observe inputs

ext/MTKExt.jl

@@ -189,7 +189,7 @@ For a given system and name, extract all the relevant meta we want to keep for t
 function _get_metadata(sys, name)
     nt = (;)
     sym = try
-        getproperty_symbolic(sys, name)
+        getproperty_symbolic(sys, name; might_contain_toplevel_ns=false)
     catch e
         if !endswith(string(name), "ˍt") # known for "internal" derivatives
             @warn "Could not extract metadata for $name $(e.msg)"

ext/MTKUtils.jl

@@ -93,18 +93,24 @@ function _collect_differentials!(found, ex)
 end
 
 """
-    getproperty_symbolic(sys, var)
+    getproperty_symbolic(sys, var; might_contain_toplevel_ns=true)
 
 Like `getproperty` but works on a greater varaity of "var"
 - var can be Num or Symbolic (resolved using genname)
-- strip namespace of sys if present
+- strip namespace of sys if present (don't strip if `might_contain_top_level_ns=false`)
 - for nested variables (foo₊bar₊baz) resolve them one by one
 """
-function getproperty_symbolic(sys, var)
+function getproperty_symbolic(sys, var; might_contain_toplevel_ns=true)
     ns = string(getname(sys))
     varname = string(getname(var))
-    varname_nons = replace(varname, r"^"*ns*"₊" => "")
-    parts = split(varname_nons, "₊")
+    # split of the toplevel namespace if necessary
+    if might_contain_toplevel_ns && startswith(varname, ns*"₊")
+        if getname(sys) ∈ getname.(ModelingToolkit.get_systems(sys))
+            @warn "Namespace :$ns appears multiple times, this might lead to unexpected, since it is not clear whether the namespace should be stripped or not."
+        end
+        varname = replace(varname, r"^"*ns*"₊" => "")
+    end
+    parts = split(varname, "₊")
     r = getproperty(sys, Symbol(parts[1]); namespace=false)
     for part in parts[2:end]
         r = getproperty(r, Symbol(part); namespace=true)

src/component_functions.jl

@@ -487,6 +487,9 @@ edges it returns a named tuple `(; src, dst)` with two symbol vectors.
 insym(c::VertexModel)::Vector{Symbol} = c.insym
 insym(c::EdgeModel)::@NamedTuple{src::Vector{Symbol},dst::Vector{Symbol}} = c.insym
 
+insym_all(c::VertexModel) = c.insym
+insym_all(c::EdgeModel) = Iterators.flatten(values(c.insym))
+
 """
     indim(c::VertexModel)::Int
     indim(c::EdgeModel)::@NamedTuple{src::Int,dst::Int}
@@ -950,16 +953,17 @@ function _fill_defaults(T, @nospecialize(kwargs))
     ####
     #### Cached outsymflat/outsymall
     ####
-    _outsym_flat = if T <: VertexModel
-        outsym
-    elseif T <: EdgeModel
-        vcat(outsym.src, outsym.dst)
-    else
-        error()
-    end
+    _outsym_flat = flatten_sym(outsym)
     dict[:_outsym_flat] = _outsym_flat
+
     dict[:_obssym_all] = setdiff(_outsym_flat, sym) ∪ obssym
 
+    insym = dict[:insym]
+    if !isnothing(insym)
+        insym_flat = flatten_sym(insym)
+        dict[:_obssym_all] = dict[:_obssym_all] ∪ insym_flat
+    end
+
     ####
     #### External Inputs
     ####
@@ -991,10 +995,8 @@ function _fill_defaults(T, @nospecialize(kwargs))
 
     _is = if isnothing(__is)
         Symbol[]
-    elseif __is isa NamedTuple
-        vcat(__is.src, __is.dst)
     else
-        __is
+        flatten_sym(insym)
     end
     if !allunique(vcat(_s, _ps, _obss, _is, _os))
         throw(ArgumentError("Symbol names must be unique. There are clashes in sym, psym, outsym, obssym and insym."))

src/symbolicindexing.jl

@@ -412,6 +412,8 @@ function SII.observed(nw::Network, snis)
     stateidx = Dict{Int, Int}()
     # mapping i -> index in output
     outidx = Dict{Int, Int}()
+    # mapping i -> index in aggbuf
+    aggidx = Dict{Int, Int}()
     # mapping i -> f(fullstate, p, t) (component observables)
     obsfuns = Dict{Int, Function}()
     for (i, sni) in enumerate(_snis)
@@ -427,12 +429,28 @@ function SII.observed(nw::Network, snis)
             elseif (idx=findfirst(isequal(sni.subidx), obssym(cf))) != nothing #found in observed
                 _obsf = _get_observed_f(nw, cf, resolvecompidx(nw, sni))
                 obsfuns[i] = (u, outbuf, aggbuf, extbuf, p, t) -> _obsf(u, outbuf, aggbuf, extbuf, p, t)[idx]
+            elseif hasinsym(cf) && sni.subidx ∈ insym_all(cf) # found in input
+                if sni isa SymbolicVertexIndex
+                    idx = findfirst(isequal(sni.subidx), insym_all(cf))
+                    aggidx[i] = nw.im.v_aggr[resolvecompidx(nw, sni)][idx]
+                elseif sni isa SymbolicEdgeIndex
+                    edge = nw.im.edgevec[resolvecompidx(nw, sni)]
+                    if (idx = findfirst(isequal(sni.subidx), insym(cf).src)) != nothing
+                        outidx[i] = nw.im.v_out[edge.src][idx]
+                    elseif (idx = findfirst(isequal(sni.subidx), insym(cf).dst)) != nothing
+                        outidx[i] = nw.im.v_out[edge.dst][idx]
+                    else
+                        error()
+                    end
+                else
+                    error()
+                end
             else
                 throw(ArgumentError("Cannot resolve observable $sni"))
             end
         end
     end
-    initbufs = !isempty(outidx) || !isempty(obsfuns)
+    initbufs = !isempty(outidx) || !isempty(aggidx) || !isempty(obsfuns)
 
