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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,147 @@ | ||
| using ModelingToolkit | ||
| @variables t | ||
| D = Differential(t) | ||
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| @variables xxx(t) | ||
| [xxx] | ||
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| function InPin(;name) | ||
| @parameters x | ||
| defaults = Dict(x => 13.37) | ||
| ODESystem(Equation[],t,Num[],[x]; name, defaults) | ||
| end | ||
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| function OutPin(;name) | ||
| vars = @variables x(t) xx(t) | ||
| defaults = Dict(x => 0.0) | ||
| ODESystem(Equation[D(x)~xx],t,[x,xx],Num[]; name, defaults) | ||
| end | ||
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| function create_pins(in::Integer, out::Integer) | ||
| inpins = [InPin(;name=Symbol("x$(i)_InPin")) for i in 1:in] | ||
| outpins = [OutPin(;name=Symbol("x$(i)_OutPin")) for i in 1:out] | ||
| inpins, outpins | ||
| end | ||
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| function Network(;name) | ||
| @variables x(t)=0.0 a(t) | ||
| @parameters p=0.4 | ||
| eqs = [D(x) ~ a, a ~ 0.1p] | ||
| ODESystem(eqs;name) | ||
| end | ||
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| function Model(;name) | ||
| op = OutPin(;name=:op) | ||
| net = Network(;name=:net) | ||
| eqs = [op.xx ~ net.x] | ||
| # eqs = Equation[] | ||
| systems = [op,net] | ||
| ODESystem(eqs,t;name,systems) | ||
| end | ||
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| # using Symbolics | ||
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| model = Model(;name=:model) | ||
| sys = structural_simplify(model) | ||
| @nonamespace n = model.net | ||
| @nonamespace o = model.op | ||
| u0 = Dict( | ||
| n.x => 0.0, | ||
| o.x => 0.0, | ||
| ) | ||
| p = Dict( | ||
| n.p => 0.4, | ||
| ) | ||
| defs = ModelingToolkit.get_defaults(sys) | ||
| prob = ODEProblem(sys, u0, (0.0,1.0), p) | ||
| sol = solve(prob, Tsit5()) | ||
| plot(sol[o.x]) | ||
| plot(sol[n.x]) | ||
| sol | ||
| plot(sol(sol.t,Val{1})[1,:]) | ||
| plot(sol(sol.t,Val{1})[2,:]) | ||
| plot(sol[1,:]) | ||
| plot(sol[2,:]) | ||
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| p = [0.1] | ||
| u0b = rand(1,10) | ||
| function prob_func(prob,i,repeat) | ||
| u0 = @view u0b[:,i] | ||
| remake(prob; u0) | ||
| end | ||
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| function output_func(sol,i) | ||
| sol[:, end], false | ||
| end | ||
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| function loss(p,model) | ||
| ensemble_prob = EnsembleProblem(prob; prob_func, output_func, safetycopy=false) # TODO: safetycopy ??? | ||
| sol = solve(ensemble_prob, Tsit5(), EnsembleThreads(), trajectories=size(u0b,2), | ||
| sensealg=InterpolatingAdjoint(autojacvec=ReverseDiffVJP(true)), save_everystep=false, save_start=false) | ||
| sum(Array(sol)) | ||
| end | ||
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| cbg = function (p,l;doplot=false) | ||
| display(l) | ||
| return false | ||
| end | ||
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| optfun = GalacticOptim.OptimizationFunction((θ,p) -> loss(θ,model), GalacticOptim.AutoZygote()) | ||
| optfunc = GalacticOptim.instantiate_function(optfun, p, GalacticOptim.AutoZygote(), nothing) | ||
| optprob = GalacticOptim.OptimizationProblem(optfunc, p) | ||
| GalacticOptim.solve(optprob, ADAM(), cb = cbg, maxiters=100) | ||
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| using DiffEqCallbacks, OrdinaryDiffEq, LinearAlgebra | ||
| prob = ODEProblem((du,u,p,t) -> du .= u, rand(4), (0.0,1.0)) | ||
| saved_values = SavedValues(Float64, Vector{Float64}) | ||
| cb = SavingCallback((u,t,integrator)->integrator(t,Val{1})[:,1], saved_values, saveat = 0.0:0.1:1.0) | ||
| sol = solve(prob, Tsit5(), saveat=0.1, callback=cb) | ||
| as = Array(sol) | ||
| plot(s) | ||
| sol[:] | ||
| saved_values.saveval | ||
| plot(saved_values.saveval) | ||
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| using ModelingToolkit | ||
| using ModelingToolkit: get_defaults | ||
| using OrdinaryDiffEq | ||
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| @variables t | ||
| D = Differential(t) | ||
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| function SubSys(;name) | ||
| @variables x(t)=0.0 | ||
| @parameters p=0.3 | ||
| ODESystem([x~p],t,[x],[p];name) | ||
| end | ||
| function Network(;name) | ||
| @variables x(t)=0.0 | ||
| @named subsys = SubSys() | ||
| ODESystem([D(x)~subsys.x],t,[x],[];name,systems=[subsys]) | ||
| end | ||
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| @named net = Network() | ||
| sys = structural_simplify(net) | ||
| prob = ODEProblem(sys,get_defaults(sys),(0.0,0.1)) | ||
| sol = solve(prob,Tsit5()) | ||
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| @nonamespace x = net.x | ||
| @nonamespace subsys = net.subsys | ||
| sol[x] | ||
| sol[subsys.x] | ||
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| sol[net.x] | ||
| sol[net.subsys.x] |
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