const DO = DI.Domain
 const PB = DI.Problem
 const ST = DI.System
-const CO = DI.Control
 const SY = DI.Symbolic
 
 function dynamicofsystem(

diff --git a/problems/gol_lazar_belta.jl b/problems/gol_lazar_belta.jl
@@ -3,7 +3,6 @@ module GolLazarBelta
 import Dionysos
 const DI = Dionysos
 const UT = DI.Utils
-const CO = DI.Control
 const PR = DI.Problem
 
 using FillArrays

diff --git a/problems/non_linear.jl b/problems/non_linear.jl
@@ -4,13 +4,11 @@ using StaticArrays, LinearAlgebra, Symbolics, IntervalArithmetic
 using MathematicalSystems, HybridSystems
 
 import Dionysos
-using Dionysos.Control
 using Dionysos.Problem
 const DI = Dionysos
 const UT = DI.Utils
 const DO = DI.Domain
 const ST = DI.System
-const CO = DI.Control
 const SY = DI.Symbolic
 const PR = DI.Problem
 

diff --git a/problems/path_planning.jl b/problems/path_planning.jl
@@ -5,7 +5,6 @@ using MathematicalSystems, HybridSystems
 using Dionysos
 const UT = Dionysos.Utils
 const DO = Dionysos.Domain
-const CO = Dionysos.Control
 const PB = Dionysos.Problem
 const ST = Dionysos.System
 

diff --git a/problems/pwa_sys.jl b/problems/pwa_sys.jl
@@ -5,13 +5,11 @@ using MathematicalSystems, HybridSystems
 using FillArrays, CDDLib
 
 import Dionysos
-using Dionysos.Control
 using Dionysos.Problem
 const DI = Dionysos
 const UT = DI.Utils
 const DO = DI.Domain
 const ST = DI.System
-const CO = DI.Control
 const SY = DI.Symbolic
 const PR = DI.Problem
 

diff --git a/problems/simple_problem.jl b/problems/simple_problem.jl
@@ -4,13 +4,11 @@ using StaticArrays, LinearAlgebra, Symbolics, IntervalArithmetic
 using MathematicalSystems, HybridSystems
 
 import Dionysos
-using Dionysos.Control
 using Dionysos.Problem
 const DI = Dionysos
 const UT = DI.Utils
 const DO = DI.Domain
 const ST = DI.System
-const CO = DI.Control
 const SY = DI.Symbolic
 const PR = DI.Problem
 

diff --git a/src/Dionysos.jl b/src/Dionysos.jl
@@ -4,7 +4,6 @@ include("utils/utils.jl")
 include("domain/domain.jl")
 include("system/system.jl")
 include("symbolic/symbolic.jl")
-include("control/control.jl")
 include("problem/problem.jl")
 include("optim/optim.jl")
 include("mapping/mapping.jl")

diff --git a/src/control/control.jl b/src/control/control.jl