Merge remote-tracking branch 'origin' into idp

Merge

Author: vyudu

.typos.toml

@@ -1,2 +1,3 @@
 [default.extend-words]
-ND = "ND"
+ND = "ND"
+Cachable = "Cachable"

Project.toml

@@ -1,7 +1,7 @@
 name = "SciMLBase"
 uuid = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
 authors = ["Chris Rackauckas <[email protected]> and contributors"]
-version = "2.72.0"
+version = "2.74.1"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
@@ -12,12 +12,12 @@ ConstructionBase = "187b0558-2788-49d3-abe0-74a17ed4e7c9"
 Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"
 DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
 EnumX = "4e289a0a-7415-4d19-859d-a7e5c4648b56"
-Expronicon = "6b7a57c9-7cc1-4fdf-b7f5-e857abae3636"
 FunctionWrappersWrappers = "77dc65aa-8811-40c2-897b-53d922fa7daf"
 IteratorInterfaceExtensions = "82899510-4779-5014-852e-03e436cf321d"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
 Markdown = "d6f4376e-aef5-505a-96c1-9c027394607a"
+Moshi = "2e0e35c7-a2e4-4343-998d-7ef72827ed2d"
 PrecompileTools = "aea7be01-6a6a-4083-8856-8a6e6704d82a"
 Preferences = "21216c6a-2e73-6563-6e65-726566657250"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
@@ -34,6 +34,7 @@ SymbolicIndexingInterface = "2efcf032-c050-4f8e-a9bb-153293bab1f5"
 [weakdeps]
 ChainRules = "082447d4-558c-5d27-93f4-14fc19e9eca2"
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
+MLStyle = "d8e11817-5142-5d16-987a-aa16d5891078"
 Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a"
 PartialFunctions = "570af359-4316-4cb7-8c74-252c00c2016b"
 PyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0"
@@ -43,6 +44,7 @@ Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [extensions]
 SciMLBaseChainRulesCoreExt = "ChainRulesCore"
+SciMLBaseMLStyleExt = "MLStyle"
 SciMLBaseMakieExt = "Makie"
 SciMLBasePartialFunctionsExt = "PartialFunctions"
 SciMLBasePyCallExt = "PyCall"
@@ -62,14 +64,15 @@ DataFrames = "1.6"
 Distributed = "1.10"
 DocStringExtensions = "0.9"
 EnumX = "1"
-Expronicon = "0.8"
 ForwardDiff = "0.10.36"
 FunctionWrappersWrappers = "0.1.3"
 IteratorInterfaceExtensions = "^1"
 LinearAlgebra = "1.10"
 Logging = "1.10"
+MLStyle = "0.4.17"
 Makie = "0.20, 0.21, 0.22"
 Markdown = "1.10"
+Moshi = "0.3"
 NonlinearSolve = "3, 4"
 PartialFunctions = "1.1"
 PrecompileTools = "1.2"
@@ -99,6 +102,7 @@ ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 DelayDiffEq = "bcd4f6db-9728-5f36-b5f7-82caef46ccdb"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
+MLStyle = "d8e11817-5142-5d16-987a-aa16d5891078"
 NonlinearSolve = "8913a72c-1f9b-4ce2-8d82-65094dcecaec"
 OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
 PartialFunctions = "570af359-4316-4cb7-8c74-252c00c2016b"
@@ -116,4 +120,4 @@ UnicodePlots = "b8865327-cd53-5732-bb35-84acbb429228"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [targets]
-test = ["Pkg", "Plots", "UnicodePlots", "SafeTestsets", "Serialization", "Test", "StableRNGs", "StaticArrays", "StochasticDiffEq", "Aqua", "Zygote", "PartialFunctions", "DataFrames", "NonlinearSolve", "OrdinaryDiffEq", "ForwardDiff", "Tables"]
+test = ["Pkg", "Plots", "UnicodePlots", "SafeTestsets", "Serialization", "Test", "StableRNGs", "StaticArrays", "StochasticDiffEq", "Aqua", "Zygote", "PartialFunctions", "DataFrames", "NonlinearSolve", "OrdinaryDiffEq", "ForwardDiff", "Tables", "MLStyle"]

docs/src/interfaces/Common_Keywords.md

@@ -102,8 +102,8 @@ Note that if a method does not have adaptivity, the following rules apply:
 ## Memory Optimizations
 
