WIP change from JSC to SynchToolkit

Author: baggepinnen

Project.toml

@@ -4,21 +4,22 @@ authors = ["Fredrik Bagge Carlson"]
 version = "0.1.1"
 
 [deps]
-DiffEqCallbacks = "459566f4-90b8-5000-8ac3-15dfb0a30def"
-JuliaSimCompiler = "8391cb6b-4921-5777-4e45-fd9aab8cb88d"
+HeptagonToolkit = "530fcfb7-09ad-4628-8fc4-9627497dc745"
 ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78"
 ModelingToolkitStandardLibrary = "16a59e39-deab-5bd0-87e4-056b12336739"
 OrdinaryDiffEqTsit5 = "b1df2697-797e-41e3-8120-5422d3b24e4a"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
+SynchToolkit = "f500ffa8-9682-42ac-8d34-0ca7926c2e94"
 
 [compat]
-DiffEqCallbacks = "~3.8"
+HeptagonToolkit = "1.0.0"
 ModelingToolkit = "9"
 ModelingToolkitStandardLibrary = "2"
 OrdinaryDiffEqTsit5 = "1.1.0"
 Random = "1"
 StableRNGs = "1"
+SynchToolkit = "0.1.0"
 julia = "1.10"
 
 [extras]

docs/src/examples/dc_motor_pi.md

@@ -110,7 +110,7 @@ Below, we re-model the system, this time with a discrete-time controller: [`Disc
 
 ```@example dc_motor_pi
 using ModelingToolkitSampledData
-using JuliaSimCompiler
+using SynchToolkit
 
 z = ShiftIndex()
 @mtkmodel DiscreteClosedLoop begin
@@ -159,7 +159,7 @@ end
 
 @named disc_model = DiscreteClosedLoop()
 disc_model = complete(disc_model)
-ssys = structural_simplify(IRSystem(disc_model)) # Conversion to an IRSystem from JuliaSimCompiler is required for sampled-data systems
+ssys = structural_simplify(disc_model; additional_passes=[SynchToolkit.compile_lustre])
 
 disc_prob = ODEProblem(ssys, [unknowns(disc_model) .=> 0.0; disc_model.pi_controller.I(z-1) => 0; disc_model.pi_controller.eI(z-1) => 0], (0, 2.0))
 disc_sol = solve(disc_prob, Tsit5())
@@ -213,7 +213,7 @@ end
 
 @named cascade = Cascade()
 cascade = complete(cascade)
-ssys = structural_simplify(IRSystem(cascade))
+ssys = structural_simplify(cascade; additional_passes=[SynchToolkit.compile_lustre])
 i = cascade.inner
 cascade_prob = ODEProblem(ssys, [
     unknowns(cascade) .=> 0.0;

docs/src/examples/onoffcontroller.md

@@ -10,7 +10,7 @@ where ``τ = 100`` is a time constant. The controller ``u = f(x)`` is an on/off
 using ModelingToolkit, ModelingToolkitSampledData, OrdinaryDiffEqTsit5, Plots
 using ModelingToolkit: t_nounits as t, D_nounits as D
 using ModelingToolkitStandardLibrary.Blocks
-using JuliaSimCompiler
+using SynchToolkit
 cl = Clock(1)
 z = ShiftIndex(cl)
 @mtkmodel OnOffModel begin
@@ -29,7 +29,7 @@ z = ShiftIndex(cl)
 end
 @named m = OnOffModel()
 m = complete(m)
-ssys = structural_simplify(IRSystem(m))
+ssys = structural_simplify(m; additional_passes=[SynchToolkit.compile_lustre])
 prob = ODEProblem(ssys, [m.onoff.y(z-1) => 0], (0.0, 40.0))
 sol = solve(prob, Tsit5(), dtmax=0.1)
 plot(sol, idxs=[m.x, m.onoff.y], title="On-off control of an unstable first-order system")

docs/src/examples/sliding_mode_control.md

@@ -13,7 +13,7 @@ which yields the switching variable ``s = ė + e``, encoded in the function ``s
 using ModelingToolkit, ModelingToolkitSampledData, OrdinaryDiffEqTsit5, Plots
 using ModelingToolkit: t_nounits as t, D_nounits as D
 using ModelingToolkitStandardLibrary.Blocks
-using JuliaSimCompiler
+using SynchToolkit
 dt = 0.01
 clock = Clock(dt)
 z = ShiftIndex(clock)
@@ -80,7 +80,7 @@ end
 end
 @named m = ClosedLoopModel()
 m = complete(m)
-ssys = structural_simplify(IRSystem(m))
+ssys = structural_simplify(m; additional_passes=[SynchToolkit.compile_lustre])
 prob = ODEProblem(ssys, [m.plant.x => -1, m.plant.xd => 0, m.controller.x(z-1) => 0], (0.0, 2π))
 sol = solve(prob, Tsit5(), dtmax=0.01)
 figy = plot(sol, idxs=[m.plant.x])

docs/src/tutorials/SampledData.md

@@ -226,10 +226,11 @@ connections = [r ~ (t >= 5)        # reference signal
 cl = complete(cl)
 ```
 
