added : tests for plot recipes

Author: franckgaga

src/plot_sim.jl

@@ -280,6 +280,7 @@ sim_getu!(mpc::PredictiveController, ry, d, ym) = moveinput!(mpc, ry, d; ym)
                 t, res.D_data[i, :]
             end
         end
+        subplot_base += nd
     end
     # --- plant states x ---
     if plotx

test/runtests.jl

@@ -5,7 +5,7 @@ using ControlSystemsBase
 using Documenter
 using LinearAlgebra
 using JuMP, OSQP, Ipopt
-using RecipesBase
+using Plots
 using Test
 
 

test/test_plot_sim.jl

@@ -12,6 +12,22 @@ sys = [ tf(1.90,[18.0,1])   tf(1.90,[18.0,1])   tf(1.90,[18.0,1]);
     @test res.X_data[:, 1] ≈ zeros(model.nx)
 end
 
+@testset "SimModel Plots" begin
+    model = LinModel(sys, Ts, i_d=[3])
+    res = sim!(model, 15, [1, 3], [-10])
+    p = plot(res, plotx=true)
+    @test p[1][1][:x] ≈ res.T_data
+    @test p[1][1][:y] ≈ res.Y_data[1, :]
+    @test p[2][1][:y] ≈ res.Y_data[2, :]
+    @test p[3][1][:y][1:2:end] ≈ res.U_data[1, :]
+    @test p[4][1][:y][1:2:end] ≈ res.U_data[2, :]
+    @test p[5][1][:y] ≈ res.D_data[1, :]
+    @test p[6][1][:y] ≈ res.X_data[1, :]
+    @test p[7][1][:y] ≈ res.X_data[2, :]
+    @test p[8][1][:y] ≈ res.X_data[3, :]
+    @test p[9][1][:y] ≈ res.X_data[4, :]
+end
+
 @testset "StateEstimator quick simulation" begin
     estim = SteadyKalmanFilter(LinModel(sys, Ts, i_d=[3]))
     res = sim!(estim, 15)
@@ -24,6 +40,37 @@ end
     @test res.X̂_data[:, 1]  ≈ zeros(estim.nx̂)
 end
 
+@testset "StateEstimator Plots" begin
+    estim = SteadyKalmanFilter(LinModel(sys, Ts, i_d=[3]))
+    res = sim!(estim, 15, [1, 3], [-10])
+    p1 = plot(res, plotx=true)
+    @test p1[1][1][:x] ≈ res.T_data
+    @test p1[end-3][1][:y] ≈ res.X_data[1,:]
+    @test p1[end-2][1][:y] ≈ res.X_data[2,:]
+    @test p1[end-1][1][:y] ≈ res.X_data[3,:]
+    @test p1[end-0][1][:y] ≈ res.X_data[4,:]
+    p2 = plot(res, plotx̂=true)
+    @test p2[1][1][:x] ≈ res.T_data
+    @test p2[end-5][1][:y] ≈ res.X̂_data[1,:]
+    @test p2[end-4][1][:y] ≈ res.X̂_data[2,:]
+    @test p2[end-3][1][:y] ≈ res.X̂_data[3,:]
+    @test p2[end-2][1][:y] ≈ res.X̂_data[4,:]
+    @test p2[end-1][1][:y] ≈ res.X̂_data[5,:]
+    @test p2[end-0][1][:y] ≈ res.X̂_data[6,:]
+    p3 = plot(res, plotxwithx̂=true)
+    @test p3[1][1][:x] ≈ res.T_data
+    @test p3[end-5][1][:y] ≈ res.X_data[1,:]
+    @test p3[end-5][2][:y] ≈ res.X̂_data[1,:]
+    @test p3[end-4][1][:y] ≈ res.X_data[2,:]
+    @test p3[end-4][2][:y] ≈ res.X̂_data[2,:]
+    @test p3[end-3][1][:y] ≈ res.X_data[3,:]
+    @test p3[end-3][2][:y] ≈ res.X̂_data[3,:]
+    @test p3[end-2][1][:y] ≈ res.X_data[4,:]
+    @test p3[end-2][2][:y] ≈ res.X̂_data[4,:]
+    @test p3[end-1][1][:y] ≈ res.X̂_data[5,:]
+    @test p3[end-0][1][:y] ≈ res.X̂_data[6,:]
+end
+
 @testset "PredictiveController quick simulation" begin
     mpc = LinMPC(LinModel(sys, Ts, i_d=[3]))
     res = sim!(mpc, 15)
@@ -34,4 +81,41 @@ end
     @test res.D_data[:, 1]  ≈ mpc.estim.model.dop
     @test res.X_data[:, 1]  ≈ zeros(mpc.estim.model.nx)
     @test res.X̂_data[:, 1]  ≈ zeros(mpc.estim.nx̂)
+end
+
+@testset "PredictiveController Plots" begin
+    mpc = LinMPC(LinModel(sys, Ts, i_d=[3]), Lwt=[0.01, 0.01])
+    res = sim!(mpc, 15)
+    p1 = plot(res, plotŷ=true)
+    @test p1[1][1][:x] ≈ res.T_data
+    @test p1[1][1][:y] ≈ res.Y_data[1,:]
+    @test p1[1][2][:y] ≈ res.Ŷ_data[1,:]
+    @test p1[2][1][:y] ≈ res.Y_data[2,:]
+    @test p1[2][2][:y] ≈ res.Ŷ_data[2,:]
+    p2 = plot(res, plotx=true)
+    @test p2[1][1][:x] ≈ res.T_data
+    @test p2[end-3][1][:y] ≈ res.X_data[1,:]
+    @test p2[end-2][1][:y] ≈ res.X_data[2,:]
+    @test p2[end-1][1][:y] ≈ res.X_data[3,:]
+    @test p2[end-0][1][:y] ≈ res.X_data[4,:]
+    p3 = plot(res, plotx̂=true)
+    @test p3[1][1][:x] ≈ res.T_data
+    @test p3[end-5][1][:y] ≈ res.X̂_data[1,:]
+    @test p3[end-4][1][:y] ≈ res.X̂_data[2,:]
+    @test p3[end-3][1][:y] ≈ res.X̂_data[3,:]
+    @test p3[end-2][1][:y] ≈ res.X̂_data[4,:]
+    @test p3[end-1][1][:y] ≈ res.X̂_data[5,:]
+    @test p3[end-0][1][:y] ≈ res.X̂_data[6,:]
+    p4 = plot(res, plotxwithx̂=true)
+    @test p4[1][1][:x] ≈ res.T_data
+    @test p4[end-5][1][:y] ≈ res.X_data[1,:]
+    @test p4[end-5][2][:y] ≈ res.X̂_data[1,:]
+    @test p4[end-4][1][:y] ≈ res.X_data[2,:]
+    @test p4[end-4][2][:y] ≈ res.X̂_data[2,:]
+    @test p4[end-3][1][:y] ≈ res.X_data[3,:]
+    @test p4[end-3][2][:y] ≈ res.X̂_data[3,:]
+    @test p4[end-2][1][:y] ≈ res.X_data[4,:]
+    @test p4[end-2][2][:y] ≈ res.X̂_data[4,:]
+    @test p4[end-1][1][:y] ≈ res.X̂_data[5,:]
+    @test p4[end-0][1][:y] ≈ res.X̂_data[6,:]
 end