   - `alias`: an `AbstractAliasSpecifier` object that holds fields specifying which variables to alias
-    when solving. For example, to tell an ODE solver to alias the `u0` array, you can use an `ODEAliases` object, 
-    and the `alias_u0` keyword argument, e.g. `solve(prob,alias = ODEAliases(alias_u0 = true))`. 
+    when solving. For example, to tell an ODE solver to alias the `u0` array, you can use an `ODEAliases` object,
+    and the `alias_u0` keyword argument, e.g. `solve(prob,alias = ODEAliases(alias_u0 = true))`.
     For more information on what can be aliased for each problem type, see the documentation for the `AbstractAliasSpecifier`
     associated with that problem type. Set to `true` to alias every variable possible, or to `false` to disable aliasing.
     Defaults to an `AbstractAliasSpecifier` instance with `nothing` for all fields, which tells the solver to use the default behavior.

ext/SciMLBaseMLStyleExt.jl

@@ -0,0 +1,73 @@
+"""
+The SciMLBaseMLStyleExt module provides backwards compatibility for those using MLStyle
+to pattern match the Clocks algebraic data type (ADT).
+
+Before this ADT was made using Expronomicon, which directly supported MLStyle.
+Now that the ADT is made using Moshi, we need to define MLStyle custom patterns.
+Using Moshi pattern matching is recommended over relying on this package extension.
+"""
+module SciMLBaseMLStyleExt
+
+using SciMLBase: TimeDomain, ContinuousClock, SolverStepClock, PeriodicClock
+using MLStyle: MLStyle
+using MLStyle.AbstractPatterns: literal, wildcard, PComp, BasicPatterns, decons
+using Moshi.Data: isa_variant
+
+# This makes Singletons also work without parentheses in matches
+MLStyle.is_enum(::Type{ContinuousClock}) = true
+MLStyle.is_enum(::Type{SolverStepClock}) = true
+function MLStyle.pattern_uncall(::Type{ContinuousClock}, self::Function, _, _, _)
+    literal(ContinuousClock())
+end
+MLStyle.pattern_uncall(T::TimeDomain, self::Function, _, _, _) = literal(T())
+
+function MLStyle.pattern_uncall(::Type{SolverStepClock}, self::Function, _, _, _)
+    literal(SolverStepClock())
+end
+
+function periodic_clock_pattern(c)
+    if c isa TimeDomain && isa_variant(c, PeriodicClock)
+        (c.dt, c.phase)
+    else
+        # These values are used in match results, but they shouldn't.
+        # This means that any wildcard pattern will be `nothing`, see broken test.
+        (nothing, nothing)
+    end
+end
+
+function MLStyle.pattern_uncall(
+        ::Type{PeriodicClock}, self::Function, type_params, type_args, args)
+    @assert isempty(type_params)
+    @assert isempty(type_args)
+    n_args = length(args)
+
+    trans(expr) = Expr(:call, periodic_clock_pattern, expr)
+    type_infer(_...) = Any
+
+    extract = if n_args <= 1
+        (expr::Any, i::Int, ::Any, ::Any) -> expr
+    else
+        (expr::Any, i::Int, ::Any, ::Any) -> Core._expr(:ref, expr, i)
+    end
+
+    comp = PComp(
+        "PeriodicClock(c)", type_infer; view = BasicPatterns.SimpleCachablePre(trans))
+
+    ps = if n_args === 0
+        []
+    elseif n_args === 1
+        [self(Expr(:call, Some, args[1]))]
+    elseif n_args === 2
+        if args[2] == :(_...)
+            [self(args[1]), wildcard]
+        else
+            [self(args[1]), self(args[2])]
+        end
+    else
+        error("too many arguments")
+    end
+
+    decons(comp, extract, ps)
+end
+
+end

src/SciMLBase.jl

@@ -23,7 +23,8 @@ import RuntimeGeneratedFunctions
 import EnumX
 import ADTypes: ADTypes, AbstractADType
 import Accessors: @set, @reset, @delete, @insert
-using Expronicon.ADT: @match
+using Moshi.Data: @data
+using Moshi.Match: @match
 
 using Reexport
 using SciMLOperators

src/clock.jl

@@ -1,33 +1,19 @@
-module Clocks
-
-export TimeDomain
-
-using Expronicon.ADT: variant_type, @adt, @match
-
-@adt TimeDomain begin
-    Continuous
+@data Clocks begin
+    ContinuousClock
     struct PeriodicClock
         dt::Union{Nothing, Float64, Rational{Int}}
         phase::Float64 = 0.0
     end
     SolverStepClock
 end
 
