Merge branch 'v0.13.1' into main

examples/jupyter-src/.gitignore

@@ -1,4 +1,4 @@
 .ipynb_checkpoints/
 params/
 *.png
-./*.gif
+*.gif

examples/pluto-src/SciMLUsingFMUs/SciMLUsingFMUs.jl

@@ -1,24 +1,19 @@
 ### A Pluto.jl notebook ###
-# v0.19.46
+# v0.20.3
 
 using Markdown
 using InteractiveUtils
 
 # This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).
 macro bind(def, element)
+    #! format: off
     quote
-        local iv = try
-            Base.loaded_modules[Base.PkgId(
-                Base.UUID("6e696c72-6542-2067-7265-42206c756150"),
-                "AbstractPlutoDingetjes",
-            )].Bonds.initial_value
-        catch
-            b -> missing
-        end
+        local iv = try Base.loaded_modules[Base.PkgId(Base.UUID("6e696c72-6542-2067-7265-42206c756150"), "AbstractPlutoDingetjes")].Bonds.initial_value catch; b -> missing; end
         local el = $(esc(element))
         global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : iv(el)
         el
     end
+    #! format: on
 end
 
 # ╔═╡ a1ee798d-c57b-4cc3-9e19-fb607f3e1e43
@@ -1941,7 +1936,7 @@ ProgressLogging = "~0.1.4"
 PLUTO_MANIFEST_TOML_CONTENTS = """
 # This file is machine-generated - editing it directly is not advised
 
-julia_version = "1.10.3"
+julia_version = "1.10.7"
 manifest_format = "2.0"
 project_hash = "79772b37e2cae2421c7159b63f3cbe881b42eaeb"
 
@@ -4744,7 +4739,7 @@ version = "0.15.1+0"
 [[deps.libblastrampoline_jll]]
 deps = ["Artifacts", "Libdl"]
 uuid = "8e850b90-86db-534c-a0d3-1478176c7d93"
-version = "5.8.0+1"
+version = "5.11.0+0"
 
 [[deps.libevdev_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]

src/layers.jl

@@ -142,48 +142,57 @@ Flux.@functor SimultaniousZeroCrossing (m,)
 ### SHIFTSCALE ###
 
 """
-ToDo.
+
+    ToDo.
+
+Shifts and scales input data to a given distribution
 """
-struct ShiftScale{T}
+struct ShiftScale{T, A}
     shift::AbstractArray{T}
     scale::AbstractArray{T}
+    activation::A
 
-    function ShiftScale{T}(shift::AbstractArray{T}, scale::AbstractArray{T}) where {T}
-        inst = new(shift, scale)
+    function ShiftScale{T, A}(shift::AbstractArray{T}, scale::AbstractArray{T}, activation::A=Flux.identity) where {T, A}
+        inst = new(shift, scale, activation)
         return inst
     end
 
-    function ShiftScale(shift::AbstractArray{T}, scale::AbstractArray{T}) where {T}
-        return ShiftScale{T}(shift, scale)
+    function ShiftScale(shift::AbstractArray{T}, scale::AbstractArray{T}, activation::A=Flux.identity) where {T, A}
+        return ShiftScale{T, A}(shift, scale, activation)
     end
 
     # initialize for data array
     function ShiftScale(
-        data::AbstractArray{<:AbstractArray{T}};
-        range::Union{Symbol,UnitRange{<:Integer}} = -1:1,
-    ) where {T}
+        data::AbstractArray{<:AbstractArray{T}},
+        activation::A=Flux.identity;
+        range::Union{Symbol,UnitRange{<:Integer}} = :Normalize,
+    ) where {T, A}
         shift = -mean.(data)
         scale = nothing
 
