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[ "MIT" ]
0.3.6
b9a419e46fe4516f3c0b52041d84e2bb5be9cb81
code
163
function concordiacurve end function concordiacurve! end function concordialine end function concordialine! end function rankorder end function rankorder! end
Isoplot
https://github.com/JuliaGeochronology/Isoplot.jl.git
[ "MIT" ]
0.3.6
b9a419e46fe4516f3c0b52041d84e2bb5be9cb81
code
25037
""" ```julia dist_ll(dist::Collection, mu::Collection, sigma::Collection, tmin::Number, tmax::Number) dist_ll(dist::Collection, analyses::Collection{<:Measurement}, tmin::Number, tmax::Number) ``` Return the log-likelihood of a set of mineral ages with means `mu` and uncertianty `sigma` being drawn from a given source (i.e., crystallization / closure) distribution `dist`, with terms to prevent runaway at low N. ### Examples ```julia mu, sigma = collect(100:0.1:101), 0.01*ones(11) ll = dist_ll(MeltsVolcanicZirconDistribution, mu, sigma, 100, 101) ``` """ function dist_ll(dist::Collection, mu::Collection, sigma::Collection, tmin::Number, tmax::Number) tmax >= tmin || return NaN any(isnan, mu) && return NaN any(x->!(x>0), sigma) && return NaN @assert issorted(mu) mu₋, sigma₋ = first(mu), first(sigma) mu₊, sigma₊ = last(mu), last(sigma) nbins = length(dist) - 1 dt = abs(tmax-tmin) # Cycle through each datum in dataset loglikelihood = zero(float(eltype(dist))) @inbounds for j in eachindex(mu, sigma) μⱼ, σⱼ = mu[j], sigma[j] # Find equivalent index position of μⱼ in the `dist` array ix = (μⱼ - tmin) / dt * nbins + 1 # If possible, prevent aliasing problems by interpolation if (σⱼ < dt / nbins) && 1 < ix < length(dist) # Interpolate corresponding distribution value f = floor(Int, ix) δ = ix - f likelihood = (dist[f+1]*δ + dist[f]*(1-δ)) / dt else # Otherwise, sum contributions from Gaussians at each point in distribution 𝑖 = 1:length(dist) likelihood = zero(float(eltype(dist))) normconst = 1/(length(dist) * σⱼ * sqrt(2 * pi)) @turbo for i in eachindex(dist, 𝑖) distx = tmin + dt * (𝑖[i] - 1) / nbins # time-position of distribution point # Likelihood curve follows a Gaussian PDF. Note: dt cancels likelihood += dist[i] * normconst * exp(-(distx - μⱼ)^2 / (2 * σⱼ * σⱼ)) end end loglikelihood += log(likelihood) end # Calculate a weighted mean and examine our MSWD (wm, wsigma, mswd) = wmean(mu, sigma, corrected=true) # Height of MSWD distribution relative to height at MSWD = 1 # (see Wendt and Carl, 1991, Chemical geology) f = length(mu) - 1 Zf = exp((f/2-1)*log(mswd) - f/2*(mswd-1)) * (f > 0) Zf = max(min(Zf, 1.0), 0.0) @assert 0 <= Zf <= 1 # To prevent instability / runaway of the MCMC for small datasets (low N), # favor the weighted mean interpretation at high Zf (MSWD close to 1) and # the youngest-zircon interpretation at low Zf (MSWD far from one). The # penalty factors used here are determined by training against synthetic datasets. # In other words, these are just context-dependent prior distributions on tmax and tmin loglikelihood -= (2/log(1+length(mu))) * ( # Scaling factor that decreases with log number of data points (i.e., no penalty at high N) log((abs(tmin - wm)+wsigma)/wsigma)*Zf + # Penalty for proposing tmin too far from the weighted mean at low MSWD (High Zf) log((abs(tmax - wm)+wsigma)/wsigma)*Zf + # Penalty for proposing tmax too far from the weighted mean at low MSWD (High Zf) log((abs(tmin - mu₋)+sigma₋)/sigma₋)*(1-Zf) + # Penalty for proposing tmin too far from youngest zircon at high MSWD (low Zf) log((abs(tmax - mu₊)+sigma₊)/sigma₊)*(1-Zf) ) # Penalty for proposing tmax too far from oldest zircon at high MSWD (low Zf) return loglikelihood end function dist_ll(dist::Collection, analyses::Collection{<:Measurement}, tmin::Number, tmax::Number) tmax >= tmin || return NaN any(isnan, analyses) && return NaN any(x->!(err(x) > 0), analyses) && return NaN old = maximum(analyses) yng = minimum(analyses) nbins = length(dist) - 1 dt = abs(tmax - tmin) # Cycle through each datum in dataset loglikelihood = zero(float(eltype(dist))) @inbounds for j in eachindex(analyses) dⱼ = analyses[j] μⱼ, σⱼ = val(dⱼ), err(dⱼ) # Find equivalent index position of μⱼ in the `dist` array ix = (μⱼ - tmin) / dt * nbins + 1 # If possible, prevent aliasing problems by interpolation if (σⱼ < dt / nbins) && 1 < ix < length(dist) # Interpolate corresponding distribution value f = floor(Int, ix) δ = ix - f likelihood = (dist[f+1]*δ + dist[f]*(1-δ)) / dt else # Otherwise, sum contributions from Gaussians at each point in distribution 𝑖 = 1:length(dist) likelihood = zero(float(eltype(dist))) normconst = 1/(length(dist) * σⱼ * sqrt(2 * pi)) @turbo for i in eachindex(dist, 𝑖) distx = tmin + dt * (𝑖[i] - 1) / nbins # time-position of distribution point # Likelihood curve follows a Gaussian PDF. Note: dt cancels likelihood += dist[i] * normconst * exp(-(distx - μⱼ)^2 / (2 * σⱼ * σⱼ)) end end loglikelihood += log(likelihood) end # Calculate a weighted mean and examine our MSWD (wm, mswd) = wmean(analyses, corrected=true) @assert wm.err > 0 # Height of MSWD distribution relative to height at MSWD = 1 # (see Wendt and Carl, 1991, Chemical geology) f = length(analyses) - 1 Zf = exp((f / 2 - 1) * log(mswd) - f / 2 * (mswd - 1)) * (f > 0) Zf = max(min(Zf, 1.0), 0.0) @assert 0 <= Zf <= 1 # To prevent instability / runaway of the MCMC for small datasets (low N), # favor the weighted mean interpretation at high Zf (MSWD close to 1) and # the youngest-zircon interpretation at low Zf (MSWD far from one). The # penalties used here were determined by training against synthetic datasets. # In other words, these are just context-dependent prior distributions on tmax and tmin loglikelihood -= (2 / log(1 + length(analyses))) * ( # Scaling factor that decreases with log number of analyses points (i.e., no penalty at high N) log((abs(tmin - wm.val) + wm.err) / wm.err) * Zf + # Penalty for proposing tmin too far from the weighted mean at low MSWD (High Zf) log((abs(tmax - wm.val) + wm.err) / wm.err) * Zf + # Penalty for proposing tmax too far from the weighted mean at low MSWD (High Zf) log((abs(tmin - yng.val) + yng.err) / yng.err) * (1 - Zf) + # Penalty for proposing tmin too far from youngest zircon at high MSWD (low Zf) log((abs(tmax - old.val) + old.err) / old.err) * (1 - Zf)) # Penalty for proposing tmax too far from oldest zircon at high MSWD (low Zf) return loglikelihood end """ ```julia metropolis_min(nsteps::Integer, dist::Collection, data::Collection{<:Measurement}; burnin::Integer=0, t0prior=Uniform(0,minimum(age68.(analyses))), lossprior=Uniform(0,100)) metropolis_min(nsteps::Integer, dist::Collection, mu::AbstractArray, sigma::AbstractArray; burnin::Integer=0) metropolis_min(nsteps::Integer, dist::Collection, analyses::Collection{<:UPbAnalysis; burnin::Integer=0) ``` Run a Metropolis sampler to estimate the minimum of a finite-range source distribution `dist` using samples drawn from that distribution -- e.g., estimate zircon eruption ages from a distribution of zircon crystallization ages. ### Examples ```julia tmindist = metropolis_min(2*10^5, MeltsVolcanicZirconDistribution, mu, sigma, burnin=10^5) tmindist, t0dist = metropolis_min(2*10^5, HalfNormalDistribution, analyses, burnin=10^5) ``` """ metropolis_min(nsteps::Integer, dist::Collection, data::Collection{<:Measurement}; kwargs...) = metropolis_min(nsteps, dist, val.(data), err.(data); kwargs...) function metropolis_min(nsteps::Integer, dist::Collection, mu::Collection, sigma::Collection; kwargs...) # Allocate ouput array tmindist = Array{float(eltype(mu))}(undef,nsteps) # Run Metropolis sampler return metropolis_min!(tmindist, nsteps, dist, mu, sigma; kwargs...) end function metropolis_min(nsteps::Integer, dist::Collection{T}, analyses::Collection{UPbAnalysis{T}}; kwargs...) where {T} # Allocate ouput arrays tmindist = Array{T}(undef,nsteps) t0dist = Array{T}(undef,nsteps) # Run Metropolis sampler metropolis_min!(tmindist, t0dist, nsteps, dist, analyses; kwargs...) return tmindist, t0dist end """ ```julia metropolis_min!(tmindist::DenseArray, nsteps::Integer, dist::Collection, mu::AbstractArray, sigma::AbstractArray; burnin::Integer=0) metropolis_min!(tmindist::DenseArray, t0dist::DenseArray, nsteps::Integer, dist::Collection, analyses::Collection{<:UPbAnalysis}; burnin::Integer=0) where {T} ``` In-place (non-allocating) version of `metropolis_min`, fills existing array `tmindist`. Run a Metropolis sampler to estimate the minimum of a finite-range source distribution `dist` using samples drawn from that distribution -- e.g., estimate zircon eruption ages from a distribution of zircon crystallization ages. ### Examples ```julia metropolis_min!(tmindist, 2*10^5, MeltsVolcanicZirconDistribution, mu, sigma, burnin=10^5) ``` """ function metropolis_min!(tmindist::DenseArray, nsteps::Integer, dist::Collection, mu::AbstractArray, sigma::AbstractArray; burnin::Integer=0) # standard deviation of the proposal function is stepfactor * last step; this is tuned to optimize accetance probability at 50% stepfactor = 2.9 # Sort the dataset from youngest to oldest sI = sortperm(mu) mu_sorted = mu[sI] # Sort means sigma_sorted = sigma[sI] # Sort uncertainty youngest, oldest = first(mu_sorted), last(mu_sorted) # Step sigma for Gaussian proposal distributions dt = oldest - youngest + first(sigma_sorted) + last(sigma_sorted) tmin_step = dt / length(mu) tmax_step = dt / length(mu) # Use oldest and youngest zircons for initial proposal tminₚ = tmin = youngest - first(sigma_sorted) tmaxₚ = tmax = oldest + last(sigma_sorted) # Log likelihood of initial proposal llₚ = ll = dist_ll(dist, mu_sorted, sigma_sorted, tmin, tmax) # Burnin for i=1:burnin # Adjust upper or lower bounds tminₚ, tmaxₚ = tmin, tmax r = rand() (r < 0.5) && (tmaxₚ += tmin_step*randn()) (r > 0.5) && (tminₚ += tmax_step*randn()) # Flip bounds if reversed (tminₚ > tmaxₚ) && ((tminₚ, tmaxₚ) = (tmaxₚ, tminₚ)) # Calculate log likelihood for new proposal llₚ = dist_ll(dist, mu_sorted, sigma_sorted, tminₚ, tmaxₚ) # Decide to accept or reject the proposal if log(rand()) < (llₚ-ll) if tminₚ != tmin tmin_step = abs(tminₚ-tmin)*stepfactor end if tmaxₚ != tmax tmax_step = abs(tmaxₚ-tmax)*stepfactor end ll = llₚ tmin = tminₚ tmax = tmaxₚ end end # Step through each of the N steps in the Markov chain @inbounds for i in eachindex(tmindist) # Adjust upper or lower bounds tminₚ, tmaxₚ = tmin, tmax r = rand() (r < 0.5) && (tmaxₚ += tmin_step*randn()) (r > 0.5) && (tminₚ += tmax_step*randn()) # Flip bounds if reversed (tminₚ > tmaxₚ) && ((tminₚ, tmaxₚ) = (tmaxₚ, tminₚ)) # Calculate log likelihood for new proposal llₚ = dist_ll(dist, mu_sorted, sigma_sorted, tminₚ, tmaxₚ) # Decide to accept or reject the proposal if log(rand()) < (llₚ-ll) if tminₚ != tmin tmin_step = abs(tminₚ-tmin)*stepfactor end if tmaxₚ != tmax tmax_step = abs(tmaxₚ-tmax)*stepfactor end ll = llₚ tmin = tminₚ tmax = tmaxₚ end tmindist[i] = tmin end return tmindist end function metropolis_min!(tmindist::DenseArray{T}, t0dist::DenseArray{T}, nsteps::Integer, dist::Collection{T}, analyses::Collection{UPbAnalysis{T}}; burnin::Integer=0, t0prior=Uniform(0,minimum(age68.(analyses))), lossprior=Uniform(0,100)) where {T} # standard deviation of the proposal function is stepfactor * last step; this is tuned to optimize accetance probability at 50% stepfactor = 2.9 # Sort the dataset from youngest to oldest # These quantities will be used more than once t0ₚ = t0 = 0.0 ellipses = Ellipse.(analyses) ages68 = log.(one(T) .+ (ellipses .|> e->e.y₀))./val(λ238U) ages = similar(ellipses, Measurement{T}) @. ages = upperintercept(t0ₚ, ellipses) youngest = minimum(ages) oldest = maximum(ages) t0step = youngest.val/50 # t0prior = Uniform(0, youngest.val) t0prior = truncated(t0prior, 0, minimum(age68.(analyses))) lossprior = truncated(lossprior, 0, 100) # Initial step sigma for Gaussian proposal distributions dt = sqrt((oldest.val - youngest.val)^2 + oldest.err^2 + youngest.err^2) tmin_step = tmax_step = dt / length(analyses) # Use oldest and youngest zircons for initial proposal tminₚ = tmin = val(youngest) tmaxₚ = tmax = val(oldest) # Log likelihood of initial proposal ll = dist_ll(dist, ages, tmin, tmax) + logpdf(t0prior, t0) for i in eachindex(ages, ages68) loss = 100*max(one(T) - (ages68[i] - t0) / (val(ages[i]) - t0), zero(T)) ll += logpdf(lossprior, loss) end llₚ = ll # Burnin for i = 1:burnin tminₚ, tmaxₚ, t0ₚ = tmin, tmax, t0 # Adjust upper or lower bounds, or Pb-loss time r = rand() if r < 0.35 tminₚ += tmin_step * randn() elseif r < 0.70 tmaxₚ += tmax_step * randn() else t0ₚ += t0step * randn() end # Flip bounds if reversed (tminₚ > tmaxₚ) && ((tminₚ, tmaxₚ) = (tmaxₚ, tminₚ)) # Calculate log likelihood for new proposal @. ages = upperintercept(t0ₚ, ellipses) llₚ = dist_ll(dist, ages, tminₚ, tmaxₚ) llₚ += logpdf(t0prior, t0ₚ) for i in eachindex(ages, ages68) loss = 100*max(one(T) - (ages68[i] - t0ₚ) / (val(ages[i]) - t0ₚ), zero(T)) llₚ += logpdf(lossprior, loss) end # Decide to accept or reject the proposal if log(rand()) < (llₚ - ll) if tminₚ != tmin tmin_step = abs(tminₚ - tmin) * stepfactor end if tmaxₚ != tmax tmax_step = abs(tmaxₚ - tmax) * stepfactor end ll = llₚ tmin = tminₚ tmax = tmaxₚ t0 = t0ₚ end end # Step through each of the N steps in the Markov chain @inbounds for i in eachindex(tmindist, t0dist) tminₚ, tmaxₚ, t0ₚ = tmin, tmax, t0 # Adjust upper or lower bounds, or Pb-loss time r = rand() if r < 0.35 tminₚ += tmin_step * randn() elseif r < 0.70 tmaxₚ += tmax_step * randn() else t0ₚ += t0step * randn() end # Flip bounds if reversed (tminₚ > tmaxₚ) && ((tminₚ, tmaxₚ) = (tmaxₚ, tminₚ)) # Calculate log likelihood for new proposal @. ages = upperintercept(t0ₚ, ellipses) llₚ = dist_ll(dist, ages, tminₚ, tmaxₚ) llₚ += logpdf(t0prior, t0ₚ) for i in eachindex(ages, ages68) loss = 100*max(one(T) - (ages68[i] - t0ₚ) / (val(ages[i]) - t0ₚ), zero(T)) llₚ += logpdf(lossprior, loss) end # Decide to accept or reject the proposal if log(rand()) < (llₚ - ll) if tminₚ != tmin tmin_step = abs(tminₚ - tmin) * stepfactor end if tmaxₚ != tmax tmax_step = abs(tmaxₚ - tmax) * stepfactor end ll = llₚ tmin = tminₚ tmax = tmaxₚ t0 = t0ₚ end tmindist[i] = tmin t0dist[i] = t0 end return tmindist end """ ```julia metropolis_minmax(nsteps::Integer, dist::Collection, data::Collection{<:Measurement}; burnin::Integer=0) metropolis_minmax(nsteps::Integer, dist::AbstractArray, data::AbstractArray, uncert::AbstractArray; burnin::Integer=0) ``` Run a Metropolis sampler to estimate the extrema of a finite-range source distribution `dist` using samples drawn from that distribution -- e.g., estimate zircon saturation and eruption ages from a distribution of zircon crystallization ages. ### Examples ```julia tmindist, tmaxdist, lldist, acceptancedist = metropolis_minmax(2*10^5, MeltsVolcanicZirconDistribution, mu, sigma, burnin=10^5) ``` """ metropolis_minmax(nsteps::Integer, dist::Collection, data::Collection{<:Measurement}; kwargs...) = metropolis_minmax(nsteps, dist, val.(data), err.(data); kwargs...) function metropolis_minmax(nsteps::Integer, dist::Collection, mu::AbstractArray, sigma::AbstractArray; kwargs...) # Allocate ouput arrays acceptancedist = falses(nsteps) lldist = Array{float(eltype(dist))}(undef,nsteps) tmaxdist = Array{float(eltype(mu))}(undef,nsteps) tmindist = Array{float(eltype(mu))}(undef,nsteps) # Run metropolis sampler return metropolis_minmax!(tmindist, tmaxdist, lldist, acceptancedist, nsteps, dist, mu, sigma; kwargs...) end function metropolis_minmax(nsteps::Integer, dist::Collection{T}, analyses::Collection{<:UPbAnalysis{T}}; kwargs...) where T # Allocate ouput arrays acceptancedist = falses(nsteps) lldist = Array{T}(undef,nsteps) t0dist = Array{T}(undef,nsteps) tmaxdist = Array{T}(undef,nsteps) tmindist = Array{T}(undef,nsteps) # Run metropolis sampler return metropolis_minmax!(tmindist, tmaxdist, t0dist, lldist, acceptancedist, nsteps, dist, analyses; kwargs...) end """ ```julia metropolis_minmax!(tmindist, tmaxdist, lldist, acceptancedist, nsteps::Integer, dist::AbstractArray, data::AbstractArray, uncert::AbstractArray; burnin::Integer=0) metropolis_minmax!(tmindist, tmaxdist, t0dist, lldist, acceptancedist, nsteps::Integer, dist::Collection, analyses::Collection{<:UPbAnalysis}; burnin::Integer=0) ``` In-place (non-allocating) version of `metropolis_minmax`, filling existing arrays Run a Metropolis sampler to estimate the extrema of a finite-range source distribution `dist` using samples drawn from that distribution -- e.g., estimate zircon saturation and eruption ages from a distribution of zircon crystallization ages. ### Examples ```julia metropolis_minmax!(tmindist, tmaxdist, lldist, acceptancedist, 2*10^5, MeltsVolcanicZirconDistribution, mu, sigma, burnin=10^5) ``` """ function metropolis_minmax!(tmindist::DenseArray, tmaxdist::DenseArray, lldist::DenseArray, acceptancedist::BitVector, nsteps::Integer, dist::Collection, mu::AbstractArray, sigma::AbstractArray; burnin::Integer=0) # standard deviation of the proposal function is stepfactor * last step; this is tuned to optimize accetance probability at 50% stepfactor = 2.9 # Sort the dataset from youngest to oldest sI = sortperm(mu) mu_sorted = mu[sI] # Sort means sigma_sorted = sigma[sI] # Sort uncertainty youngest, oldest = first(mu_sorted), last(mu_sorted) # Step sigma for Gaussian proposal distributions dt = oldest - youngest + first(sigma_sorted) + last(sigma_sorted) tmin_step = tmax_step = dt / length(mu) # Use oldest and youngest zircons for initial proposal tminₚ = tmin = youngest - first(sigma_sorted) tmaxₚ = tmax = oldest + last(sigma_sorted) # Log likelihood of initial proposal llₚ = ll = dist_ll(dist, mu_sorted, sigma_sorted, tmin, tmax) # Burnin for i=1:nsteps # Adjust upper or lower bounds tminₚ, tmaxₚ = tmin, tmax r = rand() (r < 0.5) && (tmaxₚ += tmin_step*randn()) (r > 0.5) && (tminₚ += tmax_step*randn()) # Flip bounds if reversed (tminₚ > tmaxₚ) && ((tminₚ, tmaxₚ) = (tmaxₚ, tminₚ)) # Calculate log likelihood for new proposal llₚ = dist_ll(dist, mu_sorted, sigma_sorted, tminₚ, tmaxₚ) # Decide to accept or reject the proposal if log(rand()) < (llₚ-ll) if tminₚ != tmin tmin_step = abs(tminₚ-tmin)*stepfactor end if tmaxₚ != tmax tmax_step = abs(tmaxₚ-tmax)*stepfactor end ll = llₚ tmin = tminₚ tmax = tmaxₚ end end # Step through each of the N steps in the Markov chain @inbounds for i in eachindex(tmindist, tmaxdist, lldist, acceptancedist) # Adjust upper or lower bounds tminₚ, tmaxₚ = tmin, tmax r = rand() (r < 0.5) && (tmaxₚ += tmin_step*randn()) (r > 0.5) && (tminₚ += tmax_step*randn()) # Flip bounds if reversed (tminₚ > tmaxₚ) && ((tminₚ, tmaxₚ) = (tmaxₚ, tminₚ)) # Calculate log likelihood for new proposal llₚ = dist_ll(dist, mu_sorted, sigma_sorted, tminₚ, tmaxₚ) # Decide to accept or reject the proposal if log(rand()) < (llₚ-ll) if tminₚ != tmin tmin_step = abs(tminₚ-tmin)*stepfactor end if tmaxₚ != tmax tmax_step = abs(tmaxₚ-tmax)*stepfactor end ll = llₚ tmin = tminₚ tmax = tmaxₚ acceptancedist[i]=true end tmindist[i] = tmin tmaxdist[i] = tmax lldist[i] = ll end return tmindist, tmaxdist, lldist, acceptancedist end function metropolis_minmax!(tmindist::DenseArray{T}, tmaxdist::DenseArray{T}, t0dist::DenseArray{T}, lldist::DenseArray{T}, acceptancedist::BitVector, nsteps::Integer, dist::Collection{T}, analyses::Collection{UPbAnalysis{T}}; burnin::Integer=0) where {T} # standard deviation of the proposal function is stepfactor * last step; this is tuned to optimize accetance probability at 50% stepfactor = 2.9 # Sort the dataset from youngest to oldest # These quantities will be used more than once t0ₚ = t0 = 0.0 ellipses = Ellipse.(analyses) ages = similar(ellipses, Measurement{T}) @. ages = upperintercept(t0ₚ, ellipses) youngest = minimum(ages) oldest = maximum(ages) t0step = youngest.val/50 t0prior = Uniform(0, youngest.val) # Initial step sigma for Gaussian proposal distributions dt = sqrt((oldest.val - youngest.val)^2 + oldest.err^2 + youngest.err^2) tmin_step = tmax_step = dt / length(analyses) # Use oldest and youngest zircons for initial proposal tminₚ = tmin = val(youngest) tmaxₚ = tmax = val(oldest) # Log likelihood of initial proposal ll = llₚ = dist_ll(dist, ages, tmin, tmax) + logpdf(t0prior, t0) # Burnin for i = 1:burnin tminₚ, tmaxₚ, t0ₚ = tmin, tmax, t0 # Adjust upper or lower bounds, or Pb-loss time r = rand() if r < 0.35 tminₚ += tmin_step * randn() elseif r < 0.70 tmaxₚ += tmax_step * randn() else t0ₚ += t0step * randn() end # Flip bounds if reversed (tminₚ > tmaxₚ) && ((tminₚ, tmaxₚ) = (tmaxₚ, tminₚ)) # Calculate log likelihood for new proposal @. ages = upperintercept(t0ₚ, ellipses) llₚ = dist_ll(dist, ages, tminₚ, tmaxₚ) llₚ += logpdf(t0prior, t0ₚ) # Decide to accept or reject the proposal if log(rand()) < (llₚ - ll) if tminₚ != tmin tmin_step = abs(tminₚ - tmin) * stepfactor end if tmaxₚ != tmax tmax_step = abs(tmaxₚ - tmax) * stepfactor end ll = llₚ tmin = tminₚ tmax = tmaxₚ t0 = t0ₚ end end # Step through each of the N steps in the Markov chain @inbounds for i in eachindex(tmindist, t0dist) tminₚ, tmaxₚ, t0ₚ = tmin, tmax, t0 # Adjust upper or lower bounds, or Pb-loss time r = rand() if r < 0.35 tminₚ += tmin_step * randn() elseif r < 0.70 tmaxₚ += tmax_step * randn() else t0ₚ += t0step * randn() end # Flip bounds if reversed (tminₚ > tmaxₚ) && ((tminₚ, tmaxₚ) = (tmaxₚ, tminₚ)) # Calculate log likelihood for new proposal @. ages = upperintercept(t0ₚ, ellipses) llₚ = dist_ll(dist, ages, tminₚ, tmaxₚ) llₚ += logpdf(t0prior, t0ₚ) # Decide to accept or reject the proposal if log(rand()) < (llₚ - ll) if tminₚ != tmin tmin_step = abs(tminₚ - tmin) * stepfactor end if tmaxₚ != tmax tmax_step = abs(tmaxₚ - tmax) * stepfactor end ll = llₚ tmin = tminₚ tmax = tmaxₚ t0 = t0ₚ acceptancedist[i]=true end tmindist[i] = tmin tmaxdist[i] = tmax t0dist[i] = t0 lldist[i] = ll end return tmindist, tmaxdist, t0dist, lldist, acceptancedist end
Isoplot
https://github.com/JuliaGeochronology/Isoplot.jl.git
[ "MIT" ]
0.3.6
b9a419e46fe4516f3c0b52041d84e2bb5be9cb81
code
10056
using SpecialFunctions: erfc """ Apply Chauvenet's criterion to a set of data to identify outliers. The function calculates the z-scores of the data points, and then calculates the probability `p` of observing a value as extreme as the z-score under the assumption of normal distribution. It then applies Chauvenet's criterion, marking any data point as an outlier if `2 * N * p < 1.0`, where `N` is the total number of data points. """ function chauvenet_func(μ::Vector{T}, σ::Vector) where {T} mean_val = mean(μ) N = length(μ) z_scores = abs.(μ .- mean_val) ./ σ p = 0.5 * erfc.(z_scores ./ sqrt(2.0)) criterion = 2 * N * p selected_data = criterion .>= 1.0 # add @info about number of outliers @info "Excluding $(N - sum(selected_data)) outliers based on Chauvenet's criterion." return selected_data end ## --- Weighted means """ ```julia wμ, wσ, mswd = wmean(μ, σ; corrected=true, chauvenet=false) wμ ± wσ, mswd = wmean(μ ± σ; corrected=true, chauvenet=false) ``` The weighted mean, with or without the "geochronologist's MSWD correction" to uncertainty. You may specify your means and standard deviations either as separate vectors `μ` and `σ`, or as a single vector `x` of `Measurement`s equivalent to `x = μ .± σ` In all cases, `σ` is assumed to reported as _actual_ sigma (i.e., 1-sigma). If `corrected=true`, the resulting uncertainty of the weighted mean is expanded by a factor of `sqrt(mswd)` to attempt to account for dispersion dispersion when the MSWD is greater than `1` If `chauvenet=true`, outliers will be removed before the computation of the weighted mean using Chauvenet's criterion. ### Examples ```julia julia> x = randn(10) 10-element Vector{Float64}: 0.4612989881720301 -0.7255529837975242 -0.18473979056481055 -0.4176427262202118 -0.21975911391551833 -1.6250003193791873 -1.6185557291787287 0.25315988825847513 -0.4979804844182867 1.3565281078086726 julia> y = ones(10); julia> wmean(x, y) (-0.321824416323509, 0.31622776601683794, 0.8192171477885678) julia> wmean(x .± y) (-0.32 ± 0.32, 0.8192171477885678) julia> wmean(x .± y./10) (-0.322 ± 0.032, 81.9217147788568) julia> wmean(x .± y./10, corrected=true) (-0.32 ± 0.29, 81.9217147788568) ``` """ function wmean(μ::Collection1D{T}, σ::Collection1D{T}; corrected::Bool=true, chauvenet::Bool=false) where {T} if chauvenet not_outliers = chauvenet_func(μ, σ) μ = μ[not_outliers] σ = σ[not_outliers] end sum_of_values = sum_of_weights = χ² = zero(float(T)) @inbounds for i in eachindex(μ,σ) σ² = σ[i]^2 sum_of_values += μ[i] / σ² sum_of_weights += one(T) / σ² end wμ = sum_of_values / sum_of_weights @inbounds for i in eachindex(μ,σ) χ² += (μ[i] - wμ)^2 / σ[i]^2 end mswd = χ² / (length(μ)-1) wσ = if corrected sqrt(max(mswd,1) / sum_of_weights) else sqrt(1 / sum_of_weights) end return wμ, wσ, mswd end function wmean(x::AbstractVector{Measurement{T}}; corrected::Bool=true, chauvenet::Bool=false) where {T} if chauvenet μ, σ = val.(x), err.(x) not_outliers = chauvenet_func(μ, σ) x = x[not_outliers] end wμ, wσ, mswd = wmean(val.(x), Measurements.cov(x); corrected) return wμ ± wσ, mswd end # Full covariance matrix method function wmean(x::AbstractVector{T}, C::AbstractMatrix{T}; corrected::Bool=true) where T # Weighted mean and variance, full matrix method J = ones(length(x)) σ²ₓ̄ = 1/(J'/C*J) x̄ = σ²ₓ̄*(J'/C*x) # MSWD, full matrix method r = x .- x̄ χ² = r'/C*r mswd = χ² / (length(x)-1) # Optional: expand standard error by sqrt of mswd, if mswd > 1 corrected && (σ²ₓ̄ *= max(mswd,1)) return x̄, sqrt(σ²ₓ̄), mswd end # Legacy methods, for backwards compatibility awmean(args...) = wmean(args...; corrected=false) gwmean(args...) = wmean(args...; corrected=true) distwmean(x...; corrected::Bool=true) = distwmean(x; corrected) function distwmean(x::NTuple{N, <:AbstractVector}; corrected::Bool=true) where {N} σₓ = vstd.(x) wₓ = 1 ./ σₓ.^2 wₜ = sum(wₓ) c = similar(first(x)) @inbounds for i in eachindex(x...) c[i] = sum(getindex.(x, i) .* wₓ)/wₜ if corrected c[i] += sqrt(sum(wₓ.*abs2.(getindex.(x,i).-c[i]))/(wₜ*(N-1))) * randn() end end return c end """ ```julia mswd(μ, σ) mswd(μ ± σ) ``` Return the Mean Square of Weighted Deviates (AKA the reduced chi-squared statistic) of a dataset with values `x` and one-sigma uncertainties `σ` ### Examples ```julia julia> x = randn(10) 10-element Vector{Float64}: -0.977227094347237 2.605603343967434 -0.6869683962845955 -1.0435377148872693 -1.0171093080088411 0.12776158554629713 -0.7298235147864734 -0.3164914095249262 -1.44052961622873 0.5515207382660242 julia> mswd(x, ones(10)) 1.3901517474017941 ``` """ function mswd(μ::Collection{T}, σ::Collection; chauvenet=false) where {T} if chauvenet not_outliers = chauvenet_func(μ, σ) μ = μ[not_outliers] σ = σ[not_outliers] end sum_of_values = sum_of_weights = χ² = zero(float(T)) @inbounds for i in eachindex(μ,σ) w = 1 / σ[i]^2 sum_of_values += w * μ[i] sum_of_weights += w end wx = sum_of_values / sum_of_weights @inbounds for i in eachindex(μ,σ) χ² += (μ[i] - wx)^2 / σ[i]^2 end return χ² / (length(μ)-1) end function mswd(x::AbstractVector{Measurement{T}}; chauvenet=false) where {T} if chauvenet not_outliers = chauvenet_func(val.(x), err.(x)) x = x[not_outliers] end wμ, wσ, mswd = wmean(val.(x), Measurements.cov(x)) return mswd end ## --- Simple linear regression """ ```julia (a,b) = lsqfit(x::AbstractVector, y::AbstractVector) ``` Returns the coefficients for a simple linear least-squares regression of the form `y = a + bx` ### Examples ``` julia> a, b = lsqfit(1:10, 1:10) 2-element Vector{Float64}: -1.19542133983862e-15 1.0 julia> isapprox(a, 0, atol = 1e-12) true julia> isapprox(b, 1, atol = 1e-12) true ``` """ lsqfit(x::Collection{<:Number}, y::Collection{<:Number}) = lsqfit(x, collect(y)) function lsqfit(x::Collection{T}, y::AbstractVector{<:Number}) where {T<:Number} A = Array{T}(undef, length(x), 2) A[:,1] .= one(T) A[:,2] .= x return A\y end # Identical to the one in StatGeochemBase ## -- The York (1968) two-dimensional linear regression with x and y uncertainties # as commonly used in isochrons # Custom type to hold York fit resutls struct YorkFit{T<:Number} intercept::Measurement{T} slope::Measurement{T} xm::T ym::Measurement{T} mswd::T end """ ```julia yorkfit(x, σx, y, σy, [r]) yorkfit(x::Vector{<:Measurement}, y::Vector{<:Measurement}, [r]) yorkfit(d::Vector{<:Analysis}) ``` Uses the York (1968) two-dimensional least-squares fit to calculate `a`, `b`, and uncertanties `σa`, `σb` for the equation `y = a + bx`, given `x`, `y`, uncertaintes `σx`, and `σy`, and optially covarances `r`. For further reference, see: York, Derek (1968) "Least squares fitting of a straight line with correlated errors" Earth and Planetary Science Letters 5, 320-324. doi: 10.1016/S0012-821X(68)80059-7 ### Examples ```julia julia> x = (1:100) .+ randn.(); julia> y = 2*(1:100) .+ randn.(); julia> yorkfit(x, ones(100), y, ones(100)) YorkFit{Float64}: Least-squares linear fit of the form y = a + bx where intercept a : -0.29 ± 0.2 (1σ) slope b : 2.0072 ± 0.0035 (1σ) MSWD : 0.8136665223891004 ``` """ yorkfit(x::Vector{Measurement{T}}, y::Vector{Measurement{T}}, r=zero(T); iterations=10) where {T} = yorkfit(val.(x), err.(x), val.(y), err.(y), r; iterations) function yorkfit(d::Collection{<:Analysis{T}}; iterations=10) where {T} # Using NTuples instead of Arrays here avoids allocations and should be # much more efficient for relatively small N, but could be less efficient # for large N (greater than ~100) x = ntuple(i->d[i].μ[1], length(d)) y = ntuple(i->d[i].μ[2], length(d)) σx = ntuple(i->d[i].σ[1], length(d)) σy = ntuple(i->d[i].σ[2], length(d)) r = ntuple(i->d[i].Σ[1,2], length(d)) yorkfit(x, σx, y, σy, r; iterations) end function yorkfit(x, σx, y, σy, r=vcor(x,y); iterations=10) ## For an initial estimate of slope and intercept, calculate the # ordinary least-squares fit for the equation y=a+bx a, b = lsqfit(x, y) # Prepare for York fit ∅ = zero(float(eltype(x))) ωx = 1.0 ./ σx.^2 # x weights ωy = 1.0 ./ σy.^2 # y weights α = sqrt.(ωx .* ωy) ## Perform the York fit (must iterate) Z = @. ωx*ωy / (b^2*ωy + ωx - 2*b*r*α) x̄ = vsum(Z.*x) / vsum(Z) ȳ = vsum(Z.*y) / vsum(Z) U = x .- x̄ V = y .- ȳ if Z isa NTuple Z = collect(Z) U = collect(U) V = collect(V) end sV = @. Z^2 * V * (U/ωy + b*V/ωx - r*V/α) sU = @. Z^2 * U * (U/ωy + b*V/ωx - b*r*U/α) b = vsum(sV) / vsum(sU) a = ȳ - b * x̄ for _ in 2:iterations @. Z = ωx*ωy / (b^2*ωy + ωx - 2*b*r*α) ΣZ, ΣZx, ΣZy = ∅, ∅, ∅ @inbounds for i in eachindex(Z) ΣZ += Z[i] ΣZx += Z[i] * x[i] ΣZy += Z[i] * y[i] end x̄ = ΣZx / ΣZ ȳ = ΣZy / ΣZ @. U = x - x̄ @. V = y - ȳ @. sV = Z^2 * V * (U/ωy + b*V/ωx - r*V/α) @. sU = Z^2 * U * (U/ωy + b*V/ωx - b*r*U/α) b = sum(sV) / sum(sU) a = ȳ - b * x̄ end ## 4. Calculate uncertainties and MSWD β = @. Z * (U/ωy + b*V/ωx - (b*U+V)*r/α) u = x̄ .+ β v = ȳ .+ b.*β xm = vsum(Z.*u)./vsum(Z) ym = vsum(Z.*v)./vsum(Z) σb = sqrt(1.0 ./ vsum(Z .* (u .- xm).^2)) σa = sqrt(1.0 ./ vsum(Z) + xm.^2 .* σb.^2) σym = sqrt(1.0 ./ vsum(Z)) # MSWD (reduced chi-squared) of the fit mswd = 1.0 ./ length(x) .* vsum(@. (y - a - b*x)^2 / (σy^2 + b^2 * σx^2) ) ## Results return YorkFit(a ± σa, b ± σb, xm, ym ± σym, mswd) end
Isoplot
https://github.com/JuliaGeochronology/Isoplot.jl.git
[ "MIT" ]
0.3.6
b9a419e46fe4516f3c0b52041d84e2bb5be9cb81
code
894
# Custom pretty-printing for York fit results function Base.show(io::IO, ::MIME"text/plain", x::YorkFit{T}) where T print(io, """YorkFit{$T}: Least-squares linear fit of the form y = a + bx with intercept: $(x.intercept) (1σ) slope : $(x.slope) (1σ) MSWD : $(x.mswd) """ ) end function Base.show(io::IO, ::MIME"text/plain", x::CI{T}) where T print(io, """CI{$T} $x mean : $(x.mean) sigma : $(x.sigma) median: $(x.median) lower : $(x.lower) upper : $(x.upper) """ ) end function Base.print(io::IO, x::CI) l = round(x.mean - x.lower, sigdigits=2) u = round(x.upper - x.mean, sigdigits=2) nodata = any(isnan, (x.mean, x.upper, x.lower)) d = nodata ? 0 : floor(Int, log10(abs(x.mean))) - floor(Int, log10(max(abs(l),abs(u)))) m = round(x.mean, sigdigits=3+d) print(io, "$m +$u/-$l") end
Isoplot
https://github.com/JuliaGeochronology/Isoplot.jl.git
[ "MIT" ]
0.3.6
b9a419e46fe4516f3c0b52041d84e2bb5be9cb81
code
19319
module BaseTests using Test, Statistics using Measurements using Isoplot @testset "Show" begin yf = Isoplot.YorkFit(1±1, 1±1, 0.0, 1±1, 1.0) @test display(yf) != NaN ci = CI(1:10) @test ci == CI{Float64}(5.5, 3.0276503540974917, 5.5, 1.225, 9.775) @test "$ci" === "5.5 +4.3/-4.3" @test display(ci) != NaN ci = CI(randn(100)) @test display(ci) != NaN end @testset "General" begin @test Age(0) isa Age{Float64} @test Age(0, 1) isa Age{Float64} @test Interval(0, 1) isa Interval{Float64} @test Interval(0, 1) === Interval(0,0, 1,0) @test min(Interval(0, 1)) isa Age{Float64} @test max(Interval(0, 1)) isa Age{Float64} @test min(Interval(0, 1)) === Age(0,0) === Age(0) @test max(Interval(0, 1)) === Age(1,0) === Age(1) @test Isoplot.val(1) === 1 @test Isoplot.err(1) === 0 @test Isoplot.val(1±1) === 1.0 @test Isoplot.err(1±1) === 1.0 ci = CI(1:10) @test Isoplot.val(ci) ≈ 5.5 @test Isoplot.err(ci) ≈ 3.0276503540974917 a = Age(ci) @test Isoplot.val(a) ≈ 5.5 @test Isoplot.err(a) ≈ 3.0276503540974917 end @testset "U-Pb" begin r75 = 22.6602 σ75 = 0.017516107998 r68 = 0.408643 σ68 = 0.0001716486532565 corr = 0.831838 d1 = UPbAnalysis(r75, σ75, r68, σ68, corr) d2 = UPbAnalysis([22.6602, 0.408643], [0.00030681403939759964 2.501017729814154e-6; 2.501017729814154e-6 2.9463260164770177e-8]) d3 = UPbAnalysis([22.6602, 0.408643], [0.017516107998, 0.0001716486532565], [0.00030681403939759964 2.501017729814154e-6; 2.501017729814154e-6 2.9463260164770177e-8]) @test d1 isa UPbAnalysis{Float64} @test d2 isa UPbAnalysis{Float64} @test d3 isa UPbAnalysis{Float64} @test d1.μ ≈ d2.μ ≈ d3.μ @test d1.σ ≈ d2.σ ≈ d3.σ @test d1.Σ ≈ d2.Σ ≈ d3.Σ @test !isnan(d1) a75, a68 = age(d1) @test a75.val ≈ 3209.725483265418 @test a75.err ≈ 1.9420875256761048 @test a68.val ≈ 2208.7076248184017 @test a68.err ≈ 1.422824131349332 @test age68(d1) == a68 @test age75(d1) == a75 @test discordance(d1) ≈ 31.187024051310136 @test rand(d1) isa Vector{Float64} @test rand(d1, 10) isa Matrix{Float64} @test rand(d1, 5, 5) isa Matrix{Vector{Float64}} x = [22.70307499779583, 22.681635852743682, 22.63876085494785, 22.61732500220417, 22.638764147256317, 22.68163914505215, 22.70307499779583] y = [0.4089925091091875, 0.40901969166358015, 0.4086701825543926, 0.40829349089081246, 0.4082663083364198, 0.4086158174456074, 0.4089925091091875] e1 = Ellipse(d1, npoints=7) @test e1 isa Isoplot.Ellipse @test e1.x ≈ x @test e1.y ≈ y tₗₗ = 35 ui = upperintercept(tₗₗ ± 10, d1) @test ui == 3921.343026090256 ± 2.745595368456398 ui = upperintercept(tₗₗ, d1) @test ui == 3921.343026090256 ± 0.7241111646504936 N = 10000 uis = upperintercept(tₗₗ, d1, N) @test uis isa Vector{Float64} @test mean(uis) ≈ ui.val atol=(4*ui.err/sqrt(N)) @test std(uis) ≈ ui.err rtol=0.03 # Test upper and lower intercepts of multiple-sample concordia arrays d = [UPbAnalysis(22.6602, 0.0175, 0.40864, 0.00017, 0.83183) UPbAnalysis(33.6602, 0.0175, 0.50864, 0.00017, 0.83183)] uis = upperintercept(d, N) @test mean(uis) ≈ 4601.82 atol=0.1 @test std(uis) ≈ 1.53 atol=0.1 lis = lowerintercept(d, N) @test mean(lis) ≈ 1318.12 atol=0.1 @test std(lis) ≈ 2.04 atol=0.1 uis, lis = intercepts(d, N) @test mean(uis) ≈ 4601.82 atol=0.1 @test std(uis) ≈ 1.53 atol=0.1 @test mean(lis) ≈ 1318.12 atol=0.1 @test std(lis) ≈ 2.04 atol=0.1 # Stacey-Kramers common Pb model @test stacey_kramers(0) == (18.7, 15.628) @test stacey_kramers(3700) == (11.152, 12.998) @test stacey_kramers(4567) == (9.314476625036953, 12.984667029161916) @test stacey_kramers(5000) === (NaN, NaN) end @testset "Other systems" begin μ, σ = rand(2), rand(2) @test UThAnalysis(μ, σ) isa UThAnalysis @test ReOsAnalysis(μ, σ) isa ReOsAnalysis @test LuHfAnalysis(μ, σ) isa LuHfAnalysis @test SmNdAnalysis(μ, σ) isa SmNdAnalysis @test RbSrAnalysis(μ, σ) isa RbSrAnalysis end @testset "Weighted means" begin # Weighted means x = [-3.4699, -0.875, -1.4189, 1.2993, 1.1167, 0.8357, 0.9985, 1.2789, 0.5446, 0.5639] σx = ones(10)/4 @test all(awmean(x, σx) .≈ (0.08737999999999996, 0.07905694150420949, 38.44179426844445)) @test all(gwmean(x, σx) .≈ (0.08737999999999996, 0.49016447665837415, 38.44179426844445)) wm, m = awmean(x .± σx) @test wm.val ≈ 0.08737999999999996 @test wm.err ≈ 0.07905694150420949 @test m ≈ 38.44179426844445 wm, m = gwmean(x .± σx) @test wm.val ≈ 0.08737999999999996 @test wm.err ≈ 0.49016447665837415 @test m ≈ 38.44179426844445 @test mswd(x, σx) ≈ 38.44179426844445 @test mswd(x .± σx) ≈ 38.44179426844445 σx .*= 20 # Test underdispersed data @test gwmean(x, σx) == awmean(x, σx) N = 10^6 c = randn(N).+50 a,b = randn(N), randn(N).+1 d = distwmean(a,b; corrected=false) μ,σ,_ = wmean([0,1], [1,1]; corrected=false) @test mean(d) ≈ μ atol = 0.02 @test std(d) ≈ σ atol = 0.002 d = distwmean(a,b; corrected=true) @test mean(d) ≈ μ atol = 0.1 @test std(d) ≈ std(vcat(a,b)) atol = 0.005 b .+= 9 d = distwmean(a,b; corrected=false) μ,σ,_ = wmean([0,10], [1,1]; corrected=false) @test mean(d) ≈ μ atol = 0.02 @test std(d) ≈ σ atol = 0.002 d = distwmean(a,b; corrected=true) μ,σ,_ = wmean([0,10], [1,1]; corrected=true) @test mean(d) ≈ μ atol = 0.2 @test std(d) ≈ σ atol = 0.2 @test std(d) ≈ std(vcat(a,b)) atol = 0.02 b .+= 90 d = distwmean(a,b; corrected=false) μ,σ,_ = wmean([0,100], [1,1]; corrected=false) @test mean(d) ≈ μ atol = 0.2 @test std(d) ≈ σ atol = 0.02 d = distwmean(a,b; corrected=true) μ,σ,_ = wmean([0,100], [1,1]; corrected=true) @test mean(d) ≈ μ atol = 0.2 @test std(d) ≈ σ atol = 0.2 @test std(d) ≈ std(vcat(a,b)) atol = 0.2 d = distwmean(a,b,c; corrected=false) μ,σ,_ = wmean([0,50,100], [1,1,1]; corrected=false) @test mean(d) ≈ μ atol = 0.2 @test std(d) ≈ σ atol = 0.02 d = distwmean(a,b,c; corrected=true) μ,σ,_ = wmean([0,50,100], [1,1,1]; corrected=true) @test mean(d) ≈ μ atol = 0.2 @test std(d) ≈ σ atol = 2 a,b = 2randn(N), 3randn(N).+1 d = distwmean(a,b; corrected=false) μ,σ,_ = wmean([0,1], [2,3]; corrected=false) @test mean(d) ≈ μ atol = 0.05 @test std(d) ≈ σ atol = 0.005 b .+= 9 d = distwmean(a,b; corrected=false) μ,σ,_ = wmean([0,10], [2,3]; corrected=false) @test mean(d) ≈ μ atol = 0.05 @test std(d) ≈ σ atol = 0.005 d = distwmean(a,b; corrected=true) μ,σ,_ = wmean([0,10], [2,3]; corrected=true) @test mean(d) ≈ μ atol = 0.1 @test std(d) ≈ σ atol = 1 b .+= 90 d = distwmean(a,b; corrected=false) μ,σ,_ = wmean([0,100], [2,3]; corrected=false) @test mean(d) ≈ μ atol = 0.2 @test std(d) ≈ σ atol = 0.02 d = distwmean(a,b; corrected=true) μ,σ,_ = wmean([0,100], [2,3]; corrected=true) @test mean(d) ≈ μ atol = 0.2 @test std(d) ≈ σ atol = 2 d = distwmean(a,b,c; corrected=false) μ,σ,_ = wmean([0,50,100], [2,1,3]; corrected=false) @test mean(d) ≈ μ atol = 0.2 @test std(d) ≈ σ atol = 0.02 d = distwmean(a,b,c; corrected=true) μ,σ,_ = wmean([0,50,100], [2,1,3]; corrected=true) @test mean(d) ≈ μ atol = 0.2 @test std(d) ≈ σ atol = 2 # test chauvenet criterion x = [1.2, 1.5, 1.3, 2.4, 2.0, 2.1, 1.9, 2.2, 8.0, 2.3] xσ = [1,1,1,1,1,1,1,1,1,1.] expected = [true, true, true, true, true, true, true, true, false, true] @test Isoplot.chauvenet_func(x, xσ) == expected μ,σ,MSWD = wmean(x, xσ;chauvenet=true) @test μ ≈ 1.877777777777778 @test MSWD ≈ 0.19444444444444445 μ, MSWD = wmean((x .± xσ);chauvenet=true) @test Isoplot.val(μ) ≈ 1.877777777777778 @test MSWD ≈ 0.19444444444444445 end data = [1.1009 0.00093576 0.123906 0.00002849838 0.319 1.1003 0.00077021 0.123901 0.00003531178 0.415 1.0995 0.00049477 0.123829 0.00002538494 0.434 1.0992 0.00060456 0.123813 0.00003652483 0.616 1.1006 0.00071539 0.123813 0.00002228634 0.321 1.0998 0.00076986 0.123802 0.00002537941 0.418 1.0992 0.00065952 0.123764 0.00003589156 0.509 1.0981 0.00109810 0.123727 0.00003959264 0.232 1.0973 0.00052670 0.123612 0.00002966688 0.470 1.0985 0.00087880 0.123588 0.00002842524 0.341 1.0936 0.00054680 0.123193 0.00003264614 0.575 1.0814 0.00051366 0.121838 0.00003045950 0.587 ] analyses = UPbAnalysis.(eachcol(data)...,) @testset "Regression" begin # Simple linear regression ϕ = lsqfit(1:10, 1:10) @test ϕ[1] ≈ 0 atol=1e-12 @test ϕ[2] ≈ 1 atol=1e-12 # York (1968) fit x = [0.9304, 2.2969, 2.8047, 3.7933, 5.3853, 6.1995, 6.7479, 8.1856, 8.7423, 10.2588] y = [0.8742, 2.1626, 3.042, 3.829, 5.0116, 5.5614, 6.7675, 7.8856, 9.6414, 10.4955] σx = σy = ones(10)/4 yf = yorkfit(x, σx, y, σy) @test yf isa Isoplot.YorkFit @test yf.intercept.val ≈-0.23498964673701916 @test yf.intercept.err ≈ 0.02250863813481163 @test yf.slope.val ≈ 1.041124018512526 @test yf.slope.err ≈ 0.0035683808205783673 @test yf.mswd ≈ 1.1419901440278089 yf = yorkfit(x, σx, y, σy, zeros(length(x))) @test yf isa Isoplot.YorkFit @test yf.intercept.val ≈-0.2446693790977319 @test yf.intercept.err ≈ 0.2469541320914601 @test yf.slope.val ≈ 1.0428730084538775 @test yf.slope.err ≈ 0.039561084436542084 @test yf.mswd ≈ 1.1417951538670306 x = ((1:100) .+ randn.()) .± 1 y = (2*(1:100) .+ randn.()) .± 1 yf = yorkfit(x, y) @test yf isa Isoplot.YorkFit @test yf.intercept.val ≈ 0 atol = 2 @test yf.slope.val ≈ 2 atol = 0.1 @test yf.mswd ≈ 1 atol = 0.5 yf = yorkfit(analyses) @test yf isa Isoplot.YorkFit @test yf.intercept.val ≈ 0.0050701916562521515 @test yf.intercept.err ≈ 0.004099648408656529 @test yf.slope.val ≈ 0.1079872513087868 @test yf.slope.err ≈ 0.0037392146940848233 @test yf.xm ≈ 1.096376584184683 @test yf.ym.val ≈ 0.12346488538167279 @test yf.ym.err ≈ 2.235949353726133e-5 @test yf.mswd ≈ 0.41413597765872123 ui = upperintercept(analyses) @test ui.val ≈ 752.6744316220871 @test ui.err ≈ 0.5288009504134864 li = lowerintercept(analyses) @test li.val ≈ 115.83450556482211 @test li.err ≈ 94.4384248140631 ui, li = intercepts(analyses) @test ui.val ≈ 752.6744316220871 @test ui.err ≈ 0.5288009504134864 @test li.val ≈ 115.83450556482211 @test li.err ≈ 94.4384248140631 wm, m = wmean(age68.