     if isscalar
         @closure (u, p, t) -> begin
@@ -443,6 +461,9 @@ function SII.observed(nw::Network, snis)
             elseif !isempty(outidx)
                 idx = only(outidx).second
                 outbuf[idx]
+            elseif !isempty(aggidx)
+                idx = only(aggidx).second
+                aggbuf[idx]
             else
                 obsf = only(obsfuns).second
                 obsf(u, outbuf, aggbuf, extbuf, p, t)::eltype(u)
@@ -459,6 +480,9 @@ function SII.observed(nw::Network, snis)
             for (i, outi) in outidx
                 out[i] = outbuf[outi]
             end
+            for (i, aggi) in aggidx
+                out[i] = aggbuf[aggi]
+            end
             for (i, obsf) in obsfuns
                 out[i] = obsf(u, outbuf, aggbuf, extbuf, p, t)::eltype(u)
             end

src/utils.jl

@@ -134,3 +134,6 @@ rand_inputs_fg(cf) = rand_inputs_fg(Random.default_rng(), cf)
 abstract type SymbolicIndex{C,S} end
 abstract type SymbolicStateIndex{C,S} <: SymbolicIndex{C,S} end
 abstract type SymbolicParameterIndex{C,S} <: SymbolicIndex{C,S} end
+
+flatten_sym(v::NamedTuple) = reduce(vcat, values(v))
+flatten_sym(v::AbstractVector{Symbol}) = v

test/ComponentLibrary.jl

@@ -47,19 +47,19 @@ diffusion_vertex() = VertexModel(f=diffusionvertex!, dim=1, g=1:1)
 Base.@propagate_inbounds function kuramoto_edge!(e, θ_s, θ_d, (K,), t)
     e .= K .* sin(θ_s[1] - θ_d[1])
 end
-function kuramoto_edge(; name=:kuramoto_edge)
+function kuramoto_edge(; name=:kuramoto_edge, kwargs...)
     EdgeModel(;g=AntiSymmetric(kuramoto_edge!),
-        outsym=[:P], psym=[:K], name)
+        outsym=[:P], psym=[:K], name, kwargs...)
 end
 
 Base.@propagate_inbounds function kuramoto_inertia!(dv, v, acc, p, t)
     M, D, Pm = p
     dv[1] = v[2]
     dv[2] = 1 / M * (Pm - D * v[2] + acc[1])
 end
-function kuramoto_second(; name=:kuramoto_second)
+function kuramoto_second(; name=:kuramoto_second, kwargs...)
     VertexModel(; f=kuramoto_inertia!, sym=[:δ=>0, :ω=>0],
-        psym=[:M=>1, :D=>0.1, :Pm=>1], g=StateMask(1), name)
+        psym=[:M=>1, :D=>0.1, :Pm=>1], g=StateMask(1), name, kwargs...)
 end
 
 Base.@propagate_inbounds function kuramoto_vertex!(dθ, θ, esum, (ω,), t)

test/symbolicindexing_test.jl

@@ -306,7 +306,7 @@ for idx in idxtypes
         println(idx, " => ", b.allocs, " allocations")
     end
     if VERSION ≥ v"1.11"
-        @test b.allocs <= 12
+        @test b.allocs <= 13
     end
 end
 
@@ -470,3 +470,21 @@ nw = Network(g, [n1, n2, n3], [e1, e2])
     @test s.p.e[:e2, 1] == s[EPIndex(2,1)]
     @test s.p.e[:e3, 1] == s[EPIndex(3,1)]
 end
+
+# test observed for inputs
+@testset "test observing of model input" begin
+    v1 = Lib.kuramoto_second(name=:v1, vidx=1, insym=[:Pin])
+    v2 = Lib.kuramoto_second(name=:v2, vidx=2, insym=[:Pin])
+    v3 = Lib.kuramoto_second(name=:v3, vidx=3, insym=[:Pin])
+    e1 = Lib.kuramoto_edge(name=:e1, src=1, dst=2, insym=[:δin])
+    e2 = Lib.kuramoto_edge(name=:e2, src=2, dst=3, insym=[:δin])
+    nw = Network([v1,v2,v3], [e1,e2])
+    s = NWState(nw, rand(dim(nw)), rand(pdim(nw)))
+    @test s[VIndex(:v1, :Pin)] == s[EIndex(:e1, :₋P)]
+    @test s[VIndex(:v2, :Pin)] == s[EIndex(:e1, :P)] + s[EIndex(:e2, :₋P)]
+    @test s[VIndex(:v3, :Pin)] == s[EIndex(:e2, :P)]
+    @test s[EIndex(:e1, :src₊δin)] == s[VIndex(:v1, :δ)]
+    @test s[EIndex(:e1, :dst₊δin)] == s[VIndex(:v2, :δ)]
+    @test s[EIndex(:e2, :src₊δin)] == s[VIndex(:v2, :δ)]
+    @test s[EIndex(:e2, :dst₊δin)] == s[VIndex(:v3, :δ)]
+end

test/testutils.jl

@@ -1,3 +1,7 @@
+using CUDA
+using Adapt
+using NetworkDynamics: iscudacompatible, NaiveAggregator
+
 """
 Test utility, which rebuilds the Network with all different execution styles and compares the
 results of the coreloop.