-        if range == :NormalDistribution
+        if range == :Normalize
+            range = 0:1
+        end
+
+        if range == :Standardize # NormalDistribution
             scale = 1.0 ./ std.(data)
         elseif isa(range, UnitRange{<:Integer})
             scale =
                 1.0 ./
                 (collect(max(d...) for d in data) - collect(min(d...) for d in data)) .*
                 (range[end] - range[1])
         else
-            @assert false "Unsupported scaleMode, supported is `:NormalDistribution` or `UnitRange{<:Integer}`"
+            @assert false "Unsupported range `$(range)`, supported is `:Standardize`, `:Normalize` or `UnitRange{<:Integer}`"
         end
 
-        return ShiftScale{T}(shift, scale)
+        return ShiftScale{T, A}(shift, scale, activation)
     end
 end
 export ShiftScale
 
 function (l::ShiftScale)(x)
 
-    x_proc = (x .+ l.shift) .* l.scale
+    x_proc = l.activation((x .+ l.shift) .* l.scale)
 
     return x_proc
 end
@@ -209,14 +218,14 @@ struct ScaleShift{T}
     end
 
     # init ScaleShift with inverse transformation of a given ShiftScale
-    function ScaleShift(l::ShiftScale{T}; indices = 1:length(l.scale)) where {T}
+    function ScaleShift(l::ShiftScale{T, A}; indices = 1:length(l.scale)) where {T, A}
         return ScaleShift{T}(1.0 ./ l.scale[indices], -1.0 .* l.shift[indices])
     end
 
     function ScaleShift(data::AbstractArray{<:AbstractArray{T}}) where {T}
         shift = mean.(data)
         scale = std.(data)
-        return ShiftScale{T}(scale, shift)
+        return ShiftScale(scale, shift)
     end
 end
 export ScaleShift

test/layers.jl

@@ -16,7 +16,7 @@ s = ShiftScale(shift, scale)
 @test s(input) == [6.0, 16.0, 24.0]
 
 s = ShiftScale(inputArray)
-@test s(input) == [2.5, -0.8, -1.0]
+@test s(input) == [1.25, -0.4, -0.5]
 
 s = ShiftScale(inputArray; range = -1:1)
 for i = 1:length(inputArray)
@@ -32,7 +32,7 @@ for i = 1:length(inputArray)
     @test min(res...) >= -2
 end
 
-s = ShiftScale(inputArray; range = :NormalDistribution)
+s = ShiftScale(inputArray; range = :Normalize)
 # ToDo: Test for :NormalDistribution
 
 # ScaleShift

test/solution_gradients.jl

@@ -185,7 +185,6 @@ stepCb = FunctionCallingCallback(stepCompleted; func_everystep = true, func_star
 #fmu_params = nothing
 fmu = loadFMU("BouncingBall1D", "Dymola", "2023x"; type = :ME)
 fmu_params = Dict("damping" => 0.7, "mass_radius" => 0.0, "mass_s_min" => DBL_MIN)
-fmu.executionConfig.isolatedStateDependency = true
 
 net = Chain(#Dense(W1, b1, identity),
     x -> fmu(; x = x, dx_refs = :all),

test/supported_sensitivities.jl

@@ -4,7 +4,7 @@
 #
 
 using Flux
-using DifferentialEquations
+using DifferentialEquations, Sundials
 
 import Random
 Random.seed!(5678);
@@ -58,6 +58,7 @@ for fmu in fmus
     @info "##### CHECKING FMU WITH $(fmu.modelDescription.numberOfEventIndicators) SIMULTANEOUS EVENT INDICATOR(S) #####"
     solvers = [
         Tsit5(),
+        CVODE_BDF(),
         Rosenbrock23(autodiff = false),
         Rodas5(autodiff = false),
         FBDF(autodiff = false),

test/time_solution_gradients.jl

@@ -231,7 +231,6 @@ fmu_params = Dict(
     "mass_m" => MASS,
     "mass_s_min" => DBL_MIN,
 )
-fmu.executionConfig.isolatedStateDependency = true
 
 net = Chain(#Dense(W1, b1, identity),
     x -> fmu(; x = x, dx_refs = :all),