(analyses[1:10])) @test wm.val ≈ 752.2453179272093 @test wm.err ≈ 1.4781473739306696 @test m ≈ 13.15644886325888 m = mswd(age68.(analyses[1:10])) @test m ≈ 13.15644886325888 end @testset "Concordia Metropolis" begin data = upperintercept.(0, analyses) @test Isoplot.dist_ll(ones(10), data, 751, 755) ≈ -20.09136536048026 @test Isoplot.dist_ll(ones(10), data, 750, 760) ≈ -30.459633175497830 @test Isoplot.dist_ll(ones(10), data, 752, 753) ≈ -15.305463167234748 @test Isoplot.dist_ll(ones(10), data, 751, 752) ≈ -47.386667785224034 tmindist, t0dist = metropolis_min(1000, ones(10), analyses; burnin=200) @test tmindist isa Vector{Float64} @test mean(tmindist) ≈ 751.85 atol = 1.5 @test std(tmindist) ≈ 0.40 rtol = 0.6 @test t0dist isa Vector{Float64} @test mean(t0dist) ≈ 80. atol = 90 @test std(t0dist) ≈ 50. rtol = 0.6 tmindist, tmaxdist, t0dist, lldist, acceptancedist = metropolis_minmax(10000, ones(10), analyses; burnin=200) @test tmindist isa Vector{Float64} @test mean(tmindist) ≈ 751.85 atol = 1.5 @test std(tmindist) ≈ 0.40 rtol = 0.6 @test tmaxdist isa Vector{Float64} @test mean(tmaxdist) ≈ 753.32 atol = 1.5 @test std(tmaxdist) ≈ 0.60 rtol = 0.6 @test t0dist isa Vector{Float64} @test mean(t0dist) ≈ 80. atol = 90 @test std(t0dist) ≈ 50. rtol = 0.6 @test lldist isa Vector{Float64} @test acceptancedist isa BitVector @test mean(acceptancedist) ≈ 0.6 atol=0.2 terupt = CI(tmindist) @test terupt isa CI{Float64} @test terupt.mean ≈ 751.85 atol = 1.5 @test terupt.sigma ≈ 0.40 rtol = 0.6 @test terupt.median ≈ 751.83 atol = 1.5 @test terupt.lower ≈ 750.56 atol = 1.5 @test terupt.upper ≈ 752.52 atol = 1.5 end @testset "General Metropolis" begin mu, sigma = collect(100:0.1:101), 0.01*ones(11); @test Isoplot.dist_ll(MeltsVolcanicZirconDistribution, mu, sigma, 100,101) ≈ -3.6933372932657607 tmindist = metropolis_min(2*10^5, MeltsVolcanicZirconDistribution, mu .± sigma, burnin=10^5) @test mean(tmindist) ≈ 99.9228 atol=0.015 tmindist, tmaxdist, lldist, acceptancedist = metropolis_minmax(2*10^5, MeltsVolcanicZirconDistribution, mu .± sigma, burnin=10^5) @test mean(tmindist) ≈ 99.9228 atol=0.015 @test mean(tmaxdist) ≈ 101.08 atol=0.015 @test lldist isa Vector{Float64} @test acceptancedist isa BitVector @test mean(acceptancedist) ≈ 0.6 atol=0.2 @test mean(UniformDistribution) ≈ 1 @test mean(TriangularDistribution) ≈ 1 @test mean(HalfNormalDistribution) ≈ 1 @test mean(ExponentialDistribution) ≈ 1.03 atol=0.01 @test mean(MeltsZirconDistribution) ≈ 1 atol=0.01 @test mean(MeltsVolcanicZirconDistribution) ≈ 1 atol=0.01 end end module PlotsTest using Test, Statistics using Measurements using Isoplot using ImageIO, FileIO,Plots import ..BaseTests: analyses # Base.retry_load_extensions() @testset "Plotting" begin # Plot single concordia ellipse h = plot(analyses[1], color=:blue, alpha=0.3, label="", framestyle=:box) savefig(h, "concordia.png") img = load("concordia.png") @test size(img) == (400,600) @test sum(img)/length(img) ≈ RGB{Float64}(0.8151617156862782,0.8151617156862782,0.986395212418301) rtol = 0.02 rm("concordia.png") # Plot many concordia ellipses and concordia curve h = plot(analyses, color=:blue, alpha=0.3, label="", framestyle=:box) concordiacurve!(h) savefig(h, "concordia.png") img = load("concordia.png") @test size(img) == (400,600) @test sum(img)/length(img) ≈ RGB{Float64}(0.9448600653594766,0.9448600653594766,0.9658495915032675) rtol = 0.01 rm("concordia.png") # Plot single concordia line h = concordialine(0, 100, label="") concordiacurve!(h) savefig(h, "concordia.png") img = load("concordia.png") @test size(img) == (400,600) @test sum(img)/length(img) ≈ RGB{Float64}(0.981812385620915,0.9832414705882352,0.9841176307189541) rtol = 0.01 rm("concordia.png") h = concordialine(0, 100, label="", truncate=true) concordiacurve!(h) savefig(h, "concordia.png") img = load("concordia.png") @test size(img) == (400,600) @test sum(img)/length(img) ≈ RGB{Float64}(0.981812385620915,0.9832414705882352,0.9841176307189541) rtol = 0.01 rm("concordia.png") # Plot many single concordia lines h = concordialine(10*randn(100).+10, 100*randn(100).+1000, label="") concordiacurve!(h) savefig(h, "concordia.png") img = load("concordia.png") @test size(img) == (400,600) @test sum(img)/length(img) ≈ RGB{Float64}(0.966250588235294,0.966250588235294,0.966250588235294) rtol = 0.01 rm("concordia.png") h = concordialine(10*randn(100).+10, 100*randn(100).+1000, label="", truncate=true) concordiacurve!(h) savefig(h, "concordia.png") img = load("concordia.png") @test size(img) == (400,600) @test sum(img)/length(img) ≈ RGB{Float64}(0.966250588235294,0.966250588235294,0.966250588235294) rtol = 0.01 rm("concordia.png") # Rank-order plot h = rankorder(1:10, 2*ones(10), label="") savefig(h, "rankorder.png") img = load("rankorder.png") @test size(img) == (400,600) @test sum(img)/length(img) ≈ RGB{Float64}(0.9842757843137254,0.9882103758169932,0.9906218464052285) rtol = 0.01 rm("rankorder.png") end end module MakieTest using Test, Statistics using Measurements using Isoplot using ImageIO, FileIO, CairoMakie using ColorTypes import ..BaseTests: analyses # Base.retry_load_extensions() @testset "Makie Plotting" begin f = Figure() ax = Axis(f[1,1]) plot!(analyses[1], color=(:blue,0.3)) save("concordia.png",f) img = load("concordia.png") @test size(img) == (900, 1200) @test sum(img)/length(img) ≈ RGB{Float64}(0.8524913580246877,0.8524913580246877,0.9885884168482209) rtol = 0.02 rm("concordia.png") # Plot many concordia ellipses and concordia curve f2 = Figure() ax2 = Axis(f2[1,1]) plot!.(analyses, color=(:blue, 0.3)) ages = age.(analyses) concordiacurve!(minimum(ages)[1].val-5,maximum(ages)[1].val+5) xmin, xmax, ymin, ymax = datalimits(analyses) limits!(ax2,xmin,xmax,ymin,ymax) save("concordia.png",f2) img = load("concordia.png") @test size(img) == (900, 1200) @test sum(img)/length(img) ≈ RGB{Float64}(0.9523360547065816,0.9523360547065816,0.9661779080315414) rtol = 0.01 rm("concordia.png") # Plot single concordia line f3 = Figure() ax3 = Axis(f3[1,1]) concordialine!(0, 100) concordiacurve!(0,100) save("concordia.png",f3) img = load("concordia.png") @test size(img) == (900, 1200) @test sum(img)/length(img) ≈ RGB{Float64}(0.9845678970995279,0.9845678970995279,0.9845678970995279) rtol = 0.01 rm("concordia.png") end end
Isoplot
https://github.com/JuliaGeochronology/Isoplot.jl.git
[ "MIT" ]
0.3.6
b9a419e46fe4516f3c0b52041d84e2bb5be9cb81
docs
4255
# Isoplot.jl [![Docs][docs-dev-img]][docs-dev-url] [![CI][ci-img]][ci-url] [![codecov.io][codecov-img]][codecov-url] Well someone needs to write one... ## Installation ```Julia pkg> add Isoplot ``` ## Usage ```julia using Isoplot, Plots # Example U-Pb dataset (MacLennan et al. 2020) # 207/235 1σ abs 206/238 1σ abs correlation data = [1.1009 0.00093576 0.123906 0.00002849838 0.319 1.1003 0.00077021 0.123901 0.00003531178 0.415 1.0995 0.00049477 0.123829 0.00002538494 0.434 1.0992 0.00060456 0.123813 0.00003652483 0.616 1.1006 0.00071539 0.123813 0.00002228634 0.321 1.0998 0.00076986 0.123802 0.00002537941 0.418 1.0992 0.00065952 0.123764 0.00003589156 0.509 1.0981 0.00109810 0.123727 0.00003959264 0.232 1.0973 0.00052670 0.123612 0.00002966688 0.470 1.0985 0.00087880 0.123588 0.00002842524 0.341 1.0936 0.00054680 0.123193 0.00003264614 0.575 1.0814 0.00051366 0.121838 0.00003045950 0.587 ] # Turn into UPbAnalysis objects analyses = UPbAnalysis.(eachcol(data)...,) # Screen for discordance analyses = analyses[discordance.(analyses) .< 0.2] # Plot in Wetherill concordia space hdl = plot(xlabel="²⁰⁷Pb/²³⁵U", ylabel="²⁰⁶Pb/²³⁸U", framestyle=:box) plot!(hdl, analyses, color=:darkblue, alpha=0.3, label="") concordiacurve!(hdl) # Add concordia curve savefig(hdl, "concordia.svg") display(hdl) ``` ![svg](examples/concordia.svg) ```julia # Rank-order plot of 6/8 ages hdl = plot(framestyle=:box, layout=(1,2), size=(800,400), ylims=(748, 754)) rankorder!(hdl[1], age68.(analyses), ylabel="²⁰⁶Pb/²³⁸U Age [Ma]", color=:darkblue, mscolor=:darkblue) rankorder!(hdl[2], age75.(analyses), ylabel="²⁰⁷Pb/²³⁵U Age [Ma]", color=:darkblue, mscolor=:darkblue) savefig(hdl, "rankorder.svg") display(hdl) ``` ![svg](examples/rankorder.svg) ### Pb-loss-aware Bayesian eruption age estimation Among other things implemented in this package is an extension of the method of [Keller, Schoene, and Samperton (2018)](https://doi.org/10.7185/geochemlet.1826) to the case where some analyses may have undergone significant Pb-loss: ```julia nsteps = 10^6 tmindist, t0dist = metropolis_min(nsteps, HalfNormalDistribution, analyses; burnin=10^4) tpbloss = CI(t0dist) terupt = CI(tmindist) display(terupt) println("Eruption/deposition age: $terupt Ma (95% CI)") # Add to concordia plot I = rand(1:length(tmindist), 1000) # Pick 100 random samples from the posterior distribution concordialine!(hdl, t0dist[I], tmindist[I], color=:darkred, alpha=0.02, label="Model: $terupt Ma") # Add to Concordia plot display(hdl) ``` > Eruption/deposition age: 751.952 +0.493/-0.757 Ma (95% CI) ```julia h = histogram(tmindist, xlabel="Age [Ma]", ylabel="Probability Density", normalize=true, label="Eruption age", color=:darkblue, alpha=0.65, linealpha=0.1, framestyle=:box) ylims!(h, 0, last(ylims())) savefig(h, "EruptionAge.svg") display(h) ``` ![svg](examples/eruptionage.svg) ```julia h = histogram(t0dist, xlabel="Age [Ma]", ylabel="Probability Density", normalize=true, label="Time of Pb-loss", color=:darkblue, alpha=0.65, linealpha=0.1, framestyle=:box) xlims!(h, 0, last(xlims())) ylims!(h, 0, last(ylims())) savefig(h, "PbLoss.svg") display(h) ``` ![svg](examples/pbloss.svg) Notably, In contrast to a weighted mean or a standard Bayesian eruption age, the result appears to be influenced little if at all by any decision to exclude or not exclude discordant grains, for example: Excluding four analyses with >0.07% discordance: ![svg](examples/concordiascreened.svg) Excluding nothing: ![svg](examples/concordiaall.svg) with in this example perhaps only a slight _increase_ in precision when more data are included, even if those data happen to be highly discordant. [docs-dev-img]: https://img.shields.io/badge/docs-dev-blue.svg [docs-dev-url]: https://JuliaGeochronology.github.io/Isoplot.jl/dev/ [ci-img]: https://github.com/JuliaGeochronology/Isoplot.jl/workflows/CI/badge.svg [ci-url]: https://github.com/JuliaGeochronology/Isoplot.jl/actions/workflows/CI.yml [codecov-img]: http://codecov.io/github/JuliaGeochronology/Isoplot.jl/coverage.svg?branch=main [codecov-url]: http://app.codecov.io/github/JuliaGeochronology/Isoplot.jl?branch=main
Isoplot
https://github.com/JuliaGeochronology/Isoplot.jl.git
[ "MIT" ]
0.3.6
b9a419e46fe4516f3c0b52041d84e2bb5be9cb81
docs
184
```@meta CurrentModule = Isoplot ``` # Isoplot Documentation for [Isoplot.jl](https://github.com/JuliaGeochronology/Isoplot.jl). ```@index ``` ```@autodocs Modules = [Isoplot] ```
Isoplot
https://github.com/JuliaGeochronology/Isoplot.jl.git
[ "MIT" ]
0.13.0
7fec0fac72076ea32823fb59efcc0e280cd28162
code
1818
# # Copyright (c) 2021 Tobias Thummerer, Lars Mikelsons # Licensed under the MIT license. See LICENSE file in the project root for details. # import Pkg; Pkg.develop(path = joinpath(@__DIR__, "../../FMIFlux.jl")); using Documenter, FMIFlux using Documenter: GitHubActions makedocs( sitename = "FMIFlux.jl", format = Documenter.HTML( collapselevel = 1, sidebar_sitename = false, edit_link = nothing, size_threshold_ignore = [joinpath("examples", "juliacon_2023.md")], ), warnonly = true, pages = Any[ "Introduction" => "index.md" "Examples" => [ "Overview" => "examples/overview.md" "Simple CS-NeuralFMU" => "examples/simple_hybrid_CS.md" "Simple ME-NeuralFMU" => "examples/simple_hybrid_ME.md" "Growing Horizon ME-NeuralFMU" => "examples/growing_horizon_ME.md" "JuliaCon 2023" => "examples/juliacon_2023.md" "MDPI 2022" => "examples/mdpi_2022.md" "Modelica Conference 2021" => "examples/modelica_conference_2021.md" "Pluto Workshops" => "examples/workshops.md" ] "FAQ" => "faq.md" "Library Functions" => "library.md" "Related Publication" => "related.md" "Contents" => "contents.md" ], ) function deployConfig() github_repository = get(ENV, "GITHUB_REPOSITORY", "") github_event_name = get(ENV, "GITHUB_EVENT_NAME", "") if github_event_name == "workflow_run" || github_event_name == "repository_dispatch" github_event_name = "push" end github_ref = get(ENV, "GITHUB_REF", "") return GitHubActions(github_repository, github_event_name, github_ref) end deploydocs( repo = "github.com/ThummeTo/FMIFlux.jl.git", devbranch = "main", deploy_config = deployConfig(), )
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
[ "MIT" ]
0.13.0
7fec0fac72076ea32823fb59efcc0e280cd28162
code
2117
# Copyright (c) 2023 Tobias Thummerer, Lars Mikelsons # Licensed under the MIT license. # See LICENSE (https://github.com/thummeto/FMIFlux.jl/blob/main/LICENSE) file in the project root for details. # This workshop was held at the JuliaCon2023 @ MIT (Boston) using Plots using Distributed using JLD2 using DistributedHyperOpt # add via `add "https://github.com/ThummeTo/DistributedHyperOpt.jl"` # if you want to see more messages about Hyperband working ... # ENV["JULIA_DEBUG"] = "DistributedHyperOpt" nprocs() workers = addprocs(5) @everywhere include(joinpath(@__DIR__, "workshop_module.jl")) # creating paths for log files (logs), parameter sets (params) and hyperparameter plots (plots) for dir ∈ ("logs", "params", "plots") path = joinpath(@__DIR__, dir) @info "Creating (if not already) path: $(path)" mkpath(path) end beta1 = 1.0 .- exp10.(LinRange(-4, -1, 4)) beta2 = 1.0 .- exp10.(LinRange(-6, -1, 6)) sampler = DistributedHyperOpt.Hyperband(; R = 81, η = 3, ressourceScale = 1.0 / 81.0 * NODE_Training.data.cumconsumption_t[end], ) optimization = DistributedHyperOpt.Optimization( NODE_Training.train!, DistributedHyperOpt.Parameter( "eta", (1e-5, 1e-2); type = :Log, samples = 7, round_digits = 5, ), DistributedHyperOpt.Parameter("beta1", beta1), DistributedHyperOpt.Parameter("beta2", beta2), DistributedHyperOpt.Parameter("batchDur", (0.5, 20.0); samples = 40, round_digits = 1), DistributedHyperOpt.Parameter("lastWeight", (0.1, 1.0); samples = 10, round_digits = 1), DistributedHyperOpt.Parameter("schedulerID", [:Random, :Sequential, :LossAccumulation]), DistributedHyperOpt.Parameter("loss", [:MSE, :MAE]), ) DistributedHyperOpt.optimize( optimization; sampler = sampler, plot = true, plot_ressources = true, save_plot = joinpath(@__DIR__, "plots", "hyperoptim.png"), redirect_worker_io_dir = joinpath(@__DIR__, "logs"), ) Plots.plot(optimization; size = (1024, 1024), ressources = true) minimum, minimizer, ressource = DistributedHyperOpt.results(optimization)
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
[ "MIT" ]
0.13.0
7fec0fac72076ea32823fb59efcc0e280cd28162
code
9852
# Copyright (c) 2023 Tobias Thummerer, Lars Mikelsons # Licensed under the MIT license. # See LICENSE (https://github.com/thummeto/FMIFlux.jl/blob/main/LICENSE) file in the project root for details. using LaTeXStrings import FMIFlux: roundToLength import FMIZoo: movavg import FMI: FMUSolution import FMIZoo: VLDM, VLDM_Data function singleInstanceMode(fmu::FMU2, mode::Bool) if mode # switch to a more efficient execution configuration, allocate only a single FMU instance, see: # https://thummeto.github.io/FMI.jl/dev/features/#Execution-Configuration fmu.executionConfig = FMI.FMIImport.FMU_EXECUTION_CONFIGURATION_NOTHING c, _ = FMIFlux.prepareSolveFMU( fmu, nothing, fmu.type; instantiate = true, setup = true, parameters = data.params, x0 = x0, ) else c = FMI.getCurrentInstance(fmu) # switch back to the default execution configuration, allocate a new FMU instance for every run, see: # https://thummeto.github.io/FMI.jl/dev/features/#Execution-Configuration fmu.executionConfig = FMI.FMIImport.FMU_EXECUTION_CONFIGURATION_NO_RESET FMIFlux.finishSolveFMU(fmu, c; freeInstance = false, terminate = true) end return nothing end function dataIndexForTime(t::Real) return 1 + round(Int, t / dt) end function plotEnhancements( neuralFMU::NeuralFMU, fmu::FMU2, data::FMIZoo.VLDM_Data; reductionFactor::Int = 10, mov_avg::Int = 100, filename = nothing, ) colorMin = 0 colorMax = 0 okregion = 0 label = "" tStart = data.consumption_t[1] tStop = data.consumption_t[end] x0 = FMIZoo.getStateVector(data, tStart) resultNFMU = neuralFMU( x0, (tStart, tStop); parameters = data.params, showProgress = false, recordValues = :derivatives, saveat = data.consumption_t, ) resultFMU = simulate( fmu, (tStart, tStop); parameters = data.params, showProgress = false, recordValues = :derivatives, saveat = data.consumption_t, ) # Finite differences for acceleration dt = data.consumption_t[2] - data.consumption_t[1] acceleration_val = (data.speed_val[2:end] - data.speed_val[1:end-1]) / dt acceleration_val = [acceleration_val..., 0.0] acceleration_dev = (data.speed_dev[2:end] - data.speed_dev[1:end-1]) / dt acceleration_dev = [acceleration_dev..., 0.0] ANNInputs = getValue(resultNFMU, :derivatives) # collect([0.0, 0.0, 0.0, data.speed_val[i], acceleration_val[i], data.consumption_val[i]] for i in 1:length(data.consumption_t)) ANNInputs = collect( [ ANNInputs[1][i], ANNInputs[2][i], ANNInputs[3][i], ANNInputs[4][i], ANNInputs[5][i], ANNInputs[6][i], ] for i = 1:length(ANNInputs[1]) ) ANNOutputs = getStateDerivative(resultNFMU, 5:6; isIndex = true) ANNOutputs = collect([ANNOutputs[1][i], ANNOutputs[2][i]] for i = 1:length(ANNOutputs[1])) FMUOutputs = getStateDerivative(resultFMU, 5:6; isIndex = true) FMUOutputs = collect([FMUOutputs[1][i], FMUOutputs[2][i]] for i = 1:length(FMUOutputs[1])) ANN_consumption = collect(o[2] for o in ANNOutputs) ANN_error = ANN_consumption - data.consumption_val ANN_error = collect( ANN_error[i] > 0.0 ? max(0.0, ANN_error[i] - data.consumption_dev[i]) : min(0.0, ANN_error[i] + data.consumption_dev[i]) for i = 1:length(data.consumption_t) ) FMU_consumption = collect(o[2] for o in FMUOutputs) FMU_error = FMU_consumption - data.consumption_val FMU_error = collect( FMU_error[i] > 0.0 ? max(0.0, FMU_error[i] - data.consumption_dev[i]) : min(0.0, FMU_error[i] + data.consumption_dev[i]) for i = 1:length(data.consumption_t) ) colorMin = -231.0 colorMax = 231.0 FMU_error = movavg(FMU_error, mov_avg) ANN_error = movavg(ANN_error, mov_avg) ANN_error = ANN_error .- FMU_error ANNInput_vel = collect(o[4] for o in ANNInputs) ANNInput_acc = collect(o[5] for o in ANNInputs) ANNInput_con = collect(o[6] for o in ANNInputs) _max = max(ANN_error...) _min = min(ANN_error...) neutral = 0.5 if _max > colorMax @warn "max value ($(_max)) is larger than colorMax ($(colorMax)) - values will be cut" end if _min < colorMin @warn "min value ($(_min)) is smaller than colorMin ($(colorMin)) - values will be cut" end anim = @animate for ang = 0:5:360 l = Plots.@layout [Plots.grid(3, 1) r{0.85w}] fig = Plots.plot( layout = l, size = (1600, 800), left_margin = 10Plots.mm, right_margin = 10Plots.mm, bottom_margin = 10Plots.mm, ) colorgrad = cgrad([:green, :white, :red], [0.0, 0.5, 1.0]) # , scale = :log) scatter!( fig[1], ANNInput_vel[1:reductionFactor:end], ANNInput_acc[1:reductionFactor:end], xlabel = "velocity [m/s]", ylabel = "acceleration [m/s^2]", color = colorgrad, zcolor = ANN_error[1:reductionFactor:end], label = :none, colorbar = :none, ) # scatter!( fig[2], ANNInput_acc[1:reductionFactor:end], ANNInput_con[1:reductionFactor:end], xlabel = "acceleration [m/s^2]", ylabel = "consumption [W]", color = colorgrad, zcolor = ANN_error[1:reductionFactor:end], label = :none, colorbar = :none, ) # scatter!( fig[3], ANNInput_vel[1:reductionFactor:end], ANNInput_con[1:reductionFactor:end], xlabel = "velocity [m/s]", ylabel = "consumption [W]", color = colorgrad, zcolor = ANN_error[1:reductionFactor:end], label = :none, colorbar = :none, ) # scatter!( fig[4], ANNInput_vel[1:reductionFactor:end], ANNInput_acc[1:reductionFactor:end], ANNInput_con[1:reductionFactor:end], xlabel = "velocity [m/s]", ylabel = "acceleration [m/s^2]", zlabel = "consumption [W]", color = colorgrad, zcolor = ANN_error[1:reductionFactor:end], markersize = 8, label = :none, camera = (ang, 20), colorbar_title = " \n\n\n\n" * L"ΔMAE" * " (smoothed)", ) # draw invisible dummys to scale colorbar to fixed size for i = 1:3 scatter!( fig[i], [0.0, 0.0], [0.0, 0.0], color = colorgrad, zcolor = [colorMin, colorMax], markersize = 0, label = :none, ) end for i = 4:4 scatter!( fig[i], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], color = colorgrad, zcolor = [colorMin, colorMax], markersize = 0, label = :none, ) end end if !isnothing(filename) return gif(anim, filename; fps = 10) else return gif(anim; fps = 10) end end function plotCumulativeConsumption( solutionNFMU::FMUSolution, solutionFMU::FMUSolution, data::FMIZoo.VLDM_Data; range = (0.0, 1.0), filename = nothing, ) len = length(data.consumption_t) steps = (1+round(Int, range[1] * len)):(round(Int, range[end] * len)) t = data.consumption_t nfmu_val = getState(solutionNFMU, 6; isIndex = true) fmu_val = getState(solutionFMU, 6; isIndex = true) data_val = data.cumconsumption_val data_dev = data.cumconsumption_dev mse_nfmu = FMIFlux.Losses.mse_dev(nfmu_val, data_val, data_dev) mse_fmu = FMIFlux.Losses.mse_dev(fmu_val, data_val, data_dev) mae_nfmu = FMIFlux.Losses.mae_dev(nfmu_val, data_val, data_dev) mae_fmu = FMIFlux.Losses.mae_dev(fmu_val, data_val, data_dev) max_nfmu = FMIFlux.Losses.max_dev(nfmu_val, data_val, data_dev) max_fmu = FMIFlux.Losses.max_dev(fmu_val, data_val, data_dev) fig = plot(xlabel = L"t[s]", ylabel = L"x_6 [Ws]", dpi = 600) plot!( fig, t[steps], data_val[steps]; label = "Data", ribbon = data_dev, fillalpha = 0.3, ) plot!( fig, t[steps], fmu_val[steps]; label = "FMU [ MSE:$(roundToLength(mse_fmu,10)) | MAE:$(roundToLength(mae_fmu,10)) | MAX:$(roundToLength(max_fmu,10)) ]", ) plot!( fig, t[steps], nfmu_val[steps]; label = "NeuralFMU [ MSE:$(roundToLength(mse_nfmu,10)) | MAE:$(roundToLength(mae_nfmu,10)) | MAX:$(roundToLength(max_nfmu,10)) ]", ) if !isnothing(filename) savefig(fig, filename) end return fig end function simPlotCumulativeConsumption(cycle::Symbol, filename = nothing; kwargs...) d = FMIZoo.VLDM(cycle) tStart = d.consumption_t[1] tStop = d.consumption_t[end] tSave = d.consumption_t resultNFMU = neuralFMU( x0, (tStart, tStop); parameters = d.params, showProgress = false, saveat = tSave, maxiters = 1e7, ) resultFMU = simulate( fmu, (tStart, tStop), parameters = d.params, showProgress = false, saveat = tSave, ) fig = plotCumulativeConsumption(resultNFMU, resultFMU, d, kwargs...) if !isnothing(filename) savefig(fig, filename) end return fig end
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
[ "MIT" ]
0.13.0
7fec0fac72076ea32823fb59efcc0e280cd28162
code
599
### A Pluto.jl notebook ### # v0.19.43 using Markdown using InteractiveUtils # ╔═╡ 1470df0f-40e1-45d5-a4cc-519cc3b28fb8 md""" # Hands-on: Hybrid Modeling using FMI Workshop @ MODPROD 2024 (Linköping University, Sweden) by Tobias Thummerer (University of Augsburg) *#hybridmodeling, #sciml, #neuralode, #neuralfmu, #penode* This workshop was refactored and moved to [Scientific Machine Learning using Functional Mock-up Units](https://github.com/ThummeTo/FMIFlux.jl/tree/main/examples/pluto-src/SciMLUsingFMUs/SciMLUsingFMUs.jl). """ # ╔═╡ Cell order: # ╟─1470df0f-40e1-45d5-a4cc-519cc3b28fb8
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
[ "MIT" ]
0.13.0
7fec0fac72076ea32823fb59efcc0e280cd28162
code
185368
### A Pluto.jl notebook ### # v0.19.46 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) 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 el = $(esc(element)) global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : iv(el) el end end # ╔═╡ a1ee798d-c57b-4cc3-9e19-fb607f3e1e43 using PlutoUI # Notebook UI # ╔═╡ 72604eef-5951-4934-844d-d2eb7eb0292c using FMI # load and simulate FMUs # ╔═╡ 21104cd1-9fe8-45db-9c21-b733258ff155 using FMIFlux # machine learning with FMUs # ╔═╡ 9d9e5139-d27e-48c8-a62e-33b2ae5b0086 using FMIZoo # a collection of demo FMUs # ╔═╡ eaae989a-c9d2-48ca-9ef8-fd0dbff7bcca using FMIFlux.Flux # default Julia Machine Learning library # ╔═╡ 98c608d9-c60e-4eb6-b611-69d2ae7054c9 using FMIFlux.DifferentialEquations # the mighty (O)DE solver suite # ╔═╡ ddc9ce37-5f93-4851-a74f-8739b38ab092 using ProgressLogging: @withprogress, @logprogress, @progressid, uuid4 # ╔═╡ de7a4639-e3b8-4439-924d-7d801b4b3eeb using BenchmarkTools # default benchmarking library # ╔═╡ 45c4b9dd-0b04-43ae-a715-cd120c571424 using Plots # ╔═╡ 1470df0f-40e1-45d5-a4cc-519cc3b28fb8 md""" # Scientific Machine Learning $br using Functional Mock-Up Units (former *Hybrid Modeling using FMI*) Workshop $br @ JuliaCon 2024 (Eindhoven, Netherlands) $br @ MODPROD 2024 (Linköping University, Sweden) by Tobias Thummerer (University of Augsburg) *#hybridmodeling, #sciml, #neuralode, #neuralfmu, #penode* # Abstract If there is something YOU know about a physical system, AI shouldn’t need to learn it. How to integrate YOUR system knowledge into a ML development process is the core topic of this hands-on workshop. The entire workshop evolves around a challenging use case from robotics: Modeling a robot that is able to write arbitrary messages with a pen. After introducing the topic and the considered use case, participants can experiment with their very own hybrid model topology. # Introduction This workshop focuses on the integration of Functional Mock-Up Units (FMUs) into a machine learning topology. FMUs are simulation models that can be generated within a variety of modeling tools, see the [FMI homepage](https://fmi-standard.org/). Together with deep neural networks that complement and improve the FMU prediction, so called *neural FMUs* can be created. The workshop itself evolves around the hybrid modeling of a *Selective Compliance Assembly Robot Arm* (SCARA), that is able to write user defined words on a sheet of paper. A ready to use physical simulation model (FMU) for the SCARA is given and shortly highlighted in this workshop. However, this model – as any simulation model – shows some deviations if compared to measurements from the real system. These deviations results from not modeled slip-stick-friction: The pen sticks to the paper until a force limit is reached, but then moves jerkily. A hard to model physical effect – but not for a neural FMU. More advanced code snippets are hidden by default and marked with a ghost `👻`. Computations, that are disabled for performance reasons, are marked with `ℹ️`. They offer a hint how to enable the idled computation by activating the corresponding checkbox marked with `🎬`. ## Example Video If you haven't seen such a SCARA system yet, you can watch the following video. There are many more similar videos out there. """ # ╔═╡ 7d694be0-cd3f-46ae-96a3-49d07d7cf65a html""" <iframe width="560" height="315" src="https://www.youtube.com/embed/ryIwLLr6yRA?si=ncr1IXlnuNhWPWgl" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe> """ # ╔═╡ 10cb63ad-03d7-47e9-bc33-16c7786b9f6a md""" This video is by *Alexandru Babaian* on YouTube. ## Workshop Video """ # ╔═╡ 1e0fa041-a592-42fb-bafd-c7272e346e46 html""" <iframe width="560" height="315" src="https://www.youtube.com/embed/sQ2MXSswrSo?si=XcEoe1Ai7U6hqnp5" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe> """ # ╔═╡ 6fc16c34-c0c8-48ce-87b3-011a9a0f4e7c md""" This video is from JuliaCon 2024 (Eindhoven, Netherlands). ## Requirements To follow this workshop, you should ... - ... have a rough idea what the *Functional Mock-Up Interface* is and how the standard-conform models - the *Functional Mock-Up Units* - work. If not, a good source is the homepage of the standard, see the [FMI Homepage](https://fmi-standard.org/). - ... know the *Julia Programming Language* or at least have some programming skills in another high-level programming language like *Python* or *Matlab*. An introduction to Julia can be found on the [Julia Homepage](https://julialang.org/), but there are many more introductions in different formats available on the internet. - ... have an idea of how modeling (in terms of modeling ODE and DAE systems) and simulation (solving) of such models works. The technical requirements are: | | recommended | minimum | your | | ----- | ---- | ---- | ---- | | RAM | $\geq$ 16.0GB | 8.0GB | $(round(Sys.total_memory() / 2^30; digits=1))GB | | OS | Windows | Windows / Linux | $(Sys.islinux() ? "Linux" : (Sys.iswindows() ? "Windows" : "unsupported")) | Julia | 1.10 | 1.6 | $("" * string(Int(VERSION.major)) * "." * string(Int(VERSION.minor))) | This said, we can start "programming"! The entire notebook is pre-implemented, so you can use it without writing a single line of code. Users new to Julia can use interactive UI elements to interact, while more advance users can view and manipulate corresponding code. Let's go! """ # ╔═╡ 8a82d8c7-b781-4600-8780-0a0a003b676c md""" ## Loading required Julia libraries Before starting with the actual coding, we load in the required Julia libraries. This Pluto-Notebook installs all required packages automatically. However, this will take some minutes when you start the notebook for the first time... it is recommended to not interact with the UI elements as long as the first compilation runs (orange status light in the bottom right corner). """ # ╔═╡ a02f77d1-00d2-46a3-91ba-8a7f5b4bbdc9 md""" First, we load the Pluto UI elements: """ # ╔═╡ 02f0add7-9c4e-4358-8b5e-6863bae3ee75 md""" Then, the three FMI-libraries we need for FMU loading, machine learning and the FMU itself: """ # ╔═╡ 85308992-04c4-4d20-a840-6220cab54680 md""" Some additional libraries for machine learning and ODE solvers: """ # ╔═╡ 3e2579c2-39ce-4249-ad75-228f82e616da md""" To visualize a progress bar during training: """ # ╔═╡ 93fab704-a8dd-47ec-ac88-13f32be99460 md""" And to do some benchmarking: """ # ╔═╡ 5cb505f7-01bd-4824-8876-3e0f5a922fb7 md""" Load in the plotting libraries ... """ # ╔═╡ 33d648d3-e66e-488f-a18d-e538ebe9c000 import PlotlyJS # ╔═╡ 1e9541b8-5394-418d-8c27-2831951c538d md""" ... and use the beautiful `plotly` backend for interactive plots. """ # ╔═╡ e6e91a22-7724-46a3-88c1-315c40660290 plotlyjs() # ╔═╡ 44500f0a-1b89-44af-b135-39ce0fec5810 md""" Next, we define some helper functions, that are not important to follow the workshop - they are hidden by default. However they are here, if you want to explore what it takes to write fully working code. If you do this workshop for the first time, it is recommended to skip the hidden part and directly go on. """ # ╔═╡ 74d23661-751b-4371-bf6b-986149124e81 md""" Display the table of contents: """ # ╔═╡ c88b0627-2e04-40ab-baa2-b4c1edfda0c3 TableOfContents() # ╔═╡ 915e4601-12cc-4b7e-b2fe-574e116f3a92 md""" # Loading Model (FMU) and Data We want to do hybrid modeling, so we need a simulation model and some data to work with. Fortunately, someone already prepared both for us. We start by loading some data from *FMIZoo.jl*, which is a collection of FMUs and corresponding data. """ # ╔═╡ f8e40baa-c1c5-424a-9780-718a42fd2b67 md""" ## Training Data First, we need some data to train our hybrid model on. We can load data for our SCARA (here called `RobotRR`) with the following line: """ # ╔═╡ 74289e0b-1292-41eb-b13b-a4a5763c72b0 # load training data for the `RobotRR` from the FMIZoo data_train = FMIZoo.RobotRR(:train) # ╔═╡ 33223393-bfb9-4e9a-8ea6-a3ab6e2f22aa begin # define the printing messages used at different places in this notebook LIVE_RESULTS_MESSAGE = md"""ℹ️ Live plotting is disabled to safe performance. Checkbox `Plot Results`.""" LIVE_TRAIN_MESSAGE = md"""ℹ️ Live training is disabled to safe performance. Checkbox `Start Training`.""" BENCHMARK_MESSAGE = md"""ℹ️ Live benchmarks are disabled to safe performance. Checkbox `Start Benchmark`.""" HIDDEN_CODE_MESSAGE = md"""> 👻 Hidden Code | You probably want to skip this code section on the first run.""" import FMI.FMIImport.FMICore: hasCurrentComponent, getCurrentComponent, FMU2Solution import Random function fmiSingleInstanceMode!( fmu::FMU2, mode::Bool, params = FMIZoo.getParameter(data_train, 0.0; friction = false), x0 = FMIZoo.getState(data_train, 0.0), ) fmu.executionConfig = deepcopy(FMU2_EXECUTION_CONFIGURATION_NO_RESET) # for this model, state events are generated but don't need to be handled, # we can skip that to gain performance fmu.executionConfig.handleStateEvents = false fmu.executionConfig.loggingOn = false #fmu.executionConfig.externalCallbacks = true if mode # switch to a more efficient execution configuration, allocate only a single FMU instance, see: # https://thummeto.github.io/FMI.jl/dev/features/#Execution-Configuration fmu.executionConfig.terminate = true fmu.executionConfig.instantiate = false fmu.executionConfig.reset = true fmu.executionConfig.setup = true fmu.executionConfig.freeInstance = false c, _ = FMIFlux.prepareSolveFMU( fmu, nothing, fmu.type, true, # instantiate false, # free true, # terminate true, # reset true, # setup params; x0 = x0, ) else if !hasCurrentComponent(fmu) return nothing end c = getCurrentComponent(fmu) # switch back to the default execution configuration, allocate a new FMU instance for every run, see: # https://thummeto.github.io/FMI.jl/dev/features/#Execution-Configuration fmu.executionConfig.terminate = true fmu.executionConfig.instantiate = true fmu.executionConfig.reset = true fmu.executionConfig.setup = true fmu.executionConfig.freeInstance = true FMIFlux.finishSolveFMU( fmu, c, true, # free true, ) # terminate end return nothing end function prepareSolveFMU(fmu, parameters) FMIFlux.prepareSolveFMU( fmu, nothing, fmu.type, fmu.executionConfig.instantiate, fmu.executionConfig.freeInstance, fmu.executionConfig.terminate, fmu.executionConfig.reset, fmu.executionConfig.setup, parameters, ) end function dividePath(values) last_value = values[1] paths = [] path = [] for j = 1:length(values) if values[j] == 1.0 push!(path, j) end if values[j] == 0.0 && last_value != 0.0 push!(path, j) push!(paths, path) path = [] end last_value = values[j] end if length(path) > 0 push!(paths, path) end return paths end function plotRobot(solution::FMU2Solution, t::Real) x = solution.states(t) a1 = x[5] a2 = x[3] dt = 0.01 i = 1 + round(Integer, t / dt) v = solution.values.saveval[i] l1 = 0.2 l2 = 0.1 margin = 0.05 scale = 1500 fig = plot(; title = "Time $(round(t; digits=1))s", size = ( round(Integer, (2 * margin + l1 + l2) * scale), round(Integer, (l1 + l2 + 2 * margin) * scale), ), xlims = (-margin, l1 + l2 + margin), ylims = (-l1 - margin, l2 + margin), legend = :bottomleft, ) p0 = [0.0, 0.0] p1 = p0 .+ [cos(a1) * l1, sin(a1) * l1] p2 = p1 .+ [cos(a1 + a2) * l2, sin(a1 + a2) * l2] f_norm = collect(v[3] for v in solution.values.saveval) paths = dividePath(f_norm) drawing = collect(v[1:2] for v in solution.values.saveval) for path in paths plot!( fig, collect(v[1] for v in drawing[path]), collect(v[2] for v in drawing[path]), label = :none, color = :black, style = :dot, ) end paths = dividePath(f_norm[1:i]) drawing_is = collect(v[4:5] for v in solution.values.saveval)[1:i] for path in paths plot!( fig, collect(v[1] for v in drawing_is[path]), collect(v[2] for v in drawing_is[path]), label = :none, color = :green, width = 2, ) end plot!(fig, [p0[1], p1[1]], [p0[2], p1[2]], label = :none, width = 3, color = :blue) plot!(fig, [p1[1], p2[1]], [p1[2], p2[2]], label = :none, width = 3, color = :blue) scatter!( fig, [p0[1]], [p0[2]], label = "R1 | α1=$(round(a1; digits=3)) rad", color = :red, ) scatter!( fig, [p1[1]], [p1[2]], label = "R2 | α2=$(round(a2; digits=3)) rad", color = :purple, ) scatter!(fig, [v[1]], [v[2]], label = "TCP | F=$(v[3]) N", color = :orange) end HIDDEN_CODE_MESSAGE end # begin # ╔═╡ 92ad1a99-4ad9-4b69-b6f3-84aab49db54f @bind t_train_plot Slider(0.0:0.1:data_train.t[end], default = data_train.t[1]) # ╔═╡ f111e772-a340-4217-9b63-e7715f773b2c md""" Let's have a look on the data! It's the written word *train*. You can use the slider to pick a specific point in time to plot the "robot" as recorded as part of the data. The current picked time is $(round(t_train_plot; digits=1))s. """ # ╔═╡ 909de9f1-2aca-4bf0-ba60-d3418964ba4a plotRobot(data_train.solution, t_train_plot) # ╔═╡ d8ca5f66-4f55-48ab-a6c9-a0be662811d9 md""" > 👁️ Interestingly, the first part of the word "trai" is not significantly affected by the slip-stick-effect, the actual TCP trajectory (green) lays quite good on the target position (black dashed). However, the "n" is very jerky. This can be explained by the increasing lever, the motor needs more torque to overcome the static friction the further away the TCP (orange) is from the robot base (red). Let's extract a start and stop time, as well as saving points for the later solving process: """ # ╔═╡ 41b1c7cb-5e3f-4074-a681-36dd2ef94454 tSave = data_train.t # time points to save the solution at # ╔═╡ 8f45871f-f72a-423f-8101-9ce93e5a885b tStart = tSave[1] # start time for simulation of FMU and neural FMU # ╔═╡ 57c039f7-5b24-4d63-b864-d5f808110b91 tStop = tSave[end] # stop time for simulation of FMU and neural FMU # ╔═╡ 4510022b-ad28-4fc2-836b-e4baf3c14d26 md""" Finally, also the start state can be grabbed from *FMIZoo.jl*, as well as some default parameters for the simulation model we load in the next section. How to interpret the six states is discussed in the next section where the model is loaded. """ # ╔═╡ 9589416a-f9b3-4b17-a381-a4f660a5ee4c x0 = FMIZoo.getState(data_train, tStart) # ╔═╡ 326ae469-43ab-4bd7-8dc4-64575f4a4d3e md""" The parameter array only contains the path to the training data file, the trajectory writing "train". """ # ╔═╡ 8f8f91cc-9a92-4182-8f18-098ae3e2c553 parameters = FMIZoo.getParameter(data_train, tStart; friction = false) # ╔═╡ 8d93a1ed-28a9-4a77-9ac2-5564be3729a5 md""" ## Validation Data To check whether the hybrid model was not only able to *imitate*, but *understands* the training data, we need some unknown data for validation. In this case, the written word "validate". """ # ╔═╡ 4a8de267-1bf4-42c2-8dfe-5bfa21d74b7e # load validation data for the `RobotRR` from the FMIZoo data_validation = FMIZoo.RobotRR(:validate) # ╔═╡ dbde2da3-e3dc-4b78-8f69-554018533d35 @bind t_validate_plot Slider(0.0:0.1:data_validation.t[end], default = data_validation.t[1]) # ╔═╡ 6a8b98c9-e51a-4f1c-a3ea-cc452b9616b7 md""" Let's have a look on the validation data! Again, you can use the slider to pick a specific point in time. The current time is $(round(t_validate_plot; digits=1))s. """ # ╔═╡ d42d0beb-802b-4d30-b5b8-683d76af7c10 plotRobot(data_validation.solution, t_validate_plot) # ╔═╡ e50d7cc2-7155-42cf-9fef-93afeee6ffa4 md""" > 👁️ It looks similar to the effect we know from training data, the first part "valida" is not significantly affected by the slip-stick-effect, but the "te" is very jerky. Again, think of the increasing lever ... """ # ╔═╡ 3756dd37-03e0-41e9-913e-4b4f183d8b81 md""" ## Simulation Model (FMU) The SCARA simulation model is called `RobotRR` for `Robot Rotational Rotational`, indicating that this robot consists of two rotational joints, connected by links. It is loaded with the following line of code: """ # ╔═╡ 2f83bc62-5a54-472a-87a2-4ddcefd902b6 # load the FMU named `RobotRR` from the FMIZoo # the FMU was exported from Dymola (version 2023x) # load the FMU in mode `model-exchange` (ME) fmu = fmiLoad("RobotRR", "Dymola", "2023x"; type = :ME) # ╔═╡ c228eb10-d694-46aa-b952-01d824879287 begin # We activate the single instance mode, so only one FMU instance gets allocated and is reused again an again. fmiSingleInstanceMode!