-We can now simulate the system. JuliaSimCompiler is required to simulate hybrid continuous/discrete systems, we thus convert the system to an `JuliaSimCompiler.IRSystem` before calling `structural_simplify`
+We can now simulate the system.
 ```@example clocks
-using JuliaSimCompiler, Plots
-ssys = structural_simplify(IRSystem(cl))
+using Plots
+using SynchToolkit
+ssys = structural_simplify(cl; additional_passes=[SynchToolkit.compile_lustre])
 
 prob = ODEProblem(ssys, [
     cl.f.x(k-1) => 0,

docs/src/tutorials/noise.md

@@ -14,7 +14,7 @@ using ModelingToolkitStandardLibrary.Electrical
 using ModelingToolkitStandardLibrary.Mechanical.Rotational
 using ModelingToolkitStandardLibrary.Blocks
 using ModelingToolkitSampledData
-using JuliaSimCompiler
+using SynchToolkit
 using OrdinaryDiffEq
 using Plots
 
@@ -72,7 +72,7 @@ end
 
 @named noisy_model = NoisyClosedLoop()
 noisy_model = complete(noisy_model)
-ssys = structural_simplify(IRSystem(noisy_model)) # Conversion to an IRSystem from JuliaSimCompiler is required for sampled-data systems
+ssys = structural_simplify(noisy_model; additional_passes=[SynchToolkit.compile_lustre])
 
 noise_prob = ODEProblem(ssys, [unknowns(noisy_model) .=> 0.0; noisy_model.pi_controller.I(z-1) => 0; noisy_model.pi_controller.eI(z-1) => 0; noisy_model.noise.y(z-1) => 0], (0, 2.0))
 noise_sol = solve(noise_prob, Tsit5())
@@ -105,7 +105,7 @@ A signal may be quantized to a fixed number of levels (e.g., 8-bit) using the [`
 using ModelingToolkit, ModelingToolkitSampledData, OrdinaryDiffEq, Plots
 using ModelingToolkit: t_nounits as t, D_nounits as D
 using ModelingToolkitStandardLibrary.Blocks
-using JuliaSimCompiler
+
 z = ShiftIndex(Clock(0.1))
 @mtkmodel QuantizationModel begin
     @components begin
@@ -123,7 +123,7 @@ z = ShiftIndex(Clock(0.1))
 end
 @named m = QuantizationModel()
 m = complete(m)
-ssys = structural_simplify(IRSystem(m))
+ssys = structural_simplify(m; additional_passes=[SynchToolkit.compile_lustre])
 prob = ODEProblem(ssys, [], (0.0, 2.0))
 sol = solve(prob, Tsit5())
 plot(sol, idxs=m.input.output.u)
@@ -170,7 +170,7 @@ The block [`SampleWithADEffects`](@ref) combines an ideal [`Sampler`](@ref), a [
 end
 @named m = PracticalSamplerModel()
 m = complete(m)
-ssys = structural_simplify(IRSystem(m))
+ssys = structural_simplify(m; additional_passes=[SynchToolkit.compile_lustre])
 prob = ODEProblem(ssys, [m.sampling.noise.y(z-1) => 0], (0.0, 2.0))
 sol = solve(prob, Tsit5())
 plot(sol, idxs=m.input.output.u)

src/ModelingToolkitSampledData.jl

@@ -1,6 +1,6 @@
 module ModelingToolkitSampledData
 using ModelingToolkit
-using JuliaSimCompiler
+using SynchToolkit
 using StableRNGs
 
 export get_clock
@@ -14,6 +14,4 @@ export DiscreteOnOffController
 include("discrete_blocks.jl")
 
 
-runtime(ssys::JuliaSimCompiler.ScheduledSystem) = ssys.discrete_info.callback.affect!.affect!.x
