Merge pull request SciML#3354 from visr/moshi

Replace Expronicon with Moshi

Author: ChrisRackauckas

Project.toml

@@ -23,7 +23,6 @@ DomainSets = "5b8099bc-c8ec-5219-889f-1d9e522a28bf"
 DynamicQuantities = "06fc5a27-2a28-4c7c-a15d-362465fb6821"
 EnumX = "4e289a0a-7415-4d19-859d-a7e5c4648b56"
 ExprTools = "e2ba6199-217a-4e67-a87a-7c52f15ade04"
-Expronicon = "6b7a57c9-7cc1-4fdf-b7f5-e857abae3636"
 FindFirstFunctions = "64ca27bc-2ba2-4a57-88aa-44e436879224"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 FunctionWrappers = "069b7b12-0de2-55c6-9aab-29f3d0a68a2e"
@@ -36,6 +35,7 @@ Latexify = "23fbe1c1-3f47-55db-b15f-69d7ec21a316"
 Libdl = "8f399da3-3557-5675-b5ff-fb832c97cbdb"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MLStyle = "d8e11817-5142-5d16-987a-aa16d5891078"
+Moshi = "2e0e35c7-a2e4-4343-998d-7ef72827ed2d"
 NaNMath = "77ba4419-2d1f-58cd-9bb1-8ffee604a2e3"
 NonlinearSolve = "8913a72c-1f9b-4ce2-8d82-65094dcecaec"
 OffsetArrays = "6fe1bfb0-de20-5000-8ca7-80f57d26f881"
@@ -103,7 +103,6 @@ DomainSets = "0.6, 0.7"
 DynamicQuantities = "^0.11.2, 0.12, 0.13, 1"
 EnumX = "1.0.4"
 ExprTools = "0.1.10"
-Expronicon = "0.8"
 FMI = "0.14"
 FindFirstFunctions = "1"
 ForwardDiff = "0.10.3"
@@ -121,6 +120,7 @@ LinearAlgebra = "1"
 Logging = "1"
 MLStyle = "0.4.17"
 ModelingToolkitStandardLibrary = "2.19"
+Moshi = "0.3"
 NaNMath = "0.3, 1"
 NonlinearSolve = "4.3"
 OffsetArrays = "1"
@@ -135,7 +135,7 @@ RecursiveArrayTools = "3.26"
 Reexport = "0.2, 1"
 RuntimeGeneratedFunctions = "0.5.9"
 SCCNonlinearSolve = "1.0.0"
-SciMLBase = "2.73"
+SciMLBase = "2.75"
 SciMLStructures = "1.0"
 Serialization = "1"
 Setfield = "0.7, 0.8, 1"

src/ModelingToolkit.jl

@@ -45,10 +45,13 @@ using Compat
 using AbstractTrees
 using DiffEqBase, SciMLBase, ForwardDiff
 using SciMLBase: StandardODEProblem, StandardNonlinearProblem, handle_varmap, TimeDomain,
-                 PeriodicClock, Clock, SolverStepClock, Continuous, OverrideInit, NoInit
+                 PeriodicClock, Clock, SolverStepClock, ContinuousClock, OverrideInit,
+                 NoInit
 using Distributed
 import JuliaFormatter
 using MLStyle
+import Moshi
+using Moshi.Data: @data
 using NonlinearSolve
 import SCCNonlinearSolve
 using Reexport
@@ -292,7 +295,7 @@ export @variables, @parameters, @independent_variables, @constants, @brownian
 export @named, @nonamespace, @namespace, extend, compose, complete
 export debug_system
 
-#export Continuous, Discrete, sampletime, input_timedomain, output_timedomain
+#export ContinuousClock, Discrete, sampletime, input_timedomain, output_timedomain
 #export has_discrete_domain, has_continuous_domain
 #export is_discrete_domain, is_continuous_domain, is_hybrid_domain
 export Sample, Hold, Shift, ShiftIndex, sampletime, SampleTime

src/clock.jl

@@ -1,20 +1,12 @@
-module InferredClock
-
-export InferredTimeDomain
-
-using Expronicon.ADT: @adt, @match
-using SciMLBase: TimeDomain
-
-@adt InferredTimeDomain begin
+@data InferredClock begin
     Inferred
     InferredDiscrete
 end
 
-Base.Broadcast.broadcastable(x::InferredTimeDomain) = Ref(x)
+const InferredTimeDomain = InferredClock.Type
+using .InferredClock: Inferred, InferredDiscrete
 
-end
-
-using .InferredClock
+Base.Broadcast.broadcastable(x::InferredTimeDomain) = Ref(x)
 
 struct VariableTimeDomain end
 Symbolics.option_to_metadata_type(::Val{:timedomain}) = VariableTimeDomain
@@ -29,7 +21,7 @@ true if `x` contains only continuous-domain signals.
 See also [`has_continuous_domain`](@ref)
 """
 function is_continuous_domain(x)
-    issym(x) && return getmetadata(x, VariableTimeDomain, false) == Continuous()
+    issym(x) && return getmetadata(x, VariableTimeDomain, false) == ContinuousClock()
     !has_discrete_domain(x) && has_continuous_domain(x)
 end
 