(fmu, true) using FMI.FMIImport: fmi2StringToValueReference # declare some model identifiers (inside of the FMU) STATE_I1 = fmu.modelDescription.stateValueReferences[2] STATE_I2 = fmu.modelDescription.stateValueReferences[1] STATE_A1 = fmi2StringToValueReference( fmu, "rRPositionControl_Elasticity.rr1.rotational1.revolute1.phi", ) STATE_A2 = fmi2StringToValueReference( fmu, "rRPositionControl_Elasticity.rr1.rotational2.revolute1.phi", ) STATE_dA1 = fmi2StringToValueReference( fmu, "rRPositionControl_Elasticity.rr1.rotational1.revolute1.w", ) STATE_dA2 = fmi2StringToValueReference( fmu, "rRPositionControl_Elasticity.rr1.rotational2.revolute1.w", ) DER_ddA2 = fmu.modelDescription.derivativeValueReferences[4] DER_ddA1 = fmu.modelDescription.derivativeValueReferences[6] VAR_TCP_PX = fmi2StringToValueReference(fmu, "rRPositionControl_Elasticity.tCP.p_x") VAR_TCP_PY = fmi2StringToValueReference(fmu, "rRPositionControl_Elasticity.tCP.p_y") VAR_TCP_VX = fmi2StringToValueReference(fmu, "rRPositionControl_Elasticity.tCP.v_x") VAR_TCP_VY = fmi2StringToValueReference(fmu, "rRPositionControl_Elasticity.tCP.v_y") VAR_TCP_F = fmi2StringToValueReference(fmu, "combiTimeTable.y[3]") HIDDEN_CODE_MESSAGE end # ╔═╡ 16ffc610-3c21-40f7-afca-e9da806ea626 md""" Let's check out some meta data of the FMU with `fmiInfo`: """ # ╔═╡ 052f2f19-767b-4ede-b268-fce0aee133ad fmiInfo(fmu) # ╔═╡ 746fbf6f-ed7c-43b8-8a6f-0377cd3cf85e md""" > 👁️ We can read the model name, tool information for the exporting tool, number of event indicators, states, inputs, outputs and whether the optionally implemented FMI features (like *directional derivatives*) are supported by this FMU. """ # ╔═╡ 08e1ff54-d115-4da9-8ea7-5e89289723b3 md""" All six states are listed with all their alias identifiers, that might look a bit awkward the first time. The six states - human readable - are: | variable reference | description | | -------- | ------ | | 33554432 | motor #2 current | | 33554433 | motor #1 current | | 33554434 | joint #2 angle | | 33554435 | joint #2 angular velocity | | 33554436 | joint #1 angle | | 33554437 | joint #1 angular velocity | """ # ╔═╡ 70c6b605-54fa-40a3-8bce-a88daf6a2022 md""" To simulate - or *solve* - the ME-FMU, we need an ODE solver. We use the *Tsit5* (explicit Runge-Kutta) here. """ # ╔═╡ 634f923a-5e09-42c8-bac0-bf165ab3d12a solver = Tsit5() # ╔═╡ f59b5c84-2eae-4e3f-aaec-116c090d454d md""" Let's define an array of values we want to be recorded during the first simulation of our FMU. The variable identifiers (like `DER_ddA2`) were pre-defined in the hidden code section above. """ # ╔═╡ 0c9493c4-322e-41a0-9ec7-2e2c54ae1373 recordValues = [ DER_ddA2, DER_ddA1, # mechanical accelerations STATE_A2, STATE_A1, # mechanical angles VAR_TCP_PX, VAR_TCP_PY, # tool-center-point x and y VAR_TCP_VX, VAR_TCP_VY, # tool-center-point velocity x and y VAR_TCP_F, ] # normal force pen on paper # ╔═╡ 325c3032-4c78-4408-b86e-d9aa4cfc3187 md""" Let's simulate the FMU using `fmiSimulate`. In the solution object, different information can be found, like the number of ODE, jacobian or gradient evaluations: """ # ╔═╡ 25e55d1c-388f-469d-99e6-2683c0508693 sol_fmu_train = fmiSimulate( fmu, # our FMU (tStart, tStop); # sim. from tStart to tStop solver = solver, # use the Tsit5 solver parameters = parameters, # the word "train" saveat = tSave, # saving points for the sol. recordValues = recordValues, ) # values to record # ╔═╡ 74c519c9-0eef-4798-acff-b11044bb4bf1 md""" Now that we know our model and data a little bit better, it's time to care about our hybrid model topology. # Experiments: $br Hybrid Model Topology Today is opposite day! Instead of deriving a topology step by step, the final neural FMU topology is presented in the picture below... however, three experiments are intended to make clear why it looks the way it looks. ![](https://github.com/ThummeTo/FMIFlux.jl/blob/main/examples/pluto-src/SciMLUsingFMUs/src/plan_complete.png?raw=true) The first experiment is on choosing a good interface between FMU and ANN. The second is on online data pre- and post-processing. And the third one on gates, that allow to control the influence of ANN and FMU on the resulting hybrid model dynamics. After you completed all three, you are equipped with the knowledge to cope the final challenge: Build your own neural FMU and train it! """ # ╔═╡ 786c4652-583d-43e9-a101-e28c0b6f64e4 md""" ## Choosing interface signals **between the physical and machine learning domain** When connecting an FMU with an ANN, technically different signals could be used: States, state derivatives, inputs, outputs, parameters, time itself or other observable variables. Depending on the use case, some signals are more clever to choose than others. In general, every additional signal costs a little bit of computational performance, as you will see. So picking the right subset is the key! ![](https://github.com/ThummeTo/FMIFlux.jl/blob/main/examples/pluto-src/SciMLUsingFMUs/src/plan_e1.png?raw=true) """ # ╔═╡ 5d688c3d-b5e3-4a3a-9d91-0896cc001000 md""" We start building our deep model as a `Chain` of layers. For now, there is only a single layer in it: The FMU `fmu` itself. The layer input `x` is interpreted as system state (compare to the figure above) and set in the fmu call via `x=x`. The current solver time `t` is set implicitly. Further, we want all state derivatives as layer outputs by setting `dx_refs=:all` and some additional outputs specified via `y_refs=CHOOSE_y_refs` (you can pick them using the checkboxes). """ # ╔═╡ 68719de3-e11e-4909-99a3-5e05734cc8b1 md""" Which signals are used for `y_refs`, can be selected: """ # ╔═╡ b42bf3d8-e70c-485c-89b3-158eb25d8b25 @bind CHOOSE_y_refs MultiCheckBox([ STATE_A1 => "Angle Joint 1", STATE_A2 => "Angle Joint 2", STATE_dA1 => "Angular velocity Joint 1", STATE_dA2 => "Angular velocity Joint 2", VAR_TCP_PX => "TCP position x", VAR_TCP_PY => "TCP position y", VAR_TCP_VX => "TCP velocity x", VAR_TCP_VY => "TCP velocity y", VAR_TCP_F => "TCP (normal) force z", ]) # ╔═╡ 2e08df84-a468-4e99-a277-e2813dfeae5c model = Chain(x -> fmu(; x = x, dx_refs = :all, y_refs = CHOOSE_y_refs)) # ╔═╡ c446ed22-3b23-487d-801e-c23742f81047 md""" Let's pick a state `x1` one second after simulation start to determine sensitivities for: """ # ╔═╡ fc3d7989-ac10-4a82-8777-eeecd354a7d0 x1 = FMIZoo.getState(data_train, tStart + 1.0) # ╔═╡ f4e66f76-76ff-4e21-b4b5-c1ecfd846329 begin using FMIFlux.FMISensitivity.ReverseDiff using FMIFlux.FMISensitivity.ForwardDiff prepareSolveFMU(fmu, parameters) jac_rwd = ReverseDiff.jacobian(x -> model(x), x1) A_rwd = jac_rwd[1:length(x1), :] end # ╔═╡ 0a7955e7-7c1a-4396-9613-f8583195c0a8 md""" Depending on how many signals you select, the output of the FMU-layer is extended. The first six outputs are the state derivatives, the remaining are the $(length(CHOOSE_y_refs)) additional output(s) selected above. """ # ╔═╡ 4912d9c9-d68d-4afd-9961-5d8315884f75 begin dx_y = model(x1) end # ╔═╡ 19942162-cd4e-487c-8073-ea6b262d299d md""" Derivatives: """ # ╔═╡ 73575386-673b-40cc-b3cb-0b8b4f66a604 ẋ = dx_y[1:length(x1)] # ╔═╡ 24861a50-2319-4c63-a800-a0a03279efe2 md""" Additional outputs: """ # ╔═╡ 93735dca-c9f3-4f1a-b1bd-dfe312a0644a y = dx_y[length(x1)+1:end] # ╔═╡ 13ede3cd-99b1-4e65-8a18-9043db544728 md""" For the later training, we need gradients and Jacobians. """ # ╔═╡ f7c119dd-c123-4c43-812e-d0625817d77e md""" If we use reverse-mode automatic differentiation via `ReverseDiff.jl`, the determined Jacobian $A = \frac{\partial \dot{x}}{\partial x}$ states: """ # ╔═╡ b163115b-393d-4589-842d-03859f05be9a md""" Forward-mode automatic differentiation (using *ForwardDiff.jl*)is available, too. We can determine further Jacobians for FMUs, for example the Jacobian $C = \frac{\partial y}{\partial x}$ states (using *ReverseDiff.jl*): """ # ╔═╡ ac0afa6c-b6ec-4577-aeb6-10d1ec63fa41 begin C_rwd = jac_rwd[length(x1)+1:end, :] end # ╔═╡ 5e9cb956-d5ea-4462-a649-b133a77929b0 md""" Let's check the performance of these calls, because they will have significant influence on the later training performance! """ # ╔═╡ 9dc93971-85b6-463b-bd17-43068d57de94 md""" ### Benchmark The amount of selected signals has influence on the computational performance of the model. The more signals you use, the slower is inference and gradient determination. For now, you have picked $(length(CHOOSE_y_refs)) additional signal(s). """ # ╔═╡ 476a1ed7-c865-4878-a948-da73d3c81070 begin CHOOSE_y_refs md""" 🎬 **Start Benchmark** $(@bind BENCHMARK CheckBox()) (benchmarking takes around 10 seconds) """ end # ╔═╡ 0b6b4f6d-be09-42f3-bc2c-5f17a8a9ab0e md""" The current timing and allocations for inference are: """ # ╔═╡ a1aca180-d561-42a3-8d12-88f5a3721aae begin if BENCHMARK @btime model(x1) else BENCHMARK_MESSAGE end end # ╔═╡ 3bc2b859-d7b1-4b79-88df-8fb517a6929d md""" Gradient and Jacobian computation takes a little longer of course. We use reverse-mode automatic differentiation via `ReverseDiff.jl` here: """ # ╔═╡ a501d998-6fd6-496f-9718-3340c42b08a6 begin if BENCHMARK prepareSolveFMU(fmu, parameters) function ben_rwd(x) return ReverseDiff.jacobian(model, x + rand(6) * 1e-12) end @btime ben_rwd(x1) #nothing else BENCHMARK_MESSAGE end end # ╔═╡ 83a2122d-56da-4a80-8c10-615a8f76c4c1 md""" Further, forward-mode automatic differentiation is available too via `ForwardDiff.jl`, but a little bit slower than reverse-mode: """ # ╔═╡ e342be7e-0806-4f72-9e32-6d74ed3ed3f2 begin if BENCHMARK prepareSolveFMU(fmu, parameters) function ben_fwd(x) return ForwardDiff.jacobian(model, x + rand(6) * 1e-12) end @btime ben_fwd(x1) # second run for "benchmarking" #nothing else BENCHMARK_MESSAGE end end # ╔═╡ eaf37128-0377-42b6-aa81-58f0a815276b md""" > 💡 Keep in mind that the choice of interface might has a significant impact on your inference and training performance! However, some signals are simply required to be part of the interface, because the effect we want to train for depends on them. """ # ╔═╡ c030d85e-af69-49c9-a7c8-e490d4831324 md""" ## Online Data Pre- and Postprocessing **is required for hybrid models** Now that we have defined the signals that come *from* the FMU and go *into* the ANN, we need to think about data pre- and post-processing. In ML, this is often done before the actual training starts. In hybrid modeling, we need to do this *online*, because the FMU constantly generates signals that might not be suitable for ANNs. On the other hand, the signals generated by ANNs might not suit the expected FMU input. What *suitable* means gets more clear if we have a look on the used activation functions, like e.g. the *tanh*. ![](https://github.com/ThummeTo/FMIFlux.jl/blob/main/examples/pluto-src/SciMLUsingFMUs/src/plan_e2.png?raw=true) We simplify the ANN to a single nonlinear activation function. Let's see what's happening as soon as we put the derivative *angular velocity of joint 1* (dα1) from the FMU into a `tanh` function: """ # ╔═╡ 51c200c9-0de3-4e50-8884-49fe06158560 begin fig_pre_post1 = plot( layout = grid(1, 2, widths = (1 / 4, 3 / 4)), xlabel = "t [s]", legend = :bottomright, ) plot!(fig_pre_post1[1], data_train.t, data_train.da1, label = :none, xlims = (0.0, 0.1)) plot!(fig_pre_post1[1], data_train.t, tanh.(data_train.da1), label = :none) plot!(fig_pre_post1[2], data_train.t, data_train.da1, label = "dα1") plot!(fig_pre_post1[2], data_train.t, tanh.(data_train.da1), label = "tanh(dα1)") fig_pre_post1 end # ╔═╡ 0dadd112-3132-4491-9f02-f43cf00aa1f9 md""" In general, it looks like the velocity isn't saturated too much by `tanh`. This is a good thing and not always the case! However, the very beginning of the trajectory is saturated too much (the peak value of $\approx -3$ is saturated to $\approx -1$). This is bad, because the hybrid model velocity is *slower* in this time interval and the hybrid system won't reach the same angle over time as the original FMU. We can add shift (=addition) and scale (=multiplication) operations before and after the ANN to bypass this issue. See how you can influence the output *after* the `tanh` (and the ANN respectively) to match the ranges. The goal is to choose pre- and post-processing parameters so that the signal ranges needed by the FMU are preserved by the hybrid model. """ # ╔═╡ bf6bf640-54bc-44ef-bd4d-b98e934d416e @bind PRE_POST_SHIFT Slider(-1:0.1:1.0, default = 0.0) # ╔═╡ 5c2308d9-6d04-4b38-af3b-6241da3b6871 md""" Change the `shift` value $(PRE_POST_SHIFT): """ # ╔═╡ 007d6d95-ad85-4804-9651-9ac3703d3b40 @bind PRE_POST_SCALE Slider(0.1:0.1:2.0, default = 1.0) # ╔═╡ 639889b3-b9f2-4a3c-999d-332851768fd7 md""" Change the `scale` value $(PRE_POST_SCALE): """ # ╔═╡ ed1887df-5079-4367-ab04-9d02a1d6f366 begin fun_pre = ShiftScale([PRE_POST_SHIFT], [PRE_POST_SCALE]) fun_post = ScaleShift(fun_pre) fig_pre_post2 = plot(; layout = grid(1, 2, widths = (1 / 4, 3 / 4)), xlabel = "t [s]") plot!( fig_pre_post2[2], data_train.t, data_train.da1, label = :none, title = "Shift: $(round(PRE_POST_SHIFT; digits=1)) | Scale: $(round(PRE_POST_SCALE; digits=1))", legend = :bottomright, ) plot!(fig_pre_post2[2], data_train.t, tanh.(data_train.da1), label = :none) plot!( fig_pre_post2[2], data_train.t, fun_post(tanh.(fun_pre(data_train.da1))), label = :none, ) plot!(fig_pre_post2[1], data_train.t, data_train.da1, label = "dα1", xlims = (0.0, 0.1)) plot!(fig_pre_post2[1], data_train.t, tanh.(data_train.da1), label = "tanh(dα1)") plot!( fig_pre_post2[1], data_train.t, fun_post(tanh.(fun_pre(data_train.da1))), label = "post(tanh(pre(dα1)))", ) fig_pre_post2 end # ╔═╡ 0b0c4650-2ce1-4879-9acd-81c16d06700e md""" The left plot shows the negative spike at the very beginning in more detail. In *FMIFlux.jl*, there are ready to use layers for scaling and shifting, that can automatically select appropriate parameters. These parameters are trained together with the ANN parameters by default, so they can adapt to new signal ranges that might occur during training. """ # ╔═╡ b864631b-a9f3-40d4-a6a8-0b57a37a476d md""" > 💡 In many machine learning applications, pre- and post-processing is done offline. If we combine machine learning and physical models, we need to pre- and post-process online at the interfaces. This does at least improve training performance and is a necessity if the nominal values become very large or very small. """ # ╔═╡ 0fb90681-5d04-471a-a7a8-4d0f3ded7bcf md""" ## Introducing Gates **to control how physical and machine learning model contribute and interact** ![](https://github.com/ThummeTo/FMIFlux.jl/blob/main/examples/pluto-src/SciMLUsingFMUs/src/plan_e3.png?raw=true) """ # ╔═╡ 95e14ea5-d82d-4044-8c68-090d74d95a61 md""" There are two obvious ways of connecting two blocks (the ANN and the FMU): - In **series**, so one block is getting signals from the other block and is able to *manipulate* or *correct* these signals. This way, e.g. modeling or parameterization errors can be corrected. - In **parallel**, so both are getting the same signals and calculate own outputs, these outputs must be merged afterwards. This way, additional system parts, like e.g. forces or momentum, can be learned and added to or augment the existing dynamics. The good news is, you don't have to decide this beforehand. This is something that the optimizer can decide, if we introduce a topology with parameters, that allow for both modes. This structure is referred to as *gates*. """ # ╔═╡ cbae6aa4-1338-428c-86aa-61d3304e33ed @bind GATE_INIT_FMU Slider(0.0:0.1:1.0, default = 1.0) # ╔═╡ 2fa1821b-aaec-4de4-bfb4-89560790dc39 md""" Change the opening of the **FMU gate** $(GATE_INIT_FMU) for dα1: """ # ╔═╡ 8c56acd6-94d3-4cbc-bc29-d249740268a0 @bind GATE_INIT_ANN Slider(0.0:0.1:1.0, default = 0.0) # ╔═╡ 9b52a65a-f20c-4387-aaca-5292a92fb639 md""" Change the opening of the **ANN gate** $(GATE_INIT_ANN) for dα1: """ # ╔═╡ 845a95c4-9a35-44ae-854c-57432200da1a md""" The FMU gate value for dα1 is $(GATE_INIT_FMU) and the ANN gate value is $(GATE_INIT_ANN). This means the hybrid model dα1 is composed of $(GATE_INIT_FMU*100)% of dα1 from the FMU and of $(GATE_INIT_ANN*100)% of dα1 from the ANN. """ # ╔═╡ 5a399a9b-32d9-4f93-a41f-8f16a4b102dc begin function build_model_gates() Random.seed!(123) cache = CacheLayer() # allocate a cache layer cacheRetrieve = CacheRetrieveLayer(cache) # allocate a cache retrieve layer, link it to the cache layer # we have two signals (acceleration, consumption) and two sources (ANN, FMU), so four gates: # (1) acceleration from FMU (gate=1.0 | open) # (2) consumption from FMU (gate=1.0 | open) # (3) acceleration from ANN (gate=0.0 | closed) # (4) consumption from ANN (gate=0.0 | closed) # the accelerations [1,3] and consumptions [2,4] are paired gates = ScaleSum([GATE_INIT_FMU, GATE_INIT_ANN], [[1, 2]]) # gates with sum # setup the neural FMU topology model_gates = Flux.f64( Chain( dx -> cache(dx), # cache `dx` Dense(1, 16, tanh), Dense(16, 1, tanh), # pre-process `dx` dx -> cacheRetrieve(1, dx), # dynamics FMU | dynamics ANN gates, ), ) # stack together model_input = collect([v] for v in data_train.da1) model_output = collect(model_gates(inp) for inp in model_input) ANN_output = collect(model_gates[2:3](inp) for inp in model_input) fig = plot(; ylims = (-3, 1), legend = :bottomright) plot!(fig, data_train.t, collect(v[1] for v in model_input), label = "dα1 of FMU") plot!(fig, data_train.t, collect(v[1] for v in ANN_output), label = "dα1 of ANN") plot!( fig, data_train.t, collect(v[1] for v in model_output), label = "dα1 of neural FMU", ) return fig end build_model_gates() end # ╔═╡ fd1cebf1-5ccc-4bc5-99d4-1eaa30e9762e md""" Some observations from the current gate openings are: This equals the serial topology: $((GATE_INIT_FMU==0 && GATE_INIT_ANN==1) ? "✔️" : "❌") $br This equals the parallel topology: $((GATE_INIT_FMU==1 && GATE_INIT_ANN==1) ? "✔️" : "❌") $br The neural FMU dynamics equal the FMU dynamics: $((GATE_INIT_FMU==1 && GATE_INIT_ANN==0) ? "✔️" : "❌") """ # ╔═╡ 1cd976fb-db40-4ebe-b40d-b996e16fc213 md""" > 💡 Gates allow to make parts of the architecture *learnable* while still keeping the training results interpretable. """ # ╔═╡ 93771b35-4edd-49e3-bed1-a3ccdb7975e6 md""" > 💭 **Further reading:** Optimizing the gates together with the ANN parameters seems a useful strategy if we don't know how FMU and ANN need to interact in the later application. Technically, we keep a part of the architecture *parameterizable* and therefore learnable. How far can we push this game? > > Actually to the point, that the combination of FMU and ANN is described by a single *connection* equation, that is able to express all possible combinations of both models with each other - so a connection between every pair of inputs and outputs. This is discussed in detail as part of our article [*Learnable & Interpretable Model Combination in Dynamic Systems Modeling*](https://doi.org/10.48550/arXiv.2406.08093). """ # ╔═╡ e79badcd-0396-4a44-9318-8c6b0a94c5c8 md""" Time to take care of the big picture next. """ # ╔═╡ 2a5157c5-f5a2-4330-b2a3-0c1ec0b7adff md""" # Building the neural FMU **... putting everything together** ![](https://github.com/ThummeTo/FMIFlux.jl/blob/main/examples/pluto-src/SciMLUsingFMUs/src/plan_train.png?raw=true) """ # ╔═╡ 4454c8d2-68ed-44b4-adfa-432297cdc957 md""" ## FMU inputs In general, you can use arbitrary values as input for the FMU layer, like system inputs, states or parameters. In this example, we want to use only system states as inputs for the FMU layer - to keep it easy - which are: - currents of both motors - angles of both joints - angular velocities of both joints To preserve the ODE topology (a mapping from state to state derivative), we use all system state derivatives as layer outputs. However, you can choose further outputs if you want to... and you definitely should. ## ANN inputs As input to the ANN, we choose at least the angular accelerations of both joints - this is fixed: - angular acceleration Joint 1 - angular acceleration Joint 2 Pick additional ANN layer inputs: """ # ╔═╡ d240c95c-5aba-4b47-ab8d-2f9c0eb854cd @bind y_refs MultiCheckBox([ STATE_A2 => "Angle Joint 2", STATE_A1 => "Angle Joint 1", STATE_dA1 => "Angular velocity Joint 1", STATE_dA2 => "Angular velocity Joint 2", VAR_TCP_PX => "TCP position x", VAR_TCP_PY => "TCP position y", VAR_TCP_VX => "TCP velocity x", VAR_TCP_VY => "TCP velocity y", VAR_TCP_F => "TCP (normal) force z", ]) # ╔═╡ 06937575-9ab1-41cd-960c-7eef3e8cae7f md""" It might be clever to pick additional inputs, because the effect being learned (slip-stick of the pen) might depend on these additional inputs. However, every additional signal has a little negative impact on the computational performance and a risk of learning from wrong correlations. """ # ╔═╡ 356b6029-de66-418f-8273-6db6464f9fbf md""" ## ANN size """ # ╔═╡ 5805a216-2536-44ac-a702-d92e86d435a4 md""" The ANN shall have $(@bind NUM_LAYERS Select([2, 3, 4])) layers with a width of $(@bind LAYERS_WIDTH Select([8, 16, 32])) each. """ # ╔═╡ 53e971d8-bf43-41cc-ac2b-20dceaa78667 @bind GATES_INIT Slider(0.0:0.1:1.0, default = 0.0) # ╔═╡ 68d57a23-68c3-418c-9c6f-32bdf8cafceb md""" The ANN gates shall be initialized with $(GATES_INIT), slide to change: """ # ╔═╡ e8b8c63b-2ca4-4e6a-a801-852d6149283e md""" ANN gates shall be initialized with $(GATES_INIT), meaning the ANN contributes $(GATES_INIT*100)% to the hybrid model derivatives, while the FMU contributes $(100-GATES_INIT*100)%. These parameters are adapted during training, these are only start values. """ # ╔═╡ c0ac7902-0716-4f18-9447-d18ce9081ba5 md""" ## Resulting neural FMU Our final neural FMU topology looks like this: """ # ╔═╡ 84215a73-1ab0-416d-a9db-6b29cd4f5d2a begin function build_topology(gates_init, add_y_refs, nl, lw) ANN_input_Vars = [recordValues[1:2]..., add_y_refs...] ANN_input_Vals = fmiGetSolutionValue(sol_fmu_train, ANN_input_Vars) ANN_input_Idcs = [4, 6] for i = 1:length(add_y_refs) push!(ANN_input_Idcs, i + 6) end # pre- and post-processing preProcess = ShiftScale(ANN_input_Vals) # we put in the derivatives recorded above, FMIFlux shift and scales so we have a data mean of 0 and a standard deviation of 1 #preProcess.scale[:] *= 0.1 # add some additional "buffer" postProcess = ScaleShift(preProcess; indices = [1, 2]) # initialize the postProcess as inverse of the preProcess, but only take indices 1 and 2 # cache cache = CacheLayer() # allocate a cache layer cacheRetrieve = CacheRetrieveLayer(cache) # allocate a cache retrieve layer, link it to the cache layer gates = ScaleSum( [1.0 - gates_init, 1.0 - gates_init, gates_init, gates_init], [[1, 3], [2, 4]], ) # gates with sum ANN_layers = [] push!(ANN_layers, Dense(2 + length(add_y_refs), lw, tanh)) # first layer for i = 3:nl push!(ANN_layers, Dense(lw, lw, tanh)) end push!(ANN_layers, Dense(lw, 2, tanh)) # last layer model = Flux.f64( Chain( x -> fmu(; x = x, dx_refs = :all, y_refs = add_y_refs), dxy -> cache(dxy), # cache `dx` dxy -> dxy[ANN_input_Idcs], preProcess, ANN_layers..., postProcess, dx -> cacheRetrieve(4, 6, dx), # dynamics FMU | dynamics ANN gates, # compute resulting dx from ANN + FMU dx -> cacheRetrieve(1:3, dx[1], 5, dx[2]), ), ) return model end HIDDEN_CODE_MESSAGE end # ╔═╡ bc09bd09-2874-431a-bbbb-3d53c632be39 md""" We find a `Chain` consisting of multipl layers and the corresponding parameter counts. We can evaluate it, by putting in our start state `x0`. The model computes the resulting state derivative: """ # ╔═╡ f02b9118-3fb5-4846-8c08-7e9bbca9d208 md""" On basis of this `Chain`, we can build a neural FMU very easy: """ # ╔═╡ d347d51b-743f-4fec-bed7-6cca2b17bacb md""" So let's get that thing trained! # Training After setting everything up, we can give it a try and train our created neural FMU. Depending on the chosen optimization hyperparameters, this will be more or less successful. Feel free to play around a bit, but keep in mind that for real application design, you should do hyper parameter optimization instead of playing around by yourself. """ # ╔═╡ d60d2561-51a4-4f8a-9819-898d70596e0c md""" ## Hyperparameters Besides the already introduced hyperparameters - the depth, width and initial gate opening of the hybrid model - further parameters might have significant impact on the training success. ### Optimizer For this example, we use the well-known `Adam`-Optimizer with a step size `eta` of $(@bind ETA Select([1e-4 => "1e-4", 1e-3 => "1e-3", 1e-2 => "1e-2"])). ### Batching Because data has a significant length, gradient computation over the entire simulation trajectory might not be effective. The most common approach is to *cut* data into slices and train on these subsets instead of the entire trajectory at once. In this example, data is cut in pieces with length of $(@bind BATCHDUR Select([0.05, 0.1, 0.15, 0.2])) seconds. """ # ╔═╡ c97f2dea-cb18-409d-9ae8-1d03647a6bb3 md""" This results in a batch with $(round(Integer, data_train.t[end] / BATCHDUR)) elements. """ # ╔═╡ 366abd1a-bcb5-480d-b1fb-7c76930dc8fc md""" We use a simple `Random` scheduler here, that picks a random batch element for the next training step. Other schedulers are pre-implemented in *FMIFlux.jl*. """ # ╔═╡ 7e2ffd6f-19b0-435d-8e3c-df24a591bc55 md""" ### Loss Function Different loss functions are thinkable here. Two quantities that should be considered are the motor currents and the motor revolution speeds. For this workshop we use the *Mean Average Error* (MAE) over the motor currents. Other loss functions can easily be deployed. """ # ╔═╡ caa5e04a-2375-4c56-8072-52c140adcbbb # goal is to match the motor currents (they can be recorded easily in the real application) function loss(solution::FMU2Solution, data::FMIZoo.RobotRR_Data) # determine the start/end indices `ts` and `te` (sampled with 100Hz) dt = 0.01 ts = 1 + round(Integer, solution.states.t[1] / dt) te = 1 + round(Integer, solution.states.t[end] / dt) # retrieve simulation data from neural FMU ("where we are") and data from measurements ("where we want to be") i1_value = fmiGetSolutionState(solution, STATE_I1) i2_value = fmiGetSolutionState(solution, STATE_I2) i1_data = data.i1[ts:te] i2_data = data.i2[ts:te] # accumulate our loss value Δvalue = 0.0 Δvalue += FMIFlux.Losses.mae(i1_value, i1_data) Δvalue += FMIFlux.Losses.mae(i2_value, i2_data) return Δvalue end # ╔═╡ 69657be6-6315-4655-81e2-8edef7f21e49 md""" For example, the loss function value of the plain FMU is $(round(loss(sol_fmu_train, data_train); digits=6)). """ # ╔═╡ 23ad65c8-5723-4858-9abe-750c3b65c28a md""" ## Summary To summarize, your ANN has a **depth of $(NUM_LAYERS) layers** with a **width of $(LAYERS_WIDTH)** each. The **ANN gates are initialized with $(GATES_INIT*100)%**, so all FMU gates are initialized with $(100-GATES_INIT*100)%. You decided to batch your data with a **batch element length of $(BATCHDUR)** seconds. Besides the state derivatives, you **put $(length(y_refs)) additional variables** in the ANN. Adam optimizer will try to find a good minimum with **`eta` is $(ETA)**. Batching takes a few seconds and training a few minutes (depending on the number of training steps), so this is not triggered automatically. If you are ready to go, choose a number of training steps and check the checkbox `Start Training`. This will start a training of $(@bind STEPS Select([0, 10, 100, 1000, 2500, 5000, 10000])) training steps. Alternatively, you can change the training mode to `demo` which loads parameters from a pre-trained model. """ # ╔═╡ abc57328-4de8-42d8-9e79-dd4020769dd9 md""" Select training mode: $(@bind MODE Select([:train => "Training", :demo => "Demo (pre-trained)"])) """ # ╔═╡ f9d35cfd-4ae5-4dcd-94d9-02aefc99bdfb begin using JLD2 if MODE == :train final_model = build_topology(GATES_INIT, y_refs, NUM_LAYERS, LAYERS_WIDTH) elseif MODE == :demo final_model = build_topology( 0.2, [STATE_A2, STATE_A1, VAR_TCP_VX, VAR_TCP_VY, VAR_TCP_F], 3, 32, ) end end # ╔═╡ f741b213-a20d-423a-a382-75cae1123f2c final_model(x0) # ╔═╡ 91473bef-bc23-43ed-9989-34e62166d455 begin neuralFMU = ME_NeuralFMU( fmu, # the FMU used in the neural FMU final_model, # the model we specified above (tStart, tStop),# start and stop time for solving solver; # the solver (Tsit5) saveat = tSave, ) # time points to save the solution at end # ╔═╡ 404ca10f-d944-4a9f-addb-05efebb4f159 begin import Downloads demo_path = Downloads.download( "https://github.com/ThummeTo/FMIFlux.jl/blob/main/examples/pluto-src/SciMLUsingFMUs/src/20000.jld2?raw=true", ) # in demo mode, we load parameters from a pre-trained model if MODE == :demo fmiLoadParameters(neuralFMU, demo_path) end HIDDEN_CODE_MESSAGE end # ╔═╡ e8bae97d-9f90-47d2-9263-dc8fc065c3d0 begin neuralFMU y_refs NUM_LAYERS LAYERS_WIDTH GATES_INIT ETA BATCHDUR MODE if MODE == :train md"""⚠️ The roughly estimated training time is **$(round(Integer, STEPS*10*BATCHDUR + 0.6/BATCHDUR)) seconds** (Windows, i7 @ 3.6GHz). Training might be faster if the system is less stiff than expected. Once you started training by clicking on `Start Training`, training can't be terminated easily. 🎬 **Start Training** $(@bind LIVE_TRAIN CheckBox()) """ else LIVE_TRAIN = false md"""ℹ️ No training in demo mode. Please continue with plotting results. """ end end # ╔═╡ 2dce68a7-27ec-4ffc-afba-87af4f1cb630 begin function train(eta, batchdur, steps) if steps == 0 return md"""⚠️ Number of training steps is `0`, no training.""" end prepareSolveFMU(fmu, parameters) train_t = data_train.t train_data = collect([data_train.i2[i], data_train.i1[i]] for i = 1:length(train_t)) #@info @info "Started batching ..." batch = batchDataSolution( neuralFMU, # our neural FMU model t -> FMIZoo.getState(data_train, t), # a function returning a start state for a given time point `t`, to determine start states for batch elements train_t, # data time points train_data; # data cumulative consumption batchDuration = batchdur, # duration of one batch element indicesModel = [1, 2], # model indices to train on (1 and 2 equal the `electrical current` states) plot = false, # don't show intermediate plots (try this outside of Pluto) showProgress = false, parameters = parameters, ) @info "... batching finished!" # a random element scheduler scheduler = RandomScheduler(neuralFMU, batch; applyStep = 1, plotStep = 0) lossFct = (solution::FMU2Solution) -> loss(solution, data_train) maxiters = round(Int, 1e5 * batchdur) _loss = p -> FMIFlux.Losses.loss( neuralFMU, # the neural FMU to simulate batch; # the batch to take an element from p = p, # the neural FMU training parameters (given as input) lossFct = lossFct, # our custom loss function batchIndex = scheduler.elementIndex, # the index of the batch element to take, determined by the chosen scheduler logLoss = true, # log losses after every evaluation showProgress = false, parameters = parameters, maxiters = maxiters, ) params = FMIFlux.params(neuralFMU) FMIFlux.initialize!( scheduler; p = params[1], showProgress = false, parameters = parameters, print = false, ) BETA1 = 0.9 BETA2 = 0.999 optim = Adam(eta, (BETA1, BETA2)) @info "Started training ..." @withprogress name = "iterating" begin iteration = 0 function cb() iteration += 1 @logprogress iteration / steps FMIFlux.update!(scheduler; print = false) nothing end FMIFlux.train!( _loss, # the loss function for training neuralFMU, # the parameters to train Iterators.repeated((), steps), # an iterator repeating `steps` times optim; # the optimizer to train gradient = :ReverseDiff, # use ReverseDiff, because it's much faster! cb = cb, # update the scheduler after every step proceed_on_assert = true, ) # go on if a training steps fails (e.g. because of instability) end @info "... training finished!" end HIDDEN_CODE_MESSAGE end # ╔═╡ c3f5704b-8e98-4c46-be7a-18ab4f139458 let if MODE == :train if LIVE_TRAIN train(ETA, BATCHDUR, STEPS) else LIVE_TRAIN_MESSAGE end else md"""ℹ️ No training in demo mode. Please continue with plotting results. """ end end # ╔═╡ 1a608bc8-7264-4dd3-a4e7-0e39128a8375 md""" > 💡 Playing around with hyperparameters is fun, but keep in mind that this is not a suitable method for finding good hyperparameters in real world engineering. Do a hyperparameter optimization instead. """ # ╔═╡ ff106912-d18c-487f-bcdd-7b7af2112cab md""" # Results Now it's time to find out if it worked! Plotting results makes the notebook slow, so it's deactivated by default. Activate it to plot results of your training. ## Training results Let's check out the *training* results of the freshly trained neural FMU. """ # ╔═╡ 51eeb67f-a984-486a-ab8a-a2541966fa72 begin neuralFMU MODE LIVE_TRAIN md""" 🎬 **Plot results** $(@bind LIVE_RESULTS CheckBox()) """ end # ╔═╡ 27458e32-5891-4afc-af8e-7afdf7e81cc6 begin function plotPaths!(fig, t, x, N; color = :black, label = :none, kwargs...) paths = [] path = nothing lastN = N[1] for i = 1:length(N) if N[i] == 0.0 if lastN == 1.0 push!(path, (t[i], x[i])) push!(paths, path) end end if N[i] == 1.0 if lastN == 0.0 path = [] end push!(path, (t[i], x[i])) end lastN = N[i] end if length(path) > 0 push!(paths, path) end isfirst = true for path in paths plot!( fig, collect(v[1] for v in path), collect(v[2] for v in path); label = isfirst ? label : :none, color = color, kwargs..., ) isfirst = false end return fig end HIDDEN_CODE_MESSAGE end # ╔═╡ 737e2c50-0858-4205-bef3-f541e33b85c3 md""" ### FMU Simulating the FMU (training data): """ # ╔═╡ 5dd491a4-a8cd-4baf-96f7-7a0b850bb26c begin fmu_train = fmiSimulate( fmu, (data_train.t[1], data_train.t[end]); x0 = x0, parameters = Dict{String,Any}("fileName" => data_train.params["fileName"]), recordValues = [ "rRPositionControl_Elasticity.tCP.p_x", "rRPositionControl_Elasticity.tCP.p_y", "rRPositionControl_Elasticity.tCP.N", "rRPositionControl_Elasticity.tCP.a_x", "rRPositionControl_Elasticity.tCP.a_y", ], showProgress = true, maxiters = 1e6, saveat = data_train.t, solver = Tsit5(), ) nothing end # ╔═╡ 4f27b6c0-21da-4e26-aaad-ff453c8af3da md""" ### Neural FMU Simulating the neural FMU (training data): """ # ╔═╡ 1195a30c-3b48-4bd2-8a3a-f4f74f3cd864 begin if LIVE_RESULTS result_train = neuralFMU( x0, (data_train.t[1], data_train.t[end]); parameters = Dict{String,Any}("fileName" => data_train.params["fileName"]), recordValues = [ "rRPositionControl_Elasticity.tCP.p_x", "rRPositionControl_Elasticity.tCP.p_y", "rRPositionControl_Elasticity.tCP.N", "rRPositionControl_Elasticity.tCP.v_x", "rRPositionControl_Elasticity.tCP.v_y", ], showProgress = true, maxiters = 1e6, saveat = data_train.t, ) nothing else LIVE_RESULTS_MESSAGE end end # ╔═╡ b0ce7b92-93e0-4715-8324-3bf4ff42a0b3 let if LIVE_RESULTS loss_fmu = loss(fmu_train, data_train) loss_nfmu = loss(result_train, data_train) md""" #### The word `train` The loss function value of the FMU on training data is $(round(loss_fmu; digits=6)), of the neural FMU it is $(round(loss_nfmu; digits=6)). The neural FMU is about $(round(loss_fmu/loss_nfmu; digits=1)) times more accurate. """ else LIVE_RESULTS_MESSAGE end end # ╔═╡ 919419fe-35de-44bb-89e4-8f8688bee962 let if LIVE_RESULTS fig = plot(; dpi = 300, size = (200 * 3, 60 * 3)) plotPaths!( fig, data_train.tcp_px, data_train.tcp_py, data_train.tcp_norm_f, label = "Data", color = :black, style = :dash, ) plotPaths!( fig, collect(v[1] for v in fmu_train.values.saveval), collect(v[2] for v in fmu_train.values.saveval), collect(v[3] for v in fmu_train.values.saveval), label = "FMU", color = :orange, ) plotPaths!( fig, collect(v[1] for v in result_train.values.saveval), collect(v[2] for v in result_train.values.saveval), collect(v[3] for v in result_train.values.saveval), label = "Neural FMU", color = :blue, ) else LIVE_RESULTS_MESSAGE end end # ╔═╡ ed25a535-ca2f-4cd2-b0af-188e9699f1c3 md""" #### The letter `a` """ # ╔═╡ 2918daf2-6499-4019-a04b-8c3419ee1ab7 let if LIVE_RESULTS fig = plot(; dpi = 300, size = (40 * 10, 40 * 10), xlims = (0.165, 0.205), ylims = (-0.035, 0.005), ) plotPaths!( fig, data_train.tcp_px, data_train.tcp_py, data_train.tcp_norm_f, label = "Data", color = :black, style = :dash, ) plotPaths!( fig, collect(v[1] for v in fmu_train.values.saveval), collect(v[2] for v in fmu_train.values.saveval), collect(v[3] for v in fmu_train.values.saveval), label = "FMU", color = :orange, ) plotPaths!( fig, collect(v[1] for v in result_train.values.saveval), collect(v[2] for v in result_train.values.saveval), collect(v[3] for v in result_train.values.saveval), label = "Neural FMU", color = :blue, ) else LIVE_RESULTS_MESSAGE end end # ╔═╡ d798a5d0-3017-4eab-9cdf-ee85d63dfc49 md""" #### The letter `n` """ # ╔═╡ 048e39c3-a3d9-4e6b-b050-1fd5a919e4ae let if LIVE_RESULTS fig = plot(; dpi = 300, size = (50 * 10, 40 * 10), xlims = (0.245, 0.295), ylims = (-0.04, 0.0), ) plotPaths!( fig, data_train.tcp_px, data_train.tcp_py, data_train.tcp_norm_f, label = "Data", color = :black, style = :dash, ) plotPaths!( fig, collect(v[1] for v in fmu_train.values.saveval), collect(v[2] for v in fmu_train.values.saveval), collect(v[3] for v in fmu_train.values.saveval), label = "FMU", color = :orange, ) plotPaths!( fig, collect(v[1] for v in result_train.values.saveval), collect(v[2] for v in result_train.values.saveval), collect(v[3] for v in result_train.values.saveval), label = "Neural FMU", color = :blue, ) else LIVE_RESULTS_MESSAGE end end # ╔═╡ b489f97d-ee90-48c0-af06-93b66a1f6d2e md""" ## Validation results Let's check out the *validation* results of the freshly trained neural FMU. """ # ╔═╡ 4dad3e55-5bfd-4315-bb5a-2680e5cbd11c md""" ### FMU Simulating the FMU (validation data): """ # ╔═╡ ea0ede8d-7c2c-4e72-9c96-3260dc8d817d begin fmu_validation = fmiSimulate( fmu, (data_validation.t[1], data_validation.t[end]); x0 = x0, parameters = Dict{String,Any}("fileName" => data_validation.params["fileName"]), recordValues = [ "rRPositionControl_Elasticity.tCP.p_x", "rRPositionControl_Elasticity.tCP.p_y", "rRPositionControl_Elasticity.tCP.N", ], showProgress = true, maxiters = 1e6, saveat = data_validation.t, solver = Tsit5(), ) nothing end # ╔═╡ 35f52dbc-0c0b-495e-8fd4-6edbc6fa811e md""" ### Neural FMU Simulating the neural FMU (validation data): """ # ╔═╡ 51aed933-2067-4ea8-9c2f-9d070692ecfc begin if LIVE_RESULTS result_validation = neuralFMU( x0, (data_validation.t[1], data_validation.t[end]); parameters = Dict{String,Any}("fileName" => data_validation.params["fileName"]), recordValues = [ "rRPositionControl_Elasticity.tCP.p_x", "rRPositionControl_Elasticity.tCP.p_y", "rRPositionControl_Elasticity.tCP.N", ], showProgress = true, maxiters = 1e6, saveat = data_validation.t, ) nothing else LIVE_RESULTS_MESSAGE end end # ╔═╡ 8d9dc86e-f38b-41b1-80c6-b2ab6f488a3a begin if LIVE_RESULTS loss_fmu = loss(fmu_validation, data_validation) loss_nfmu = loss(result_validation, data_validation) md""" #### The word `validate` The loss function value of the FMU on validation data is $(round(loss_fmu; digits=6)), of the neural FMU it is $(round(loss_nfmu; digits=6)). The neural FMU is about $(round(loss_fmu/loss_nfmu; digits=1)) times more accurate. """ else LIVE_RESULTS_MESSAGE end end # ╔═╡ 74ef5a39-1dd7-404a-8baf-caa1021d3054 let if LIVE_RESULTS fig = plot(; dpi = 300, size = (200 * 3, 40 * 3)) plotPaths!( fig, data_validation.tcp_px, data_validation.tcp_py, data_validation.tcp_norm_f, label = "Data", color = :black, style = :dash, ) plotPaths!( fig, collect(v[1] for v in fmu_validation.values.saveval), collect(v[2] for v in fmu_validation.values.saveval), collect(v[3] for v in fmu_validation.values.saveval), label = "FMU", color = :orange, ) plotPaths!( fig, collect(v[1] for v in result_validation.values.saveval), collect(v[2] for v in result_validation.values.saveval), collect(v[3] for v in result_validation.values.saveval), label = "Neural FMU", color = :blue, ) else LIVE_RESULTS_MESSAGE end end # ╔═╡ 347d209b-9d41-48b0-bee6-0d159caacfa9 md""" #### The letter `d` """ # ╔═╡ 05281c4f-dba8-4070-bce3-dc2f1319902e let if LIVE_RESULTS fig = plot(; dpi = 300, size = (35 * 10, 50 * 10), xlims = (0.188, 0.223), ylims = (-0.025, 0.025), ) plotPaths!( fig, data_validation.tcp_px, data_validation.tcp_py, data_validation.tcp_norm_f, label = "Data", color = :black, style = :dash, ) plotPaths!( fig, collect(v[1] for v in fmu_validation.values.saveval), collect(v[2] for v in fmu_validation.values.saveval), collect(v[3] for v in fmu_validation.values.saveval), label = "FMU", color = :orange, ) plotPaths!( fig, collect(v[1] for v in result_validation.values.saveval), collect(v[2] for v in result_validation.values.saveval), collect(v[3] for v in result_validation.values.saveval), label = "Neural FMU", color = :blue, ) else LIVE_RESULTS_MESSAGE end end # ╔═╡ 590d7f24-c6b6-4524-b3db-0c93d9963b74 md""" #### The letter `t` """ # ╔═╡ 67cfe7c5-8e62-4bf0-996b-19597d5ad5ef let if LIVE_RESULTS fig = plot(; dpi = 300, size = (25 * 10, 50 * 10), xlims = (0.245, 0.27), ylims = (-0.025, 0.025), legend = :topleft, ) plotPaths!( fig, data_validation.tcp_px, data_validation.tcp_py, data_validation.tcp_norm_f, label = "Data", color = :black, style = :dash, ) plotPaths!( fig, collect(v[1] for v in fmu_validation.values.saveval), collect(v[2] for v in fmu_validation.values.saveval), collect(v[3] for v in fmu_validation.values.saveval), label = "FMU", color = :orange, ) plotPaths!( fig, collect(v[1] for v in result_validation.values.saveval), collect(v[2] for v in result_validation.values.saveval), collect(v[3] for v in result_validation.values.saveval), label = "Neural FMU", color = :blue, ) else LIVE_RESULTS_MESSAGE end end # ╔═╡ e6dc8aab-82c1-4dc9-a1c8-4fe9c137a146 md""" #### The letter `e` """ # ╔═╡ dfee214e-bd13-4d4f-af8e-20e0c4e0de9b let if LIVE_RESULTS fig = plot(; dpi = 300, size = (25 * 10, 30 * 10), xlims = (0.265, 0.29), ylims = (-0.025, 0.005), legend = :topleft, ) plotPaths!( fig, data_validation.tcp_px, data_validation.tcp_py, data_validation.tcp_norm_f, label = "Data", color = :black, style = :dash, ) plotPaths!( fig, collect(v[1] for v in fmu_validation.values.saveval), collect(v[2] for v in fmu_validation.values.saveval), collect(v[3] for v in fmu_validation.values.saveval), label = "FMU", color = :orange, ) plotPaths!( fig, collect(v[1] for v in result_validation.values.saveval), collect(v[2] for v in result_validation.values.saveval), collect(v[3] for v in result_validation.values.saveval), label = "Neural FMU", color = :blue, ) else LIVE_RESULTS_MESSAGE end end # ╔═╡ 88884204-79e4-4412-b861-ebeb5f6f7396 md""" # Conclusion Hopefully you got a good first insight in the topic hybrid modeling using FMI and collected your first sense of achievement. Did you find a nice optimum? In case you don't, some rough hyper parameters are given below. ## Hint If your results are not *that* promising, here is a set of hyperparameters to check. It is *not* a optimal set of parameters, but a *good* set, so feel free to explore the *best*! | Parameter | Value | | ----- | ----- | | eta | 1e-3 | | layer count | 3 | | layer width | 32 | | initial gate opening | 0.2 | | batch element length | 0.05s | | training steps | $\geq$ 10 000 | | additional variables | Joint 1 Angle $br Joint 2 Angle $br TCP velocity x $br TCP velocity y $br TCP nominal force | ## Citation If you find this workshop useful for your own work and/or research, please cite our related publication: Tobias Thummerer, Johannes Stoljar and Lars Mikelsons. 