-
 end

src/discrete_blocks.jl

@@ -343,7 +343,6 @@ end
 """
 @mtkmodel ClockChanger begin
     begin
-        isdefined(Main, :JuliaSimCompiler) || error("JuliaSimCompiler must be defined in the Main module for the ClockChanger component to work. Run `import JuliaSimCompiler`.")
         @warn "The ClockChanger component is experimental and has known correctness issues. Please use with caution."
     end
     @extend u, y = siso = SISO()
@@ -355,7 +354,7 @@ end
         O = 0 # This is just a dummy to workaround a bug in JSComp
     end
     @equations begin
-        y(ShiftIndex(to)) ~ Main.JuliaSimCompiler.ClockChange(; to, from)(u(ShiftIndex(from))) + O
+        y(ShiftIndex(to)) ~ ClockChange(; to, from)(u(ShiftIndex(from))) + O
     end
 end
 
@@ -918,7 +917,7 @@ z = ShiftIndex(cl)
 end
 @named m = SlweRateLimiterModel()
 m = complete(m)
-ssys = structural_simplify(IRSystem(m))
+ssys = structural_simplify(m; additional_passes=[SynchToolkit.compile_lustre])
 prob = ODEProblem(ssys, [m.limiter.y(z-1) => 0], (0.0, 2.0))
 sol = solve(prob, Tsit5(), dtmax=0.01)
 plot(sol, idxs=[m.input.output.u, m.limiter.y], title="Slew rate limited sine wave")

test/runtests.jl

@@ -1,6 +1,6 @@
 using ModelingToolkitSampledData
 using ModelingToolkit
-using JuliaSimCompiler
+using SynchToolkit
 using Test
 
 @testset "ModelingToolkitSampledData.jl" begin

test/test_discrete_blocks.jl

@@ -3,7 +3,7 @@ using ModelingToolkit, ModelingToolkitStandardLibrary, OrdinaryDiffEqTsit5
 using ModelingToolkitStandardLibrary.Blocks
 using ModelingToolkit: t_nounits as t, D_nounits as D
 using OrdinaryDiffEqTsit5: ReturnCode.Success
-using JuliaSimCompiler
+using SynchToolkit
 using Test
 Difference = ModelingToolkitSampledData.Difference
 
@@ -31,7 +31,7 @@ an integrator with a constant input is often used together with the system under
 
     @named model = TestDiscreteOnly()
     model = complete(model)
-    ssys = structural_simplify(IRSystem(model))
+    ssys = structural_simplify(model; additional_passes=[SynchToolkit.compile_lustre])
     prob = ODEProblem(ssys, [model.x(k-1) => 1.0], (0.0, 10.0))
     sol = solve(prob, Tsit5())
     @test sol[model.x] == dt .^ (1:21)
@@ -53,7 +53,7 @@ end
         t,
         systems = [sampler, int, intc, c])
     model = complete(iosys)
-    sys = structural_simplify(IRSystem(model))
+    sys = structural_simplify(model; additional_passes=[SynchToolkit.compile_lustre])
     prob = ODEProblem(sys, Pair[int.x(k - 1) => 1
                                 int.u(k - 1) => 0], (0.0, 1.0))
     sol = solve(prob, Tsit5())
@@ -70,7 +70,7 @@ end
         t,
         systems = [sampler, int, intc, c])
     model = complete(iosys)
-    sys = structural_simplify(IRSystem(model))
+    sys = structural_simplify(model; additional_passes=[SynchToolkit.compile_lustre])