@@ -50,16 +42,16 @@ has_time_domain(_, x) = has_time_domain(x)
 Determine if variable `x` has a time-domain attributed to it.
 """
 function has_time_domain(x::Symbolic)
-    # getmetadata(x, Continuous, nothing) !== nothing ||
+    # getmetadata(x, ContinuousClock, nothing) !== nothing ||
     # getmetadata(x, Discrete,   nothing) !== nothing
     getmetadata(x, VariableTimeDomain, nothing) !== nothing
 end
 has_time_domain(x::Num) = has_time_domain(value(x))
 has_time_domain(x) = false
 
 for op in [Differential]
-    @eval input_timedomain(::$op, arg = nothing) = Continuous()
-    @eval output_timedomain(::$op, arg = nothing) = Continuous()
+    @eval input_timedomain(::$op, arg = nothing) = ContinuousClock()
+    @eval output_timedomain(::$op, arg = nothing) = ContinuousClock()
 end
 
 """
@@ -104,8 +96,8 @@ function is_discrete_domain(x)
     !has_discrete_domain(x) && has_continuous_domain(x)
 end
 
-sampletime(c) = @match c begin
-    PeriodicClock(dt, _...) => dt
+sampletime(c) = Moshi.Match.@match c begin
+    PeriodicClock(dt) => dt
     _ => nothing
 end
 

src/discretedomain.jl

@@ -226,28 +226,28 @@ Base.:-(k::ShiftIndex, i::Int) = k + (-i)
 """
     input_timedomain(op::Operator)
 
-Return the time-domain type (`Continuous` or `InferredDiscrete`) that `op` operates on.
+Return the time-domain type (`ContinuousClock()` or `InferredDiscrete()`) that `op` operates on.
 """
 function input_timedomain(s::Shift, arg = nothing)
     if has_time_domain(arg)
         return get_time_domain(arg)
     end
-    InferredDiscrete
+    InferredDiscrete()
 end
 
 """
     output_timedomain(op::Operator)
 
-Return the time-domain type (`Continuous` or `InferredDiscrete`) that `op` results in.
+Return the time-domain type (`ContinuousClock()` or `InferredDiscrete()`) that `op` results in.
 """
 function output_timedomain(s::Shift, arg = nothing)
     if has_time_domain(t, arg)
         return get_time_domain(t, arg)
     end
-    InferredDiscrete
+    InferredDiscrete()
 end
 
-input_timedomain(::Sample, _ = nothing) = Continuous()
+input_timedomain(::Sample, _ = nothing) = ContinuousClock()
 output_timedomain(s::Sample, _ = nothing) = s.clock
 
 function input_timedomain(h::Hold, arg = nothing)
@@ -256,7 +256,7 @@ function input_timedomain(h::Hold, arg = nothing)
     end
     InferredDiscrete # the Hold accepts any discrete
 end
-output_timedomain(::Hold, _ = nothing) = Continuous()
+output_timedomain(::Hold, _ = nothing) = ContinuousClock()
 
 sampletime(op::Sample, _ = nothing) = sampletime(op.clock)
 sampletime(op::ShiftIndex, _ = nothing) = sampletime(op.clock)

src/systems/abstractsystem.jl

@@ -631,8 +631,8 @@ SymbolicUtils.promote_symtype(::Type{Initial}, T) = T
 SymbolicUtils.isbinop(::Initial) = false
 Base.nameof(::Initial) = :Initial
 Base.show(io::IO, x::Initial) = print(io, "Initial")
-input_timedomain(::Initial, _ = nothing) = Continuous()
-output_timedomain(::Initial, _ = nothing) = Continuous()
+input_timedomain(::Initial, _ = nothing) = ContinuousClock()
+output_timedomain(::Initial, _ = nothing) = ContinuousClock()
 
 function (f::Initial)(x)
     # wrap output if wrapped input

src/systems/clock_inference.jl

@@ -8,8 +8,8 @@ end
 function ClockInference(ts::TransformationState)
     @unpack structure = ts
     @unpack graph = structure
-    eq_domain = TimeDomain[Continuous() for _ in 1:nsrcs(graph)]
-    var_domain = TimeDomain[Continuous() for _ in 1:ndsts(graph)]
+    eq_domain = TimeDomain[ContinuousClock() for _ in 1:nsrcs(graph)]
+    var_domain = TimeDomain[ContinuousClock() for _ in 1:ndsts(graph)]
     inferred = BitSet()
     for (i, v) in enumerate(get_fullvars(ts))
         d = get_time_domain(ts, v)
@@ -151,7 +151,7 @@ function split_system(ci::ClockInference{S}) where {S}
             get!(clock_to_id, d) do
                 cid = (cid_counter[] += 1)
                 push!(id_to_clock, d)
-                if d == Continuous()
+                if d == ContinuousClock()
                     continuous_id[] = cid
                 end
                 cid