2022. **NeuralFMU: presenting a workflow for integrating hybrid neuralODEs into real-world applications.** Electronics 11, 19, 3202. DOI: 10.3390/electronics11193202 ## Acknowlegments - the FMU was created using the excellent Modelica library *Servomechanisms* $br (https://github.com/afrhu/Servomechanisms) - the linked YouTube video in the introduction is by *Alexandru Babaian* $br (https://www.youtube.com/watch?v=ryIwLLr6yRA) """ # ╔═╡ 00000000-0000-0000-0000-000000000001 PLUTO_PROJECT_TOML_CONTENTS = """ [deps] BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf" Downloads = "f43a241f-c20a-4ad4-852c-f6b1247861c6" FMI = "14a09403-18e3-468f-ad8a-74f8dda2d9ac" FMIFlux = "fabad875-0d53-4e47-9446-963b74cae21f" FMIZoo = "724179cf-c260-40a9-bd27-cccc6fe2f195" JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819" PlotlyJS = "f0f68f2c-4968-5e81-91da-67840de0976a" Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" PlutoUI = "7f904dfe-b85e-4ff6-b463-dae2292396a8" ProgressLogging = "33c8b6b6-d38a-422a-b730-caa89a2f386c" Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" [compat] BenchmarkTools = "~1.5.0" FMI = "~0.13.3" FMIFlux = 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"35661453-b289-5fab-8a00-3d9160c6a3a4" version = "1.4.6+0" [[deps.Xorg_xkeyboard_config_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_xkbcomp_jll"] git-tree-sha1 = "691634e5453ad362044e2ad653e79f3ee3bb98c3" uuid = "33bec58e-1273-512f-9401-5d533626f822" version = "2.39.0+0" [[deps.Xorg_xtrans_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl"] git-tree-sha1 = "e92a1a012a10506618f10b7047e478403a046c77" uuid = "c5fb5394-a638-5e4d-96e5-b29de1b5cf10" version = "1.5.0+0" [[deps.ZipFile]] deps = ["Libdl", "Printf", "Zlib_jll"] git-tree-sha1 = "f492b7fe1698e623024e873244f10d89c95c340a" uuid = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea" version = "0.10.1" [[deps.Zlib_jll]] deps = ["Libdl"] uuid = "83775a58-1f1d-513f-b197-d71354ab007a" version = "1.2.13+1" [[deps.Zstd_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl"] git-tree-sha1 = "e678132f07ddb5bfa46857f0d7620fb9be675d3b" uuid = "3161d3a3-bdf6-5164-811a-617609db77b4" version = "1.5.6+0" [[deps.Zygote]] deps = ["AbstractFFTs", "ChainRules", "ChainRulesCore", "DiffRules", "Distributed", "FillArrays", "ForwardDiff", "GPUArrays", "GPUArraysCore", "IRTools", "InteractiveUtils", "LinearAlgebra", "LogExpFunctions", "MacroTools", "NaNMath", "PrecompileTools", "Random", "Requires", "SparseArrays", "SpecialFunctions", "Statistics", "ZygoteRules"] git-tree-sha1 = "19c586905e78a26f7e4e97f81716057bd6b1bc54" uuid = "e88e6eb3-aa80-5325-afca-941959d7151f" version = "0.6.70" weakdeps = ["Colors", "Distances", "Tracker"] [deps.Zygote.extensions] ZygoteColorsExt = "Colors" ZygoteDistancesExt = "Distances" ZygoteTrackerExt = "Tracker" [[deps.ZygoteRules]] deps = ["ChainRulesCore", "MacroTools"] git-tree-sha1 = "27798139afc0a2afa7b1824c206d5e87ea587a00" uuid = "700de1a5-db45-46bc-99cf-38207098b444" version = "0.2.5" [[deps.eudev_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "gperf_jll"] git-tree-sha1 = "431b678a28ebb559d224c0b6b6d01afce87c51ba" uuid = "35ca27e7-8b34-5b7f-bca9-bdc33f59eb06" version = "3.2.9+0" [[deps.fzf_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl"] git-tree-sha1 = "a68c9655fbe6dfcab3d972808f1aafec151ce3f8" uuid = "214eeab7-80f7-51ab-84ad-2988db7cef09" version = "0.43.0+0" [[deps.gperf_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "3516a5630f741c9eecb3720b1ec9d8edc3ecc033" uuid = "1a1c6b14-54f6-533d-8383-74cd7377aa70" version = "3.1.1+0" [[deps.libaec_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl"] git-tree-sha1 = "46bf7be2917b59b761247be3f317ddf75e50e997" uuid = "477f73a3-ac25-53e9-8cc3-50b2fa2566f0" version = "1.1.2+0" [[deps.libaom_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl"] git-tree-sha1 = "1827acba325fdcdf1d2647fc8d5301dd9ba43a9d" uuid = "a4ae2306-e953-59d6-aa16-d00cac43593b" version = "3.9.0+0" [[deps.libass_jll]] deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "HarfBuzz_jll", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"] git-tree-sha1 = "5982a94fcba20f02f42ace44b9894ee2b140fe47" uuid = "0ac62f75-1d6f-5e53-bd7c-93b484bb37c0" version = "0.15.1+0" [[deps.libblastrampoline_jll]] deps = ["Artifacts", "Libdl"] uuid = "8e850b90-86db-534c-a0d3-1478176c7d93" version = "5.8.0+1" [[deps.libevdev_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "141fe65dc3efabb0b1d5ba74e91f6ad26f84cc22" uuid = "2db6ffa8-e38f-5e21-84af-90c45d0032cc" version = "1.11.0+0" [[deps.libfdk_aac_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "daacc84a041563f965be61859a36e17c4e4fcd55" uuid = "f638f0a6-7fb0-5443-88ba-1cc74229b280" version = "2.0.2+0" [[deps.libinput_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "eudev_jll", "libevdev_jll", "mtdev_jll"] git-tree-sha1 = "ad50e5b90f222cfe78aa3d5183a20a12de1322ce" uuid = "36db933b-70db-51c0-b978-0f229ee0e533" version = "1.18.0+0" [[deps.libpng_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Zlib_jll"] git-tree-sha1 = "d7015d2e18a5fd9a4f47de711837e980519781a4" uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f" version = "1.6.43+1" [[deps.libvorbis_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"] git-tree-sha1 = "b910cb81ef3fe6e78bf6acee440bda86fd6ae00c" uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a" version = "1.3.7+1" [[deps.mtdev_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "814e154bdb7be91d78b6802843f76b6ece642f11" uuid = "009596ad-96f7-51b1-9f1b-5ce2d5e8a71e" version = "1.1.6+0" [[deps.nghttp2_jll]] deps = ["Artifacts", "Libdl"] uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d" version = "1.52.0+1" [[deps.oneTBB_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl"] git-tree-sha1 = "7d0ea0f4895ef2f5cb83645fa689e52cb55cf493" uuid = "1317d2d5-d96f-522e-a858-c73665f53c3e" version = "2021.12.0+0" [[deps.p7zip_jll]] deps = ["Artifacts", "Libdl"] uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0" version = "17.4.0+2" [[deps.x264_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "4fea590b89e6ec504593146bf8b988b2c00922b2" uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a" version = "2021.5.5+0" [[deps.x265_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9" uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76" version = "3.5.0+0" [[deps.xkbcommon_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"] git-tree-sha1 = "9c304562909ab2bab0262639bd4f444d7bc2be37" uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd" version = "1.4.1+1" """ # ╔═╡ Cell order: # ╟─1470df0f-40e1-45d5-a4cc-519cc3b28fb8 # ╟─7d694be0-cd3f-46ae-96a3-49d07d7cf65a # ╟─10cb63ad-03d7-47e9-bc33-16c7786b9f6a # ╟─1e0fa041-a592-42fb-bafd-c7272e346e46 # ╟─6fc16c34-c0c8-48ce-87b3-011a9a0f4e7c # ╟─8a82d8c7-b781-4600-8780-0a0a003b676c # ╟─a02f77d1-00d2-46a3-91ba-8a7f5b4bbdc9 # ╠═a1ee798d-c57b-4cc3-9e19-fb607f3e1e43 # ╟─02f0add7-9c4e-4358-8b5e-6863bae3ee75 # 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FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
[ "MIT" ]
0.13.0
7fec0fac72076ea32823fb59efcc0e280cd28162
code
1103
# # Copyright (c) 2021 Tobias Thummerer, Lars Mikelsons # Licensed under the MIT license. See LICENSE file in the project root for details. # module JLD2Ext using FMIFlux, JLD2 using FMIFlux.Flux function FMIFlux.saveParameters(nfmu::NeuralFMU, path::String; keyword = "parameters") params = Flux.params(nfmu) JLD2.save(path, Dict(keyword => params[1])) end function FMIFlux.loadParameters( nfmu::NeuralFMU, path::String; flux_model = nothing, keyword = "parameters", ) paramsLoad = JLD2.load(path, keyword) nfmu_params = Flux.params(nfmu) flux_model_params = nothing if flux_model != nothing flux_model_params = Flux.params(flux_model) end numParams = length(nfmu_params[1]) l = 1 p = 1 for i = 1:numParams nfmu_params[1][i] = paramsLoad[i] if flux_model != nothing flux_model_params[l][p] = paramsLoad[i] p += 1 if p > length(flux_model_params[l]) l += 1 p = 1 end end end return nothing end end # JLD2Ext
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
[ "MIT" ]
0.13.0
7fec0fac72076ea32823fb59efcc0e280cd28162
code
1679
# # Copyright (c) 2021 Tobias Thummerer, Lars Mikelsons # Licensed under the MIT license. See LICENSE file in the project root for details. # module FMIFlux import FMISensitivity import FMISensitivity.ForwardDiff import FMISensitivity.Zygote import FMISensitivity.ReverseDiff import FMISensitivity.FiniteDiff @debug "Debugging messages enabled for FMIFlux ..." if VERSION < v"1.7.0" @warn "Training under Julia 1.6 is very slow, please consider using Julia 1.7 or newer." maxlog = 1 end import FMIImport.FMIBase: hasCurrentInstance, getCurrentInstance, unsense import FMISensitivity.ChainRulesCore: ignore_derivatives import FMIImport import Flux using FMIImport include("optimiser.jl") include("hotfixes.jl") include("convert.jl") include("flux_overload.jl") include("neural.jl") include("misc.jl") include("layers.jl") include("deprecated.jl") include("batch.jl") include("losses.jl") include("scheduler.jl") include("compatibility_check.jl") # optional extensions using FMIImport.FMIBase.Requires using FMIImport.FMIBase.PackageExtensionCompat function __init__() @require_extensions end # JLD2.jl function saveParameters end function loadParameters end # FMI_neural.jl export ME_NeuralFMU, CS_NeuralFMU, NeuralFMU # misc.jl export mse_interpolate, transferParams!, transferFlatParams!, lin_interp # scheduler.jl export WorstElementScheduler, WorstGrowScheduler, RandomScheduler, SequentialScheduler, LossAccumulationScheduler # batch.jl export batchDataSolution, batchDataEvaluation # layers.jl # >>> layers are exported inside the file itself # deprecated.jl # >>> deprecated functions are exported inside the file itself end # module
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
[ "MIT" ]
0.13.0
7fec0fac72076ea32823fb59efcc0e280cd28162
code
18243
# # Copyright (c) 2021 Tobias Thummerer, Lars Mikelsons # Licensed under the MIT license. See LICENSE file in the project root for details. # import FMIImport.FMIBase: FMUSnapshot import FMIImport: fmi2Real, fmi2FMUstate, fmi2EventInfo, fmi2ComponentState using FMIImport.FMIBase.DiffEqCallbacks: FunctionCallingCallback abstract type FMU2BatchElement end mutable struct FMULoss{T} loss::T step::Integer time::Real function FMULoss{T}(loss::T, step::Integer = 0, time::Real = time()) where {T} inst = new{T}(loss, step, time) return inst end function FMULoss(loss, step::Integer = 0, time::Real = time()) loss = unsense(loss) T = typeof(loss) inst = new{T}(loss, step, time) return inst end end function nominalLoss(l::FMULoss{T}) where {T<:AbstractArray} return unsense(sum(l.loss)) end function nominalLoss(l::FMULoss{T}) where {T<:Real} return unsense(l.loss) end function nominalLoss(::Nothing) return Inf end function nominalLoss(b::FMU2BatchElement) return nominalLoss(b.loss) end mutable struct FMU2SolutionBatchElement{D} <: FMU2BatchElement snapshot::Union{FMUSnapshot,Nothing} xStart::Union{Vector{fmi2Real},Nothing} xdStart::Union{Vector{D},Nothing} tStart::fmi2Real tStop::fmi2Real # initialState::Union{fmi2FMUstate, Nothing} # initialComponentState::fmi2ComponentState # initialEventInfo::Union{fmi2EventInfo, Nothing} loss::FMULoss # the current loss losses::Array{<:FMULoss} # logged losses (if used) step::Integer saveat::Union{AbstractVector{<:Real},Nothing} targets::Union{AbstractArray,Nothing} indicesModel::Any solution::FMUSolution scalarLoss::Bool # canGetSetState::Bool function FMU2SolutionBatchElement{D}(; scalarLoss::Bool = true) where {D} inst = new() inst.snapshot = nothing inst.xStart = nothing inst.xdStart = nothing inst.tStart = -Inf inst.tStop = Inf # inst.initialState = nothing # inst.initialEventInfo = nothing inst.loss = FMULoss(Inf) inst.losses = Array{FMULoss,1}() inst.step = 0 inst.saveat = nothing inst.targets = nothing inst.indicesModel = nothing inst.scalarLoss = scalarLoss # inst.canGetSetState = canGetSetState return inst end end mutable struct FMU2EvaluationBatchElement <: FMU2BatchElement tStart::fmi2Real tStop::fmi2Real loss::FMULoss losses::Array{<:FMULoss} step::Integer saveat::Union{AbstractVector{<:Real},Nothing} targets::Union{AbstractArray,Nothing} features::Union{AbstractArray,Nothing} indicesModel::Any result::Any scalarLoss::Bool function FMU2EvaluationBatchElement(; scalarLoss::Bool = true) inst = new() inst.tStart = -Inf inst.tStop = Inf inst.loss = FMULoss(Inf) inst.losses = Array{FMULoss,1}() inst.step = 0 inst.saveat = nothing inst.features = nothing inst.targets = nothing inst.indicesModel = nothing inst.result = nothing inst.scalarLoss = scalarLoss return inst end end function pasteFMUState!(fmu::FMU2, batchElement::FMU2SolutionBatchElement) c = getCurrentInstance(fmu) FMIBase.apply!(c, batchElement.snapshot) @info "Pasting snapshot @$(batchElement.snapshot.t)" return nothing end function copyFMUState!(fmu::FMU2, batchElement::FMU2SolutionBatchElement) c = getCurrentInstance(fmu) if isnothing(batchElement.snapshot) batchElement.snapshot = FMIBase.snapshot!(c) #batchElement.snapshot.t = batchElement.tStart @debug "New snapshot @$(batchElement.snapshot.t)" else #tBefore = batchElement.snapshot.t FMIBase.update!(c, batchElement.snapshot) #batchElement.snapshot.t = batchElement.tStart #tAfter = batchElement.snapshot.t # [Note] for discontinuous batches (time offsets inside batch), # it might be necessary to correct the new snapshot time to fit the old one. # if tBefore != tAfter # batchElement.snapshot.t = max(tBefore, tAfter) # logInfo(fmu, "Corrected snapshot time from $(tAfter) to $(tBefore)") # end @debug "Updated snapshot @$(batchElement.snapshot.t)" end return nothing end function run!( neuralFMU::ME_NeuralFMU, batchElement::FMU2SolutionBatchElement; nextBatchElement = nothing, kwargs..., ) neuralFMU.customCallbacksAfter = [] neuralFMU.customCallbacksBefore = [] # STOP CALLBACK if !isnothing(nextBatchElement) stopcb = FunctionCallingCallback( (u, t, integrator) -> copyFMUState!(neuralFMU.fmu, nextBatchElement); funcat = [batchElement.tStop], ) push!(neuralFMU.customCallbacksAfter, stopcb) end writeSnapshot = nothing readSnapshot = nothing # on first run of the element, there is no snapshot if isnothing(batchElement.snapshot) c = getCurrentInstance(neuralFMU.fmu) batchElement.snapshot = snapshot!(c) writeSnapshot = batchElement.snapshot # needs to be updated, therefore write else readSnapshot = batchElement.snapshot end @debug "Running $(batchElement.tStart) with snapshot: $(!isnothing(batchElement.snapshot))..." batchElement.solution = neuralFMU( batchElement.xStart, (batchElement.tStart, batchElement.tStop); readSnapshot = readSnapshot, writeSnapshot = writeSnapshot, saveat = batchElement.saveat, kwargs..., ) # @assert batchElement.solution.states.t == batchElement.saveat "Batch element simulation failed, missmatch between `states.t` and `saveat`." neuralFMU.customCallbacksBefore = [] neuralFMU.customCallbacksAfter = [] batchElement.step += 1 return batchElement.solution end function run!(model, batchElement::FMU2EvaluationBatchElement, p = nothing) if isnothing(p) # implicite parameter model batchElement.result = collect(model(f)[batchElement.indicesModel] for f in batchElement.features) else # explicite parameter model batchElement.result = collect(model(p)(f)[batchElement.indicesModel] for f in batchElement.features) end end function plot(batchElement::FMU2SolutionBatchElement; targets::Bool = true, plotkwargs...) fig = Plots.plot(; xlabel = "t [s]", plotkwargs...) # , title="loss[$(batchElement.step)] = $(nominalLoss(batchElement.losses[end]))") for i = 1:length(batchElement.indicesModel) if !isnothing(batchElement.solution) @assert batchElement.solution.states.t == batchElement.saveat "Batch element plotting failed, missmatch between `states.t` and `saveat`." Plots.plot!( fig, batchElement.solution.states.t, collect( unsense(u[batchElement.indicesModel[i]]) for u in batchElement.solution.states.u ), label = "Simulation #$(i)", ) end if targets Plots.plot!( fig, batchElement.saveat, collect(d[i] for d in batchElement.targets), label = "Targets #$(i)", ) end end return fig end function plot( batchElement::FMU2BatchElement; targets::Bool = true, features::Bool = true, plotkwargs..., ) fig = Plots.plot(; xlabel = "t [s]", plotkwargs...) # , title="loss[$(batchElement.step)] = $(nominalLoss(batchElement.losses[end]))") if batchElement.features != nothing && features for i = 1:length(batchElement.features[1]) Plots.plot!( fig, batchElement.saveat, collect(d[i] for d in batchElement.features), style = :dash, label = "Features #$(i)", ) end end for i = 1:length(batchElement.indicesModel) if batchElement.result != nothing Plots.plot!( fig, batchElement.saveat, collect(ForwardDiff.value(u[i]) for u in batchElement.result), label = "Evaluation #$(i)", ) end if targets Plots.plot!( fig, batchElement.saveat, collect(d[i] for d in batchElement.targets), label = "Targets #$(i)", ) end end return fig end function plot( batch::AbstractArray{<:FMU2BatchElement}; plot_mean::Bool = true, plot_shadow::Bool = true, plotkwargs..., ) num = length(batch) xs = 1:num ys = collect((nominalLoss(b) != Inf ? nominalLoss(b) : 0.0) for b in batch) fig = Plots.plot(; xlabel = "Batch ID", ylabel = "Loss", plotkwargs...) if plot_shadow ys_shadow = collect( (length(b.losses) > 1 ? nominalLoss(b.losses[end-1]) : 0.0) for b in batch ) Plots.bar!( fig, xs, ys_shadow; label = "Previous loss", color = :green, bar_width = 1.0, ) end Plots.bar!(fig, xs, ys; label = "Current loss", color = :blue, bar_width = 0.5) if plot_mean avgsum = mean(ys) Plots.plot!(fig, [1, num], [avgsum, avgsum]; label = "mean") end return fig end function plotLoss(batchElement::FMU2BatchElement; xaxis::Symbol = :steps) @assert length(batchElement.losses) > 0 "Can't plot, no losses!" ts = nothing tlabel = "" if xaxis == :time ts = collect(l.time for l in batchElement.losses) tlabel = "t [s]" elseif xaxis == :steps ts = collect(l.step for l in batchElement.losses) tlabel = "steps [/]" else @assert false "unsupported keyword for `xaxis`." end ls = collect(l.loss for l in batchElement.losses) fig = Plots.plot(ts, ls, xlabel = tlabel, ylabel = "Loss") return fig end function loss!(batchElement::FMU2SolutionBatchElement, lossFct; logLoss::Bool = false) loss = 0.0 # will be incremented if hasmethod(lossFct, Tuple{FMUSolution}) loss = lossFct(batchElement.solution) elseif hasmethod(lossFct, Tuple{FMUSolution,Union{}}) loss = lossFct(batchElement.solution, batchElement.targets) else # hasmethod(lossFct, Tuple{Union{}, Union{}}) if batchElement.solution.success if batchElement.scalarLoss for i = 1:length(batchElement.indicesModel) dataTarget = collect(d[i] for d in batchElement.targets) modelOutput = collect( u[batchElement.indicesModel[i]] for u in batchElement.solution.states.u ) loss += lossFct(modelOutput, dataTarget) end else dataTarget = batchElement.targets modelOutput = collect( u[batchElement.indicesModel] for u in batchElement.solution.states.u ) loss = lossFct(modelOutput, dataTarget) end else @warn "Can't compute loss for batch element, because solution is invalid (`success=false`) for batch element\n$(batchElement)." end end batchElement.loss.step = batchElement.step batchElement.loss.time = time() batchElement.loss.loss = unsense(loss) ignore_derivatives() do if logLoss push!(batchElement.losses, deepcopy(batchElement.loss)) end end return loss end function loss!(batchElement::FMU2EvaluationBatchElement, lossFct; logLoss::Bool = true) loss = 0.0 # will be incremented if batchElement.scalarLoss for i = 1:length(batchElement.indicesModel) dataTarget = collect(d[i] for d in batchElement.targets) modelOutput = collect(u[i] for u in batchElement.result) loss += lossFct(modelOutput, dataTarget) end else dataTarget = batchElement.targets modelOutput = batchElement.result loss = lossFct(modelOutput, dataTarget) end batchElement.loss.step = batchElement.step batchElement.loss.time = time() batchElement.loss.loss = unsense(loss) ignore_derivatives() do if logLoss push!(batchElement.losses, deepcopy(batchElement.loss)) end end return loss end function _batchDataSolution!( batch::AbstractArray{<:FMIFlux.FMU2SolutionBatchElement}, neuralFMU::NeuralFMU, x0_fun, train_t::AbstractArray{<:AbstractArray{<:Real}}, targets::AbstractArray; kwargs..., ) len = length(train_t) for i = 1:len _batchDataSolution!(batch, neuralFMU, x0_fun, train_t[i], targets[i]; kwargs...) end return nothing end function _batchDataSolution!( batch::AbstractArray{<:FMIFlux.FMU2SolutionBatchElement}, neuralFMU::NeuralFMU, x0_fun, train_t::AbstractArray{<:Real}, targets::AbstractArray; batchDuration::Real = (train_t[end] - train_t[1]), indicesModel = 1:length(targets[1]), plot::Bool = false, scalarLoss::Bool = true, ) @assert length(train_t) == length(targets) "Timepoints in `train_t` ($(length(train_t))) must match number of `targets` ($(length(targets)))" canGetSetState = canGetSetFMUState(neuralFMU.fmu) if !canGetSetState logWarning( neuralFMU.fmu, "This FMU can't set/get a FMU state. This is suboptimal for batched training.", ) end # c, _ = prepareSolveFMU(neuralFMU.fmu, nothing, neuralFMU.fmu.type, nothing, nothing, nothing, nothing, nothing, nothing, neuralFMU.tspan[1], neuralFMU.tspan[end], nothing; handleEvents=FMIFlux.handleEvents) # indicesData = 1:1 tStart = train_t[1] # iStart = timeToIndex(train_t, tStart) # iStop = timeToIndex(train_t, tStart + batchDuration) # startElement = FMIFlux.FMU2SolutionBatchElement(;scalarLoss=scalarLoss) # startElement.tStart = train_t[iStart] # startElement.tStop = train_t[iStop] # startElement.xStart = x0_fun(tStart) # startElement.saveat = train_t[iStart:iStop] # startElement.targets = targets[iStart:iStop] # startElement.indicesModel = indicesModel # push!(batch, startElement) numElements = floor(Integer, (train_t[end] - train_t[1]) / batchDuration) D = eltype(neuralFMU.fmu.modelDescription.discreteStateValueReferences) for i = 1:numElements element = FMIFlux.FMU2SolutionBatchElement{D}(; scalarLoss = scalarLoss) iStart = FMIFlux.timeToIndex(train_t, tStart + (i - 1) * batchDuration) iStop = FMIFlux.timeToIndex(train_t, tStart + i * batchDuration) element.tStart = train_t[iStart] element.tStop = train_t[iStop] element.xStart = x0_fun(element.tStart) element.saveat = train_t[iStart:iStop] element.targets = targets[iStart:iStop] element.indicesModel = indicesModel push!(batch, element) end return nothing end function batchDataSolution( neuralFMU::NeuralFMU, x0_fun, train_t, targets; batchDuration::Real = (train_t[end] - train_t[1]), indicesModel = 1:length(targets[1]), plot::Bool = false, scalarLoss::Bool = true, restartAtJump::Bool = true, solverKwargs..., ) batch = Array{FMIFlux.FMU2SolutionBatchElement,1}() _batchDataSolution!( batch, neuralFMU, x0_fun, train_t, targets; batchDuration = batchDuration, indicesModel = indicesModel, plot = plot, scalarLoss = scalarLoss, ) numElements = length(batch) for i = 1:numElements nextBatchElement = nothing if i < numElements && batch[i].tStop == batch[i+1].tStart nextBatchElement = batch[i+1] end FMIFlux.run!( neuralFMU, batch[i]; nextBatchElement = nextBatchElement, solverKwargs..., ) if plot fig = FMIFlux.plot(batch[i]) display(fig) end end return batch end function batchDataEvaluation( train_t::AbstractArray{<:Real}, targets::AbstractArray, features::Union{AbstractArray,Nothing} = nothing; batchDuration::Real = (train_t[end] - train_t[1]), indicesModel = 1:length(targets[1]), plot::Bool = false, round_digits = 3, scalarLoss::Bool = true, ) batch = Array{FMIFlux.FMU2EvaluationBatchElement,1}() indicesData = 1:1 tStart = train_t[1] iStart = timeToIndex(train_t, tStart) iStop = timeToIndex(train_t, tStart + batchDuration) startElement = FMIFlux.FMU2EvaluationBatchElement(; scalarLoss = scalarLoss) startElement.tStart = train_t[iStart] startElement.tStop = train_t[iStop] startElement.saveat = train_t[iStart:iStop] startElement.targets = targets[iStart:iStop] if features != nothing startElement.features = features[iStart:iStop] else startElement.features = startElement.targets end startElement.indicesModel = indicesModel push!(batch, startElement) for i = 2:floor(Integer, (train_t[end] - train_t[1]) / batchDuration) push!(batch, FMIFlux.FMU2EvaluationBatchElement(; scalarLoss = scalarLoss)) iStart = timeToIndex(train_t, tStart + (i - 1) * batchDuration) iStop = timeToIndex(train_t, tStart + i * batchDuration) batch[i].tStart = train_t[iStart] batch[i].tStop = train_t[iStop] batch[i].saveat = train_t[iStart:iStop] batch[i].targets = targets[iStart:iStop] if features != nothing batch[i].features = features[iStart:iStop] else batch[i].features = batch[i].targets end batch[i].indicesModel = indicesModel if plot fig = FMIFlux.plot(batch[i-1]) display(fig) end end return batch end
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
[ "MIT" ]
0.13.0
7fec0fac72076ea32823fb59efcc0e280cd28162
code
9292
# # Copyright (c) 2021 Tobias Thummerer, Lars Mikelsons # Licensed under the MIT license. See LICENSE file in the project root for details. # # checks gradient determination for all available sensitivity configurations, see: # https://docs.sciml.ai/SciMLSensitivity/stable/manual/differential_equation_sensitivities/ using FMISensitivity.SciMLSensitivity function checkSensalgs!( loss, neuralFMU::Union{ME_NeuralFMU,CS_NeuralFMU}; gradients = (:ReverseDiff, :Zygote, :ForwardDiff), # :FiniteDiff is slow ... max_msg_len = 192, chunk_size = DEFAULT_CHUNK_SIZE, OtD_autojacvecs = ( false, true, TrackerVJP(), ZygoteVJP(), ReverseDiffVJP(false), ReverseDiffVJP(true), ), # EnzymeVJP() deadlocks in the current release xD OtD_sensealgs = ( BacksolveAdjoint, InterpolatingAdjoint, QuadratureAdjoint, GaussAdjoint, ), OtD_checkpointings = (true, false), DtO_sensealgs = (ReverseDiffAdjoint, ForwardDiffSensitivity, TrackerAdjoint), # TrackerAdjoint, ZygoteAdjoint freeze the REPL multiObjective::Bool = false, bestof::Int = 2, timeout_seconds::Real = 60.0, gradient_gt::Symbol = :FiniteDiff, kwargs..., ) params = Flux.params(neuralFMU) initial_sensalg = neuralFMU.fmu.executionConfig.sensealg best_timing = Inf best_gradient = nothing best_sensealg = nothing printstyled("Mode: Ground-Truth ($(gradient_gt)))\n") grads, _ = runGrads(loss, params, gradient_gt, chunk_size, multiObjective) # jac = zeros(length(params[1])) # FiniteDiff.finite_difference_gradient!(jac, loss, params[1]) # step = 1e-6 # for i in 1:length(params[1]) # params[1][i] -= step/2.0 # neg = loss(params[1]) # params[1][i] += step # pos = loss(params[1]) # params[1][i] -= step/2.0 # jac[i] = (pos-neg)/step # end # @info "Jac: $(jac)" # grads = [jac] grad_gt_val = collect(sum(abs.(grad)) for grad in grads)[1] printstyled("\tGround Truth: $(grad_gt_val)\n", color = :green) @assert grad_gt_val > 0.0 "Loss gradient is zero, grad_gt_val == 0.0" printstyled("Mode: Optimize-then-Discretize\n") for gradient ∈ gradients printstyled("\tGradient: $(gradient)\n") for sensealg ∈ OtD_sensealgs printstyled("\t\tSensealg: $(sensealg)\n") for checkpointing ∈ OtD_checkpointings printstyled("\t\t\tCheckpointing: $(checkpointing)\n") if sensealg ∈ (QuadratureAdjoint, GaussAdjoint) && checkpointing printstyled( "\t\t\t\t$(sensealg) doesn't implement checkpointing, skipping ...\n", ) continue end for autojacvec ∈ OtD_autojacvecs printstyled("\t\t\t\tAutojacvec: $(autojacvec)\n") if sensealg ∈ (BacksolveAdjoint, InterpolatingAdjoint) neuralFMU.fmu.executionConfig.sensealg = sensealg(; autojacvec = autojacvec, chunk_size = chunk_size, checkpointing = checkpointing, ) else neuralFMU.fmu.executionConfig.sensealg = sensealg(; autojacvec = autojacvec, chunk_size = chunk_size) end call = () -> _tryrun( loss, params, gradient, chunk_size, 5, max_msg_len, multiObjective; timeout_seconds = timeout_seconds, grad_gt_val = grad_gt_val, ) for i = 1:bestof timing, valid = call() if valid && timing < best_timing best_timing = timing best_gradient = gradient best_sensealg = neuralFMU.fmu.executionConfig.sensealg end end end end end end printstyled("Mode: Discretize-then-Optimize\n") for gradient ∈ gradients printstyled("\tGradient: $(gradient)\n") for sensealg ∈ DtO_sensealgs printstyled("\t\tSensealg: $(sensealg)\n") if sensealg == ForwardDiffSensitivity neuralFMU.fmu.executionConfig.sensealg = sensealg(; chunk_size = chunk_size, convert_tspan = true) else neuralFMU.fmu.executionConfig.sensealg = sensealg() end call = () -> _tryrun( loss, params, gradient, chunk_size, 3, max_msg_len, multiObjective; timeout_seconds = timeout_seconds, grad_gt_val = grad_gt_val, ) for i = 1:bestof timing, valid = call() if valid && timing < best_timing best_timing = timing best_gradient = gradient best_sensealg = neuralFMU.fmu.executionConfig.sensealg end end end end neuralFMU.fmu.executionConfig.sensealg = initial_sensalg printstyled( "------------------------------\nBest time: $(best_timing)\nBest gradient: $(best_gradient)\nBest sensealg: $(best_sensealg)\n", color = :blue, ) return best_timing, best_gradient, best_sensealg end # Thanks to: # https://discourse.julialang.org/t/help-writing-a-timeout-macro/16591/11 function timeout(f, arg, seconds, fail) tsk = @task f(arg...) schedule(tsk) Timer(seconds) do timer istaskdone(tsk) || Base.throwto(tsk, InterruptException()) end try fetch(tsk) catch _ fail end end function runGrads(loss, params, gradient, chunk_size, multiObjective) tstart = time() grads = nothing if multiObjective dim = loss(params[1]) grads = zeros(Float64, length(params[1]), length(dim)) else grads = zeros(Float64, length(params[1])) end computeGradient!(grads, loss, params[1], gradient, chunk_size, multiObjective) timing = time() - tstart if length(grads[1]) == 1 grads = [grads] end return grads, timing end function _tryrun( loss, params, gradient, chunk_size, ts, max_msg_len, multiObjective::Bool = false; print_stdout::Bool = true, print_stderr::Bool = true, timeout_seconds::Real = 60.0, grad_gt_val::Real = 0.0, reltol = 1e-2, ) spacing = "" for t in ts spacing *= "\t" end message = "" color = :black timing = Inf valid = false original_stdout = stdout original_stderr = stderr (rd_stdout, wr_stdout) = redirect_stdout() (rd_stderr, wr_stderr) = redirect_stderr() try #grads, timing = timeout(runGrads, (loss, params, gradient, chunk_size, multiObjective), timeout_seconds, ([Inf], -1.0)) grads, timing = runGrads(loss, params, gradient, chunk_size, multiObjective) if timing == -1.0 message = spacing * "TIMEOUT\n" color = :red else val = collect(sum(abs.(grad)) for grad in grads)[1] tol = abs(1.0 - val / grad_gt_val) if tol > reltol message = spacing * "WRONG $(round(tol*100;digits=2))% > $(round(reltol*100;digits=2))% | $(round(timing; digits=3))s | GradAbsSum: $(round.(val; digits=6))\n" color = :yellow valid = false else message = spacing * "SUCCESS $(round(tol*100;digits=2))% <= $(round(reltol*100;digits=2))% | $(round(timing; digits=3))s | GradAbsSum: $(round.(val; digits=6))\n" color = :green valid = true end end catch e msg = "$(e)" msg = length(msg) > max_msg_len ? first(msg, max_msg_len) * "..." : msg message = spacing * "$(msg)\n" color = :red end redirect_stdout(original_stdout) redirect_stderr(original_stderr) close(wr_stdout) close(wr_stderr) if print_stdout msg = read(rd_stdout, String) if length(msg) > 0 msg = length(msg) > max_msg_len ? first(msg, max_msg_len) * "..." : msg printstyled(spacing * "STDOUT: $(msg)\n", color = :yellow) end end if print_stderr msg = read(rd_stderr, String) if length(msg) > 0 msg = length(msg) > max_msg_len ? first(msg, max_msg_len) * "..." : msg printstyled(spacing * "STDERR: $(msg)\n", color = :yellow) end end printstyled(message, color = color) return timing, valid end
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
[ "MIT" ]
0.13.0
7fec0fac72076ea32823fb59efcc0e280cd28162
code
477
# # Copyright (c) 2021 Tobias Thummerer, Lars Mikelsons # Licensed under the MIT license. See LICENSE file in the project root for details. # function is64(model::Flux.Chain) params = Flux.params(model) for i = 1:length(params) for j = 1:length(params[i]) if !isa(params[i][j], Float64) return false end end end return true end function convert64(model::Flux.Chain) Flux.fmap(Flux.f64, model) end
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
[ "MIT" ]
0.13.0
7fec0fac72076ea32823fb59efcc0e280cd28162
code
4832
# # Copyright (c) 2021 Tobias Thummerer, Lars Mikelsons # Licensed under the MIT license. See LICENSE file in the project root for details. # using FMIImport.FMIBase: FMI2Struct """ DEPRECATED: Performs something similar to `fmiDoStep` for ME-FMUs (note, that fmiDoStep is for CS-FMUs only). Event handling (state- and time-events) is supported. If you don't want events to be handled, you can disable event-handling for the NeuralFMU `nfmu` with the attribute `eventHandling = false`. Optional, additional FMU-values can be set via keyword arguments `setValueReferences` and `setValues`. Optional, additional FMU-values can be retrieved by keyword argument `getValueReferences`. Function takes the current system state array ("x") and returns an array with state derivatives ("x dot") and optionally the FMU-values for `getValueReferences`. Setting the FMU time via argument `t` is optional, if not set, the current time of the ODE solver around the NeuralFMU is used. """ function fmi2EvaluateME( fmu::FMU2, x::Array{<:Real}, t,#::Real, setValueReferences::Union{Array{fmi2ValueReference},Nothing} = nothing, setValues::Union{Array{<:Real},Nothing} = nothing, getValueReferences::Union{Array{fmi2ValueReference},Nothing} = nothing, ) y = nothing y_refs = getValueReferences u = setValues u_refs = setValueReferences if y_refs != nothing y = zeros(length(y_refs)) end dx = zeros(length(x)) c = fmu.components[end] y, dx = c(dx = dx, y = y, y_refs = y_refs, x = x, u = u, u_refs = u_refs, t = t) return [(dx == nothing ? [] : dx)..., (y == nothing ? [] : y)...] end export fmi2EvaluateME """ DEPRECATED: Wrapper. Call ```fmi2EvaluateME``` for more information. """ function fmiEvaluateME( str::FMI2Struct, x::Array{<:Real}, t::Real = (typeof(str) == FMU2 ? str.components[end].t : str.t), setValueReferences::Union{Array{fmi2ValueReference},Nothing} = nothing, setValues::Union{Array{<:Real},Nothing} = nothing, getValueReferences::Union{Array{fmi2ValueReference},Nothing} = nothing, ) fmi2EvaluateME(str, x, t, setValueReferences, setValues, getValueReferences) end export fmiEvaluateME """ DEPRECATED: Wrapper. Call ```fmi2DoStepCS``` for more information. """ function fmiDoStepCS( str::FMI2Struct, dt::Real, setValueReferences::Array{fmi2ValueReference} = zeros(fmi2ValueReference, 0), setValues::Array{<:Real} = zeros(Real, 0), getValueReferences::Array{fmi2ValueReference} = zeros(fmi2ValueReference, 0), ) fmi2DoStepCS(str, dt, setValueReferences, setValues, getValueReferences) end export fmiDoStepCS """ DEPRECATED: Wrapper. Call ```fmi2InputDoStepCSOutput``` for more information. """ function fmiInputDoStepCSOutput(str::FMI2Struct, dt::Real, u::Array{<:Real}) fmi2InputDoStepCSOutput(str, dt, u) end export fmiInputDoStepCSOutput """ DEPRECATED: fmi2InputDoStepCSOutput(comp::FMU2Component, dt::Real, u::Array{<:Real}) Sets all FMU inputs to `u`, performs a ´´´fmi2DoStep´´´ and returns all FMU outputs. """ function fmi2InputDoStepCSOutput(fmu::FMU2, dt::Real, u::Array{<:Real}) @assert fmi2IsCoSimulation(fmu) [ "fmi2InputDoStepCSOutput(...): As in the name, this function only supports CS-FMUs.", ] # fmi2DoStepCS(fmu, dt, # fmu.modelDescription.inputValueReferences, # u, # fmu.modelDescription.outputValueReferences) y_refs = fmu.modelDescription.outputValueReferences u_refs = fmu.modelDescription.inputValueReferences y = zeros(length(y_refs)) c = fmu.components[end] y, _ = c(y = y, y_refs = y_refs, u = u, u_refs = u_refs) # ignore_derivatives() do # fmi2DoStep(c, dt) # end return y end export fmi2InputDoStepCSOutput function fmi2DoStepCS( fmu::FMU2, dt::Real, setValueReferences::Array{fmi2ValueReference} = zeros(fmi2ValueReference, 0), setValues::Array{<:Real} = zeros(Real, 0), getValueReferences::Array{fmi2ValueReference} = zeros(fmi2ValueReference, 0), ) y_refs = setValueReferences u_refs = getValueReferences y = zeros(length(y_refs)) u = setValues c = fmu.components[end] y, _ = c(y = y, y_refs = y_refs, u = u, u_refs = u_refs) # ignore_derivatives() do # fmi2DoStep(c, dt) # end return y end export fmi2DoStepCS # FMU wrappers function fmi2EvaluateME(comp::FMU2Component, args...; kwargs...) fmi2EvaluateME(comp.fmu, args...; kwargs...) end function fmi2DoStepCS(comp::FMU2Component, args...; kwargs...) fmi2DoStepCS(comp.fmu, args...; kwargs...) end function fmi2InputDoStepCSOutput(comp::FMU2Component, args...; kwargs...) fmi2InputDoStepCSOutput(comp.fmu, args...; kwargs...) end
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
[ "MIT" ]
0.13.0
7fec0fac72076ea32823fb59efcc0e280cd28162
code
179
# # Copyright (c) 2021 Tobias Thummerer, Lars Mikelsons # Licensed under the MIT license. See LICENSE file in the project root for details. # # feed through params = Flux.params
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
[ "MIT" ]
0.13.0
7fec0fac72076ea32823fb59efcc0e280cd28162
code
4085
# # Copyright (c) 2021 Tobias Thummerer, Lars Mikelsons # Licensed under the MIT license. See LICENSE file in the project root for details. # # ToDo: Quick-fixes until patch release SciMLSensitivity v0.7.XX import FMISensitivity.SciMLSensitivity: FakeIntegrator, u_modified! import FMISensitivity.SciMLSensitivity.DiffEqBase: set_u! function u_modified!(::FakeIntegrator, ::Bool) return nothing end function set_u!(::FakeIntegrator, u) return nothing end # import FMISensitivity.ReverseDiff: increment_deriv! # function increment_deriv!(t::AbstractArray{<:ReverseDiff.TrackedReal}, x::ReverseDiff.ZeroTangent, args...) # return nothing # end ##### # [ToDo] This allows ITP solving also for ReverseDiff.TrackedReal borders, see: # https://github.com/SciML/DiffEqBase.jl/blob/c7d949e062d9f382e6ef289d6d28e3c53e7202bc/src/internal_itp.jl#L13 using FMISensitivity.SciMLSensitivity.SciMLBase using FMISensitivity.SciMLSensitivity.DiffEqBase using FMISensitivity.SciMLSensitivity.DiffEqBase: InternalITP, nextfloat_tdir, prevfloat_tdir, ReturnCode import FMISensitivity.SciMLSensitivity.SciMLBase: solve function SciMLBase.solve( prob::IntervalNonlinearProblem{IP,Tuple{T,T2}}, alg::InternalITP, args...; maxiters = 1000, kwargs..., ) where {IP,T,T2} f = Base.Fix2(prob.f, prob.p) left, right = prob.tspan # a and b fl, fr = f(left), f(right) ϵ = eps(T) if iszero(fl) return SciMLBase.build_solution( prob, alg, left, fl; retcode = ReturnCode.ExactSolutionLeft, left = left, right = right, ) elseif iszero(fr) return SciMLBase.build_solution( prob, alg, right, fr; retcode = ReturnCode.ExactSolutionRight, left = left, right = right, ) end #defining variables/cache k1 = T(alg.k1) k2 = T(alg.k2) n0 = T(alg.n0) n_h = ceil(log2(abs(right - left) / (2 * ϵ))) mid = (left + right) / 2 x_f = (fr * left - fl * right) / (fr - fl) xt = left xp = left r = zero(left) #minmax radius δ = zero(left) # truncation error σ = 1.0 ϵ_s = ϵ * 2^(n_h + n0) i = 0 #iteration while i <= maxiters #mid = (left + right) / 2 span = abs(right - left) r = ϵ_s - (span / 2) δ = k1 * (span^k2) ## Interpolation step ## x_f = left + (right - left) * (fl / (fl - fr)) ## Truncation step ## σ = sign(mid - x_f) if δ <= abs(mid - x_f) xt = x_f + (σ * δ) else xt = mid end ## Projection step ## if abs(xt - mid) <= r xp = xt else xp = mid - (σ * r) end ## Update ## tmin, tmax = minmax(left, right) xp >= tmax && (xp = prevfloat(tmax)) xp <= tmin && (xp = nextfloat(tmin)) yp = f(xp) yps = yp * sign(fr) if yps > 0 right = xp fr = yp elseif yps < 0 left = xp fl = yp else left = prevfloat_tdir(xp, prob.tspan...) right = xp return SciMLBase.build_solution( prob, alg, left, f(left); retcode = ReturnCode.Success, left = left, right = right, ) end i += 1 mid = (left + right) / 2 ϵ_s /= 2 if nextfloat_tdir(left, prob.tspan...) == right return SciMLBase.build_solution( prob, alg, left, fl; retcode = ReturnCode.FloatingPointLimit, left = left, right = right, ) end end return SciMLBase.build_solution( prob, alg, left, fl; retcode = ReturnCode.MaxIters, left = left, right = right, ) end
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
[ "MIT" ]
0.13.0
7fec0fac72076ea32823fb59efcc0e280cd28162
code
8098