-Base.Broadcast.broadcastable(d::TimeDomain) = Ref(d)
-
-const DiscriminatorType = typeof(variant_type(Continuous))
-
-function Base.write(io::IO, x::DiscriminatorType)
-    write(io, Base.reinterpret(UInt32, x))
-end
-
-function Base.read(io::IO, ::Type{DiscriminatorType})
-    Base.reinterpret(DiscriminatorType, read(io, UInt32))
-end
-
-end
+# for backwards compatibility
+const TimeDomain = Clocks.Type
+using .Clocks: ContinuousClock, PeriodicClock, SolverStepClock
+const Continuous = ContinuousClock()
+(clock::TimeDomain)() = clock
 
-using .Clocks
+Base.Broadcast.broadcastable(d::TimeDomain) = Ref(d)
 
 """
     Clock(dt)
@@ -55,27 +41,27 @@ filters.
 """ SolverStepClock
 
 isclock(c) = @match c begin
-    PeriodicClock(_...) => true
+    PeriodicClock() => true
     _ => false
 end
 
 issolverstepclock(c) = @match c begin
-    &SolverStepClock => true
+    SolverStepClock() => true
     _ => false
 end
 
 iscontinuous(c) = @match c begin
-    &Continuous => true
+    ContinuousClock() => true
     _ => false
 end
 
 is_discrete_time_domain(c) = !iscontinuous(c)
 
 function first_clock_tick_time(c, t0)
     @match c begin
-        PeriodicClock(dt, _...) => ceil(t0 / dt) * dt
-        &SolverStepClock => t0
-        &Continuous => error("Continuous is not a discrete clock")
+        PeriodicClock(dt) => ceil(t0 / dt) * dt
+        SolverStepClock() => t0
+        ContinuousClock() => error("ContinuousClock() is not a discrete clock")
     end
 end
 
@@ -90,13 +76,13 @@ function canonicalize_indexed_clock(ic::IndexedClock, sol::AbstractTimeseriesSol
     c = ic.clock
 
     return @match c begin
-        PeriodicClock(dt, _...) => ceil(sol.prob.tspan[1] / dt) * dt .+ (ic.idx .- 1) .* dt
-        &SolverStepClock => begin
+        PeriodicClock(dt) => ceil(sol.prob.tspan[1] / dt) * dt .+ (ic.idx .- 1) .* dt
+        SolverStepClock() => begin
             ssc_idx = findfirst(eachindex(sol.discretes)) do i
                 !isa(sol.discretes[i].t, AbstractRange)
             end
             sol.discretes[ssc_idx].t[ic.idx]
         end
-        &Continuous => sol.t[ic.idx]
+        ContinuousClock() => sol.t[ic.idx]
     end
 end

src/initialization.jl

@@ -295,3 +295,23 @@ end
 function is_trivial_initialization(prob::AbstractSciMLProblem)
     is_trivial_initialization(prob.f)
 end
+
+@enum DETERMINED_STATUS OVERDETERMINED FULLY_DETERMINED UNDERDETERMINED
+
+function initialization_status(prob::AbstractSciMLProblem)
+    has_initialization_data(prob.f) || return nothing
+
+    sys = prob.f.initialization_data.initializeprob.f.sys
+    isnothing(sys) && return nothing
+
+    neqs = length(equations(sys))
+    nunknowns = length(unknowns(sys))
+
+    if neqs > nunknowns
+        return OVERDETERMINED
+    elseif neqs < nunknowns
+        return UNDERDETERMINED
+    else
+        return FULLY_DETERMINED
+    end
+end

src/problems/analytical_problems.jl

@@ -29,7 +29,6 @@ end
 
 export AnalyticalProblem, AbstractAnalyticalProblem
 
-
 @doc doc"""
 
 Holds information on what variables to alias
@@ -66,5 +65,3 @@ struct AnalyticalAliasSpecifier <: AbstractAliasSpecifier
         end
     end
 end
-
-

src/problems/bvp_problems.jl

@@ -474,6 +474,3 @@ struct BVPAliasSpecifier <: AbstractAliasSpecifier
         end
     end
 end
-
-
-

src/problems/dae_problems.jl

@@ -155,6 +155,3 @@ struct DAEAliasSpecifier
         end
     end
 end
-
-
-