     prob = ODEProblem(sys, Pair[int.x(k - 1) => 1
                                 int.u(k - 1) => 0], (0.0, 1.0))
     sol = solve(prob, Tsit5())
@@ -88,7 +88,7 @@ end
         t,
         systems = [sampler, int, intc, c])
     model = complete(iosys)
-    sys = structural_simplify(IRSystem(model))
+    sys = structural_simplify(model; additional_passes=[SynchToolkit.compile_lustre])
     prob = ODEProblem(sys, Pair[int.x(k - 1) => 1
                                 int.u(k - 1) => 0], (0.0, 1.0))
     sol = solve(prob, Tsit5())
@@ -128,7 +128,7 @@ end
 @named model = ClosedLoop()
 model = complete(model)
 # ci, varmap = infer_clocks(expand_connections(model))
-ssys = structural_simplify(IRSystem(model))
+ssys = structural_simplify(model; additional_passes=[SynchToolkit.compile_lustre])
 
 Tf = 5
 timevec = 0:(dt):Tf
@@ -197,7 +197,7 @@ end
 @named model = ClosedLoop()
 model = complete(model)
 # ci, varmap = infer_clocks(expand_connections(model))
-ssys = structural_simplify(IRSystem(model))
+ssys = structural_simplify(model; additional_passes=[SynchToolkit.compile_lustre])
 
 Tf = 5
 timevec = 0:(dt):Tf
@@ -256,7 +256,7 @@ end
 
     @named m = DelayModel()
     m = complete(m)
-    ssys = structural_simplify(IRSystem(m))
+    ssys = structural_simplify(m; additional_passes=[SynchToolkit.compile_lustre])
     prob = ODEProblem(
         ssys, [m.delay.u(k - 3) => 0, m.delay.u(k - 2) => 0, m.delay.u(k - 1) => 0], (
             0.0, 10.0))
@@ -289,7 +289,7 @@ using ModelingToolkitStandardLibrary.Blocks
 
     @named m = DiffModel()
     m = complete(m)
-    ssys = structural_simplify(IRSystem(m))
+    ssys = structural_simplify(m; additional_passes=[SynchToolkit.compile_lustre])
     prob = ODEProblem(ssys, Dict(m.diff.u(k - 1) => 0), (0.0, 10.0))
     sol = solve(prob, Tsit5(), dtmax = 0.01)
     @test reduce(vcat, sol((0:10) .+ 1e-2))[:]≈[zeros(2); 1; zeros(8)] atol=1e-2
@@ -319,7 +319,7 @@ using Statistics
 
     @named m = NoiseModel()
     m = complete(m)
-    ssys = structural_simplify(IRSystem(m))
+    ssys = structural_simplify(m; additional_passes=[SynchToolkit.compile_lustre])
     prob = ODEProblem(ssys, [m.noise.y(k-1) => 0], (0.0, 10.0))
     sol = solve(prob, Tsit5())
     @test !all(iszero, sol.u)
@@ -345,7 +345,7 @@ end
 
     @named m = NoiseModel()
     m = complete(m)
-    ssys = structural_simplify(IRSystem(m))
+    ssys = structural_simplify(m; additional_passes=[SynchToolkit.compile_lustre])
     prob = ODEProblem(ssys, [m.noise.y(k-1) => 0], (0.0, 10.0))
     sol = solve(prob, Tsit5())
     @test !all(iszero, sol.u)
@@ -375,7 +375,7 @@ end
 
 #     @named m = PlantModel()
 #     m = complete(m)
-#     ssys = structural_simplify(IRSystem(m))
+#     ssys = structural_simplify(m; additional_passes=[SynchToolkit.compile_lustre])