# # Copyright (c) 2021 Tobias Thummerer, Lars Mikelsons # Licensed under the MIT license. See LICENSE file in the project root for details. # using Statistics: mean, std ### FMUParameterRegistrator ### """ ToDo. """ struct FMUParameterRegistrator{T} fmu::FMU2 p_refs::AbstractArray{<:fmi2ValueReference} p::AbstractArray{T} function FMUParameterRegistrator{T}( fmu::FMU2, p_refs::fmi2ValueReferenceFormat, p::AbstractArray{T}, ) where {T} @assert length(p_refs) == length(p) "`p_refs` and `p` need to be the same length!" p_refs = prepareValueReference(fmu, p_refs) fmu.default_p_refs = p_refs fmu.default_p = p for c in fmu.instances c.default_p_refs = p_refs c.default_p = p end return new{T}(fmu, p_refs, p) end function FMUParameterRegistrator( fmu::FMU2, p_refs::fmi2ValueReferenceFormat, p::AbstractArray{T}, ) where {T} return FMUParameterRegistrator{T}(fmu, p_refs, p) end end export FMUParameterRegistrator function (l::FMUParameterRegistrator)(x) l.fmu.default_p_refs = l.p_refs l.fmu.default_p = l.p for c in l.fmu.instances c.default_p_refs = l.p_refs c.default_p = l.p end return x end Flux.@functor FMUParameterRegistrator (p,) ### TimeLayer ### """ A neutral layer that calls a function `fct` with current FMU time as input. """ struct FMUTimeLayer{F,O} fmu::FMU2 fct::F offset::O function FMUTimeLayer{F,O}(fmu::FMU2, fct::F, offset::O) where {F,O} return new{F,O}(fmu, fct, offset) end function FMUTimeLayer(fmu::FMU2, fct::F, offset::O) where {F,O} return FMUTimeLayer{F,O}(fmu, fct, offset) end end export FMUTimeLayer function (l::FMUTimeLayer)(x) if hasCurrentInstance(l.fmu) c = getCurrentInstance(l.fmu) l.fct(c.default_t + l.offset[1]) end return x end Flux.@functor FMUTimeLayer (offset,) ### ParameterRegistrator ### """ ToDo. """ struct ParameterRegistrator{T} p::AbstractArray{T} function ParameterRegistrator{T}(p::AbstractArray{T}) where {T} return new{T}(p) end function ParameterRegistrator(p::AbstractArray{T}) where {T} return ParameterRegistrator{T}(p) end end export ParameterRegistrator function (l::ParameterRegistrator)(x) return x end Flux.@functor ParameterRegistrator (p,) ### SimultaniousZeroCrossing ### """ Forces a simultaniuos zero crossing together with a given value by function. """ struct SimultaniousZeroCrossing{T,F} m::T # scaling factor fct::F function SimultaniousZeroCrossing{T,F}(m::T, fct::F) where {T,F} return new{T,F}(m, fct) end function SimultaniousZeroCrossing(m::T, fct::F) where {T,F} return SimultaniousZeroCrossing{T,F}(m, fct) end end export SimultaniousZeroCrossing function (l::SimultaniousZeroCrossing)(x) return x * l.m * l.fct() end Flux.@functor SimultaniousZeroCrossing (m,) ### SHIFTSCALE ### """ ToDo. """ struct ShiftScale{T} shift::AbstractArray{T} scale::AbstractArray{T} function ShiftScale{T}(shift::AbstractArray{T}, scale::AbstractArray{T}) where {T} inst = new(shift, scale) return inst end function ShiftScale(shift::AbstractArray{T}, scale::AbstractArray{T}) where {T} return ShiftScale{T}(shift, scale) end # initialize for data array function ShiftScale( data::AbstractArray{<:AbstractArray{T}}; range::Union{Symbol,UnitRange{<:Integer}} = -1:1, ) where {T} shift = -mean.(data) scale = nothing if range == :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}`" end return ShiftScale{T}(shift, scale) end end export ShiftScale function (l::ShiftScale)(x) x_proc = (x .+ l.shift) .* l.scale return x_proc end Flux.@functor ShiftScale (shift, scale) ### SCALESHIFT ### """ ToDo. """ struct ScaleShift{T} scale::AbstractArray{T} shift::AbstractArray{T} function ScaleShift{T}(scale::AbstractArray{T}, shift::AbstractArray{T}) where {T} inst = new(scale, shift) return inst end function ScaleShift(scale::AbstractArray{T}, shift::AbstractArray{T}) where {T} return ScaleShift{T}(scale, shift) end # init ScaleShift with inverse transformation of a given ShiftScale function ScaleShift(l::ShiftScale{T}; indices = 1:length(l.scale)) where {T} 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) end end export ScaleShift function (l::ScaleShift)(x) x_proc = (x .* l.scale) .+ l.shift return x_proc end Flux.@functor ScaleShift (scale, shift) ### ScaleSum ### struct ScaleSum{T} scale::AbstractArray{T} groups::Union{AbstractVector{<:AbstractVector{<:Integer}},Nothing} function ScaleSum{T}( scale::AbstractArray{T}, groups::Union{AbstractVector{<:AbstractVector{<:Integer}},Nothing} = nothing, ) where {T} inst = new(scale, groups) return inst end function ScaleSum( scale::AbstractArray{T}, groups::Union{AbstractVector{<:AbstractVector{<:Integer}},Nothing} = nothing, ) where {T} return ScaleSum{T}(scale, groups) end end export ScaleSum function (l::ScaleSum)(x) if isnothing(l.groups) x_proc = sum(x .* l.scale) return [x_proc] else return collect(sum(x[g] .* l.scale[g]) for g in l.groups) end end Flux.@functor ScaleSum (scale,) ### CACHE ### mutable struct CacheLayer cache::AbstractArray{<:AbstractArray} function CacheLayer() inst = new() inst.cache = Array{Array,1}(undef, Threads.nthreads()) return inst end end export CacheLayer function (l::CacheLayer)(x) tid = Threads.threadid() l.cache[tid] = x return x end ### CACHERetrieve ### struct CacheRetrieveLayer cacheLayer::CacheLayer function CacheRetrieveLayer(cacheLayer::CacheLayer) inst = new(cacheLayer) return inst end end export CacheRetrieveLayer function (l::CacheRetrieveLayer)(args...) tid = Threads.threadid() values = zeros(Real, 0) for arg in args if isa(arg, Integer) val = l.cacheLayer.cache[tid][arg] push!(values, val) elseif isa(arg, AbstractArray) && length(arg) == 0 @warn "Deploying empty arrays `[]` in CacheRetrieveLayer is not necessary anymore, just remove them.\nThis warning is only printed once." maxlog = 1 # nothing to do here elseif isa(arg, AbstractArray{<:Integer}) && length(arg) == 1 @warn "Deploying single element arrays `$(arg)` in CacheRetrieveLayer is not necessary anymore, just write `$(arg[1])`.\nThis warning is only printed once." maxlog = 1 val = l.cacheLayer.cache[tid][arg] push!(values, val...) elseif isa(arg, UnitRange{<:Integer}) || isa(arg, AbstractArray{<:Integer}) val = l.cacheLayer.cache[tid][arg] push!(values, val...) elseif isa(arg, Real) push!(values, arg) elseif isa(arg, AbstractArray{<:Real}) push!(values, arg...) else @assert false "CacheRetrieveLayer: Unknown argument `$(arg)` with type `$(typeof(arg))`" end end # [Todo] this is only a quick fix! values = [values...] # promote common data type return values end
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
[ "MIT" ]
0.13.0
7fec0fac72076ea32823fb59efcc0e280cd28162
code
10240
# # Copyright (c) 2021 Tobias Thummerer, Lars Mikelsons # Licensed under the MIT license. See LICENSE file in the project root for details. # module Losses using Flux import ..FMIFlux: FMU2BatchElement, NeuralFMU, loss!, run!, ME_NeuralFMU, FMUSolution import ..FMIFlux.FMIImport.FMIBase: unsense, logWarning mse = Flux.Losses.mse mae = Flux.Losses.mae function last_element_rel(fun, a::AbstractArray, b::AbstractArray, lastElementRatio::Real) return (1.0 - lastElementRatio) * fun(a[1:end-1], b[1:end-1]) + lastElementRatio * fun(a[end], b[end]) end function mse_last_element_rel( a::AbstractArray, b::AbstractArray, lastElementRatio::Real = 0.25, ) return last_element_rel(mse, a, b, lastElementRatio) end function mae_last_element_rel( a::AbstractArray, b::AbstractArray, lastElementRatio::Real = 0.25, ) return last_element_rel(mae, a, b, lastElementRatio) end function mse_last_element(a::AbstractArray, b::AbstractArray) return mse(a[end], b[end]) end function mae_last_element(a::AbstractArray, b::AbstractArray) return mae(a[end], b[end]) end function deviation(a::AbstractArray, b::AbstractArray, dev::AbstractArray) Δ = abs.(a .- b) Δ -= abs.(dev) Δ = collect(max(val, 0.0) for val in Δ) return Δ end function mae_dev(a::AbstractArray, b::AbstractArray, dev::AbstractArray) num = length(a) Δ = deviation(a, b, dev) Δ = sum(Δ) / num return Δ end function mse_dev(a::AbstractArray, b::AbstractArray, dev::AbstractArray) num = length(a) Δ = deviation(a, b, dev) Δ = sum(Δ .^ 2) / num return Δ end function max_dev(a::AbstractArray, b::AbstractArray, dev::AbstractArray) Δ = deviation(a, b, dev) Δ = max(Δ...) return Δ end function mae_last_element_rel_dev( a::AbstractArray, b::AbstractArray, dev::AbstractArray, lastElementRatio::Real, ) num = length(a) Δ = deviation(a, b, dev) Δ[1:end-1] .*= (1.0 - lastElementRatio) Δ[end] *= lastElementRatio Δ = sum(Δ) / num return Δ end function mse_last_element_rel_dev( a::AbstractArray, b::AbstractArray, dev::AbstractArray, lastElementRatio::Real, ) num = length(a) Δ = deviation(a, b, dev) Δ = Δ .^ 2 Δ[1:end-1] .*= (1.0 - lastElementRatio) Δ[end] *= lastElementRatio Δ = sum(Δ) / num return Δ end function stiffness_corridor( solution::FMUSolution, corridor::AbstractArray{<:AbstractArray{<:Tuple{Real,Real}}}; lossFct = Flux.Losses.mse, ) @assert !isnothing(solution.eigenvalues) "stiffness_corridor: Need eigenvalue information, that is not present in the given `FMUSolution`. Use keyword `recordEigenvalues=true` for FMU or NeuralFMU simulation." eigs_over_time = solution.eigenvalues.saveval num_eigs_over_time = length(eigs_over_time) @assert num_eigs_over_time == length(corridor) "stiffness_corridor: length of time points with eigenvalues $(num_eigs_over_time) doesn't match time points in corridor $(length(corridor))." l = 0.0 for i = 2:num_eigs_over_time eigs = eigs_over_time[i] num_eigs = Int(length(eigs) / 2) for j = 1:num_eigs re = eigs[(j-1)*2+1] im = eigs[j*2] c_min, c_max = corridor[i][j] if re > c_max l += lossFct(re, c_max) / num_eigs / num_eigs_over_time end if re < c_min l += lossFct(c_min, re) / num_eigs / num_eigs_over_time end end end return l end function stiffness_corridor( solution::FMUSolution, corridor::AbstractArray{<:Tuple{Real,Real}}; lossFct = Flux.Losses.mse, ) @assert !isnothing(solution.eigenvalues) "stiffness_corridor: Need eigenvalue information, that is not present in the given `FMUSolution`. Use keyword `recordEigenvalues=true` for FMU or NeuralFMU simulation." eigs_over_time = solution.eigenvalues.saveval num_eigs_over_time = length(eigs_over_time) @assert num_eigs_over_time == length(corridor) "stiffness_corridor: length of time points with eigenvalues $(num_eigs_over_time) doesn't match time points in corridor $(length(corridor))." l = 0.0 for i = 2:num_eigs_over_time eigs = eigs_over_time[i] num_eigs = Int(length(eigs) / 2) c_min, c_max = corridor[i] for j = 1:num_eigs re = eigs[(j-1)*2+1] im = eigs[j*2] if re > c_max l += lossFct(re, c_max) / num_eigs / num_eigs_over_time end if re < c_min l += lossFct(c_min, re) / num_eigs / num_eigs_over_time end end end return l end function stiffness_corridor( solution::FMUSolution, corridor::Tuple{Real,Real}; lossFct = Flux.Losses.mse, ) @assert !isnothing(solution.eigenvalues) "stiffness_corridor: Need eigenvalue information, that is not present in the given `FMUSolution`. Use keyword `recordEigenvalues=true` for FMU or NeuralFMU simulation." eigs_over_time = solution.eigenvalues.saveval num_eigs_over_time = length(eigs_over_time) c_min, c_max = corridor l = 0.0 for i = 2:num_eigs_over_time eigs = eigs_over_time[i] num_eigs = Int(length(eigs) / 2) for j = 1:num_eigs re = eigs[(j-1)*2+1] im = eigs[j*2] if re > c_max l += lossFct(re, c_max) / num_eigs / num_eigs_over_time end if re < c_min l += lossFct(re, c_min) / num_eigs / num_eigs_over_time end end end return l end function loss( model, batchElement::FMU2BatchElement; logLoss::Bool = true, lossFct = Flux.Losses.mse, p = nothing, ) model = nfmu.neuralODE.model[layers] # evaluate model result = run!(model, batchElement, p = p) return loss!(batchElement, lossFct; logLoss = logLoss) end function loss( nfmu::NeuralFMU, batch::AbstractArray{<:FMU2BatchElement}; batchIndex::Integer = rand(1:length(batch)), lossFct = Flux.Losses.mse, logLoss::Bool = true, solvekwargs..., ) # cut out data batch from data targets_data = batch[batchIndex].targets nextBatchElement = nothing if batchIndex < length(batch) && batch[batchIndex].tStop == batch[batchIndex+1].tStart nextBatchElement = batch[batchIndex+1] end solution = run!( nfmu, batch[batchIndex]; nextBatchElement = nextBatchElement, progressDescr = "Sim. Batch $(batchIndex)/$(length(batch)) |", solvekwargs..., ) if !solution.success logWarning( nfmu.fmu, "Solving the NeuralFMU as part of the loss function failed with return code `$(solution.states.retcode)`.\nThis is often because the ODE cannot be solved. Did you initialize the NeuralFMU model?\nOften additional solver errors/warnings are printed before this warning.\nHowever, it is tried to compute a loss on the partial retrieved solution from $(unsense(solution.states.t[1]))s to $(unsense(solution.states.t[end]))s.", ) return Inf else return loss!(batch[batchIndex], lossFct; logLoss = logLoss) end end function loss( model, batch::AbstractArray{<:FMU2BatchElement}; batchIndex::Integer = rand(1:length(batch)), lossFct = Flux.Losses.mse, logLoss::Bool = true, p = nothing, ) run!(model, batch[batchIndex], p) return loss!(batch[batchIndex], lossFct; logLoss = logLoss) end function batch_loss( neuralFMU::ME_NeuralFMU, batch::AbstractArray{<:FMU2BatchElement}; update::Bool = false, logLoss::Bool = false, lossFct = nothing, kwargs..., ) accu = nothing if update @assert lossFct != nothing "update=true, but no keyword lossFct provided. Please provide one." numBatch = length(batch) for i = 1:numBatch b = batch[i] b_next = nothing if i < numBatch && batch[i].tStop == batch[i+1].tStart b_next = batch[i+1] end if !isnothing(b.xStart) run!( neuralFMU, b; nextBatchElement = b_next, progressDescr = "Sim. Batch $(i)/$(numBatch) |", kwargs..., ) end if isnothing(accu) accu = loss!(b, lossFct; logLoss = logLoss) else accu += loss!(b, lossFct; logLoss = logLoss) end end else for b in batch @assert length(b.losses) > 0 "batch_loss(): `update=false` but no existing losses for batch element $(b)" if isnothing(accu) accu = b.losses[end].loss else accu += b.losses[end].loss end end end return accu end function batch_loss( model, batch::AbstractArray{<:FMU2BatchElement}; update::Bool = false, logLoss::Bool = false, lossFct = nothing, p = nothing, ) accu = nothing if update @assert lossFct != nothing "update=true, but no keyword lossFct provided. Please provide one." numBatch = length(batch) for i = 1:numBatch b = batch[i] run!(model, b, p) if isnothing(accu) accu = loss!(b, lossFct; logLoss = logLoss) else accu += loss!(b, lossFct; logLoss = logLoss) end end else for b in batch if isnothing(accu) accu = nominalLoss(b) else accu += nominalLoss(b) end end end return accu end mutable struct ToggleLoss index::Int losses::Any function ToggleLoss(losses...) @assert length(losses) >= 2 "ToggleLoss needs at least 2 losses, $(length(losses)) given." return new(1, losses) end end function (t::ToggleLoss)(args...; kwargs...) ret = t.losses[t.index](args...; kwargs...) t.index += 1 if t.index > length(t.losses) t.index = 1 end return ret end end # module
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
[ "MIT" ]
0.13.0
7fec0fac72076ea32823fb59efcc0e280cd28162
code
4280
# # Copyright (c) 2021 Tobias Thummerer, Lars Mikelsons # Licensed under the MIT license. See LICENSE file in the project root for details. # """ Compares non-equidistant (or equidistant) datapoints by linear interpolating and comparing at given interpolation points `t_comp`. (Zygote-friendly: Zygote can differentiate through via AD.) """ function mse_interpolate(t1, x1, t2, x2, t_comp) #lin1 = LinearInterpolation(t1, x1) #lin2 = LinearInterpolation(t2, x2) ar1 = collect(lin_interp(t1, x1, t_sample) for t_sample in t_comp) #lin1.(t_comp) ar2 = collect(lin_interp(t2, x2, t_sample) for t_sample in t_comp) #lin2.(t_comp) Flux.Losses.mse(ar1, ar2) end # Helper: simple linear interpolation function lin_interp(t, x, t_sample) if t_sample <= t[1] return x[1] end if t_sample >= t[end] return x[end] end i = 1 while t_sample > t[i] i += 1 end x_left = x[i-1] x_right = x[i] t_left = t[i-1] t_right = t[i] dx = x_right - x_left dt = t_right - t_left h = t_sample - t_left x_left + dx / dt * h end """ Writes/Copies flatted (Flux.destructure) training parameters `p_net` to non-flat model `net` with data offset `c`. """ function transferFlatParams!(net, p_net, c = 1; netRange = nothing) if netRange == nothing netRange = 1:length(net.layers) end for l in netRange if !isa(net.layers[l], Flux.Dense) continue end ni = size(net.layers[l].weight, 2) no = size(net.layers[l].weight, 1) w = zeros(no, ni) b = zeros(no) for i = 1:ni for o = 1:no w[o, i] = p_net[1][c+(i-1)*no+(o-1)] end end c += ni * no for o = 1:no b[o] = p_net[1][c+(o-1)] end c += no copy!(net.layers[l].weight, w) copy!(net.layers[l].bias, b) end end function transferParams!(net, p_net, c = 1; netRange = nothing) if netRange == nothing netRange = 1:length(net.layers) end for l in netRange if !(net.layers[l] isa Flux.Dense) continue end for w = 1:length(net.layers[l].weight) net.layers[l].weight[w] = p_net[1+(l-1)*2][w] end for b = 1:length(net.layers[l].bias) net.layers[l].bias[b] = p_net[l*2][b] end end end # this is needed by Zygote, but not defined by default function Base.ndims(::Tuple{Float64}) return 1 end # transposes a vector of vectors function transpose(vec::AbstractVector{<:AbstractVector{<:Real}}) return collect(eachrow(reduce(hcat, vec))) end function timeToIndex(ts::AbstractArray{<:Real}, target::Real) tStart = ts[1] tStop = ts[end] tLen = length(ts) @assert target >= tStart "timeToIndex(...): Time ($(target)) < tStart ($(tStart))" # because of the event handling condition, `target` can be outside of the simulation interval! # OLD: @assert target <= tStop "timeToIndex(...): Time ($(target)) > tStop ($(tStop))" # NEW: if target > tStop target = tStop end if target == tStart return 1 elseif target == tStop return tLen end # i = 1 # while ts[i] < target # i += 1 # end # return i # estimate start value steps = 0 i = min(max(round(Integer, (target - tStart) / (tStop - tStart) * tLen), 1), tLen) lastStep = Inf while !(ts[i] <= target && ts[i+1] > target) dt = target - ts[i] step = round(Integer, dt / (tStop - tStart) * tLen) if abs(step) >= lastStep step = Int(sign(dt)) * (lastStep - 1) end if step == 0 step = Int(sign(dt)) end lastStep = abs(step) #@info "$i += $step = $(i+step)" i += step if i < 1 i = 1 elseif i > tLen i = tLen end steps += 1 @assert steps < tLen "Steps reached max." end #@info "$steps" t = ts[i] next_t = ts[i+1] @assert t <= target && next_t >= target "No fitting time found, numerical issue." if (target - t) < (next_t - target) return i else return i + 1 end end
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
[ "MIT" ]
0.13.0
7fec0fac72076ea32823fb59efcc0e280cd28162
code
67217
# # Copyright (c) 2021 Tobias Thummerer, Lars Mikelsons # Licensed under the MIT license. See LICENSE file in the project root for details. # import FMIImport.FMIBase: assert_integrator_valid, isdual, istracked, issense, undual, unsense, unsense_copy, untrack, FMUSnapshot import FMIImport: finishSolveFMU, handleEvents, prepareSolveFMU, snapshot_if_needed!, getSnapshot import Optim import FMIImport.FMIBase.ProgressMeter import FMISensitivity.SciMLSensitivity.SciMLBase: CallbackSet, ContinuousCallback, ODESolution, ReturnCode, RightRootFind, VectorContinuousCallback, set_u!, terminate!, u_modified!, build_solution import OrdinaryDiffEq: isimplicit, alg_autodiff using FMISensitivity.ReverseDiff: TrackedArray import FMISensitivity.SciMLSensitivity: InterpolatingAdjoint, ReverseDiffVJP, AutoForwardDiff import ThreadPools import FMIImport.FMIBase using FMIImport.FMIBase.DiffEqCallbacks using FMIImport.FMIBase.SciMLBase: ODEFunction, ODEProblem, solve using FMIImport.FMIBase: fmi2ComponentState, fmi2ComponentStateContinuousTimeMode, fmi2ComponentStateError, fmi2ComponentStateEventMode, fmi2ComponentStateFatal, fmi2ComponentStateInitializationMode, fmi2ComponentStateInstantiated, fmi2ComponentStateTerminated, fmi2StatusOK, fmi2Type, fmi2TypeCoSimulation, fmi2TypeModelExchange, logError, logInfo, logWarning, fast_copy! using FMISensitivity.SciMLSensitivity: ForwardDiffSensitivity, InterpolatingAdjoint, ReverseDiffVJP, ZygoteVJP import DifferentiableEigen import DifferentiableEigen.LinearAlgebra: I import FMIImport.FMIBase: EMPTY_fmi2Real, EMPTY_fmi2ValueReference import FMIImport.FMIBase import FMISensitivity: NoTangent, ZeroTangent DEFAULT_PROGRESS_DESCR = "Simulating ME-NeuralFMU ..." DEFAULT_CHUNK_SIZE = 32 """ The mutable struct representing an abstract (simulation mode unknown) NeuralFMU. """ abstract type NeuralFMU end """ Structure definition for a NeuralFMU, that runs in mode `Model Exchange` (ME). """ mutable struct ME_NeuralFMU{M,R} <: NeuralFMU model::M p::AbstractArray{<:Real} re::R solvekwargs::Any re_model::Any re_p::Any fmu::FMU tspan::Any saveat::Any saved_values::Any recordValues::Any solver::Any valueStack::Any customCallbacksBefore::Array customCallbacksAfter::Array x0::Union{Array{Float64},Nothing} firstRun::Bool tolerance::Union{Real,Nothing} parameters::Union{Dict{<:Any,<:Any},Nothing} modifiedState::Bool execution_start::Real condition_buffer::Union{AbstractArray{<:Real},Nothing} snapshots::Bool function ME_NeuralFMU{M,R}(model::M, p::AbstractArray{<:Real}, re::R) where {M,R} inst = new() inst.model = model inst.p = p inst.re = re inst.x0 = nothing inst.saveat = nothing inst.re_model = nothing inst.re_p = nothing inst.modifiedState = false # inst.startState = nothing # inst.stopState = nothing # inst.startEventInfo = nothing # inst.stopEventInfo = nothing inst.customCallbacksBefore = [] inst.customCallbacksAfter = [] inst.execution_start = 0.0 inst.condition_buffer = nothing inst.snapshots = false return inst end end """ Structure definition for a NeuralFMU, that runs in mode `Co-Simulation` (CS). """ mutable struct CS_NeuralFMU{F,C} <: NeuralFMU model::Any fmu::F tspan::Any p::Union{AbstractArray{<:Real},Nothing} re::Any # restructure function snapshots::Bool function CS_NeuralFMU{F,C}() where {F,C} inst = new{F,C}() inst.re = nothing inst.p = nothing inst.snapshots = false return inst end end function evaluateModel(nfmu::ME_NeuralFMU, c::FMU2Component, x; p = nfmu.p, t = c.default_t) @assert getCurrentInstance(nfmu.fmu) == c "Thread `$(Threads.threadid())` wants to evaluate wrong component!" # [ToDo]: Skip array check, e.g. by using a flag #if p !== nfmu.re_p || p != nfmu.re_p # || isnothing(nfmu.re_model) # nfmu.re_p = p # fast_copy!(nfmu, :re_p, p) # nfmu.re_model = nfmu.re(p) #end #return nfmu.re_model(x) @debug "evaluateModel(t=$(t)) [out-of-place dx]" #nfmu.p = p c.default_t = t return nfmu.re(p)(x) end function evaluateModel( nfmu::ME_NeuralFMU, c::FMU2Component, dx, x; p = nfmu.p, t = c.default_t, ) @assert getCurrentInstance(nfmu.fmu) == c "Thread `$(Threads.threadid())` wants to evaluate wrong component!" # [ToDo]: Skip array check, e.g. by using a flag #if p !== nfmu.re_p || p != nfmu.re_p # || isnothing(nfmu.re_model) # nfmu.re_p = p # fast_copy!(nfmu, :re_p, p) # nfmu.re_model = nfmu.re(p) #end #dx[:] = nfmu.re_model(x) @debug "evaluateModel(t=$(t)) [in-place dx]" #nfmu.p = p c.default_t = t dx[:] = nfmu.re(p)(x) return nothing end ##### EVENT HANDLING START function startCallback( integrator, nfmu::ME_NeuralFMU, c::Union{FMU2Component,Nothing}, t, writeSnapshot, readSnapshot, ) ignore_derivatives() do nfmu.execution_start = time() t = unsense(t) @assert t == nfmu.tspan[1] "startCallback(...): Called for non-start-point t=$(t)" c, x0 = prepareSolveFMU( nfmu.fmu, c, fmi2TypeModelExchange; parameters = nfmu.parameters, t_start = nfmu.tspan[1], t_stop = nfmu.tspan[end], tolerance = nfmu.tolerance, x0 = nfmu.x0, handleEvents = FMIFlux.handleEvents, cleanup = true, ) if c.eventInfo.nextEventTime == t && c.eventInfo.nextEventTimeDefined == fmi2True @debug "Initial time event detected!" else @debug "No initial time events ..." end if nfmu.snapshots FMIBase.snapshot!(c.solution) end if !isnothing(writeSnapshot) FMIBase.update!(c, writeSnapshot) end if !isnothing(readSnapshot) @assert c == readSnapshot.instance "Snapshot instance mismatch, snapshot instance is $(readSnapshot.instance.compAddr), current component is $(c.compAddr)" # c = readSnapshot.instance if t != readSnapshot.t logWarning( c.fmu, "Snapshot time mismatch, snapshot time = $(readSnapshot.t), but start time is $(t)", ) end @debug "ME_NeuralFMU: Applying snapshot..." FMIBase.apply!(c, readSnapshot; t = t) @debug "ME_NeuralFMU: Snapshot applied." end end return c end function stopCallback(nfmu::ME_NeuralFMU, c::FMU2Component, t) @assert getCurrentInstance(nfmu.fmu) == c "Thread `$(Threads.threadid())` wants to evaluate wrong component!" ignore_derivatives() do t = unsense(t) @assert t == nfmu.tspan[end] "stopCallback(...): Called for non-start-point t=$(t)" end return c end # Read next time event from fmu and provide it to the integrator function time_choice(nfmu::ME_NeuralFMU, c::FMU2Component, integrator, tStart, tStop) @assert getCurrentInstance(nfmu.fmu) == c "Thread `$(Threads.threadid())` wants to evaluate wrong component!" @assert c.fmu.executionConfig.handleTimeEvents "time_choice(...) was called, but execution config disables time events.\nPlease open a issue." # assert_integrator_valid(integrator) # last call may be after simulation end if c == nothing return nothing end c.solution.evals_timechoice += 1 if c.eventInfo.nextEventTimeDefined == fmi2True if c.eventInfo.nextEventTime >= tStart && c.eventInfo.nextEventTime <= tStop @debug "time_choice(...): At $(integrator.t) next time event announced @$(c.eventInfo.nextEventTime)s" return c.eventInfo.nextEventTime else # the time event is outside the simulation range! @debug "Next time event @$(c.eventInfo.nextEventTime)s is outside simulation time range ($(tStart), $(tStop)), skipping." return nothing end else return nothing end end # [ToDo] for now, ReverseDiff (together with the rrule) seems to have a problem with the SubArray here (when `collect` it accesses array elements that are #undef), # so I added an additional (single allocating) dispatch... # Type is ReverseDiff.TrackedReal{Float64, Float64, ReverseDiff.TrackedArray{Float64, Float64, 1, Vector{Float64}, Vector{Float64}}}[#undef, #undef, #undef, ...] function condition!( nfmu::ME_NeuralFMU, c::FMU2Component, out::AbstractArray{<:ReverseDiff.TrackedReal}, x, t, integrator, handleEventIndicators, ) if !isassigned(out, 1) if isnothing(nfmu.condition_buffer) logInfo( nfmu.fmu, "There is currently an issue with the condition buffer pre-allocation, the buffer can't be overwritten by the generated rrule.\nBuffer is generated automatically.", ) @assert length(out) == length(handleEventIndicators) "Number of event indicators to handle ($(handleEventIndicators)) doesn't fit buffer size $(length(out))." nfmu.condition_buffer = zeros(eltype(out), length(out)) elseif eltype(out) != eltype(nfmu.condition_buffer) || length(out) != length(nfmu.condition_buffer) nfmu.condition_buffer = zeros(eltype(out), length(out)) end out[:] = nfmu.condition_buffer end invoke( condition!, Tuple{ME_NeuralFMU,FMU2Component,Any,Any,Any,Any,Any}, nfmu, c, out, x, t, integrator, handleEventIndicators, ) return nothing end function condition!( nfmu::ME_NeuralFMU, c::FMU2Component, out, x, t, integrator, handleEventIndicators, ) @assert getCurrentInstance(nfmu.fmu) == c "Thread `$(Threads.threadid())` wants to evaluate wrong component!" @assert c.state == fmi2ComponentStateContinuousTimeMode "condition!(...):\n" * FMIBase.ERR_MSG_CONT_TIME_MODE # [ToDo] Evaluate on light-weight model (sub-model) without fmi2GetXXX or similar and the bottom ANN. # Basically only the layers from very top to FMU need to be evaluated here. prev_t = c.default_t prev_ec = c.default_ec prev_ec_idcs = c.default_ec_idcs c.default_t = t c.default_ec = out c.default_ec_idcs = handleEventIndicators evaluateModel(nfmu, c, x) # write back to condition buffer if (!isdual(out) && isdual(c.output.ec)) || (!istracked(out) && istracked(c.output.ec)) out[:] = unsense(c.output.ec) else out[:] = c.output.ec # [ToDo] This seems not to be necessary, because of `c.default_ec = out` end # reset c.default_t = prev_t c.default_ec = prev_ec c.default_ec_idcs = prev_ec_idcs c.solution.evals_condition += 1 @debug "condition!(...) -> [typeof=$(typeof(out))]\n$(unsense(out))" return nothing end global lastIndicator = nothing global lastIndicatorX = nothing global lastIndicatorT = nothing function conditionSingle(nfmu::ME_NeuralFMU, c::FMU2Component, index, x, t, integrator) @assert c.state == fmi2ComponentStateContinuousTimeMode "condition(...):\n" * FMIBase.ERR_MSG_CONT_TIME_MODE @assert getCurrentInstance(nfmu.fmu) == c "Thread `$(Threads.threadid())` wants to evaluate wrong component!" if c.fmu.handleEventIndicators != nothing && index ∉ c.fmu.handleEventIndicators return 1.0 end global lastIndicator if lastIndicator == nothing || length(lastIndicator) != c.fmu.modelDescription.numberOfEventIndicators lastIndicator = zeros(c.fmu.modelDescription.numberOfEventIndicators) end # [ToDo] Evaluate on light-weight model (sub-model) without fmi2GetXXX or similar and the bottom ANN c.default_t = t c.default_ec = lastIndicator evaluateModel(nfmu, c, x) c.default_t = -1.0 c.default_ec = EMPTY_fmi2Real c.solution.evals_condition += 1 return lastIndicator[index] end function smoothmax(vec::AbstractVector; alpha = 0.5) dividend = 0.0 divisor = 0.0 e = Float64(ℯ) for x in vec dividend += x * e^(alpha * x) divisor += e^(alpha * x) end return dividend / divisor end function smoothmax(a, b; kwargs...) return smoothmax([a, b]; kwargs...) end # [ToDo] Check, that the new determined state is the right root of the event instant! function f_optim( x, nfmu::ME_NeuralFMU, c::FMU2Component, right_x_fmu, idx, sign::Real, out, indicatorValue, handleEventIndicators; _unsense::Bool = false, ) prev_ec = c.default_ec prev_ec_idcs = c.default_ec_idcs prev_y_refs = c.default_y_refs prev_y = c.default_y #@info "\ndx: $(c.default_dx)\n x: $(x)" c.default_ec = out c.default_ec_idcs = handleEventIndicators c.default_y_refs = c.fmu.modelDescription.stateValueReferences c.default_y = zeros(typeof(x[1]), length(c.fmu.modelDescription.stateValueReferences)) evaluateModel(nfmu, c, x; p = unsense(nfmu.p)) # write back to condition buffer # if (!isdual(out) && isdual(c.output.ec)) || (!istracked(out) && istracked(c.output.ec)) # @assert false "Type missmatch! Can't propagate sensitivities!" # out[:] = unsense(c.output.ec) # else # out[:] = c.output.ec # [ToDo] This seems not to be necessary, because of `c.default_ec = out` # end # reset c.default_ec = prev_ec c.default_ec_idcs = prev_ec_idcs c.default_y_refs = prev_y_refs c.default_y = prev_y # propagete the new state-guess `x` through the NeuralFMU #condition!(nfmu, c, buffer, x, c.t, nothing, handleEventIndicators) ec = c.output.ec[idx] y = c.output.y #@info "\nec: $(ec)\n-> $(unsense(ec))\ny: $(y)\n-> $(unsense(y))" errorIndicator = Flux.Losses.mae(indicatorValue, ec) + smoothmax(-sign * ec * 1000.0, 0.0) # if errorIndicator > 0.0 # errorIndicator = max(errorIndicator, 1.0) # end errorState = Flux.Losses.mae(right_x_fmu, y) #@info "ErrorState: $(errorState) | ErrorIndicator: $(errorIndicator)" ret = errorState + errorIndicator # if _unsense # ret = unsense(ret) # end return ret end function sampleStateChangeJacobian(nfmu, c, left_x, right_x, t, idx::Integer; step = 1e-8) @debug "sampleStateChangeJacobian(x = $(left_x))" c.solution.evals_∂xr_∂xl += 1 numStates = length(left_x) jac = zeros(numStates, numStates) # first, jump to before the event instance # if length(c.solution.snapshots) > 0 # c.t != t # sn = getSnapshot(c.solution, t) # FMIBase.apply!(c, sn; x_c=left_x, t=t) # #@info "[d] Set snapshot @ t=$(t) (sn.t=$(sn.t))" # end # indicator_sign = idx > 0 ? sign(fmi2GetEventIndicators(c)[idx]) : 1.0 # [ToDo] ONLY A TEST new_left_x = copy(left_x) if length(c.solution.snapshots) > 0 # c.t != t sn = getSnapshot(c.solution, t) FMIBase.apply!(c, sn; x_c = new_left_x, t = t) #@info "[?] Set snapshot @ t=$(t) (sn.t=$(sn.t))" end new_right_x = stateChange!(nfmu, c, new_left_x, t, idx; snapshots = false) statesChanged = (c.eventInfo.valuesOfContinuousStatesChanged == fmi2True) # [ToDo: these tests should be included, but will drastically fail on FMUs with no support for get/setState] # @assert statesChanged "Can't reproduce event (statesChanged)!" # @assert left_x == new_left_x "Can't reproduce event (left_x)!" # @assert right_x == new_right_x "Can't reproduce event (right_x)!" at_least_one_state_change = false for i = 1:numStates #new_left_x[:] .= left_x new_left_x = copy(left_x) new_left_x[i] += step # first, jump to before the event instance if length(c.solution.snapshots) > 0 # c.t != t sn = getSnapshot(c.solution, t) FMIBase.apply!(c, sn; x_c = new_left_x, t = t) #@info "[e] Set snapshot @ t=$(t) (sn.t=$(sn.t))" end # [ToDo] Don't check if event was handled via event-indicator, because there is no guarantee that it is reset (like for the bouncing ball) # to match the sign from before the event! Better check if FMU detects a new event! # fmi2EnterEventMode(c) # handleEvents(c) new_right_x = stateChange!(nfmu, c, new_left_x, t, idx; snapshots = false) statesChanged = (c.eventInfo.valuesOfContinuousStatesChanged == fmi2True) at_least_one_state_change = statesChanged || at_least_one_state_change #new_indicator_sign = idx > 0 ? sign(fmi2GetEventIndicators(c)[idx]) : 1.0 #@info "Sample P: t:$(t) $(new_left_x) -> $(new_right_x)" grad = (new_right_x .- right_x) ./ step # (left_x .- new_left_x) # choose other direction if !statesChanged #@info "New_indicator sign is $(new_indicator_sign) (should be $(indicator_sign)), retry..." #new_left_x[:] .= left_x new_left_x = copy(left_x) new_left_x[i] -= step if length(c.solution.snapshots) > 0 # c.t != t sn = getSnapshot(c.solution, t) FMIBase.apply!(c, sn; x_c = new_left_x, t = t) #@info "[e] Set snapshot @ t=$(t) (sn.t=$(sn.t))" end #fmi2EnterEventMode(c) #handleEvents(c) new_right_x = stateChange!(nfmu, c, new_left_x, t, idx; snapshots = false) statesChanged = (c.eventInfo.valuesOfContinuousStatesChanged == fmi2True) at_least_one_state_change = statesChanged || at_least_one_state_change #new_indicator_sign = idx > 0 ? sign(fmi2GetEventIndicators(c)[idx]) : 1.0 #@info "Sample N: t:$(t) $(new_left_x) -> $(new_right_x)" if statesChanged grad = (new_right_x .- right_x) ./ -step # (left_x .- new_left_x) else grad = (right_x .- right_x) # ... so zero, this state is not sensitive at all! end end # if length(c.solution.snapshots) > 0 # c.t != t # sn = getSnapshot(c.solution, t) # FMIBase.apply!(c, sn; x_c=new_left_x, t=t) # #@info "[e] Set snapshot @ t=$(t) (sn.t=$(sn.t))" # end # new_right_x = stateChange!(nfmu, c, new_left_x, t, idx; snapshots=false) # [ToDo] check if the SAME event indicator was triggered! #@info "t=$(t) idx=$(idx)\n left_x: $(left_x) -> right_x: $(right_x) [$(indicator_sign)]\nnew_left_x: $(new_left_x) -> new_right_x: $(new_right_x) [$(new_indicator_sign)]" jac[i, :] = grad end #@assert at_least_one_state_change "Sampling state change jacobian failed, can't find another state that triggers the event!" if !at_least_one_state_change @info "Sampling state change jacobian failed, can't find another state that triggers the event!\ncommon reasons for that are:\n(a) The FMU is not able to revisit events (which should be possible with fmiXGet/SetState).\n(b) The state change is not dependent on the previous state (hard reset).\nThis is printed only 3 times." maxlog = 3 end # finally, jump back to the correct FMU state # if length(c.solution.snapshots) > 0 # c.t != t # @info "Reset snapshot @ t = $(t)" # sn = getSnapshot(c.solution, t) # FMIBase.apply!(c, sn; x_c=left_x, t=t) # end # stateChange!(nfmu, c, left_x, t, idx) if length(c.solution.snapshots) > 0 #@info "Reset exact snapshot @t=$(t)" sn = getSnapshot(c.solution, t; exact = true) if !isnothing(sn) FMIBase.apply!(c, sn; x_c = left_x, t = t) end end #@info "Jac:\n$(jac)" #@assert isapprox(jac, [0.0 0.0; 0.0 -0.7]; atol=1e-4) "Jac missmatch, is $(jac)" return jac end function is_integrator_sensitive(integrator) return istracked(integrator.u) || istracked(integrator.t) || isdual(integrator.u) || isdual(integrator.t) end function stateChange!( nfmu, c, left_x::AbstractArray{<:Float64}, t::Float64, idx; snapshots = nfmu.snapshots, ) # unpack references # if typeof(cRef) != UInt64 # cRef = UInt64(cRef) # end # c = unsafe_pointer_to_objref(Ptr{Nothing}(cRef)) # if typeof(nfmuRef) != UInt64 # nfmuRef = UInt64(nfmuRef) # end # nfmu = unsafe_pointer_to_objref(Ptr{Nothing}(nfmuRef)) # unpack references done # if length(c.solution.snapshots) > 0 # c.t != t # sn = getSnapshot(c.solution, t) # @info "[x] Set snapshot @ t=$(t) (sn.t=$(sn.t))" # FMIBase.apply!(c, sn; x_c=left_x, t=t) # end # [ToDo]: Debugging, remove this! #@assert fmi2GetContinuousStates(c) == left_x "$(left_x) != $(fmi2GetContinuousStates(c))" #@debug "stateChange!, state is $(fmi2GetContinuousStates(c))" fmi2EnterEventMode(c) handleEvents(c) #snapshots = true # nfmu.snapshots || snapshotsNeeded(nfmu, integrator) # ignore_derivatives() do # if idx == 0 # time_affect!(integrator) # else # #affect_right!(integrator, idx) # end # end right_x = left_x if c.eventInfo.valuesOfContinuousStatesChanged == fmi2True ignore_derivatives() do if idx == 0 @debug "stateChange!($(idx)): NeuralFMU time event with state change.\nt = $(t)\nleft_x = $(left_x)" else @debug "stateChange!($(idx)): NeuralFMU state event with state change by indicator $(idx).\nt = $(t)\nleft_x = $(left_x)" end end right_x_fmu = fmi2GetContinuousStates(c) # the new FMU state after handled events # if there is an ANN above the FMU, propaget FMU state through top ANN by optimization if nfmu.modifiedState before = fmi2GetEventIndicators(c) buffer = copy(before) handleEventIndicators = Vector{UInt32}( collect( i for i = 1:length(nfmu.fmu.modelDescription.numberOfEventIndicators) ), ) _f(_x) = f_optim( _x, nfmu, c, right_x_fmu, idx, sign(before[idx]), buffer, before[idx], handleEventIndicators; _unsense = true, ) _f_g(_x) = f_optim( _x, nfmu, c, right_x_fmu, idx, sign(before[idx]), buffer, before[idx], handleEventIndicators; _unsense = false, ) function _g!(G, x) #if istracked(integrator.u) # ReverseDiff.gradient!(G, _f_g, x) #else # if isdual(integrator.u) ForwardDiff.gradient!(G, _f_g, x) # else # @assert false "Unknown AD framework! -> $(typeof(integrator.u[1]))" #end #@info "G: $(G)" end result = Optim.optimize(_f, _g!, left_x, Optim.BFGS()) right_x = Optim.minimizer(result) after = fmi2GetEventIndicators(c) if sign(before[idx]) != sign(after[idx]) logError( nfmu.fmu, "Eventhandling failed,\nRight state: $(right_x)\nRight FMU state: $(right_x_fmu)\nIndicator (bef.): $(before[idx])\nIndicator (aft.): $(after[idx])", ) end else # if there is no ANN above, then: right_x = right_x_fmu end else ignore_derivatives() do if idx == 0 @debug "stateChange!($(idx)): NeuralFMU time event without state change.\nt = $(t)\nx = $(left_x)" else @debug "stateChange!($(idx)): NeuralFMU state event without state change by indicator $(idx).\nt = $(t)\nx = $(left_x)" end end # [Note] enabling this causes serious issues with time events! (wrong sensitivities!) # u_modified!(integrator, false) end if snapshots s = snapshot_if_needed!(c.solution, t) # if !isnothing(s) # @info "Add snapshot @t=$(s.t)" # end end # [ToDo] This is only correct, if every state is only depenent on itself. # This should only be done in the frule/rrule, the actual affect should do a hard "set state" #logWarning(c.fmu, "Before: integrator.u = $(integrator.u)") # if nfmu.fmu.executionConfig.isolatedStateDependency # for i in 1:length(left_x) # if abs(left_x[i]) > 1e-16 # left_x[i] != 0.0 # # scale = right_x[i] / left_x[i] # integrator.u[i] *= scale # else # integrator state zero can't be scaled, need to add (but no sensitivities in this case!) # shift = right_x[i] - left_x[i] # integrator.u[i] += shift # #integrator.u[i] = right_x[i] # #logWarning(c.fmu, "Probably wrong sensitivities @t=$(unsense(t)) for ∂x^+ / ∂x^-\nCan't scale zero state #$(i) from $(left_x[i]) to $(right_x[i])\nNew state after transform is: $(integrator.u[i])") # end # end # else # integrator.u[:] = right_x # end return right_x end # Handles the upcoming event function affectFMU!(nfmu::ME_NeuralFMU, c::FMU2Component, integrator, idx) @debug "affectFMU!" @assert getCurrentInstance(nfmu.fmu) == c "Thread `$(Threads.threadid())` wants to evaluate wrong component!" # assert_integrator_valid(integrator) @assert c.state == fmi2ComponentStateContinuousTimeMode "affectFMU!(...):\n" * FMIBase.ERR_MSG_CONT_TIME_MODE # [NOTE] Here unsensing is OK, because we just want to reset the FMU to the correct state! # The values come directly from the integrator and are NOT function arguments! t = unsense(integrator.t) left_x = unsense_copy(integrator.u) right_x = nothing ignore_derivatives() do # if snapshots && length(c.solution.snapshots) > 0 # sn = getSnapshot(c.solution, t) # FMIBase.apply!(c, sn) # end #if c.x != left_x # capture status of `force` mode = c.force c.force = true # there are fx-evaluations before the event is handled, reset the FMU state to the current integrator step evaluateModel(nfmu, c, left_x; t = t) # evaluate NeuralFMU (set new states) # [NOTE] No need to reset time here, because we did pass a event instance! # c.default_t = -1.0 c.force = mode #end end integ_sens = nfmu.snapshots right_x = stateChange!(nfmu, c, left_x, t, idx) # sensitivities needed if integ_sens jac = I if c.eventInfo.valuesOfContinuousStatesChanged == fmi2True jac = sampleStateChangeJacobian(nfmu, c, left_x, right_x, t, idx) end VJP = jac * integrator.u #tgrad = tvec .* integrator.t staticOff = right_x .- unsense(VJP) # .- unsense(tgrad) # [ToDo] add (sampled) time gradient integrator.u[:] = staticOff + VJP # + tgrad else integrator.u[:] = right_x end #@info "affect right_x = $(right_x)" # [Note] enabling this causes serious issues with time events! (wrong sensitivities!) # u_modified!