src/systems/systemstructure.jl

@@ -672,7 +672,8 @@ function structural_simplify!(state::TearingState, io = nothing; simplify = fals
                 Dict(v => 0.0 for v in Iterators.flatten(inputs)))
         end
         ps = [sym isa CallWithMetadata ? sym :
-              setmetadata(sym, VariableTimeDomain, get(time_domains, sym, Continuous()))
+              setmetadata(
+                  sym, VariableTimeDomain, get(time_domains, sym, ContinuousClock()))
               for sym in get_ps(sys)]
         @set! sys.ps = ps
     else

test/clock.jl

@@ -1,5 +1,5 @@
 using ModelingToolkit, Test, Setfield, OrdinaryDiffEq, DiffEqCallbacks
-using ModelingToolkit: Continuous
+using ModelingToolkit: ContinuousClock
 using ModelingToolkit: t_nounits as t, D_nounits as D
 
 function infer_clocks(sys)
@@ -77,19 +77,19 @@ k = ShiftIndex(d)
 
 d = Clock(dt)
 # Note that TearingState reorders the equations
-@test eqmap[1] == Continuous()
+@test eqmap[1] == ContinuousClock()
 @test eqmap[2] == d
 @test eqmap[3] == d
 @test eqmap[4] == d
-@test eqmap[5] == Continuous()
-@test eqmap[6] == Continuous()
+@test eqmap[5] == ContinuousClock()
+@test eqmap[6] == ContinuousClock()
 
 @test varmap[yd] == d
 @test varmap[ud] == d
 @test varmap[r] == d
-@test varmap[x] == Continuous()
-@test varmap[y] == Continuous()
-@test varmap[u] == Continuous()
+@test varmap[x] == ContinuousClock()
+@test varmap[y] == ContinuousClock()
+@test varmap[u] == ContinuousClock()
 
 @info "Testing shift normalization"
 dt = 0.1
@@ -192,10 +192,10 @@ eqs = [yd ~ Sample(dt)(y)
     @test varmap[ud1] == d
     @test varmap[yd2] == d2
     @test varmap[ud2] == d2
-    @test varmap[r] == Continuous()
-    @test varmap[x] == Continuous()
-    @test varmap[y] == Continuous()
-    @test varmap[u] == Continuous()
+    @test varmap[r] == ContinuousClock()
+    @test varmap[x] == ContinuousClock()
+    @test varmap[y] == ContinuousClock()
+    @test varmap[u] == ContinuousClock()
 
     @info "test composed systems"
 
@@ -241,14 +241,14 @@ eqs = [yd ~ Sample(dt)(y)
     ci, varmap = infer_clocks(cl)
 
     @test varmap[f.x] == Clock(0.5)
-    @test varmap[p.x] == Continuous()
-    @test varmap[p.y] == Continuous()
+    @test varmap[p.x] == ContinuousClock()
+    @test varmap[p.y] == ContinuousClock()
     @test varmap[c.ud] == Clock(0.5)
     @test varmap[c.yd] == Clock(0.5)
-    @test varmap[c.y] == Continuous()
+    @test varmap[c.y] == ContinuousClock()
     @test varmap[f.y] == Clock(0.5)
     @test varmap[f.u] == Clock(0.5)
-    @test varmap[p.u] == Continuous()
+    @test varmap[p.u] == ContinuousClock()
     @test varmap[c.r] == Clock(0.5)
 
     ## Multiple clock rates
@@ -281,9 +281,9 @@ eqs = [yd ~ Sample(dt)(y)
     @test varmap[ud1] == d
     @test varmap[yd2] == d2
     @test varmap[ud2] == d2
-    @test varmap[x] == Continuous()
-    @test varmap[y] == Continuous()
-    @test varmap[u] == Continuous()
+    @test varmap[x] == ContinuousClock()
+    @test varmap[y] == ContinuousClock()
+    @test varmap[u] == ContinuousClock()
 
     ss = structural_simplify(cl)
     ss_nosplit = structural_simplify(cl; split = false)
@@ -398,13 +398,13 @@ eqs = [yd ~ Sample(dt)(y)
 
     ci, varmap = infer_clocks(expand_connections(_model))
 
-    @test varmap[_model.plant.input.u] == Continuous()
-    @test varmap[_model.plant.u] == Continuous()
-    @test varmap[_model.plant.x] == Continuous()
-    @test varmap[_model.plant.y] == Continuous()
-    @test varmap[_model.plant.output.u] == Continuous()
-    @test varmap[_model.holder.output.u] == Continuous()
-    @test varmap[_model.sampler.input.u] == Continuous()
+    @test varmap[_model.plant.input.u] == ContinuousClock()
+    @test varmap[_model.plant.u] == ContinuousClock()
+    @test varmap[_model.plant.x] == ContinuousClock()
+    @test varmap[_model.plant.y] == ContinuousClock()
+    @test varmap[_model.plant.output.u] == ContinuousClock()
+    @test varmap[_model.holder.output.u] == ContinuousClock()
+    @test varmap[_model.sampler.input.u] == ContinuousClock()
     @test varmap[_model.controller.u] == d
     @test varmap[_model.holder.input.u] == d
     @test varmap[_model.controller.output.u] == d