 #     prob = ODEProblem(ssys, Dict(m.plant.u(k - 1) => 0), (0.0, 10.0))
 #     sol = solve(prob, Tsit5(), dtmax = 0.01)
 #     @test reduce(vcat, sol((0:10) .+ 1e-2))[:]≈[zeros(2); 1; zeros(8)] atol=1e-2
@@ -401,7 +401,7 @@ end
     end
     @named m = SlewRateLimiterModel()
     m = complete(m)
-    ssys = structural_simplify(IRSystem(m))
+    ssys = structural_simplify(m; additional_passes=[SynchToolkit.compile_lustre])
     prob = ODEProblem(ssys, [m.limiter.y(z-1) => 0], (0.0, 2.0))
     sol = solve(prob, Tsit5(), dtmax=0.01)
     # plot(sol, idxs=[m.input.output.u, m.limiter.y], title="Slew rate limited sine wave")
@@ -457,7 +457,7 @@ end
     end
     @named m = QuantizationModel()
     m = complete(m)
-    ssys = structural_simplify(IRSystem(m))
+    ssys = structural_simplify(m; additional_passes=[SynchToolkit.compile_lustre])
     prob = ODEProblem(ssys, [], (0.0, 10.0))
     sol = solve(prob, Tsit5(), dtmax=0.01)
     y = sol[m.quant.y]
@@ -476,7 +476,7 @@ end
     end
     @named m = QuantizationModel2()
     m = complete(m)
-    ssys = structural_simplify(IRSystem(m))
+    ssys = structural_simplify(m; additional_passes=[SynchToolkit.compile_lustre])
     prob = ODEProblem(ssys, [], (0.0, 10.0))
     sol = solve(prob, Tsit5(), dtmax=0.01)
     y = sol[m.quant.y]
@@ -505,7 +505,7 @@ end
     end
     @named m = OnOffModel()
     m = complete(m)
-    ssys = structural_simplify(IRSystem(m))
+    ssys = structural_simplify(m; additional_passes=[SynchToolkit.compile_lustre])
     prob = ODEProblem(ssys, [m.onoff.y(z-1) => 0], (0.0, 4.0))
     sol = solve(prob, Tsit5(), dtmax=0.1)
     # plot(sol, idxs=[m.x, m.onoff.y], title="On-off control of an unstable first-order system")
@@ -530,7 +530,7 @@ end
     end
     @named m = ExponentialFilterModel()
     m = complete(m)
-    ssys = structural_simplify(IRSystem(m))
+    ssys = structural_simplify(m; additional_passes=[SynchToolkit.compile_lustre])
     prob = ODEProblem(ssys, [m.filter.y(z-1) => 0], (0.0, 10.0))
     sol = solve(prob, Tsit5(), dtmax=0.1)
     @test sol(10, idxs=m.filter.y) ≈ 1 atol=0.001
@@ -558,7 +558,7 @@ end
 
     @named m = MovingAverageFilterModel()
     m = complete(m)
-    ssys = structural_simplify(IRSystem(m))
+    ssys = structural_simplify(m; additional_passes=[SynchToolkit.compile_lustre])
     prob = ODEProblem(ssys, [m.filter.u(z-i) => 0 for i = 0:3], (0.0, 2.0))
     sol = solve(prob, Tsit5(), dtmax=0.1)
     # plot(sol, idxs=m.filter.y)
@@ -587,7 +587,7 @@ end
     end
     @named m = PracticalSamplerModel()
     m = complete(m)
-    ssys = structural_simplify(IRSystem(m))
+    ssys = structural_simplify(m; additional_passes=[SynchToolkit.compile_lustre])
     prob = ODEProblem(ssys, [m.sampling.noise.y(z-1) => 0], (0.0, 2.0))
     sol = solve(prob, Tsit5())