(integrator, true) if c.eventInfo.nominalsOfContinuousStatesChanged == fmi2True # [ToDo] Do something with that information, e.g. use for FiniteDiff sampling step size determination x_nom = fmi2GetNominalsOfContinuousStates(c) end ignore_derivatives() do if idx != -1 _left_x = left_x _right_x = isnothing(right_x) ? _left_x : unsense_copy(right_x) #@assert c.eventInfo.valuesOfContinuousStatesChanged == (_left_x != _right_x) "FMU says valuesOfContinuousStatesChanged $(c.eventInfo.valuesOfContinuousStatesChanged), but states say different!" e = FMUEvent(unsense(t), UInt64(idx), _left_x, _right_x) push!(c.solution.events, e) end # calculates state events per second pt = t - nfmu.tspan[1] ne = 0 for event in c.solution.events #if t - event.t < pt if event.indicator > 0 # count only state events ne += 1 end #end end ratio = ne / pt if ne >= 100 && ratio > c.fmu.executionConfig.maxStateEventsPerSecond logError( c.fmu, "Event chattering detected $(round(Integer, ratio)) state events/s (allowed are $(c.fmu.executionConfig.maxStateEventsPerSecond)), aborting at t=$(t) (rel. t=$(pt)) at state event $(ne):", ) for i = 1:c.fmu.modelDescription.numberOfEventIndicators num = 0 for e in c.solution.events if e.indicator == i num += 1 end end if num > 0 logError( c.fmu, "\tEvent indicator #$(i) triggered $(num) ($(round(num/ne*100.0; digits=1))%)", ) end end terminate!(integrator) end end c.solution.evals_affect += 1 return nothing end # Does one step in the simulation. function stepCompleted( nfmu::ME_NeuralFMU, c::FMU2Component, x, t, integrator, tStart, tStop, ) # assert_integrator_valid(integrator) # [Note] enabling this causes serious issues with time events! (wrong sensitivities!) # u_modified!(integrator, false) c.solution.evals_stepcompleted += 1 # if snapshots # FMIBase.snapshot!(c.solution) # end if !isnothing(c.progressMeter) t = unsense(t) dt = unsense(integrator.t) - unsense(integrator.tprev) events = length(c.solution.events) steps = c.solution.evals_stepcompleted simLen = tStop - tStart c.progressMeter.desc = "t=$(roundToLength(t, 10))s | Δt=$(roundToLength(dt, 10))s | STPs=$(steps) | EVTs=$(events) |" #@info "$(tStart) $(tStop) $(t)" if simLen > 0.0 ProgressMeter.update!( c.progressMeter, floor(Integer, 1000.0 * (t - tStart) / simLen), ) end end if c != nothing (status, enterEventMode, terminateSimulation) = fmi2CompletedIntegratorStep(c, fmi2True) if terminateSimulation == fmi2True logError(c.fmu, "stepCompleted(...): FMU requested termination!") end if enterEventMode == fmi2True affectFMU!(nfmu, c, integrator, -1) end @debug "Step completed at $(unsense(t)) with $(unsense(x))" end # assert_integrator_valid(integrator) end # [ToDo] (1) This must be in-place # (2) getReal must be replaced with the inplace getter within c(...) # (3) remove unsense to determine save value sensitivities # save FMU values function saveValues(nfmu::ME_NeuralFMU, c::FMU2Component, recordValues, _x, _t, integrator) t = unsense(_t) x = unsense(_x) c.solution.evals_savevalues += 1 # ToDo: Evaluate on light-weight model (sub-model) without fmi2GetXXX or similar and the bottom ANN evaluateModel(nfmu, c, x; t = t) # evaluate NeuralFMU (set new states) values = fmi2GetReal(c, recordValues) @debug "Save values @t=$(t)\nintegrator.t=$(unsense(integrator.t))\n$(values)" # Todo set inputs return (values...,) end function saveEigenvalues( nfmu::ME_NeuralFMU, c::FMU2Component, _x, _t, integrator, sensitivity::Symbol, ) @assert c.state == fmi2ComponentStateContinuousTimeMode "saveEigenvalues(...):\n" * FMIBase.ERR_MSG_CONT_TIME_MODE c.solution.evals_saveeigenvalues += 1 A = nothing if sensitivity == :ForwardDiff A = ForwardDiff.jacobian(x -> evaluateModel(nfmu, c, x; t = _t), _x) # TODO: chunk_size! elseif sensitivity == :ReverseDiff A = ReverseDiff.jacobian(x -> evaluateModel(nfmu, c, x; t = _t), _x) elseif sensitivity == :Zygote A = Zygote.jacobian(x -> evaluateModel(nfmu, c, x; t = _t), _x)[1] elseif sensitivity == :none A = ForwardDiff.jacobian(x -> evaluateModel(nfmu, c, x; t = _t), unsense(_x)) end eigs, _ = DifferentiableEigen.eigen(A) return (eigs...,) end function fx( nfmu::ME_NeuralFMU, c::FMU2Component, dx,#::Array{<:Real}, x,#::Array{<:Real}, p,#::Array, t, )#::Real) if isnothing(c) # this should never happen! @warn "fx() called without allocated FMU instance!" return zeros(length(x)) end ############ evaluateModel(nfmu, c, dx, x; p = p, t = t) ignore_derivatives() do c.solution.evals_fx_inplace += 1 end return dx end function fx( nfmu::ME_NeuralFMU, c::FMU2Component, x,#::Array{<:Real}, p,#::Array, t, )#::Real) if c === nothing # this should never happen! return zeros(length(x)) end ignore_derivatives() do c.solution.evals_fx_outofplace += 1 end return evaluateModel(nfmu, c, x; p = p, t = t) end ##### EVENT HANDLING END """ Constructs a ME-NeuralFMU where the FMU is at an arbitrary location inside of the NN. # Arguments - `fmu` the considered FMU inside the NN - `model` the NN topology (e.g. Flux.chain) - `tspan` simulation time span - `solver` an ODE Solver (default=`nothing`, heurisitically determine one) # Keyword arguments - `recordValues` additionally records internal FMU variables """ function ME_NeuralFMU( fmu::FMU2, model, tspan, solver = nothing; recordValues = nothing, saveat = nothing, solvekwargs..., ) if !is64(model) model = convert64(model) logInfo( fmu, "Model is not Float64, but this is necessary for (Neural)FMUs.\nModel parameters are automatically converted to Float64.", ) end p, re = Flux.destructure(model) nfmu = ME_NeuralFMU{typeof(model),typeof(re)}(model, p, re) ###### nfmu.fmu = fmu nfmu.saved_values = nothing nfmu.recordValues = prepareValueReference(fmu, recordValues) nfmu.tspan = tspan nfmu.solver = solver nfmu.saveat = saveat nfmu.solvekwargs = solvekwargs nfmu.parameters = nothing ###### nfmu end """ Constructs a CS-NeuralFMU where the FMU is at an arbitrary location inside of the ANN. # Arguents - `fmu` the considered FMU inside the ANN - `model` the ANN topology (e.g. Flux.Chain) - `tspan` simulation time span # Keyword arguments - `recordValues` additionally records FMU variables """ function CS_NeuralFMU(fmu::FMU2, model, tspan; recordValues = []) if !is64(model) model = convert64(model) logInfo( fmu, "Model is not Float64, but this is necessary for (Neural)FMUs.\nModel parameters are automatically converted to Float64.", ) end nfmu = CS_NeuralFMU{FMU2,FMU2Component}() nfmu.fmu = fmu nfmu.model = model nfmu.tspan = tspan nfmu.p, nfmu.re = Flux.destructure(nfmu.model) return nfmu end function CS_NeuralFMU(fmus::Vector{<:FMU2}, model, tspan; recordValues = []) if !is64(model) model = convert64(model) for fmu in fmus logInfo( fmu, "Model is not Float64, but this is necessary for (Neural)FMUs.\nModel parameters are automatically converted to Float64.", ) end end nfmu = CS_NeuralFMU{Vector{FMU2},Vector{FMU2Component}}() nfmu.fmu = fmus nfmu.model = model nfmu.tspan = tspan nfmu.p, nfmu.re = Flux.destructure(nfmu.model) return nfmu end function checkExecTime(integrator, nfmu::ME_NeuralFMU, c, max_execution_duration::Real) dist = max(nfmu.execution_start + max_execution_duration - time(), 0.0) if dist <= 0.0 logInfo( nfmu.fmu, "Reached max execution duration ($(max_execution_duration)), terminating integration ...", ) terminate!(integrator) end return 1.0 end function getInstance(nfmu::NeuralFMU) return hasCurrentInstance(nfmu.fmu) ? getCurrentInstance(nfmu.fmu) : nothing end # ToDo: Separate this: NeuralFMU creation and solving! """ nfmu(x_start, tspan; kwargs) Evaluates the ME_NeuralFMU `nfmu` in the timespan given during construction or in a custom timespan from `t_start` to `t_stop` for a given start state `x_start`. # Keyword arguments [ToDo] """ function (nfmu::ME_NeuralFMU)( x_start::Union{Array{<:Real},Nothing} = nfmu.x0, tspan::Tuple{Float64,Float64} = nfmu.tspan; showProgress::Bool = false, progressDescr::String = DEFAULT_PROGRESS_DESCR, tolerance::Union{Real,Nothing} = nothing, parameters::Union{Dict{<:Any,<:Any},Nothing} = nothing, p = nfmu.p, solver = nfmu.solver, saveEventPositions::Bool = false, max_execution_duration::Real = -1.0, recordValues::fmi2ValueReferenceFormat = nfmu.recordValues, recordEigenvaluesSensitivity::Symbol = :none, recordEigenvalues::Bool = (recordEigenvaluesSensitivity != :none), saveat = nfmu.saveat, # ToDo: Data type sensealg = nfmu.fmu.executionConfig.sensealg, # ToDo: AbstractSensitivityAlgorithm writeSnapshot::Union{FMUSnapshot,Nothing} = nothing, readSnapshot::Union{FMUSnapshot,Nothing} = nothing, cleanSnapshots::Bool = true, solvekwargs..., ) if !isnothing(saveat) if saveat[1] != tspan[1] || saveat[end] != tspan[end] logWarning( nfmu.fmu, "NeuralFMU changed time interval, start time is $(tspan[1]) and stop time is $(tspan[end]), but saveat from constructor gives $(saveat[1]) and $(saveat[end]).\nPlease provide correct `saveat` via keyword with matching start/stop time.", 1, ) saveat = collect(saveat) while saveat[1] < tspan[1] popfirst!(saveat) end while saveat[end] > tspan[end] pop!(saveat) end end end recordValues = prepareValueReference(nfmu.fmu, recordValues) saving = (length(recordValues) > 0) t_start = tspan[1] t_stop = tspan[end] nfmu.tspan = tspan nfmu.x0 = x_start nfmu.p = p ignore_derivatives() do @debug "ME_NeuralFMU(showProgress=$(showProgress), tspan=$(tspan), x0=$(nfmu.x0))" nfmu.firstRun = true nfmu.tolerance = tolerance if isnothing(parameters) if !isnothing(nfmu.fmu.default_p_refs) nfmu.parameters = Dict(nfmu.fmu.default_p_refs .=> unsense(nfmu.fmu.default_p)) end else nfmu.parameters = parameters end end callbacks = [] c = getInstance(nfmu) @debug "ME_NeuralFMU: Starting callback..." c = startCallback(nothing, nfmu, c, t_start, writeSnapshot, readSnapshot) ignore_derivatives() do c.solution = FMUSolution(c) @debug "ME_NeuralFMU: Defining callbacks..." # custom callbacks for cb in nfmu.customCallbacksBefore push!(callbacks, cb) end nfmu.fmu.hasStateEvents = (c.fmu.modelDescription.numberOfEventIndicators > 0) nfmu.fmu.hasTimeEvents = (c.eventInfo.nextEventTimeDefined == fmi2True) # time event handling if nfmu.fmu.executionConfig.handleTimeEvents && nfmu.fmu.hasTimeEvents timeEventCb = IterativeCallback( (integrator) -> time_choice(nfmu, c, integrator, t_start, t_stop), (integrator) -> affectFMU!(nfmu, c, integrator, 0), Float64; initial_affect = (c.eventInfo.nextEventTime == t_start), # already checked in the outer closure: c.eventInfo.nextEventTimeDefined == fmi2True save_positions = (saveEventPositions, saveEventPositions), ) push!(callbacks, timeEventCb) end # state event callback if c.fmu.hasStateEvents && c.fmu.executionConfig.handleStateEvents handleIndicators = nothing # if we want a specific subset if !isnothing(c.fmu.handleEventIndicators) handleIndicators = c.fmu.handleEventIndicators else # handle all handleIndicators = collect( UInt32(i) for i = 1:c.fmu.modelDescription.numberOfEventIndicators ) end numEventInds = length(handleIndicators) if c.fmu.executionConfig.useVectorCallbacks eventCb = VectorContinuousCallback( (out, x, t, integrator) -> condition!(nfmu, c, out, x, t, integrator, handleIndicators), (integrator, idx) -> affectFMU!(nfmu, c, integrator, idx), numEventInds; rootfind = RightRootFind, save_positions = (saveEventPositions, saveEventPositions), interp_points = c.fmu.executionConfig.rootSearchInterpolationPoints, ) push!(callbacks, eventCb) else for idx = 1:c.fmu.modelDescription.numberOfEventIndicators eventCb = ContinuousCallback( (x, t, integrator) -> conditionSingle(nfmu, c, idx, x, t, integrator), (integrator) -> affectFMU!(nfmu, c, integrator, idx); rootfind = RightRootFind, save_positions = (saveEventPositions, saveEventPositions), interp_points = c.fmu.executionConfig.rootSearchInterpolationPoints, ) push!(callbacks, eventCb) end end end if max_execution_duration > 0.0 terminateCb = ContinuousCallback( (x, t, integrator) -> checkExecTime(integrator, nfmu, c, max_execution_duration), (integrator) -> terminate!(integrator); save_positions = (false, false), ) push!(callbacks, terminateCb) logInfo(nfmu.fmu, "Setting max execeution time to $(max_execution_duration)") end # custom callbacks for cb in nfmu.customCallbacksAfter push!(callbacks, cb) end if showProgress c.progressMeter = ProgressMeter.Progress(1000; desc = progressDescr, color = :blue, dt = 1.0) ProgressMeter.update!(c.progressMeter, 0) # show it! else c.progressMeter = nothing end # integrator step callback stepCb = FunctionCallingCallback( (x, t, integrator) -> stepCompleted(nfmu, c, x, t, integrator, t_start, t_stop); func_everystep = true, func_start = true, ) push!(callbacks, stepCb) # [ToDo] Allow for AD-primitives for sensitivity analysis of recorded values if saving c.solution.values = SavedValues( Float64, Tuple{collect(Float64 for i = 1:length(recordValues))...}, ) c.solution.valueReferences = recordValues if isnothing(saveat) savingCB = SavingCallback( (x, t, integrator) -> saveValues(nfmu, c, recordValues, x, t, integrator), c.solution.values, ) else savingCB = SavingCallback( (x, t, integrator) -> saveValues(nfmu, c, recordValues, x, t, integrator), c.solution.values, saveat = saveat, ) end push!(callbacks, savingCB) end if recordEigenvalues @assert recordEigenvaluesSensitivity ∈ (:none, :ForwardDiff, :ReverseDiff, :Zygote) "Keyword `recordEigenvaluesSensitivity` must be one of (:none, :ForwardDiff, :ReverseDiff, :Zygote)" recordEigenvaluesType = nothing if recordEigenvaluesSensitivity == :ForwardDiff recordEigenvaluesType = FMISensitivity.ForwardDiff.Dual elseif recordEigenvaluesSensitivity == :ReverseDiff recordEigenvaluesType = FMISensitivity.ReverseDiff.TrackedReal elseif recordEigenvaluesSensitivity ∈ (:none, :Zygote) recordEigenvaluesType = fmi2Real end dtypes = collect( recordEigenvaluesType for _ = 1:2*length(c.fmu.modelDescription.stateValueReferences) ) c.solution.eigenvalues = SavedValues(recordEigenvaluesType, Tuple{dtypes...}) savingCB = nothing if isnothing(saveat) savingCB = SavingCallback( (u, t, integrator) -> saveEigenvalues( nfmu, c, u, t, integrator, recordEigenvaluesSensitivity, ), c.solution.eigenvalues, ) else savingCB = SavingCallback( (u, t, integrator) -> saveEigenvalues( nfmu, c, u, t, integrator, recordEigenvaluesSensitivity, ), c.solution.eigenvalues, saveat = saveat, ) end push!(callbacks, savingCB) end end # ignore_derivatives prob = nothing function fx_ip(dx, x, p, t) fx(nfmu, c, dx, x, p, t) return nothing end # function fx_op(x, p, t) # return fx(nfmu, c, x, p, t) # end # function fx_jac(J, x, p, t) # J[:] = ReverseDiff.jacobian(_x -> fx_op(_x, p, t), x) # return nothing # end # function jvp(Jv, v, x, p, t) # n = length(x) # J = similar(x, (n, n)) # fx_jac(J, x, p, t) # Jv[:] = J * v # return nothing # end # function vjp(Jv, v, x, p, t) # n = length(x) # J = similar(x, (n, n)) # fx_jac(J, x, p, t) # Jv[:] = v' * J # return nothing # end ff = ODEFunction{true}(fx_ip) # ; jvp=jvp, vjp=vjp, jac=fx_jac) # tgrad=nothing prob = ODEProblem{true}(ff, nfmu.x0, nfmu.tspan, p) # [TODO] that (using ReverseDiffAdjoint) should work now with `autodiff=false` if isnothing(sensealg) #if isnothing(solver) # logWarning(nfmu.fmu, "No solver keyword detected for NeuralFMU.\nOnly relevant if you use AD: Continuous adjoint method is applied, which requires solving backward in time.\nThis might be not supported by every FMU.", 1) # sensealg = InterpolatingAdjoint(; autojacvec=ReverseDiffVJP(true), checkpointing=true) # elseif isimplicit(solver) # @assert !(alg_autodiff(solver) isa AutoForwardDiff) "Implicit solver using `autodiff=true` detected for NeuralFMU.\nThis is currently not supported, please use `autodiff=false` as solver keyword.\nExample: `Rosenbrock23(autodiff=false)` instead of `Rosenbrock23()`." # logWarning(nfmu.fmu, "Implicit solver detected for NeuralFMU.\nOnly relevant if you use AD: Continuous adjoint method is applied, which requires solving backward in time.\nThis might be not supported by every FMU.", 1) # sensealg = InterpolatingAdjoint(; autojacvec=ReverseDiffVJP(true), checkpointing=true) # else sensealg = ReverseDiffAdjoint() #end end args = Vector{Any}() kwargs = Dict{Symbol,Any}(nfmu.solvekwargs..., solvekwargs...) if !isnothing(saveat) kwargs[:saveat] = saveat end ignore_derivatives() do if !isnothing(solver) push!(args, solver) end end #kwargs[:callback]=CallbackSet(callbacks...) #kwargs[:sensealg]=sensealg #kwargs[:u0] = nfmu.x0 # this is because of `IntervalNonlinearProblem has no field u0` @debug "ME_NeuralFMU: Start solving ..." c.solution.states = solve( prob, args...; callback = CallbackSet(callbacks...), sensealg = sensealg, u0 = nfmu.x0, kwargs..., ) @debug "ME_NeuralFMU: ... finished solving!" ignore_derivatives() do @assert !isnothing(c.solution.states) "Solving NeuralODE returned `nothing`!" # ReverseDiff returns an array instead of an ODESolution, this needs to be corrected # [TODO] doesn`t Array cover the TrackedArray case? if isa(c.solution.states, TrackedArray) || isa(c.solution.states, Array) @assert !isnothing(saveat) "Keyword `saveat` is nothing, please provide the keyword when using ReverseDiff." t = collect(saveat) while t[1] < tspan[1] popfirst!(t) end while t[end] > tspan[end] pop!(t) end u = c.solution.states c.solution.success = (size(u) == (length(nfmu.x0), length(t))) if size(u)[2] > 0 # at least some recorded points c.solution.states = build_solution(prob, solver, t, collect(u[:, i] for i = 1:size(u)[2])) end else c.solution.success = (c.solution.states.retcode == ReturnCode.Success) end end # ignore_derivatives @debug "ME_NeuralFMU: Stopping callback..." # stopCB stopCallback(nfmu, c, t_stop) # cleanup snapshots to release memory if cleanSnapshots for snapshot in c.solution.snapshots FMIBase.freeSnapshot!(snapshot) end c.solution.snapshots = Vector{FMUSnapshot}(undef, 0) end return c.solution end function (nfmu::ME_NeuralFMU)(x0::Union{Array{<:Real},Nothing}, t::Real; p = nothing) c = nothing return fx(nfmu, c, x0, p, t) end """ ToDo: Docstring for Arguments, Keyword arguments, ... Evaluates the CS_NeuralFMU in the timespan given during construction or in a custum timespan from `t_start` to `t_stop` with a given time step size `t_step`. Via optional argument `reset`, the FMU is reset every time evaluation is started (default=`true`). """ function (nfmu::CS_NeuralFMU{F,C})( inputFct, t_step::Real, tspan::Tuple{Float64,Float64} = nfmu.tspan; p = nfmu.p, tolerance::Union{Real,Nothing} = nothing, parameters::Union{Dict{<:Any,<:Any},Nothing} = nothing, ) where {F,C} t_start, t_stop = tspan c = (hasCurrentInstance(nfmu.fmu) ? getCurrentInstance(nfmu.fmu) : nothing) c, _ = prepareSolveFMU( nfmu.fmu, c, fmi2TypeCoSimulation; parameters = parameters, t_start = t_start, t_stop = t_stop, tolerance = tolerance, cleanup = true, ) ts = collect(t_start:t_step:t_stop) model_input = inputFct.(ts) firstStep = true function simStep(input) y = nothing if !firstStep ignore_derivatives() do fmi2DoStep(c, t_step) end else firstStep = false end if p == nothing # structured, implicite parameters y = nfmu.model(input) else # flattened, explicite parameters @assert !isnothing(nfmu.re) "Using explicite parameters without destructing the model." y = nfmu.re(p)(input) end return y end valueStack = simStep.(model_input) ignore_derivatives() do c.solution.success = true end c.solution.values = SavedValues{typeof(ts[1]),typeof(valueStack[1])}(ts, valueStack) # [ToDo] check if this is still the case for current releases of related libraries # this is not possible in CS (pullbacks are sometimes called after the finished simulation), clean-up happens at the next call # c = finishSolveFMU(nfmu.fmu, c, freeInstance, terminate) return c.solution end function (nfmu::CS_NeuralFMU{Vector{F},Vector{C}})( inputFct, t_step::Real, tspan::Tuple{Float64,Float64} = nfmu.tspan; p = nothing, tolerance::Union{Real,Nothing} = nothing, parameters::Union{Vector{Union{Dict{<:Any,<:Any},Nothing}},Nothing} = nothing, ) where {F,C} t_start, t_stop = tspan numFMU = length(nfmu.fmu) cs = nothing ignore_derivatives() do cs = Vector{Union{FMU2Component,Nothing}}(undef, numFMU) for i = 1:numFMU cs[i] = ( hasCurrentInstance(nfmu.fmu[i]) ? getCurrentInstance(nfmu.fmu[i]) : nothing ) end end for i = 1:numFMU cs[i], _ = prepareSolveFMU( nfmu.fmu[i], cs[i], fmi2TypeCoSimulation; parameters = parameters, t_start = t_start, t_stop = t_stop, tolerance = tolerance, cleanup = true, ) end solution = FMUSolution(nothing) ts = collect(t_start:t_step:t_stop) model_input = inputFct.(ts) firstStep = true function simStep(input) y = nothing if !firstStep ignore_derivatives() do for c in cs fmi2DoStep(c, t_step) end end else firstStep = false end if p == nothing # structured, implicite parameters y = nfmu.model(input) else # flattened, explicite parameters @assert nfmu.re != nothing "Using explicite parameters without destructing the model." if length(p) == 1 y = nfmu.re(p[1])(input) else y = nfmu.re(p)(input) end end return y end valueStack = simStep.(model_input) ignore_derivatives() do solution.success = true # ToDo: Check successful simulation! end solution.values = SavedValues{typeof(ts[1]),typeof(valueStack[1])}(ts, valueStack) # [ToDo] check if this is still the case for current releases of related libraries # this is not possible in CS (pullbacks are sometimes called after the finished simulation), clean-up happens at the next call # cs = finishSolveFMU(nfmu.fmu, cs, freeInstance, terminate) return solution end # adapting the Flux functions function Flux.params(nfmu::ME_NeuralFMU; destructure::Bool = false) if destructure nfmu.p, nfmu.re = Flux.destructure(nfmu.model) end ps = Flux.params(nfmu.p) if issense(ps) @warn "Parameters include AD-primitives, this indicates that something did go wrong in before." end return ps end function Flux.params(nfmu::CS_NeuralFMU; destructure::Bool = false) # true) if destructure nfmu.p, nfmu.re = Flux.destructure(nfmu.model) # else # return Flux.params(nfmu.model) end ps = Flux.params(nfmu.p) if issense(ps) @warn "Parameters include AD-primitives, this indicates that something did go wrong in before." end return ps end function computeGradient!( jac, loss, params, gradient::Symbol, chunk_size::Union{Symbol,Int}, multiObjective::Bool, ) if gradient == :ForwardDiff if chunk_size == :auto_forwarddiff if multiObjective conf = ForwardDiff.JacobianConfig(loss, params) ForwardDiff.jacobian!(jac, loss, params, conf) else conf = ForwardDiff.GradientConfig(loss, params) ForwardDiff.gradient!(jac, loss, params, conf) end elseif chunk_size == :auto_fmiflux chunk_size = DEFAULT_CHUNK_SIZE if multiObjective conf = ForwardDiff.JacobianConfig( loss, params, ForwardDiff.Chunk{min(chunk_size, length(params))}(), ) ForwardDiff.jacobian!(jac, loss, params, conf) else conf = ForwardDiff.GradientConfig( loss, params, ForwardDiff.Chunk{min(chunk_size, length(params))}(), ) ForwardDiff.gradient!(jac, loss, params, conf) end else if multiObjective conf = ForwardDiff.JacobianConfig( loss, params, ForwardDiff.Chunk{min(chunk_size, length(params))}(), ) ForwardDiff.jacobian!(jac, loss, params, conf) else conf = ForwardDiff.GradientConfig( loss, params, ForwardDiff.Chunk{min(chunk_size, length(params))}(), ) ForwardDiff.gradient!(jac, loss, params, conf) end end elseif gradient == :Zygote if multiObjective jac[:] = Zygote.jacobian(loss, params)[1] else jac[:] = Zygote.gradient(loss, params)[1] end elseif gradient == :ReverseDiff if multiObjective ReverseDiff.jacobian!(jac, loss, params) else ReverseDiff.gradient!(jac, loss, params) end elseif gradient == :FiniteDiff if multiObjective FiniteDiff.finite_difference_jacobian!(jac, loss, params) else FiniteDiff.finite_difference_gradient!(jac, loss, params) end else @assert false "Unknown `gradient=$(gradient)`, supported are `:ForwardDiff`, `:Zygote`, `:FiniteDiff` and `:ReverseDiff`." end ### check gradient is valid # [Todo] Better! grads = nothing if multiObjective grads = collect(jac[i, :] for i = 1:size(jac)[1]) else grads = [jac] end all_zero = any(collect(all(iszero.(grad)) for grad in grads)) has_nan = any(collect(any(isnan.(grad)) for grad in grads)) has_nothing = any(collect(any(isnothing.(grad)) for grad in grads)) || any(isnothing.(grads)) @assert !all_zero "Determined gradient containes only zeros.\nThis might be because the loss function is:\n(a) not sensitive regarding the model parameters or\n(b) sensitivities regarding the model parameters are not traceable via AD." if gradient != :ForwardDiff && (has_nan || has_nothing) @warn "Gradient determination with $(gradient) failed, because gradient contains `NaNs` and/or `nothing`.\nThis might be because the FMU is throwing redundant events, which is currently not supported.\nTrying ForwardDiff as back-up.\nIf this message gets printed (almost) every step, consider using keyword `gradient=:ForwardDiff` to fix ForwardDiff as sensitivity system." gradient = :ForwardDiff computeGradient!(jac, loss, params, gradient, chunk_size, multiObjective) if multiObjective grads = collect(jac[i, :] for i = 1:size(jac)[1]) else grads = [jac] end end has_nan = any(collect(any(isnan.(grad)) for grad in grads)) has_nothing = any(collect(any(isnothing.(grad)) for grad in grads)) || any(isnothing.(grads)) @assert !has_nan "Gradient determination with $(gradient) failed, because gradient contains `NaNs`.\nNo back-up options available." @assert !has_nothing "Gradient determination with $(gradient) failed, because gradient contains `nothing`.\nNo back-up options available." return nothing end lk_TrainApply = ReentrantLock() function trainStep( loss, params, gradient, chunk_size, optim::FMIFlux.AbstractOptimiser, printStep, proceed_on_assert, cb, multiObjective, ) global lk_TrainApply #try for j = 1:length(params) step = FMIFlux.apply!(optim, params[j]) lock(lk_TrainApply) do params[j] .-= step if printStep @info "Step: min(abs()) = $(min(abs.(step)...)) max(abs()) = $(max(abs.(step)...))" end end end # catch e # if proceed_on_assert # msg = "$(e)" # msg = length(msg) > 4096 ? first(msg, 4096) * "..." : msg # @error "Training asserted, but continuing: $(msg)" # else # throw(e) # end # end if cb != nothing if isa(cb, AbstractArray) for _cb in cb _cb() end else cb() end end end """ train!(loss, neuralFMU::Union{ME_NeuralFMU, CS_NeuralFMU}, data, optim; gradient::Symbol=:ReverseDiff, kwargs...) A function analogous to Flux.train! but with additional features and explicit parameters (faster). # Arguments - `loss` a loss function in the format `loss(p)` - `neuralFMU` a object holding the neuralFMU with its parameters - `data` the training data (or often an iterator) - `optim` the optimizer used for training # Keywords - `gradient` a symbol determining the AD-library for gradient computation, available are `:ForwardDiff`, `:Zygote` and :ReverseDiff (default) - `cb` a custom callback function that is called after every training step (default `nothing`) - `chunk_size` the chunk size for AD using ForwardDiff (ignored for other AD-methods) (default `:auto_fmiflux`) - `printStep` a boolean determining wheater the gradient min/max is printed after every step (for gradient debugging) (default `false`) - `proceed_on_assert` a boolean that determins wheater to throw an ecxeption on error or proceed training and just print the error (default `false`) - `multiThreading`: a boolean that determins if multiple gradients are generated in parallel (default `false`) - `multiObjective`: set this if the loss function returns multiple values (multi objective optimization), currently gradients are fired to the optimizer one after another (default `false`) """ function train!( loss, neuralFMU::Union{ME_NeuralFMU,CS_NeuralFMU}, data, optim; gradient::Symbol = :ReverseDiff, kwargs..., ) params = Flux.params(neuralFMU) snapshots = neuralFMU.snapshots # [Note] :ReverseDiff, :Zygote need it for state change sampling and the rrule # :ForwardDiff needs it for state change sampling neuralFMU.snapshots = true _train!(loss, params, data, optim; gradient = gradient, kwargs...) neuralFMU.snapshots = snapshots neuralFMU.p = unsense(neuralFMU.p) return nothing end # Dispatch for FMIFlux.jl [FMIFlux.AbstractOptimiser] function _train!( loss, params::Union{Flux.Params,Zygote.Params,AbstractVector{<:AbstractVector{<:Real}}}, data, optim::FMIFlux.AbstractOptimiser; gradient::Symbol = :ReverseDiff, cb = nothing, chunk_size::Union{Integer,Symbol} = :auto_fmiflux, printStep::Bool = false, proceed_on_assert::Bool = false, multiThreading::Bool = false, multiObjective::Bool = false, ) if length(params) <= 0 || length(params[1]) <= 0 @warn "train!(...): Empty parameter array, training on an empty parameter array doesn't make sense." return end if multiThreading && Threads.nthreads() == 1 @warn "train!(...): Multi-threading is set via flag `multiThreading=true`, but this Julia process does not have multiple threads. This will not result in a speed-up. Please spawn Julia in multi-thread mode to speed-up training." end _trainStep = (i,) -> trainStep( loss, params, gradient, chunk_size, optim, printStep, proceed_on_assert, cb, multiObjective, ) if multiThreading ThreadPools.qforeach(_trainStep, 1:length(data)) else foreach(_trainStep, 1:length(data)) end end # Dispatch for Flux.jl [Flux.Optimise.AbstractOptimiser] function _train!( loss, params::Union{Flux.Params,Zygote.Params,AbstractVector{<:AbstractVector{<:Real}}}, data, optim::Flux.Optimise.AbstractOptimiser; gradient::Symbol = :ReverseDiff, chunk_size::Union{Integer,Symbol} = :auto_fmiflux, multiObjective::Bool = false, kwargs..., ) grad_buffer = nothing if multiObjective dim = loss(params[1]) grad_buffer = zeros(Float64, length(params[1]), length(dim)) else grad_buffer = zeros(Float64, length(params[1])) end grad_fun! = (G, p) -> computeGradient!(G, loss, p, gradient, chunk_size, multiObjective) _optim = FluxOptimiserWrapper(optim, grad_fun!, grad_buffer) _train!( loss, params, data, _optim; gradient = gradient, chunk_size = chunk_size, multiObjective = multiObjective, kwargs..., ) end # Dispatch for Optim.jl [Optim.AbstractOptimizer] function _train!( loss, params::Union{Flux.Params,Zygote.Params,AbstractVector{<:AbstractVector{<:Real}}}, data, optim::Optim.AbstractOptimizer; gradient::Symbol = :ReverseDiff, chunk_size::Union{Integer,Symbol} = :auto_fmiflux, multiObjective::Bool = false, kwargs..., ) if length(params) <= 0 || length(params[1]) <= 0 @warn "train!(...): Empty parameter array, training on an empty parameter array doesn't make sense." return end grad_fun! = (G, p) -> computeGradient!(G, loss, p, gradient, chunk_size, multiObjective) _optim = OptimOptimiserWrapper(optim, grad_fun!, loss, params[1]) _train!( loss, params, data, _optim; gradient = gradient, chunk_size = chunk_size, multiObjective = multiObjective, kwargs..., ) end
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
[ "MIT" ]
0.13.0
7fec0fac72076ea32823fb59efcc0e280cd28162
code
2510
# # Copyright (c) 2021 Tobias Thummerer, Lars Mikelsons # Licensed under the MIT license. See LICENSE file in the project root for details. # import Flux import Optim abstract type AbstractOptimiser end ### Optim.jl ### struct OptimOptimiserWrapper{G} <: AbstractOptimiser optim::Optim.AbstractOptimizer grad_fun!::G state::Union{Optim.AbstractOptimizerState,Nothing} d::Union{Optim.OnceDifferentiable,Nothing} options::Any function OptimOptimiserWrapper( optim::Optim.AbstractOptimizer, grad_fun!::G, loss, params, ) where {G} options = Optim.Options( outer_iterations = 1, iterations = 1, g_calls_limit = 1, f_calls_limit = 5, ) # should be ignored anyway, because function `g!` is given autodiff = :forward # = ::finite inplace = true d = Optim.promote_objtype(optim, params, autodiff, inplace, loss, grad_fun!) state = Optim.initial_state(optim, options, d, params) return new{G}(optim, grad_fun!, state, d, options) end end export OptimOptimiserWrapper function apply!(optim::OptimOptimiserWrapper, params) res = Optim.optimize(optim.d, params, optim.optim, optim.options, optim.state) step = params .- Optim.minimizer(res) return step end ### Flux.Optimisers ### struct FluxOptimiserWrapper{G} <: AbstractOptimiser optim::Flux.Optimise.AbstractOptimiser grad_fun!::G grad_buffer::Union{AbstractVector{Float64},AbstractMatrix{Float64}} multiGrad::Bool function FluxOptimiserWrapper( optim::Flux.Optimise.AbstractOptimiser, grad_fun!::G, grad_buffer::AbstractVector{Float64}, ) where {G} return new{G}(optim, grad_fun!, grad_buffer, false) end function FluxOptimiserWrapper( optim::Flux.Optimise.AbstractOptimiser, grad_fun!::G, grad_buffer::AbstractMatrix{Float64}, ) where {G} return new{G}(optim, grad_fun!, grad_buffer, true) end end export FluxOptimiserWrapper function apply!(optim::FluxOptimiserWrapper, params) optim.grad_fun!(optim.grad_buffer, params) if optim.multiGrad return collect( Flux.Optimise.apply!(optim.optim, params, optim.grad_buffer[:, i]) for i = 1:size(optim.grad_buffer)[2] ) else return Flux.Optimise.apply!(optim.optim, params, optim.grad_buffer) end end ### generic FMIFlux.AbstractOptimiser ###
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
[ "MIT" ]
0.13.0
7fec0fac72076ea32823fb59efcc0e280cd28162
code
13760
# # Copyright (c) 2021 Tobias Thummerer, Lars Mikelsons # Licensed under the MIT license. See LICENSE file in the project root for details. # import Printf using Colors abstract type BatchScheduler end # ToDo: DocString update. """ Computes all batch element losses. Picks the batch element with the greatest loss as next training element. """ mutable struct WorstElementScheduler <: BatchScheduler ### mandatory ### step::Integer elementIndex::Integer applyStep::Integer plotStep::Integer batch::Any neuralFMU::NeuralFMU losses::Vector{Float64} logLoss::Bool ### type specific ### lossFct::Any runkwargs::Any printMsg::String updateStep::Integer excludeIndices::Any function WorstElementScheduler( neuralFMU::NeuralFMU, batch, lossFct = Flux.Losses.mse; applyStep::Integer = 1, plotStep::Integer = 1, updateStep::Integer = 1, excludeIndices = nothing, ) inst = new() inst.neuralFMU = neuralFMU inst.step = 0 inst.elementIndex = 0 inst.batch = batch inst.lossFct = lossFct inst.applyStep = applyStep inst.plotStep = plotStep inst.losses = [] inst.logLoss = false inst.printMsg = "" inst.updateStep = updateStep inst.excludeIndices = excludeIndices return inst end end # ToDo: DocString update. """ Computes all batch element losses. Picks the batch element with the greatest accumulated loss as next training element. If picked, accumulated loss is resetted. (Prevents starvation of batch elements with little loss) """ mutable struct LossAccumulationScheduler <: BatchScheduler ### mandatory ### step::Integer elementIndex::Integer applyStep::Integer plotStep::Integer batch::Any neuralFMU::NeuralFMU losses::Vector{Float64} logLoss::Bool ### type specific ### lossFct::Any runkwargs::Any printMsg::String lossAccu::Array{<:Real} updateStep::Integer function LossAccumulationScheduler( neuralFMU::NeuralFMU, batch, lossFct = Flux.Losses.mse; applyStep::Integer = 1, plotStep::Integer = 1, updateStep::Integer = 1, ) inst = new() inst.neuralFMU = neuralFMU inst.step = 0 inst.elementIndex = 0 inst.batch = batch inst.lossFct = lossFct inst.applyStep = applyStep inst.plotStep = plotStep inst.updateStep = updateStep inst.losses = [] inst.logLoss = false inst.printMsg = "" inst.lossAccu = zeros(length(batch)) return inst end end # ToDo: DocString update. """ Computes all batch element losses. Picks the batch element with the greatest grow in loss (derivative) as next training element. """ mutable struct WorstGrowScheduler <: BatchScheduler ### mandatory ### step::Integer elementIndex::Integer applyStep::Integer plotStep::Integer batch::Any neuralFMU::NeuralFMU losses::Vector{Float64} logLoss::Bool ### type specific ### lossFct::Any runkwargs::Any printMsg::String function WorstGrowScheduler( neuralFMU::NeuralFMU, batch, lossFct = Flux.Losses.mse; applyStep::Integer = 1, plotStep::Integer = 1, ) inst = new() inst.neuralFMU = neuralFMU inst.step = 0 inst.elementIndex = 0 inst.batch = batch inst.lossFct = lossFct inst.applyStep = applyStep inst.plotStep = plotStep inst.losses = [] inst.logLoss = true # this is because this scheduler estimates derivatives inst.printMsg = "" return inst end end # ToDo: DocString update. """ Picks a random batch element as next training element. """ mutable struct RandomScheduler <: BatchScheduler ### mandatory ### step::Integer elementIndex::Integer applyStep::Integer plotStep::Integer batch::Any neuralFMU::NeuralFMU losses::Vector{Float64} logLoss::Bool ### type specific ### printMsg::String function RandomScheduler( neuralFMU::NeuralFMU, batch; applyStep::Integer = 1, plotStep::Integer = 1, ) inst = new() inst.neuralFMU = neuralFMU inst.step = 0 inst.elementIndex = 0 inst.batch = batch inst.applyStep = applyStep inst.plotStep = plotStep inst.losses = [] inst.logLoss = false inst.printMsg = "" return inst end end # ToDo: DocString update. """ Sequentially runs over all elements. """ mutable struct SequentialScheduler <: BatchScheduler ### mandatory ### step::Integer elementIndex::Integer applyStep::Integer plotStep::Integer batch::Any neuralFMU::NeuralFMU losses::Vector{Float64} logLoss::Bool ### type specific ### printMsg::String function SequentialScheduler( neuralFMU::NeuralFMU, batch; applyStep::Integer = 1, plotStep::Integer = 1, ) inst = new() inst.neuralFMU = neuralFMU inst.step = 0 inst.elementIndex = 0 inst.batch = batch inst.applyStep = applyStep inst.plotStep = plotStep inst.losses = [] inst.logLoss = false inst.printMsg = "" return inst end end function initialize!(scheduler::BatchScheduler; print::Bool = true, runkwargs...) lastIndex = 0 scheduler.step = 0 scheduler.elementIndex = 0 if hasfield(typeof(scheduler), :runkwargs) scheduler.runkwargs = runkwargs end scheduler.elementIndex = apply!(scheduler; print = print) if scheduler.plotStep > 0 plot(scheduler, lastIndex) end end function update!(scheduler::BatchScheduler; print::Bool = true) lastIndex = scheduler.elementIndex scheduler.step += 1 if scheduler.applyStep > 0 && scheduler.step % scheduler.applyStep == 0 scheduler.elementIndex = apply!(scheduler; print = print) end # max/avg error num = length(scheduler.batch) losssum = 0.0 avgsum = 0.0 maxe = 0.0 for i = 1:num l = nominalLoss(scheduler.batch[i]) l = l == Inf ? 0.0 : l losssum += l avgsum += l / num if l > maxe maxe = l end end push!(scheduler.losses, losssum) if print scheduler.printMsg = "AVG: $(roundToLength(avgsum, 8)) | MAX: $(roundToLength(maxe, 8)) | SUM: $(roundToLength(losssum, 8))" @info scheduler.printMsg end if scheduler.plotStep > 0 && scheduler.step % scheduler.plotStep == 0 plot(scheduler, lastIndex) end end function plot(scheduler::BatchScheduler, lastIndex::Integer) num = length(scheduler.batch) xs = 1:num ys = collect((nominalLoss(b) != Inf ? nominalLoss(b) : 0.0) for b in scheduler.batch) ys_shadow = collect( (length(b.losses) > 1 ? nominalLoss(b.losses[end-1]) : 1e-16) for b in scheduler.batch ) title = "[$(scheduler.step)]" if hasfield(typeof(scheduler), :printMsg) title = title * " " * scheduler.printMsg end fig = Plots.plot(; layout = Plots.grid(2, 1), size = (480, 960), xlabel = "Batch ID", ylabel = "Loss", background_color_legend = colorant"rgba(255,255,255,0.5)", title = title, ) if hasfield(typeof(scheduler), :lossAccu) normScale = max(ys..., ys_shadow...) / max(scheduler.lossAccu...) Plots.bar!( fig[1], xs, scheduler.lossAccu .* normScale, label = "Accum. loss (norm.)", color = :blue, bar_width = 1.0, alpha = 0.2, ) end good = [] bad = [] for i = 1:num if ys[i] > ys_shadow[i] push!(bad, i) else push!(good, i) end end Plots.bar!( fig[1], xs[good], ys[good], label = "Loss (better)", color = :green, bar_width = 1.0, ) Plots.bar!( fig[1], xs[bad], ys[bad], label = "Loss (worse)", color = :orange, bar_width = 1.0, ) for i = 1:length(ys_shadow) Plots.plot!( fig[1], [xs[i] - 0.5, xs[i] + 0.5], [ys_shadow[i], ys_shadow[i]], label = (i == 1 ? "Last loss" : :none), linewidth = 2, color = :black, ) end if lastIndex > 0 Plots.plot!( fig[1], [lastIndex], [0.0], color = :pink, marker = :circle, label = "Current ID [$(lastIndex)]", markersize = 5.0, ) # current batch element end Plots.plot!( fig[1], [scheduler.elementIndex], [0.0], color = :pink, marker = :circle, label = "Next ID [$(scheduler.elementIndex)]", markersize = 3.0, ) # next batch element Plots.plot!(fig[2], 1:length(scheduler.losses), scheduler.losses; yaxis = :log) display(fig) end """ Rounds a given `number` to a string with a maximum length of `len`. Exponentials are used if suitable. """ function roundToLength(number::Real, len::Integer) @assert len >= 5 "`len` must be at least `5`." if number == 0.0 return "0.0" end isneg = false if number < 0.0 isneg = true number = -number len -= 1 # we need one digit for the "-" end expLen = 0 if abs(number) <= 1.0 expLen = Integer(floor(log10(1.0 / number))) + 1 else expLen = Integer(floor(log10(number))) + 1 end len -= 4 # spaces needed for "+" (or "-"), "e", "." and leading number if expLen >= 100 len -= 3 # 3 spaces needed for large exponent else len -= 2 # 2 spaces needed for regular exponent end if isneg number = -number end return Printf.format(Printf.Format("%.$(len)e"), number) end function apply!(scheduler::WorstElementScheduler; print::Bool = true) avgsum = 0.0 losssum = 0.0 maxe = 0.0 maxind = 0 updateAll = (scheduler.step % scheduler.updateStep == 0) num = length(scheduler.batch) for i = 1:num l = (nominalLoss(scheduler.batch[i]) != Inf ? nominalLoss(scheduler.batch[i]) : 0.0) if updateAll FMIFlux.run!(scheduler.neuralFMU, scheduler.batch[i]; scheduler.runkwargs...) FMIFlux.loss!( scheduler.batch[i], scheduler.lossFct; logLoss = scheduler.logLoss, ) l = nominalLoss(scheduler.batch[i]) end losssum += l avgsum += l / num if isnothing(scheduler.excludeIndices) || i ∉ scheduler.excludeIndices if l > maxe maxe = l maxind = i end end end return maxind end function apply!(scheduler::LossAccumulationScheduler; print::Bool = true) avgsum = 0.0 losssum = 0.0 maxe = 0.0 nextind = 1 # reset current accu loss if scheduler.elementIndex > 0 scheduler.lossAccu[scheduler.elementIndex] = 0.0 end updateAll = (scheduler.step % scheduler.updateStep == 0) num = length(scheduler.batch) for i = 1:num l = (nominalLoss(scheduler.batch[i]) != Inf ? nominalLoss(scheduler.batch[i]) : 0.0) if updateAll FMIFlux.run!(scheduler.neuralFMU, scheduler.batch[i]; scheduler.runkwargs...) FMIFlux.loss!( scheduler.batch[i], scheduler.lossFct; logLoss = scheduler.logLoss, ) l = nominalLoss(scheduler.batch[i]) end scheduler.lossAccu[i] += l losssum += l avgsum += l / num if l > maxe maxe = l end end # find largest accumulated loss for i = 1:num if scheduler.lossAccu[i] > scheduler.lossAccu[nextind] nextind = i end end return nextind end function apply!(scheduler::WorstGrowScheduler; print::Bool = true) avgsum = 0.0 losssum = 0.0 maxe = 0.0 maxe_der = -Inf maxind = 0 num = length(scheduler.batch) for i = 1:num FMIFlux.run!(scheduler.neuralFMU, scheduler.batch[i]; scheduler.runkwargs...) l = FMIFlux.loss!( scheduler.batch[i], scheduler.lossFct; logLoss = scheduler.logLoss, ) l_der = l # fallback for first run (greatest error) if length(scheduler.batch[i].losses) >= 2 l_der = (l - nominalLoss(scheduler.batch[i].losses[end-1])) end losssum += l avgsum += l / num if l > maxe maxe = l end if l_der > maxe_der maxe_der = l_der maxind = i end end return maxind end function apply!(scheduler::RandomScheduler; print::Bool = true) next = rand(1:length(scheduler.batch)) if print @info "Current step: $(scheduler.step) | Current element=$(scheduler.elementIndex) | Next element=$(next)" end return next end function apply!(scheduler::SequentialScheduler; print::Bool = true) next = scheduler.elementIndex + 1 if next > length(scheduler.batch) next = 1 end if print @info "Current step: $(scheduler.step) | Current element=$(scheduler.elementIndex) | Next element=$(next)" end return next end
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
[ "MIT" ]
0.13.0
7fec0fac72076ea32823fb59efcc0e280cd28162
code
2203
# # Copyright (c) 2021 Tobias Thummerer, Lars Mikelsons # Licensed under the MIT license. See LICENSE file in the project root for details. # using FMIFlux.Flux import Random Random.seed!(1234); t_start = 0.0 t_step = 0.01 t_stop = 50.0 tData = t_start:t_step:t_stop # generate training data posData, velData, accData = syntTrainingData(tData) # load FMU for NeuralFMU fmu = loadFMU("SpringPendulum1D", EXPORTINGTOOL, EXPORTINGVERSION; type = :ME) using FMIFlux.FMIImport using FMIFlux.FMIImport.FMICore # loss function for training losssum_single = function (p) global problem, X0, posData solution = problem(X0; p = p, showProgress = false, saveat = tData) if !solution.success return Inf end posNet = getState(solution, 1; isIndex = true) return Flux.Losses.mse(posNet, posData) end losssum_multi = function (p) global problem, X0, posData solution = problem(X0; p = p, showProgress = false, saveat = tData) if !solution.success return [Inf, Inf] end posNet = getState(solution, 1; isIndex = true) velNet = getState(solution, 2; isIndex = true) return [Flux.Losses.mse(posNet, posData), Flux.Losses.mse(velNet, velData)] end numStates = length(fmu.modelDescription.stateValueReferences) c1 = CacheLayer() c2 = CacheRetrieveLayer(c1) # the "Chain" for training net = Chain( x -> fmu(; x = x, dx_refs = :all), dx -> c1(dx), Dense(numStates, 12, tanh), Dense(12, 1, identity), dx -> c2(1, dx[1]), ) solver = Tsit5() problem = ME_NeuralFMU(fmu, net, (t_start, t_stop), solver; saveat = tData) @test problem != nothing # before p_net = Flux.params(problem) lossBefore = losssum_single(p_net[1]) # single objective optim = OPTIMISER(ETA) FMIFlux.train!( losssum_single, problem, Iterators.repeated((), NUMSTEPS), optim; gradient = GRADIENT, ) # multi objective # lastLoss = sum(losssum_multi(p_net[1])) # optim = OPTIMISER(ETA) # FMIFlux.train!(losssum_multi, problem, Iterators.repeated((), NUMSTEPS), optim; gradient=GRADIENT, multiObjective=true) # after lossAfter = losssum_single(p_net[1]) @test lossAfter < lossBefore @test length(fmu.components) <= 1 unloadFMU(fmu)
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
[ "MIT" ]
0.13.0
7fec0fac72076ea32823fb59efcc0e280cd28162
code
322
using PkgEval using FMIFlux using Test config = Configuration(; julia = "1.10", time_limit = 120 * 60); package = Package(; name = "FMIFlux"); @info "PkgEval" result = evaluate([config], [package]) @info "Result" println(result) @info "Log" println(result[1, :log]) @test result[1, :status] == :ok
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
[ "MIT" ]
0.13.0
7fec0fac72076ea32823fb59efcc0e280cd28162
code
2526
# # Copyright (c) 2021 Tobias Thummerer, Lars Mikelsons # Licensed under the MIT license. See LICENSE file in the project root for details. # using Flux using DifferentialEquations: Tsit5 import Random Random.seed!(1234); t_start = 0.0 t_step = 0.01 t_stop = 5.0 tData = t_start:t_step:t_stop # generate training data posData, velData, accData = syntTrainingData(tData) # load FMU for NeuralFMU # [TODO] Replace by a suitable discontinuous FMU fmu = loadFMU("SpringPendulum1D", EXPORTINGTOOL, EXPORTINGVERSION; type = :ME) using FMIFlux.FMIImport using FMIFlux.FMIImport.FMICore c = fmi2Instantiate!(fmu) fmi2SetupExperiment(c, 0.0, 1.0) fmi2EnterInitializationMode(c) fmi2ExitInitializationMode(c) p_refs = fmu.modelDescription.parameterValueReferences p = fmi2GetReal(c, p_refs) # loss function for training losssum = function (p) #@info "$p" global problem, X0, posData, solution solution = problem(X0; p = p, showProgress = true, saveat = tData) if !solution.success return Inf end posNet = getState(solution, 1; isIndex = true) return Flux.Losses.mse(posNet, posData) end numStates = length(fmu.modelDescription.stateValueReferences) # the "Chain" for training net = Chain(FMUParameterRegistrator(fmu, p_refs, p), x -> fmu(x = x, dx_refs = :all)) # , fmuLayer(p)) optim = OPTIMISER(ETA) solver = Tsit5() problem = ME_NeuralFMU(fmu, net, (t_start, t_stop), solver) problem.modifiedState = false @test problem != nothing solutionBefore = problem(X0; saveat = tData) @test solutionBefore.success @test length(solutionBefore.states.t) == length(tData) @test solutionBefore.states.t[1] == t_start @test solutionBefore.states.t[end] == t_stop # train it ... p_net = Flux.params(problem) @test length(p_net) == 1 @test length(p_net[1]) == 7 lossBefore = losssum(p_net[1]) @info "Start-Loss for net: $(lossBefore)" # [ToDo] Discontinuous system? # j_fin = FiniteDiff.finite_difference_gradient(losssum, p_net[1]) # j_fwd = ForwardDiff.gradient(losssum, p_net[1]) # j_rwd = ReverseDiff.gradient(losssum, p_net[1]) FMIFlux.train!( losssum, problem, Iterators.repeated((), NUMSTEPS), optim; gradient = GRADIENT, ) # check results solutionAfter = problem(X0; saveat = tData) @test solutionAfter.success @test length(solutionAfter.states.t) == length(tData) @test solutionAfter.states.t[1] == t_start @test solutionAfter.states.t[end] == t_stop lossAfter = losssum(p_net[1]) @test lossAfter < lossBefore @test length(fmu.components) <= 1 unloadFMU(fmu)
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
[ "MIT" ]
0.13.0
7fec0fac72076ea32823fb59efcc0e280cd28162
code
1645
# # Copyright (c) 2021 Tobias Thummerer, Lars Mikelsons # Licensed under the MIT license. See LICENSE file in the project root for details. # using Flux import Random Random.seed!(1234); t_start = 0.0 t_step = 0.01 t_stop = 5.0 tData = t_start:t_step:t_stop # generate training data posData, velData, accData = syntTrainingData(tData) fmu = loadFMU("SpringPendulumExtForce1D", EXPORTINGTOOL, EXPORTINGVERSION; type = :CS) # sine(t) as external force extForce = function (t) return [sin(t)] end # loss function for training losssum = function (p) solution = problem(extForce, t_step; p = p) if !solution.success return Inf end accNet = getValue(solution, 1; isIndex = true) Flux.Losses.mse(accNet, accData) end # NeuralFMU setup numInputs = length(fmu.modelDescription.inputValueReferences) numOutputs = length(fmu.modelDescription.outputValueReferences) net = Chain( u -> fmu(; u_refs = fmu.modelDescription.inputValueReferences, u = u, y_refs = fmu.modelDescription.outputValueReferences, ), Dense(numOutputs, 16, tanh; init = Flux.identity_init), Dense(16, 16, tanh; init = Flux.identity_init), Dense(16, numOutputs; init = Flux.identity_init), ) problem = CS_NeuralFMU(fmu, net, (t_start, t_stop)) @test problem != nothing # train it ... p_net = Flux.params(problem) lossBefore = losssum(p_net[1]) optim = OPTIMISER(ETA) FMIFlux.train!( losssum, problem, Iterators.repeated((), NUMSTEPS), optim; gradient = GRADIENT, ) lossAfter = losssum(p_net[1]) @test lossAfter < lossBefore @test length(fmu.components) <= 1 unloadFMU(fmu)
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
[ "MIT" ]
0.13.0
7fec0fac72076ea32823fb59efcc0e280cd28162
code
6475
# # Copyright (c) 2021 Tobias Thummerer, Lars Mikelsons # Licensed under the MIT license. See LICENSE file in the project root for details. # using Flux using DifferentialEquations import Random Random.seed!(1234); t_start = 0.0 t_step = 0.01 t_stop = 5.0 tData = t_start:t_step:t_stop # generate training data posData, velData, accData = syntTrainingData(tData) # load FMU for NeuralFMU fmu = loadFMU("SpringPendulum1D", EXPORTINGTOOL, EXPORTINGVERSION; type = :ME) # loss function for training losssum = function (p) global problem, X0, posData solution = problem(X0; p = p, saveat = tData) if !solution.success return Inf end posNet = getState(solution, 1; isIndex = true) velNet = getState(solution, 2; isIndex = true) return Flux.Losses.mse(posNet, posData) + Flux.Losses.mse(velNet, velData) end numStates = length(fmu.modelDescription.stateValueReferences) # some NeuralFMU setups nets = [] c1 = CacheLayer() c2 = CacheRetrieveLayer(c1) c3 = CacheLayer() c4 = CacheRetrieveLayer(c3) init = Flux.glorot_uniform getVRs = [stringToValueReference(fmu, "mass.s")] numGetVRs = length(getVRs) y = zeros(fmi2Real, numGetVRs) setVRs = [stringToValueReference(fmu, "mass.m")] numSetVRs = length(setVRs) setVal = [1.1] # 1. default ME-NeuralFMU (learn dynamics and states, almost-neutral setup, parameter count << 100) net = Chain( x -> c1(x), Dense(numStates, 1, tanh; init = init), x -> c2(x[1], 1), x -> fmu(; x = x, dx_refs = :all), x -> c3(x), Dense(numStates, 1, tanh; init = init), x -> c4(1, x[1]), ) push!(nets, net) # 2. default ME-NeuralFMU (learn dynamics) net = Chain( x -> fmu(; x = x, dx_refs = :all), x -> c3(x), Dense(numStates, 8, tanh; init = init), Dense(8, 16, tanh; init = init), Dense(16, 1, tanh; init = init), x -> c4(1, x[1]), ) push!(nets, net) # 3. default ME-NeuralFMU (learn states) net = Chain( x -> c1(x), Dense(numStates, 16, tanh; init = init), Dense(16, 1, tanh; init = init), x -> c2(x[1], 1), x -> fmu(; x = x, dx_refs = :all), ) push!(nets, net) # 4. default ME-NeuralFMU (learn dynamics and states) net = Chain( x -> c1(x), Dense(numStates, 8, tanh; init = init), Dense(8, 16, tanh; init = init), Dense(16, 1, tanh; init = init), x -> c2(x[1], 1), x -> fmu(; x = x, dx_refs = :all), x -> c3(x), Dense(numStates, 8, tanh, init = init), Dense(8, 16, tanh; init = init), Dense(16, 1, tanh, init = init), x -> c4(1, x[1]), ) push!(nets, net) # 5. NeuralFMU with hard setting time to 0.0 net = Chain( states -> fmu(; x = states, t = 0.0, dx_refs = :all), x -> c3(x), Dense(numStates, 8, tanh; init = init), Dense(8, 16, tanh; init = init), Dense(16, 1, tanh; init = init), x -> c4(1, x[1]), ) push!(nets, net) # 6. NeuralFMU with additional getter net = Chain( x -> fmu(; x = x, y_refs = getVRs, dx_refs = :all), x -> c3(x), Dense(numStates + numGetVRs, 8, tanh; init = init), Dense(8, 16, tanh; init = init), Dense(16, 1, tanh; init = init), x -> c4(1, x[1]), ) push!(nets, net) # 7. NeuralFMU with additional setter net = Chain( x -> fmu(; x = x, u_refs = setVRs, u = setVal, dx_refs = :all), x -> c3(x), Dense(numStates, 8, tanh; init = init), Dense(8, 16, tanh; init = init), Dense(16, 1, tanh; init = init), x -> c4(1, x[1]), ) push!(nets, net) # 8. NeuralFMU with additional setter and getter net = Chain( x -> fmu(; x = x, u_refs = setVRs, u = setVal, y_refs = getVRs, dx_refs = :all), x -> c3(x), Dense(numStates + numGetVRs, 8, tanh; init = init), Dense(8, 16, tanh; init = init), Dense(16, 1, tanh; init = init), x -> c4(1, x[1]), ) push!(nets, net) # 9. an empty NeuralFMU (this does only make sense for debugging) net = Chain(x -> fmu(x = x, dx_refs = :all)) push!(nets, net) solvers = [Tsit5()]#, Rosenbrock23(autodiff=false)] for solver in solvers @testset "Solver: $(solver)" begin for i = 1:length(nets) @testset "Net setup $(i)/$(length(nets)) (Continuous NeuralFMU)" begin global nets, problem, iterCB global LAST_LOSS, FAILED_GRADIENTS # if i ∈ (1, 3, 4) # @warn "Currently skipping nets $(i) ∈ (1, 3, 4)" # continue # end optim = OPTIMISER(ETA) net = nets[i] problem = ME_NeuralFMU(fmu, net, (t_start, t_stop), solver) @test problem != nothing # [Note] this is not needed from a mathematical perspective, because the system is continuous differentiable if i ∈ (1, 3, 4) problem.modifiedState = true end # train it ... p_net = Flux.params(problem) @test length(p_net) == 1 solutionBefore = problem(X0; p = p_net[1], saveat = tData) if solutionBefore.success @test length(solutionBefore.states.t) == length(tData) @test solutionBefore.states.t[1] == t_start @test solutionBefore.states.t[end] == t_stop end LAST_LOSS = losssum(p_net[1]) @info "Start-Loss for net #$i: $(LAST_LOSS)" if length(p_net[1]) == 0 @info "The following warning is not an issue, because training on zero parameters must throw a warning:" end FAILED_GRADIENTS = 0 FMIFlux.train!( losssum, problem, Iterators.repeated((), NUMSTEPS), optim; gradient = GRADIENT, cb = () -> callback(p_net), ) @info "Failed Gradients: $(FAILED_GRADIENTS) / $(NUMSTEPS)" @test FAILED_GRADIENTS <= FAILED_GRADIENTS_QUOTA * NUMSTEPS # check results solutionAfter = problem(X0; p = p_net[1], saveat = tData) if solutionAfter.success @test length(solutionAfter.states.t) == length(tData) @test solutionAfter.states.t[1] == t_start @test solutionAfter.states.t[end] == t_stop end end end end end @test length(fmu.components) <= 1 unloadFMU(fmu)
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
[ "MIT" ]
0.13.0
7fec0fac72076ea32823fb59efcc0e280cd28162
code
7919
# # Copyright (c) 2021 Tobias Thummerer, Lars Mikelsons # Licensed under the MIT license. See LICENSE file in the project root for details. # using Flux using DifferentialEquations import Random Random.seed!(1234); t_start = 0.0 t_step = 0.01 t_stop = 5.0 tData = t_start:t_step:t_stop # generate training data posData = collect(abs(cos(u .* 1.0)) for u in tData) * 2.0 fmu = loadFMU("BouncingBall1D", "Dymola", "2023x"; type = :ME) # loss function for training losssum = function (p) global problem, X0, posData solution = problem(X0; p = p, saveat = tData) if !solution.success return Inf end posNet = getState(solution, 1; isIndex = true) #velNet = getState(solution, 2; isIndex=true) return Flux.Losses.mse(posNet, posData) #+ Flux.Losses.mse(velNet, velData) end numStates = length(fmu.modelDescription.stateValueReferences) # some NeuralFMU setups nets = [] c1 = CacheLayer() c2 = CacheRetrieveLayer(c1) c3 = CacheLayer() c4 = CacheRetrieveLayer(c3) init = Flux.glorot_uniform getVRs = [stringToValueReference(fmu, "mass_s")] numGetVRs = length(getVRs) y = zeros(fmi2Real, numGetVRs) setVRs = [stringToValueReference(fmu, "damping")] numSetVRs = length(setVRs) setVal = [0.8] # 1. default ME-NeuralFMU (learn dynamics and states, almost-neutral setup, parameter count << 100) net1 = function () net = Chain( x -> c1(x), Dense(numStates, 1, tanh; init = init), x -> c2(x[1], 1), x -> fmu(; x = x, dx_refs = :all), x -> c3(x), Dense(numStates, 1, tanh; init = init), x -> c4(1, x[1]), ) end push!(nets, net1) # 2. default ME-NeuralFMU (learn dynamics) net2 = function () net = Chain( x -> fmu(; x = x, dx_refs = :all), x -> c3(x), Dense(numStates, 8, tanh; init = init), Dense(8, 16, tanh; init = init), Dense(16, 1, tanh; init = init), x -> c4(1, x[1]), ) end push!(nets, net2) # 3. default ME-NeuralFMU (learn states) net3 = function () net = Chain( x -> c1(x), Dense(numStates, 16, tanh; init = init), Dense(16, 1, tanh; init = init), x -> c2(x[1], 1), x -> fmu(; x = x, dx_refs = :all), ) end push!(nets, net3) # 4. default ME-NeuralFMU (learn dynamics and states) net4 = function () net = Chain( x -> c1(x), Dense(numStates, 8, tanh; init = init), Dense(8, 16, tanh; init = init), Dense(16, 1, tanh; init = init), x -> c2(x[1], 1), x -> fmu(; x = x, dx_refs = :all), x -> c3(x), Dense(numStates, 8, tanh, init = init), Dense(8, 16, tanh; init = init), Dense(16, 1, tanh, init = init), x -> c4(1, x[1]), ) end push!(nets, net4) # 5. NeuralFMU with hard setting time to 0.0 net5 = function () net = Chain( states -> fmu(; x = states, t = 0.0, dx_refs = :all), x -> c3(x), Dense(numStates, 8, tanh; init = init), Dense(8, 16, tanh; init = init), Dense(16, 1, tanh; init = init), x -> c4(1, x[1]), ) end push!(nets, net5) # 6. NeuralFMU with additional getter net6 = function () net = Chain( x -> fmu(; x = x, y_refs = getVRs, dx_refs = :all), x -> c3(x), Dense(numStates + numGetVRs, 8, tanh; init = init), Dense(8, 16, tanh; init = init), Dense(16, 1, tanh; init = init), x -> c4(1, x[1]), ) end push!(nets, net6) # 7. NeuralFMU with additional setter net7 = function () net = Chain( x -> fmu(; x = x, u_refs = setVRs, u = setVal, dx_refs = :all), x -> c3(x), Dense(numStates, 8, tanh; init = init), Dense(8, 16, tanh; init = init), Dense(16, 1, tanh; init = init), x -> c4(1, x[1]), ) end push!(nets, net7) # 8. NeuralFMU with additional setter and getter net8 = function () net = Chain( x -> fmu(; x = x, u_refs = setVRs, u = setVal, y_refs = getVRs, dx_refs = :all), x -> c3(x), Dense(numStates + numGetVRs, 8, tanh; init = init), Dense(8, 16, tanh; init = init), Dense(16, 1, tanh; init = init), x -> c4(1, x[1]), ) end push!(nets, net8) # 9. an empty NeuralFMU (this does only make sense for debugging) net9 = function () net = Chain(x -> fmu(x = x, dx_refs = :all)) end push!(nets, net9) solvers = [Tsit5()]#, Rosenbrock23(autodiff=false)] for solver in solvers @testset "Solver: $(solver)" begin for i = 1:length(nets) @testset "Net setup $(i)/$(length(nets)) (Discontinuous NeuralFMU)" begin global nets, problem, iterCB global LAST_LOSS, FAILED_GRADIENTS if i ∈ (1, 3, 4) @warn "Currently skipping net $(i) ∈ (1, 3, 4) for disc. FMUs (ANN before FMU)" continue end optim = OPTIMISER(ETA) net_constructor = nets[i] problem = nothing p_net = nothing tries = 0 maxtries = 1000 while true net = net_constructor() problem = ME_NeuralFMU(fmu, net, (t_start, t_stop), solver) if i ∈ (1, 3, 4) problem.modifiedState = true end p_net = Flux.params(problem) solutionBefore = problem(X0; p = p_net[1], saveat = tData) ne = length(solutionBefore.events) if ne > 0 && ne <= 10 break else if tries >= maxtries @warn "Solution before did not trigger an acceptable event count (=$(ne) ∉ [1,10]) for net $(i)! Can't find a valid start configuration ($(maxtries) tries)!" break end tries += 1 end end @test !isnothing(problem) # train it ... p_net = Flux.params(problem) @test length(p_net) == 1 solutionBefore = problem(X0; p = p_net[1], saveat = tData) if solutionBefore.success @test length(solutionBefore.states.t) == length(tData) @test solutionBefore.states.t[1] == t_start @test solutionBefore.states.t[end] == t_stop end LAST_LOSS = losssum(p_net[1]) @info "Start-Loss for net #$i: $(LAST_LOSS)" if length(p_net[1]) == 0 @info "The following warning is not an issue, because training on zero parameters must throw a warning:" end FAILED_GRADIENTS = 0 FMIFlux.train!( losssum, problem, Iterators.repeated((), NUMSTEPS), optim; gradient = GRADIENT, cb = () -> callback(p_net), ) @info "Failed Gradients: $(FAILED_GRADIENTS) / $(NUMSTEPS)" @test FAILED_GRADIENTS <= FAILED_GRADIENTS_QUOTA * NUMSTEPS # check results solutionAfter = problem(X0; p = p_net[1], saveat = tData) if solutionAfter.success @test length(solutionAfter.states.t) == length(tData) @test solutionAfter.states.t[1] == t_start @test solutionAfter.states.t[end] == t_stop end # fig = plot(solutionAfter; title="Net $(i) - $(FAILED_GRADIENTS) / $(FAILED_GRADIENTS_QUOTA * NUMSTEPS)") # plot!(fig, tData, posData) # display(fig) end end end end @test length(fmu.components) <= 1 unloadFMU(fmu)
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
[ "MIT" ]
0.13.0
7fec0fac72076ea32823fb59efcc0e280cd28162
code
1315
# # Copyright (c) 2021 Tobias Thummerer, Lars Mikelsons # Licensed under the MIT license. See LICENSE file in the project root for details. # using Statistics: mean, std shift = [1.0, 2.0, 3.0] scale = [4.0, 5.0, 6.0] input = [0.5, 1.2, 1.0] inputArray = [[-3.0, -2.0, -1.0], [3.0, 2.0, 1.0], [1.0, 2.0, 3.0]] # ShiftScale 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] s = ShiftScale(inputArray; range = -1:1) for i = 1:length(inputArray) res = s(collect(inputArray[j][i] for j = 1:length(inputArray[i]))) @test max(res...) <= 1 @test min(res...) >= -1 end s = ShiftScale(inputArray; range = -2:2) for i = 1:length(inputArray) res = s(collect(inputArray[j][i] for j = 1:length(inputArray[i]))) @test max(res...) <= 2 @test min(res...) >= -2 end s = ShiftScale(inputArray; range = :NormalDistribution) # ToDo: Test for :NormalDistribution # ScaleShift s = ScaleShift(scale, shift) @test s(input) == [3.0, 8.0, 9.0] s = ScaleShift(inputArray) @test s(input) == [-3.0, 4.4, 4.0] p = ShiftScale(inputArray) s = ScaleShift(p) for i = 1:length(inputArray) in = collect(inputArray[j][i] for j = 1:length(inputArray[i])) @test p(in) != in @test s(p(in)) == in end # ToDo: Add remaining layers
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
[ "MIT" ]
0.13.0
7fec0fac72076ea32823fb59efcc0e280cd28162
code
2062
# # Copyright (c) 2021 Tobias Thummerer, Lars Mikelsons # Licensed under the MIT license. See LICENSE file in the project root for details. # using Flux using DifferentialEquations: Tsit5 import Random Random.seed!(1234); t_start = 0.0 t_step = 0.01 t_stop = 5.0 tData = t_start:t_step:t_stop # generate training data posData, velData, accData = syntTrainingData(tData) # setup traing data extForce = function (t) return [sin(t), cos(t)] end # loss function for training losssum = function (p) solution = problem(extForce, t_step; p = p) if !solution.success return Inf end accNet = getValue(solution, 1; isIndex = true) FMIFlux.Losses.mse(accNet, accData) end # Load FMUs fmus = Vector{FMU2}() for i = 1:2 # how many FMUs do you want? _fmu = loadFMU("SpringPendulumExtForce1D", EXPORTINGTOOL, EXPORTINGVERSION; type = :CS) push!(fmus, _fmu) end # NeuralFMU setup total_fmu_outdim = sum(map(x -> length(x.modelDescription.outputValueReferences), fmus)) evalFMU = function (i, u) fmus[i](; u_refs = fmus[i].modelDescription.inputValueReferences, u = u, y_refs = fmus[i].modelDescription.outputValueReferences, ) end net = Chain( Parallel(vcat, inputs -> evalFMU(1, inputs[1:1]), inputs -> evalFMU(2, inputs[2:2])), Dense(total_fmu_outdim, 16, tanh; init = Flux.identity_init), Dense(16, 16, tanh; init = Flux.identity_init), Dense( 16, length(fmus[1].modelDescription.outputValueReferences); init = Flux.identity_init, ), ) problem = CS_NeuralFMU(fmus, net, (t_start, t_stop)) @test problem != nothing solutionBefore = problem(extForce, t_step) # train it ... p_net = Flux.params(problem) optim = OPTIMISER(ETA) lossBefore = losssum(p_net[1]) FMIFlux.train!( losssum, problem, Iterators.repeated((), NUMSTEPS), optim; gradient = GRADIENT, ) lossAfter = losssum(p_net[1]) @test lossAfter < lossBefore # check results solutionAfter = problem(extForce, t_step) for i = 1:length(fmus) unloadFMU(fmus[i]) end
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
[ "MIT" ]
0.13.0
7fec0fac72076ea32823fb59efcc0e280cd28162
code
4769
# # Copyright (c) 2021 Tobias Thummerer, Lars Mikelsons # Licensed under the MIT license. See LICENSE file in the project root for details. # using Flux using DifferentialEquations: Tsit5, Rosenbrock23 import Random Random.seed!(5678); t_start = 0.0 t_step = 0.01 t_stop = 5.0 tData = t_start:t_step:t_stop # generate training data posData, velData, accData = syntTrainingData(tData) # load FMU for training fmu = loadFMU("SpringFrictionPendulum1D", EXPORTINGTOOL, EXPORTINGVERSION; type = :ME) # loss function for training losssum = function (p) global problem, X0, posData solution = problem(X0; p = p, showProgress = true, saveat = tData) if !solution.success return Inf end # posNet = getState(solution, 1; isIndex=true) velNet = getState(solution, 2; isIndex = true) return FMIFlux.Losses.mse(velNet, velData) # Flux.Losses.mse(posNet, posData) end # callback function for training global iterCB = 0 global lastLoss = 0.0 callb = function (p) global iterCB += 1 global lastLoss if iterCB % 5 == 0 loss = losssum(p[1]) @info "[$(iterCB)] Loss: $loss" # This test condition is not good, because when the FMU passes an event, the error might increase. @test (loss < lastLoss) && (loss != lastLoss) lastLoss = loss end end numStates = length(fmu.modelDescription.stateValueReferences) # some NeuralFMU setups nets = [] c1 = CacheLayer() c2 = CacheRetrieveLayer(c1) c3 = CacheLayer() c4 = CacheRetrieveLayer(c3) # 1. Discontinuous ME-NeuralFMU (learn dynamics and states) net = Chain( x -> c1(x), Dense(numStates, 16, tanh), Dense(16, 1, identity), x -> c2(x[1], 1), x -> fmu(; x = x, dx_refs = :all), x -> c3(x), Dense(numStates, 16, tanh), Dense(16, 16, tanh), Dense(16, 1, identity), x -> c4(1, x[1]), ) push!(nets, net) for i = 1:length(nets) @testset "Net setup $(i)/$(length(nets))" begin global nets, problem, lastLoss, iterCB net = nets[i] solver = Tsit5() problem = ME_NeuralFMU(fmu, net, (t_start, t_stop), solver; saveat = tData) @test problem !== nothing solutionBefore = problem(X0) if solutionBefore.success @test length(solutionBefore.states.t) == length(tData) @test solutionBefore.states.t[1] == t_start @test solutionBefore.states.t[end] == t_stop end # train it ... p_net = Flux.params(problem) p_start = copy(p_net[1]) iterCB = 0 lastLoss = losssum(p_net[1]) startLoss = lastLoss @info "[ $(iterCB)] Loss: $lastLoss" p_net[1][:] = p_start[:] lastLoss = startLoss st = time() optim = OPTIMISER(ETA) FMIFlux.train!( losssum, problem, Iterators.repeated((), NUMSTEPS), optim; cb = () -> callb(p_net), multiThreading = false, gradient = GRADIENT, ) dt = round(time() - st; digits = 2) @info "Training time single threaded (not pre-compiled): $(dt)s" p_net[1][:] = p_start[:] lastLoss = startLoss st = time() optim = OPTIMISER(ETA) FMIFlux.train!( losssum, problem, Iterators.repeated((), NUMSTEPS), optim; cb = () -> callb(p_net), multiThreading = false, gradient = GRADIENT, ) dt = round(time() - st; digits = 2) @info "Training time single threaded (pre-compiled): $(dt)s" # [ToDo] currently not implemented # p_net[1][:] = p_start[:] # lastLoss = startLoss # st = time() # optim = OPTIMISER(ETA) # FMIFlux.train!(losssum, problem, Iterators.repeated((), NUMSTEPS), optim; cb=()->callb(p_net), multiThreading=true, gradient=GRADIENT) # dt = round(time()-st; digits=2) # @info "Training time multi threaded x$(Threads.nthreads()) (not pre-compiled): $(dt)s" # p_net[1][:] = p_start[:] # lastLoss = startLoss # st = time() # optim = OPTIMISER(ETA) # FMIFlux.train!(losssum, problem, Iterators.repeated((), NUMSTEPS), optim; cb=()->callb(p_net), multiThreading=true, gradient=GRADIENT) # dt = round(time()-st; digits=2) # @info "Training time multi threaded x$(Threads.nthreads()) (pre-compiled): $(dt)s" # check results solutionAfter = problem(X0) if solutionAfter.success @test length(solutionAfter.states.t) == length(tData) @test solutionAfter.states.t[1] == t_start @test solutionAfter.states.t[end] == t_stop end end end unloadFMU(fmu)
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
[ "MIT" ]
0.13.0
7fec0fac72076ea32823fb59efcc0e280cd28162
code
6625
# # Copyright (c) 2021 Tobias Thummerer, Lars Mikelsons # Licensed under the MIT license. See LICENSE file in the project root for details. # using Flux using DifferentialEquations using FMIFlux.Optim import Random Random.seed!(1234); t_start = 0.0 t_step = 0.01 t_stop = 5.0 tData = t_start:t_step:t_stop # generate training data posData, velData, accData = syntTrainingData(tData) # load FMU for NeuralFMU fmu = loadFMU("SpringPendulum1D", EXPORTINGTOOL, EXPORTINGVERSION; type = :ME) # loss function for training losssum = function (p) global problem, X0, posData solution = problem(X0; p = p, saveat = tData) if !solution.success return Inf end posNet = getState(solution, 1; isIndex = true) velNet = getState(solution, 2; isIndex = true) return Flux.Losses.mse(posNet, posData) + Flux.Losses.mse(velNet, velData) end numStates = length(fmu.modelDescription.stateValueReferences) # some NeuralFMU setups nets = [] c1 = CacheLayer() c2 = CacheRetrieveLayer(c1) c3 = CacheLayer() c4 = CacheRetrieveLayer(c3) init = Flux.glorot_uniform getVRs = [stringToValueReference(fmu, "mass.s")] numGetVRs = length(getVRs) y = zeros(fmi2Real, numGetVRs) setVRs = [stringToValueReference(fmu, "mass.m")] numSetVRs = length(setVRs) setVal = [1.1] # 1. default ME-NeuralFMU (learn dynamics and states, almost-neutral setup, parameter count << 100) net = Chain( x -> c1(x), Dense(numStates, 1, tanh; init = init), x -> c2(x[1], 1), x -> fmu(; x = x, dx_refs = :all), x -> c3(x), Dense(numStates, 1, tanh; init = init), x -> c4(1, x[1]), ) push!(nets, net) # 2. default ME-NeuralFMU (learn dynamics) net = Chain( x -> fmu(; x = x, dx_refs = :all), x -> c3(x), Dense(numStates, 8, tanh; init = init), Dense(8, 16, tanh; init = init), Dense(16, 1, tanh; init = init), x -> c4(1, x[1]), ) push!(nets, net) # 3. default ME-NeuralFMU (learn states) net = Chain( x -> c1(x), Dense(numStates, 16, tanh; init = init), Dense(16, 1, tanh; init = init), x -> c2(x[1], 1), x -> fmu(; x = x, dx_refs = :all), ) push!(nets, net) # 4. default ME-NeuralFMU (learn dynamics and states) net = Chain( x -> c1(x), Dense(numStates, 8, tanh; init = init), Dense(8, 16, tanh; init = init), Dense(16, 1, tanh; init = init), x -> c2(x[1], 1), x -> fmu(; x = x, dx_refs = :all), x -> c3(x), Dense(numStates, 8, tanh, init = init), Dense(8, 16, tanh; init = init), Dense(16, 1, tanh, init = init), x -> c4(1, x[1]), ) push!(nets, net) # 5. NeuralFMU with hard setting time to 0.0 net = Chain( states -> fmu(; x = states, t = 0.0, dx_refs = :all), x -> c3(x), Dense(numStates, 8, tanh; init = init), Dense(8, 16, tanh; init = init), Dense(16, 1, tanh; init = init), x -> c4(1, x[1]), ) push!(nets, net) # 6. NeuralFMU with additional getter net = Chain( x -> fmu(; x = x, y_refs = getVRs, dx_refs = :all), x -> c3(x), Dense(numStates + numGetVRs, 8, tanh; init = init), Dense(8, 16, tanh; init = init), Dense(16, 1, tanh; init = init), x -> c4(1, x[1]), ) push!(nets, net) # 7. NeuralFMU with additional setter net = Chain( x -> fmu(; x = x, u_refs = setVRs, u = setVal, dx_refs = :all), x -> c3(x), Dense(numStates, 8, tanh; init = init), Dense(8, 16, tanh; init = init), Dense(16, 1, tanh; init = init), x -> c4(1, x[1]), ) push!(nets, net) # 8. NeuralFMU with additional setter and getter net = Chain( x -> fmu(; x = x, u_refs = setVRs, u = setVal, y_refs = getVRs, dx_refs = :all), x -> c3(x), Dense(numStates + numGetVRs, 8, tanh; init = init), Dense(8, 16, tanh; init = init), Dense(16, 1, tanh; init = init), x -> c4(1, x[1]), ) push!(nets, net) # 9. an empty NeuralFMU (this does only make sense for debugging) net = Chain(x -> fmu(x = x, dx_refs = :all)) push!(nets, net) solvers = [Tsit5()]#, Rosenbrock23(autodiff=false)] for solver in solvers @testset "Solver: $(solver)" begin for i = 1:length(nets) @testset "Net setup $(i)/$(length(nets)) (Continuous NeuralFMU)" begin global nets, problem, iterCB global LAST_LOSS, FAILED_GRADIENTS # if i ∈ (1, 3, 4) # @warn "Currently skipping nets $(i) ∈ (1, 3, 4)" # continue # end optim = GradientDescent(; alphaguess = ETA, linesearch = Optim.LineSearches.Static(), ) # BFGS() net = nets[i] problem = ME_NeuralFMU(fmu, net, (t_start, t_stop), solver) @test problem != nothing # [Note] this is not needed from a mathematical perspective, because the system is continuous differentiable if i ∈ (1, 3, 4) problem.modifiedState = true end # train it ... p_net = Flux.params(problem) @test length(p_net) == 1 solutionBefore = problem(X0; p = p_net[1], saveat = tData) if solutionBefore.success @test length(solutionBefore.states.t) == length(tData) @test solutionBefore.states.t[1] == t_start @test solutionBefore.states.t[end] == t_stop end LAST_LOSS = losssum(p_net[1]) @info "Start-Loss for net #$i: $(LAST_LOSS)" if length(p_net[1]) == 0 @info "The following warning is not an issue, because training on zero parameters must throw a warning:" end FAILED_GRADIENTS = 0 FMIFlux.train!( losssum, problem, Iterators.repeated((), NUMSTEPS), optim; gradient = GRADIENT, cb = () -> callback(p_net), ) @info "Failed Gradients: $(FAILED_GRADIENTS) / $(NUMSTEPS)" @test FAILED_GRADIENTS <= FAILED_GRADIENTS_QUOTA * NUMSTEPS # check results solutionAfter = problem(X0; p = p_net[1], saveat = tData) if solutionAfter.success @test length(solutionAfter.states.t) == length(tData) @test solutionAfter.states.t[1] == t_start @test solutionAfter.states.t[end] == t_stop end end end end end @test length(fmu.components) <= 1 unloadFMU(fmu)
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
[ "MIT" ]
0.13.0
7fec0fac72076ea32823fb59efcc0e280cd28162
code
5184
# # Copyright (c) 2021 Tobias Thummerer, Lars Mikelsons # Licensed under the MIT license. See LICENSE file in the project root for details. # using FMIFlux using Test using FMIZoo using FMIFlux.FMIImport using FMIFlux.FMIImport.FMICore using FMIFlux.Flux import FMIFlux.FMISensitivity: FiniteDiff, ForwardDiff, ReverseDiff using FMIFlux.FMIImport: stringToValueReference, fmi2ValueReference, prepareSolveFMU, fmi2Real using FMIFlux.FMIImport: FMU_EXECUTION_CONFIGURATIONS using FMIFlux.FMIImport: getState, getValue, getTime exportingToolsWindows = [("Dymola", "2022x")] # [("ModelicaReferenceFMUs", "0.0.25")] exportingToolsLinux = [("Dymola", "2022x")] # number of training steps to perform global NUMSTEPS = 30 global ETA = 1e-5 global GRADIENT = nothing global EXPORTINGTOOL = nothing global EXPORTINGVERSION = nothing global X0 = [2.0, 0.0] global OPTIMISER = Descent global FAILED_GRADIENTS_QUOTA = 1/3 # callback for bad optimization steps counter global FAILED_GRADIENTS = 0 global LAST_LOSS callback = function (p) global LAST_LOSS, FAILED_GRADIENTS loss = losssum(p[1]) if loss >= LAST_LOSS FAILED_GRADIENTS += 1 end #@info "$(loss)" LAST_LOSS = loss end # training data for pendulum experiment function syntTrainingData(tData) posData = cos.(tData * 3.0) * 2.0 velData = sin.(tData * 3.0) * -6.0 accData = cos.(tData * 3.0) * -18.0 return posData, velData, accData end # enable assertions for warnings/errors for all default execution configurations - we want strict tests here for exec in FMU_EXECUTION_CONFIGURATIONS exec.assertOnError = true exec.assertOnWarning = true end function runtests(exportingTool) global EXPORTINGTOOL = exportingTool[1] global EXPORTINGVERSION = exportingTool[2] @info "Testing FMUs exported from $(EXPORTINGTOOL) ($(EXPORTINGVERSION))" @testset "Testing FMUs exported from $(EXPORTINGTOOL) ($(EXPORTINGVERSION))" begin @info "Solution Gradients (solution_gradients.jl)" @testset "Solution Gradients" begin include("solution_gradients.jl") end @info "Time Event Solution Gradients (time_solution_gradients.jl)" @testset "Time Event Solution Gradients" begin include("time_solution_gradients.jl") end for _GRADIENT ∈ (:ReverseDiff, :ForwardDiff) # , :FiniteDiff) global GRADIENT = _GRADIENT @info "Gradient: $(GRADIENT)" @testset "Gradient: $(GRADIENT)" begin @info "Layers (layers.jl)" @testset "Layers" begin include("layers.jl") end @info "ME-NeuralFMU (Continuous) (hybrid_ME.jl)" @testset "ME-NeuralFMU (Continuous)" begin include("hybrid_ME.jl") end @info "ME-NeuralFMU (Discontinuous) (hybrid_ME_dis.jl)" @testset "ME-NeuralFMU (Discontinuous)" begin include("hybrid_ME_dis.jl") end @info "NeuralFMU with FMU parameter optimization (fmu_params.jl)" @testset "NeuralFMU with FMU parameter optimization" begin include("fmu_params.jl") end @info "Training modes (train_modes.jl)" @testset "Training modes" begin include("train_modes.jl") end @warn "Multi-threading Test Skipped" # @info "Multi-threading (multi_threading.jl)" # @testset "Multi-threading" begin # include("multi_threading.jl") # end @info "CS-NeuralFMU (hybrid_CS.jl)" @testset "CS-NeuralFMU" begin include("hybrid_CS.jl") end @info "Multiple FMUs (multi.jl)" @testset "Multiple FMUs" begin include("multi.jl") end @info "Batching (batching.jl)" @testset "Batching" begin include("batching.jl") end @info "Optimizers from Optim.jl (optim.jl)" @testset "Optim" begin include("optim.jl") end end end @info "Checking supported sensitivities skipped" # @info "Benchmark: Supported sensitivities (supported_sensitivities.jl)" # @testset "Benchmark: Supported sensitivities " begin # include("supported_sensitivities.jl") # end end end @testset "FMIFlux.jl" begin if Sys.iswindows() @info "Automated testing is supported on Windows." for exportingTool in exportingToolsWindows runtests(exportingTool) end elseif Sys.islinux() @info "Automated testing is supported on Linux." for exportingTool in exportingToolsLinux runtests(exportingTool) end elseif Sys.isapple() @warn "Test-sets are currrently using Windows- and Linux-FMUs, automated testing for macOS is currently not supported." end end
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
[ "MIT" ]
0.13.0
7fec0fac72076ea32823fb59efcc0e280cd28162
code
15216
# # Copyright (c) 2021 Tobias Thummerer, Lars Mikelsons # Licensed under the MIT license. See LICENSE file in the project root for details. # using Flux using Statistics using DifferentialEquations using FMIFlux, FMIZoo, Test import FMIFlux.FMISensitivity.SciMLSensitivity.SciMLBase: RightRootFind, LeftRootFind import FMIFlux.FMIImport.FMIBase: unsense using FMIFlux.FMISensitivity.SciMLSensitivity.ForwardDiff, FMIFlux.FMISensitivity.SciMLSensitivity.ReverseDiff, FMIFlux.FMISensitivity.SciMLSensitivity.FiniteDiff, FMIFlux.FMISensitivity.SciMLSensitivity.Zygote using FMIFlux.FMIImport, FMIFlux.FMIImport.FMICore, FMIZoo import LinearAlgebra: I import FMIFlux: isimplicit import Random Random.seed!(5678); global solution = nothing global events = 0 ENERGY_LOSS = 0.7 RADIUS = 0.0 GRAVITY = 9.81 DBL_MIN = 1e-10 # 2.2250738585072013830902327173324040642192159804623318306e-308 NUMEVENTS = 4 t_start = 0.0 t_step = 0.05 t_stop = 2.0 tData = t_start:t_step:t_stop posData = ones(Float64, length(tData)) x0_bb = [1.0, 0.0] solvekwargs = Dict{Symbol,Any}(:saveat => tData, :abstol => 1e-6, :reltol => 1e-6, :dtmax => 1e-2) numStates = 2 solvers = [Tsit5(), Rosenbrock23(autodiff = false)]#, FBDF(autodiff=false)] Wr = rand(2, 2) * 1e-4 # zeros(2,2) # br = rand(2) * 1e-4 # zeros(2) # W1 = [1.0 0.0; 0.0 1.0] - Wr b1 = [0.0, 0.0] - br W2 = [1.0 0.0; 0.0 1.0] - Wr b2 = [0.0, 0.0] - br ∂xn_∂xp = [0.0 0.0; 0.0 -ENERGY_LOSS] # setup BouncingBallODE fx = function (x) return [x[2], -GRAVITY] end fx_bb = function (dx, x, p, t) dx[:] = re_bb(p)(x) return nothing end net_bb = Chain(#Dense(W1, b1, identity), fx, Dense(W2, b2, identity), ) p_net_bb, re_bb = Flux.destructure(net_bb) ff = ODEFunction{true}(fx_bb) prob_bb = ODEProblem{true}(ff, x0_bb, (t_start, t_stop), p_net_bb) condition = function (out, x, t, integrator) #x = re_bb(p_net_bb)[1](x) out[1] = x[1] - RADIUS end time_choice = function (integrator) ts = [0.451523640985728, 1.083656738365748, 1.5261499065317576, 1.8358951242479626] i = 1 while ts[i] <= integrator.t i += 1 if i > length(ts) return nothing end end return ts[i] end affect_right! = function (integrator, idx) #@info "affect_right! triggered by #$(idx)" # if idx == 1 # # event #1 is handeled as "dummy" (e.g. discrete state change) # return # end if idx > 0 out = zeros(1) x = integrator.u t = integrator.t condition(out, unsense(x), unsense(t), integrator) if sign(out[idx]) > 0.0 @info "Event for bouncing ball (white-box) triggered, but not valid!" return nothing end end s_new = RADIUS + DBL_MIN v_new = -integrator.u[2] * ENERGY_LOSS u_new = [s_new, v_new] global events events += 1 #@info "[$(events)] New state at $(integrator.t) is $(u_new) triggered by #$(idx)" integrator.u .= u_new end affect_left! = function (integrator, idx) #@info "affect_left! triggered by #$(idx)" # if idx == 1 # # event #1 is handeled as "dummy" (e.g. discrete state change) # return # end out = zeros(1) x = integrator.u t = integrator.t condition(out, unsense(x), unsense(t), integrator) if sign(out[idx]) < 0.0 @warn "Event for bouncing ball triggered, but not valid!" return nothing end s_new = integrator.u[1] v_new = -integrator.u[2] * ENERGY_LOSS u_new = [s_new, v_new] global events events += 1 #@info "[$(events)] New state at $(integrator.t) is $(u_new)" integrator.u .= u_new end stepCompleted = function (x, t, integrator) end NUMEVENTINDICATORS = 1 # 2 rightCb = VectorContinuousCallback( condition, #_double, affect_right!, NUMEVENTINDICATORS; rootfind = RightRootFind, save_positions = (false, false), ) leftCb = VectorContinuousCallback( condition, #_double, affect_left!, NUMEVENTINDICATORS; rootfind = LeftRootFind, save_positions = (false, false), ) timeCb = IterativeCallback( time_choice, (indicator) -> affect_right!(indicator, 0), Float64; initial_affect = false, save_positions = (false, false), ) stepCb = FunctionCallingCallback(stepCompleted; func_everystep = true, func_start = true) # load FMU for NeuralFMU #fmu = loadFMU("BouncingBall", "ModelicaReferenceFMUs", "0.0.25"; type=:ME) #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), Dense(W2, b2, identity), ) prob = ME_NeuralFMU(fmu, net, (t_start, t_stop)) prob.snapshots = true # needed for correct sensitivities # ANNs losssum = function (p; sensealg = nothing, solver = nothing) global posData posNet = mysolve(p; sensealg = sensealg, solver = solver) return Flux.Losses.mae(posNet, posData) end losssum_bb = function (p; sensealg = nothing, root = :Right, solver = nothing) global posData posNet = mysolve_bb(p; sensealg = sensealg, root = root, solver = solver) return Flux.Losses.mae(posNet, posData) end mysolve = function (p; sensealg = nothing, solver = nothing) global solution, events # write global prob, x0_bb, posData # read-only events = 0 solution = prob( x0_bb; p = p, solver = solver, parameters = fmu_params, sensealg = sensealg, cleanSnapshots = false, solvekwargs..., ) return collect(u[1] for u in solution.states.u) end mysolve_bb = function (p; sensealg = nothing, root = :Right, solver = nothing) global solution # write global prob_bb, events # read events = 0 callback = nothing if root == :Right callback = CallbackSet(rightCb, stepCb) elseif root == :Left callback = CallbackSet(leftCb, stepCb) elseif root == :Time callback = CallbackSet(timeCb, stepCb) else @assert false "unknwon root `$(root)`" end solution = solve( prob_bb; p = p, alg = solver, callback = callback, sensealg = sensealg, solvekwargs..., ) if !isa(solution, AbstractArray) if solution.retcode != FMIFlux.ReturnCode.Success @error "Solution failed!" return Inf end return collect(u[1] for u in solution.u) else return solution[1, :] # collect(solution[:,i] for i in 1:size(solution)[2]) end end p_net = Flux.params(prob)[1] using FMIFlux.FMISensitivity.SciMLSensitivity sensealg = ReverseDiffAdjoint() # InterpolatingAdjoint(autojacvec=ReverseDiffVJP(false)) # c = nothing c, _ = FMIFlux.prepareSolveFMU( prob.fmu, c, fmi2TypeModelExchange; parameters = prob.parameters, t_start = prob.tspan[1], t_stop = prob.tspan[end], x0 = prob.x0, handleEvents = FMIFlux.handleEvents, cleanup = true, ) ### START CHECK CONDITIONS condition_bb_check = function (x) buffer = similar(x, 1) condition(buffer, x, t_start, nothing) return buffer end condition_nfmu_check = function (x) buffer = similar(x, 1) FMIFlux.condition!( prob, FMIFlux.getInstance(prob), buffer, x, t_start, nothing, [UInt32(1)], ) return buffer end jac_fwd1 = ForwardDiff.jacobian(condition_bb_check, x0_bb) jac_fwd2 = ForwardDiff.jacobian(condition_nfmu_check, x0_bb) jac_rwd1 = ReverseDiff.jacobian(condition_bb_check, x0_bb) jac_rwd2 = ReverseDiff.jacobian(condition_nfmu_check, x0_bb) jac_fin1 = FiniteDiff.finite_difference_jacobian(condition_bb_check, x0_bb) jac_fin2 = FiniteDiff.finite_difference_jacobian(condition_nfmu_check, x0_bb) atol = 1e-6 @test isapprox(jac_fin1, jac_fwd1; atol = atol) @test isapprox(jac_fin1, jac_rwd1; atol = atol) @test isapprox(jac_fin2, jac_fwd2; atol = atol) @test isapprox(jac_fin2, jac_rwd2; atol = atol) ### START CHECK AFFECT affect_bb_check = function (x) # convert TrackedArrays to Array{<:TrackedReal,1} if !isa(x, AbstractVector{<:Float64}) x = [x...] else x = copy(x) end integrator = (t = t_start, u = x) affect_right!(integrator, 1) return integrator.u end affect_nfmu_check = function (x) global prob # convert TrackedArrays to Array{<:TrackedReal,1} if !isa(x, AbstractVector{<:Float64}) x = [x...] else x = copy(x) end c, _ = FMIFlux.prepareSolveFMU( prob.fmu, nothing, fmi2TypeModelExchange; parameters = fmu_params, t_start = prob.tspan[1], t_stop = prob.tspan[end], x0 = unsense(x), handleEvents = FMIFlux.handleEvents, cleanup = true, ) integrator = (t = t_start, u = x, opts = (internalnorm = (a, b) -> 1.0,)) FMIFlux.affectFMU!(prob, c, integrator, 1) return integrator.u end #t_event_time = 0.451523640985728 x_event_left = [-1.0, -1.0] # [-3.808199081191736e-15, -4.429446918069994] x_event_right = [0.0, 0.7] # [2.2250738585072014e-308, 3.1006128426489954] x_no_event = [0.1, -1.0] @test isapprox(affect_bb_check(x_event_left), x_event_right; atol = 1e-4) @test isapprox(affect_nfmu_check(x_event_left), x_event_right; atol = 1e-4) jac_con1 = ForwardDiff.jacobian(affect_bb_check, x_event_left) jac_con2 = ForwardDiff.jacobian(affect_nfmu_check, x_event_left) @test isapprox(jac_con1, ∂xn_∂xp; atol = 1e-4) @test isapprox(jac_con2, ∂xn_∂xp; atol = 1e-4) jac_con1 = ReverseDiff.jacobian(affect_bb_check, x_event_left) jac_con2 = ReverseDiff.jacobian(affect_nfmu_check, x_event_left) @test isapprox(jac_con1, ∂xn_∂xp; atol = 1e-4) @test isapprox(jac_con2, ∂xn_∂xp; atol = 1e-4) # [Note] checking via FiniteDiff is not possible here, because finite differences offsets might not trigger the events at all # no-event @test isapprox(affect_bb_check(x_no_event), x_no_event; atol = 1e-4) @test isapprox(affect_nfmu_check(x_no_event), x_no_event; atol = 1e-4) jac_con1 = ForwardDiff.jacobian(affect_bb_check, x_no_event) jac_con2 = ForwardDiff.jacobian(affect_nfmu_check, x_no_event) @test isapprox(jac_con1, I; atol = 1e-4) @test isapprox(jac_con2, I; atol = 1e-4) jac_con1 = ReverseDiff.jacobian(affect_bb_check, x_no_event) jac_con2 = ReverseDiff.jacobian(affect_nfmu_check, x_no_event) @test isapprox(jac_con1, I; atol = 1e-4) @test isapprox(jac_con2, I; atol = 1e-4) ### for solver in solvers @info "Solver: $(solver)" # Solution (plain) losssum(p_net; sensealg = sensealg, solver = solver) @test length(solution.events) == NUMEVENTS losssum_bb(p_net_bb; sensealg = sensealg, solver = solver) @test events == NUMEVENTS # Solution FWD (FMU) grad_fwd_f = ForwardDiff.gradient(p -> losssum(p; sensealg = sensealg, solver = solver), p_net) @test length(solution.events) == NUMEVENTS # Solution FWD (right) root = :Right grad_fwd_r = ForwardDiff.gradient( p -> losssum_bb(p; sensealg = sensealg, root = root, solver = solver), p_net_bb, ) @test events == NUMEVENTS # Solution FWD (left) root = :Left grad_fwd_l = ForwardDiff.gradient( p -> losssum_bb(p; sensealg = sensealg, root = root, solver = solver), p_net_bb, ) @test events == NUMEVENTS # Solution FWD (time) root = :Time grad_fwd_t = ForwardDiff.gradient( p -> losssum_bb(p; sensealg = sensealg, root = root, solver = solver), p_net_bb, ) @test events == NUMEVENTS # Solution RWD (FMU) grad_rwd_f = ReverseDiff.gradient(p -> losssum(p; sensealg = sensealg, solver = solver), p_net) @test length(solution.events) == NUMEVENTS # Solution RWD (right) root = :Right grad_rwd_r = ReverseDiff.gradient( p -> losssum_bb(p; sensealg = sensealg, root = root, solver = solver), p_net_bb, ) @test events == NUMEVENTS # Solution RWD (left) root = :Left grad_rwd_l = ReverseDiff.gradient( p -> losssum_bb(p; sensealg = sensealg, root = root, solver = solver), p_net_bb, ) @test events == NUMEVENTS # Solution RWD (time) root = :Time grad_rwd_t = ReverseDiff.gradient( p -> losssum_bb(p; sensealg = sensealg, root = root, solver = solver), p_net_bb, ) @test events == NUMEVENTS # Ground Truth absstep = 1e-6 grad_fin_r = FiniteDiff.finite_difference_gradient( p -> losssum_bb(p; sensealg = sensealg, root = :Right, solver = solver), p_net_bb, Val{:central}; absstep = absstep, ) grad_fin_l = FiniteDiff.finite_difference_gradient( p -> losssum_bb(p; sensealg = sensealg, root = :Left, solver = solver), p_net_bb, Val{:central}; absstep = absstep, ) grad_fin_t = FiniteDiff.finite_difference_gradient( p -> losssum_bb(p; sensealg = sensealg, root = :Time, solver = solver), p_net_bb, Val{:central}; absstep = absstep, ) grad_fin_f = FiniteDiff.finite_difference_gradient( p -> losssum(p; sensealg = sensealg, solver = solver), p_net, Val{:central}; absstep = absstep, ) local atol = 1e-3 # check if finite differences match together @test isapprox(grad_fin_f, grad_fin_r; atol = atol) @test isapprox(grad_fin_f, grad_fin_l; atol = atol) @test isapprox(grad_fin_f, grad_fwd_f; atol = 0.2) # [ToDo: this is too much!] @test isapprox(grad_fin_f, grad_rwd_f; atol = atol) # Jacobian Test jac_fwd_r = ForwardDiff.jacobian( p -> mysolve_bb(p; sensealg = sensealg, solver = solver), p_net, ) jac_fwd_f = ForwardDiff.jacobian(p -> mysolve(p; sensealg = sensealg, solver = solver), p_net) jac_rwd_r = ReverseDiff.jacobian( p -> mysolve_bb(p; sensealg = sensealg, solver = solver), p_net, ) jac_rwd_f = ReverseDiff.jacobian(p -> mysolve(p; sensealg = sensealg, solver = solver), p_net) # [TODO] why this?! jac_rwd_r[2:end, :] = jac_rwd_r[2:end, :] .- jac_rwd_r[1:end-1, :] jac_rwd_f[2:end, :] = jac_rwd_f[2:end, :] .- jac_rwd_f[1:end-1, :] jac_fin_r = FiniteDiff.finite_difference_jacobian( p -> mysolve_bb(p; sensealg = sensealg, solver = solver), p_net, Val{:central}; absstep = absstep, ) jac_fin_f = FiniteDiff.finite_difference_jacobian( p -> mysolve(p; sensealg = sensealg, solver = solver), p_net, Val{:central}; absstep = absstep, ) ### local atol = 1e-3 @test isapprox(jac_fin_f, jac_fin_r; atol = atol) @test isapprox(jac_fin_f, jac_fwd_f; atol = 1e1) # [ToDo] this is too much! @test mean(abs.(jac_fin_f .- jac_fwd_f)) < 0.15 # added another test for this case... @test isapprox(jac_fin_f, jac_rwd_f; atol = atol) @test isapprox(jac_fin_r, jac_fwd_r; atol = atol) @test isapprox(jac_fin_r, jac_rwd_r; atol = atol) ### end unloadFMU(fmu)
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
[ "MIT" ]
0.13.0
7fec0fac72076ea32823fb59efcc0e280cd28162
code
2204
# # Copyright (c) 2021 Tobias Thummerer, Lars Mikelsons # Licensed under the MIT license. See LICENSE file in the project root for details. # using Flux using DifferentialEquations import Random Random.seed!(5678); # boundaries t_start = 0.0 t_step = 0.1 t_stop = 5.0 tData = t_start:t_step:t_stop tspan = (t_start, t_stop) posData = ones(Float64, length(tData)) nfmu = nothing # load FMU for NeuralFMU fmus = [] # this is a non-simultaneous event system (one event per time instant) f = loadFMU("BouncingBall", "ModelicaReferenceFMUs", "0.0.25"; type = :ME) @assert f.modelDescription.numberOfEventIndicators == 1 "Wrong number of event indicators: $(f.modelDescription.numberOfEventIndicators) != 1" push!(fmus, f) # this is a simultaneous event system (two events per time instant) f = loadFMU("BouncingBall1D", "Dymola", "2023x"; type = :ME) @assert f.modelDescription.numberOfEventIndicators == 2 "Wrong number of event indicators: $(f.modelDescription.numberOfEventIndicators) != 2" push!(fmus, f) x0_bb = [1.0, 0.0] numStates = length(x0_bb) function net_const(fmu) net = Chain(x -> fmu(; x = x, dx_refs = :all), Dense(2, 16, tanh), Dense(16, 2, identity)) return net end # loss function for training losssum = function (p) global nfmu, x0_bb, posData solution = nfmu(x0_bb; p = p, saveat = tData) if !solution.success return Inf end posNet = getState(solution, 1; isIndex = true) return FMIFlux.Losses.mse(posNet, posData) end for fmu in fmus @info "##### CHECKING FMU WITH $(fmu.modelDescription.numberOfEventIndicators) SIMULTANEOUS EVENT INDICATOR(S) #####" solvers = [ Tsit5(), Rosenbrock23(autodiff = false), Rodas5(autodiff = false), FBDF(autodiff = false), ] for solver in solvers global nfmu @info "##### SOLVER: $(solver) #####" net = net_const(fmu) nfmu = ME_NeuralFMU(fmu, net, (t_start, t_stop), solver; saveat = tData) nfmu.modifiedState = false nfmu.snapshots = true best_timing, best_gradient, best_sensealg = FMIFlux.checkSensalgs!(losssum, nfmu) #@test best_timing != Inf end end unloadFMU.(fmus)
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
[ "MIT" ]
0.13.0
7fec0fac72076ea32823fb59efcc0e280cd28162
code
16529
# # Copyright (c) 2021 Tobias Thummerer, Lars Mikelsons # Licensed under the MIT license. See LICENSE file in the project root for details. # using FMIFlux.Flux using DifferentialEquations using FMIFlux, FMIZoo, Test import FMIFlux.FMISensitivity.SciMLSensitivity.SciMLBase: RightRootFind, LeftRootFind using FMIFlux.FMIImport.FMIBase: unsense using FMIFlux.FMISensitivity.SciMLSensitivity.ForwardDiff, FMIFlux.FMISensitivity.SciMLSensitivity.ReverseDiff, FMIFlux.FMISensitivity.SciMLSensitivity.FiniteDiff, FMIFlux.FMISensitivity.SciMLSensitivity.Zygote using FMIFlux.FMIImport, FMIFlux.FMIImport.FMICore, FMIZoo import LinearAlgebra: I import Random Random.seed!(5678); global solution = nothing global events = 0 ENERGY_LOSS = 0.7 RADIUS = 0.0 GRAVITY = 9.81 GRAVITY_SIGN = -1 DBL_MIN = 1e-10 # 2.2250738585072013830902327173324040642192159804623318306e-308 TIME_FREQ = 1.0 MASS = 1.0 INTERP_POINTS = 10 NUMEVENTS = 4 t_start = 0.0 t_step = 0.05 t_stop = 2.0 tData = t_start:t_step:t_stop posData = ones(Float64, length(tData)) x0_bb = [0.5, 0.0] solvekwargs = Dict{Symbol,Any}(:saveat => tData, :abstol => 1e-6, :reltol => 1e-6, :dtmax => 1e-2) numStates = 2 solvers = [Tsit5()]#, Rosenbrock23(autodiff=false)] Wr = rand(2, 2) * 1e-4 br = rand(2) * 1e-4 W1 = [1.0 0.0; 0.0 1.0] - Wr b1 = [0.0, 0.0] - br W2 = [1.0 0.0; 0.0 1.0] - Wr b2 = [0.0, 0.0] - br ∂xn_∂xp = [0.0 0.0; 0.0 -ENERGY_LOSS] # setup BouncingBallODE global fx_dx_cache = zeros(Real, 2) fx = function (x, t; kwargs...) return _fx(x, t; kwargs...) end _fx = function (x, t) global fx_dx_cache fx_dx_cache[1] = x[2] fx_dx_cache[2] = GRAVITY_SIGN * GRAVITY / MASS return fx_dx_cache end fx_bb = function (dx, x, p, t) dx[:] = re_bb(p)(x) return nothing end net_bb = Chain(#Dense(W1, b1, identity), x -> fx(x, 0.0), Dense(W2, b2, identity), ) p_net_bb, re_bb = Flux.destructure(net_bb) ff = ODEFunction{true}(fx_bb) prob_bb = ODEProblem{true}(ff, x0_bb, (t_start, t_stop), p_net_bb) condition = function (out, x, t, integrator) #x = re_bb(p_net_bb)[1](x) out[1] = x[1] - RADIUS out[2] = x[1] - RADIUS end import FMIFlux: unsense time_choice = function (integrator) next = (floor(integrator.t / TIME_FREQ) + 1) * TIME_FREQ if next <= t_stop #@info "next: $(next)" return unsense(next) else return nothing end end time_affect! = function (integrator) global GRAVITY_SIGN GRAVITY_SIGN = -GRAVITY_SIGN global events events += 1 #u_modified!(integrator, false) end affect_right! = function (integrator, idx) #@info "affect_right! triggered by #$(idx)" # if idx == 1 # # event #1 is handeled as "dummy" (e.g. discrete state change) # return # end if idx > 0 out = zeros(NUMEVENTINDICATORS) x = integrator.u t = integrator.t condition(out, unsense(x), unsense(t), integrator) if sign(out[idx]) > 0.0 @info "Event for bouncing ball (white-box) triggered, but not valid!" return nothing end end s_new = RADIUS + DBL_MIN v_new = -1.0 * unsense(integrator.u[2]) * ENERGY_LOSS left_x = unsense(integrator.u) right_x = [s_new, v_new] global events events += 1 #@info "[$(events)] New state at $(integrator.t) is $(u_new) triggered by #$(idx)" #integrator.u[:] .= u_new for i = 1:length(left_x) if left_x[i] != 0.0 # abs(left_x[i]) > 1e-128 scale = right_x[i] / left_x[i] integrator.u[i] *= scale else # integrator state zero can't be scaled, need to add (but no sensitivities in this case!) shift = right_x[i] - left_x[i] integrator.u[i] += shift #integrator.u[i] = right_x[i] logWarning( c.fmu, "Probably wrong sensitivities @t=$(unsense(t)) for ∂x^+ / ∂x^-\nCan't scale zero state #$(i) from $(left_x[i]) to $(right_x[i])\nNew state after transform is: $(integrator.u[i])", ) end end return nothing end affect_left! = function (integrator, idx) #@info "affect_left! triggered by #$(idx)" # if idx == 1 # # event #1 is handeled as "dummy" (e.g. discrete state change) # return # end out = zeros(NUMEVENTINDICATORS) x = integrator.u t = integrator.t condition(out, unsense(x), unsense(t), integrator) if sign(out[idx]) < 0.0 @warn "Event for bouncing ball triggered, but not valid!" return nothing end s_new = integrator.u[1] v_new = -integrator.u[2] * ENERGY_LOSS u_new = [s_new, v_new] global events events += 1 #@info "[$(events)] New state at $(integrator.t) is $(u_new)" integrator.u .= u_new end stepCompleted = function (x, t, integrator) end NUMEVENTINDICATORS = 2 rightCb = VectorContinuousCallback( condition, #_double, affect_right!, NUMEVENTINDICATORS; rootfind = RightRootFind, save_positions = (false, false), interp_points = INTERP_POINTS, ) leftCb = VectorContinuousCallback( condition, #_double, affect_left!, NUMEVENTINDICATORS; rootfind = LeftRootFind, save_positions = (false, false), interp_points = INTERP_POINTS, ) gravityCb = IterativeCallback( time_choice, time_affect!, Float64; initial_affect = false, save_positions = (false, false), ) stepCb = FunctionCallingCallback(stepCompleted; func_everystep = true, func_start = true) # load FMU for NeuralFMU fmu = loadFMU("BouncingBallGravitySwitch1D", "Dymola", "2023x"; type = :ME) fmu_params = Dict( "damping" => ENERGY_LOSS, "mass_radius" => RADIUS, "gravity" => GRAVITY, "period" => TIME_FREQ, "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), Dense(W2, b2, identity), ) prob = ME_NeuralFMU(fmu, net, (t_start, t_stop)) prob.snapshots = true # needed for correct sensitivities # ANNs losssum = function (p; sensealg = nothing, solver = nothing) global posData posNet = mysolve(p; sensealg = sensealg, solver = solver) return Flux.Losses.mae(posNet, posData) end losssum_bb = function (p; sensealg = nothing, root = :Right, solver = nothing) global posData posNet = mysolve_bb(p; sensealg = sensealg, root = root, solver = solver) return Flux.Losses.mae(posNet, posData) end mysolve = function (p; sensealg = nothing, solver = nothing) global solution, events # write global prob, x0_bb, posData # read-only events = 0 solution = prob( x0_bb; p = p, solver = solver, parameters = fmu_params, sensealg = sensealg, solvekwargs..., ) return collect(u[1] for u in solution.states.u) end mysolve_bb = function (p; sensealg = nothing, root = :Right, solver = nothing) global solution, GRAVITY_SIGN global prob_bb, events # read events = 0 callback = nothing if root == :Right callback = CallbackSet(gravityCb, rightCb, stepCb) elseif root == :Left callback = CallbackSet(gravityCb, leftCb, stepCb) else @assert false "unknwon root `$(root)`" end GRAVITY_SIGN = -1 solution = solve( prob_bb, solver; u0 = x0_bb, p = p, callback = callback, sensealg = sensealg, solvekwargs..., ) if !isa(solution, AbstractArray) if solution.retcode != FMIFlux.ReturnCode.Success @error "Solution failed!" return Inf end return collect(u[1] for u in solution.u) else return solution[1, :] # collect(solution[:,i] for i in 1:size(solution)[2]) end end p_net = Flux.params(prob)[1] using FMIFlux.FMISensitivity.SciMLSensitivity sensealg = ReverseDiffAdjoint() # InterpolatingAdjoint(autojacvec=ReverseDiffVJP(false)) # c = nothing c, _ = FMIFlux.prepareSolveFMU( prob.fmu, c, fmi2TypeModelExchange; parameters = prob.parameters, t_start = prob.tspan[1], t_stop = prob.tspan[end], x0 = prob.x0, handleEvents = FMIFlux.handleEvents, cleanup = true, ) ### START CHECK CONDITIONS condition_bb_check = function (x) buffer = similar(x, NUMEVENTINDICATORS) condition(buffer, x, t_start, nothing) return buffer end condition_nfmu_check = function (x) buffer = similar(x, fmu.modelDescription.numberOfEventIndicators) inds = collect(UInt32(i) for i = 1:fmu.modelDescription.numberOfEventIndicators) FMIFlux.condition!(prob, FMIFlux.getInstance(prob), buffer, x, t_start, nothing, inds) return buffer end jac_fwd1 = ForwardDiff.jacobian(condition_bb_check, x0_bb) jac_fwd2 = ForwardDiff.jacobian(condition_nfmu_check, x0_bb) jac_rwd1 = ReverseDiff.jacobian(condition_bb_check, x0_bb) jac_rwd2 = ReverseDiff.jacobian(condition_nfmu_check, x0_bb) jac_fin1 = FiniteDiff.finite_difference_jacobian(condition_bb_check, x0_bb) jac_fin2 = FiniteDiff.finite_difference_jacobian(condition_nfmu_check, x0_bb) atol = 1e-6 @test isapprox(jac_fin1, jac_fwd1; atol = atol) @test isapprox(jac_fin1, jac_rwd1; atol = atol) @test isapprox(jac_fin2, jac_fwd2; atol = atol) @test isapprox(jac_fin2, jac_rwd2; atol = atol) ### START CHECK AFFECT affect_bb_check = function (x, t, idx = 1) # convert TrackedArrays to Array{<:TrackedReal,1} if !isa(x, AbstractVector{<:Float64}) x = [x...] else x = copy(x) end integrator = (t = t, u = x) if idx == 0 time_affect!(integrator) else affect_right!(integrator, idx) end return integrator.u end affect_nfmu_check = function (x, t, idx = 1) global prob # convert TrackedArrays to Array{<:TrackedReal,1} if !isa(x, AbstractVector{<:Float64}) x = [x...] else x = copy(x) end c, _ = FMIFlux.prepareSolveFMU( prob.fmu, nothing, fmi2TypeModelExchange; parameters = fmu_params, t_start = unsense(t), t_stop = prob.tspan[end], x0 = unsense(x), handleEvents = FMIFlux.handleEvents, cleanup = true, ) integrator = (t = t, u = x, opts = (internalnorm = (a, b) -> 1.0,)) FMIFlux.affectFMU!(prob, c, integrator, idx) return integrator.u end #t_event_time = 0.451523640985728 x_event_left = [-1.0, -1.0] # [-3.808199081191736e-15, -4.429446918069994] x_event_right = [0.0, 0.7] # [2.2250738585072014e-308, 3.1006128426489954] x_no_event = [0.1, -1.0] t_no_event = t_start # [ToDo] the following tests fail for some FMUs # @test isapprox(affect_bb_check(x_event_left, t_no_event), x_event_right; atol=1e-4) # @test isapprox(affect_nfmu_check(x_event_left, t_no_event), x_event_right; atol=1e-4) # jac_con1 = ForwardDiff.jacobian(x -> affect_bb_check(x, t_no_event), x_event_left) # jac_con2 = ForwardDiff.jacobian(x -> affect_nfmu_check(x, t_no_event), x_event_left) # @test isapprox(jac_con1, ∂xn_∂xp; atol=1e-4) # @test isapprox(jac_con2, ∂xn_∂xp; atol=1e-4) # jac_con1 = ReverseDiff.jacobian(x -> affect_bb_check(x, t_no_event), x_event_left) # jac_con2 = ReverseDiff.jacobian(x -> affect_nfmu_check(x, t_no_event), x_event_left) # @test isapprox(jac_con1, ∂xn_∂xp; atol=1e-4) # @test isapprox(jac_con2, ∂xn_∂xp; atol=1e-4) # [Note] checking via FiniteDiff is not possible here, because finite differences offsets might not trigger the events at all # no-event @test isapprox(affect_bb_check(x_no_event, t_no_event), x_no_event; atol = 1e-4) @test isapprox(affect_nfmu_check(x_no_event, t_no_event), x_no_event; atol = 1e-4) jac_con1 = ForwardDiff.jacobian(x -> affect_bb_check(x, t_no_event), x_no_event) jac_con2 = ForwardDiff.jacobian(x -> affect_nfmu_check(x, t_no_event), x_no_event) @test isapprox(jac_con1, I; atol = 1e-4) @test isapprox(jac_con2, I; atol = 1e-4) jac_con1 = ReverseDiff.jacobian(x -> affect_bb_check(x, t_no_event), x_no_event) jac_con2 = ReverseDiff.jacobian(x -> affect_nfmu_check(x, t_no_event), x_no_event) @test isapprox(jac_con1, I; atol = 1e-4) @test isapprox(jac_con2, I; atol = 1e-4) ### TIME-EVENTS t_event = t_start + 1.1 @test isapprox(affect_bb_check(x_no_event, t_event, 0), x_no_event; atol = 1e-4) @test isapprox(affect_nfmu_check(x_no_event, t_event, 0), x_no_event; atol = 1e-4) jac_con1 = ForwardDiff.jacobian(x -> affect_bb_check(x, t_event, 0), x_no_event) jac_con2 = ForwardDiff.jacobian(x -> affect_nfmu_check(x, t_event, 0), x_no_event) @test isapprox(jac_con1, I; atol = 1e-4) @test isapprox(jac_con2, I; atol = 1e-4) jac_con1 = ReverseDiff.jacobian(x -> affect_bb_check(x, t_event, 0), x_no_event) jac_con2 = ReverseDiff.jacobian(x -> affect_nfmu_check(x, t_event, 0), x_no_event) @test isapprox(jac_con1, I; atol = 1e-4) @test isapprox(jac_con2, I; atol = 1e-4) jac_con1 = ReverseDiff.jacobian(t -> affect_bb_check(x_event_left, t[1], 0), [t_event]) jac_con2 = ReverseDiff.jacobian(t -> affect_nfmu_check(x_event_left, t[1], 0), [t_event]) ### NUMEVENTS = 4 for solver in solvers @info "Solver: $(solver)" global GRAVITY_SIGN # Solution (plain) GRAVITY_SIGN = -1 losssum(p_net; sensealg = sensealg, solver = solver) @test length(solution.events) == NUMEVENTS GRAVITY_SIGN = -1 losssum_bb(p_net_bb; sensealg = sensealg, solver = solver) @test events == NUMEVENTS # Solution FWD (FMU) GRAVITY_SIGN = -1 grad_fwd_f = ForwardDiff.gradient(p -> losssum(p; sensealg = sensealg, solver = solver), p_net) @test length(solution.events) == NUMEVENTS # Solution FWD (right) GRAVITY_SIGN = -1 root = :Right grad_fwd_r = ForwardDiff.gradient( p -> losssum_bb(p; sensealg = sensealg, root = root, solver = solver), p_net_bb, ) @test events == NUMEVENTS # Solution RWD (FMU) GRAVITY_SIGN = -1 grad_rwd_f = ReverseDiff.gradient(p -> losssum(p; sensealg = sensealg, solver = solver), p_net) @test length(solution.events) == NUMEVENTS # Solution RWD (right) GRAVITY_SIGN = -1 root = :Right grad_rwd_r = ReverseDiff.gradient( p -> losssum_bb(p; sensealg = sensealg, root = root, solver = solver), p_net_bb, ) @test events == NUMEVENTS # Ground Truth grad_fin_r = FiniteDiff.finite_difference_gradient( p -> losssum_bb(p; sensealg = sensealg, root = :Right, solver = solver), p_net_bb, Val{:central}; absstep = 1e-6, ) grad_fin_f = FiniteDiff.finite_difference_gradient( p -> losssum(p; sensealg = sensealg, solver = solver), p_net, Val{:central}; absstep = 1e-6, ) local atol = 1e-3 # check if finite differences match together @test isapprox(grad_fin_f, grad_fin_r; atol = atol) @test isapprox(grad_fin_f, grad_fwd_f; atol = atol) @test isapprox(grad_fin_f, grad_rwd_f; atol = atol) @test isapprox(grad_fwd_r, grad_rwd_r; atol = atol) # Jacobian Test jac_fwd_r = ForwardDiff.jacobian( p -> mysolve_bb(p; sensealg = sensealg, solver = solver), p_net, ) @test !any(isnan.(jac_fwd_r)) jac_fwd_f = ForwardDiff.jacobian(p -> mysolve(p; sensealg = sensealg, solver = solver), p_net) @test !any(isnan.(jac_fwd_f)) jac_rwd_r = ReverseDiff.jacobian( p -> mysolve_bb(p; sensealg = sensealg, solver = solver), p_net, ) @test !any(isnan.(jac_rwd_r)) jac_rwd_f = ReverseDiff.jacobian(p -> mysolve(p; sensealg = sensealg, solver = solver), p_net) @test !any(isnan.(jac_rwd_f)) # [TODO] why this?! jac_rwd_r[2:end, :] = jac_rwd_r[2:end, :] .- jac_rwd_r[1:end-1, :] jac_rwd_f[2:end, :] = jac_rwd_f[2:end, :] .- jac_rwd_f[1:end-1, :] jac_fin_r = FiniteDiff.finite_difference_jacobian( p -> mysolve_bb(p; sensealg = sensealg, solver = solver), p_net, ) jac_fin_f = FiniteDiff.finite_difference_jacobian( p -> mysolve(p; sensealg = sensealg, solver = solver), p_net, ) ### local atol = 1e-3 @test isapprox(jac_fin_f, jac_fin_r; atol = atol) @test isapprox(jac_fin_f, jac_fwd_f; atol = atol) # [ToDo] whyever... but this is not required to work (but: too much atol here!) @test isapprox(jac_fin_f, jac_rwd_f; atol = 0.5) @test isapprox(jac_fin_r, jac_fwd_r; atol = atol) @test isapprox(jac_fin_r, jac_rwd_r; atol = atol) ### end unloadFMU(fmu)
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
[ "MIT" ]
0.13.0
7fec0fac72076ea32823fb59efcc0e280cd28162
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# # Copyright (c) 2021 Tobias Thummerer, Lars Mikelsons # Licensed under the MIT license. See LICENSE file in the project root for details. # using Flux using DifferentialEquations: Tsit5, Rosenbrock23 import FMIFlux.FMIImport: fmi2FreeInstance! import Random Random.seed!(5678); t_start = 0.0 t_step = 0.01 t_stop = 5.0 tData = t_start:t_step:t_stop # generate training data posData, velData, accData = syntTrainingData(tData) # load FMU for NeuralFMU fmu = loadFMU("SpringFrictionPendulum1D", EXPORTINGTOOL, EXPORTINGVERSION; type = :ME) # loss function for training losssum = function (p) global problem, X0, posData solution = problem(X0; p = p, saveat = tData) if !solution.success return Inf end #posNet = getState(solution, 1; isIndex=true) velNet = getState(solution, 2; isIndex = true) return Flux.Losses.mse(velNet, velData) # Flux.Losses.mse(posNet, posData) end vr = stringToValueReference(fmu, "mass.m") numStates = length(fmu.modelDescription.stateValueReferences) # some NeuralFMU setups nets = [] global comp comp = nothing for handleEvents in [true, false] @testset "handleEvents: $handleEvents" begin for config in FMU_EXECUTION_CONFIGURATIONS if config == FMU_EXECUTION_CONFIGURATION_NOTHING @info "Skipping train modes testing for `FMU_EXECUTION_CONFIGURATION_NOTHING`." continue end configstr = "$(config)" @testset "config: $(configstr[1:64])..." begin global problem, lastLoss, iterCB, comp fmu.executionConfig = config fmu.executionConfig.handleStateEvents = handleEvents fmu.executionConfig.handleTimeEvents = handleEvents fmu.executionConfig.externalCallbacks = true fmu.executionConfig.loggingOn = true fmu.executionConfig.assertOnError = true fmu.executionConfig.assertOnWarning = true @info "handleEvents: $(handleEvents) | instantiate: $(fmu.executionConfig.instantiate) | reset: $(fmu.executionConfig.reset) | terminate: $(fmu.executionConfig.terminate) | freeInstance: $(fmu.executionConfig.freeInstance)" # if fmu.executionConfig.instantiate == false # @info "instantiate = false, instantiating..." # instantiate = true # comp, _ = prepareSolveFMU(fmu, comp, :ME, instantiate, nothing, nothing, nothing, nothing, nothing, t_start, t_stop, nothing; x0=X0, handleEvents=FMIFlux.handleEvents, cleanup=true) # end c1 = CacheLayer() c2 = CacheRetrieveLayer(c1) net = Chain( states -> fmu(; x = states, dx_refs = :all), dx -> c1(dx), Dense(numStates, 16, tanh), Dense(16, 1, identity), dx -> c2(1, dx[1]), ) optim = OPTIMISER(ETA) solver = Tsit5() problem = ME_NeuralFMU(fmu, net, (t_start, t_stop), solver) @test problem != nothing solutionBefore = problem(X0; saveat = tData) if solutionBefore.success @test length(solutionBefore.states.t) == length(tData) @test solutionBefore.states.t[1] == t_start @test solutionBefore.states.t[end] == t_stop end # train it ... p_net = Flux.params(problem) iterCB = 0 lastLoss = losssum(p_net[1]) lastInstCount = length(problem.fmu.components) @info "Start-Loss for net: $lastLoss" lossBefore = losssum(p_net[1]) FMIFlux.train!( losssum, problem, Iterators.repeated((), NUMSTEPS), optim; gradient = GRADIENT, ) lossAfter = losssum(p_net[1]) @test lossAfter < lossBefore # check results solutionAfter = problem(X0; saveat = tData) if solutionAfter.success @test length(solutionAfter.states.t) == length(tData) @test solutionAfter.states.t[1] == t_start @test solutionAfter.states.t[end] == t_stop end # this is not possible, because some pullbacks are evaluated after simulation end while length(problem.fmu.components) > 1 fmi2FreeInstance!(problem.fmu.components[end]) end # if length(problem.fmu.components) == 1 # fmi2Reset(problem.fmu.components[end]) # end end end end end unloadFMU(fmu)
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
[ "MIT" ]
0.13.0
7fec0fac72076ea32823fb59efcc0e280cd28162
docs
8232
![FMIFlux.jl Logo](https://github.com/ThummeTo/FMIFlux.jl/blob/main/logo/dark/fmifluxjl_logo_640_320.png?raw=true "FMIFlux.jl Logo") # FMIFlux.jl ## What is FMIFlux.jl? [*FMIFlux.jl*](https://github.com/ThummeTo/FMIFlux.jl) is a free-to-use software library for the Julia programming language, which offers the ability to simply place your FMU ([fmi-standard.org](http://fmi-standard.org/)) everywhere inside of your ML topologies and still keep the resulting models trainable with a standard (or custom) FluxML training process. This includes for example: - NeuralODEs including FMUs, so called *Neural Functional Mock-up Units* (NeuralFMUs): You can place FMUs inside of your ML topology. - PINNs including FMUs, so called *Functional Mock-Up Unit informed Neural Networks* (FMUINNs): You can evaluate FMUs inside of your loss function. [![Dev Docs](https://img.shields.io/badge/docs-dev-blue.svg)](https://ThummeTo.github.io/FMIFlux.jl/dev) [![Test (latest)](https://github.com/ThummeTo/FMIFlux.jl/actions/workflows/TestLatest.yml/badge.svg)](https://github.com/ThummeTo/FMIFlux.jl/actions/workflows/TestLatest.yml) [![Test (LTS)](https://github.com/ThummeTo/FMIFlux.jl/actions/workflows/TestLTS.yml/badge.svg)](https://github.com/ThummeTo/FMIFlux.jl/actions/workflows/TestLTS.yml) [![Examples](https://github.com/ThummeTo/FMIFlux.jl/actions/workflows/Example.yml/badge.svg)](https://github.com/ThummeTo/FMIFlux.jl/actions/workflows/Example.yml) [![Build Docs](https://github.com/ThummeTo/FMIFlux.jl/actions/workflows/Documentation.yml/badge.svg)](https://github.com/ThummeTo/FMIFlux.jl/actions/workflows/Documentation.yml) [![Run PkgEval](https://github.com/ThummeTo/FMIFlux.jl/actions/workflows/Eval.yml/badge.svg)](https://github.com/ThummeTo/FMIFlux.jl/actions/workflows/Eval.yml) [![Coverage](https://codecov.io/gh/ThummeTo/FMIFlux.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/ThummeTo/FMIFlux.jl) [![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor's%20Guide-blueviolet)](https://github.com/SciML/ColPrac) [![SciML Code Style](https://img.shields.io/static/v1?label=code%20style&message=SciML&color=9558b2&labelColor=389826)](https://github.com/SciML/SciMLStyle) ## How can I use FMIFlux.jl? 1\. Open a Julia-REPL, switch to package mode using `]`, activate your preferred environment. 2\. Install [*FMIFlux.jl*](https://github.com/ThummeTo/FMIFlux.jl): ```julia-repl (@v1) pkg> add FMIFlux ``` 3\. If you want to check that everything works correctly, you can run the tests bundled with [*FMIFlux.jl*](https://github.com/ThummeTo/FMIFlux.jl): ```julia-repl (@v1) pkg> test FMIFlux ``` 4\. Have a look inside the [examples folder](https://github.com/ThummeTo/FMIFlux.jl/tree/examples/examples) in the examples branch or the [examples section](https://thummeto.github.io/FMIFlux.jl/dev/examples/overview/) of the documentation. All examples are available as Julia-Script (*.jl*), Jupyter-Notebook (*.ipynb*) and Markdown (*.md*). ## What is currently supported in FMIFlux.jl? - building and training ME-NeuralFMUs (NeuralODEs) with support for event-handling (*DiffEqCallbacks.jl*) and discontinuous sensitivity analysis (*SciMLSensitivity.jl*) - building and training CS-NeuralFMUs - building and training NeuralFMUs consisting of multiple FMUs - building and training FMUINNs (PINNs) - different AD-frameworks: ForwardDiff.jl (CI-tested), ReverseDiff.jl (CI-tested, default setting), FiniteDiff.jl (not CI-tested) and Zygote.jl (not CI-tested) - use `Flux.jl` optimizers as well as the ones from `Optim.jl` - using the entire *DifferentialEquations.jl* solver suite (`autodiff=false` for implicit solvers, not all are tested, see following section) - ... ## (Current) Limitations - Not all implicit solvers work for challenging, hybrid models (stiff FMUs with events), currently tested are: `Rosenbrock23(autodiff=false)`. - Implicit solvers using `autodiff=true` is not supported (now), but you can use implicit solvers with `autodiff=false`. - Sensitivity information over state change by event $\partial x^{+} / \partial x^{-}$ can't be accessed in FMI. These sensitivities are sampled if the FMU supports `fmiXGet/SetState`. If this feature is not available, wrong sensitivities are computed, which my influence your optimization (dependent on the use case). This issue is also part of the [*OpenScaling*](https://itea4.org/project/openscaling.html) research project. - If continuous adjoints instead of automatic differentiation through the ODE solver (discrete adjoint) are applied, this might lead to issues, because FMUs are by design not capable of being simulated backwards in time. On the other hand, many FMUs are capable of doing so. This issue is also part of the [*OpenScaling*](https://itea4.org/project/openscaling.html) research project. - For now, only FMI version 2.0 is supported, but FMI 3.0 support is coming with the [*OpenScaling*](https://itea4.org/project/openscaling.html) research project. ## What is under development in FMIFlux.jl? - performance optimizations - multi threaded CPU training - improved documentation - more examples - FMI3 integration - ... ## What Platforms are supported? [*FMIFlux.jl*](https://github.com/ThummeTo/FMIFlux.jl) is tested (and testing) under Julia versions *v1.6* (LTS) and *v1* (latest) on Windows (latest) and Ubuntu (latest). MacOS should work, but untested. All shipped examples are automatically tested under Julia version *v1* (latest) on Windows (latest). ## What FMI.jl-Library should I use? ![FMI.jl Family](https://github.com/ThummeTo/FMI.jl/blob/main/docs/src/assets/FMI_JL_family.png?raw=true "FMI.jl Family") To keep dependencies nice and clean, the original package [*FMI.jl*](https://github.com/ThummeTo/FMI.jl) had been split into new packages: - [*FMI.jl*](https://github.com/ThummeTo/FMI.jl): High level loading, manipulating, saving or building entire FMUs from scratch - [*FMIImport.jl*](https://github.com/ThummeTo/FMIImport.jl): Importing FMUs into Julia - [*FMIExport.jl*](https://github.com/ThummeTo/FMIExport.jl): Exporting stand-alone FMUs from Julia Code - [*FMIBase.jl*](https://github.com/ThummeTo/FMIBase.jl): Common concepts for import and export of FMUs - [*FMICore.jl*](https://github.com/ThummeTo/FMICore.jl): C-code wrapper for the FMI-standard - [*FMISensitivity.jl*](https://github.com/ThummeTo/FMISensitivity.jl): Static and dynamic sensitivities over FMUs - [*FMIBuild.jl*](https://github.com/ThummeTo/FMIBuild.jl): Compiler/Compilation dependencies for FMIExport.jl - [*FMIFlux.jl*](https://github.com/ThummeTo/FMIFlux.jl): Machine Learning with FMUs - [*FMIZoo.jl*](https://github.com/ThummeTo/FMIZoo.jl): A collection of testing and example FMUs ## Video-Workshops ### JuliaCon 2024 (Eindhoven University of Technology, Netherlands) [![YouTube Video of Workshop](https://img.youtube.com/vi/sQ2MXSswrSo/0.jpg)](https://www.youtube.com/watch?v=sQ2MXSswrSo) ### JuliaCon 2023 (Massachusetts Institute of Technology, United States) [![YouTube Video of Workshop](https://img.youtube.com/vi/X_u0KlZizD4/0.jpg)](https://www.youtube.com/watch?v=X_u0KlZizD4) ## How to cite? Tobias Thummerer, Johannes Stoljar and Lars Mikelsons. 2022. **NeuralFMU: presenting a workflow for integrating hybrid NeuralODEs into real-world applications.** Electronics 11, 19, 3202. [DOI: 10.3390/electronics11193202](https://doi.org/10.3390/electronics11193202) Tobias Thummerer, Lars Mikelsons and Josef Kircher. 2021. **NeuralFMU: towards structural integration of FMUs into neural networks.** Martin Sjölund, Lena Buffoni, Adrian Pop and Lennart Ochel (Ed.). Proceedings of 14th Modelica Conference 2021, Linköping, Sweden, September 20-24, 2021. Linköping University Electronic Press, Linköping (Linköping Electronic Conference Proceedings ; 181), 297-306. [DOI: 10.3384/ecp21181297](https://doi.org/10.3384/ecp21181297) ## Related publications? Tobias Thummerer, Johannes Tintenherr, Lars Mikelsons 2021. **Hybrid modeling of the human cardiovascular system using NeuralFMUs** Journal of Physics: Conference Series 2090, 1, 012155. [DOI: 10.1088/1742-6596/2090/1/012155](https://doi.org/10.1088/1742-6596/2090/1/012155)
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
[ "MIT" ]
0.13.0
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docs
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# Creation of the SSH deploy key for the CompatHelper ## 1. Create an ssh key pair. This command is avaible for Windows (`cmd`) and Linux (`bash`). ``` ssh-keygen -N "" -f compathelper_key -t ed25519 -C compathelper ``` ## 2. Copy the **private** key. Copy the output to your clipboard. 1. Windows ``` type compathelper_key ``` 1. Linux ``` cat compathelper_key ``` ## 3. Create a GitHub secret. 1. Open the repository on the GitHub page. 1. Click on the **Settings** tab. 1. Click on **Secrets**. 1. Click on **Actions**. 1. Click on the **New repository secret** button. 1. Name the secret `COMPATHELPER_PRIV`. 1. Paste the **private** key as content. ## 4. Copy the **public** key. Copy the output to your clipboard. 1. Windows ``` type compathelper_key.pub ``` 1. Linux ``` cat compathelper_key.pub ``` ## 5. Create a GitHub deploy key. 1. Open the repository on the GitHub page. 1. Click on the **Settings** tab. 1. Click on **Deploy keys**. 1. Click on the **Add deploy key** button. 1. Name the deploy key `COMPATHELPER_PUB`. 1. Paste the **public** key as content. 1. Enable the write access for the deploy key. ## 6. Delete the ssh key pair. 1. Windows ``` del compathelper_key compathelper_key.pub ``` 1. Linux ``` rm -f compathelper_key compathelper_key.pub ``` For more Information click [here](https://docs.juliahub.com/CompatHelper/GCWpz/2.0.1/#Instructions-for-setting-up-the-SSH-deploy-key).
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
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```@contents Depth = 2 ```
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
[ "MIT" ]
0.13.0
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# FAQ This list some common - often numerical - errors, that can be fixed by better understanding the ODE-Problem inside your FMU. ## Double callback crossing ### Description Error message, a double zero-crossing happened, often during training a NeuralFMU. ### Example - `Double callback crossing floating pointer reducer errored. Report this issue.` ### Reason This could be, because the event inside of a NeuralFMU can't be located (often when using Zygote). ### Fix - Try to increase the root search interpolation points, this is computational expensive for FMUs with many events- and event-indicators. This can be done using `fmu.executionConfig.rootSearchInterpolationPoints = 100` (default value is `10`).
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
[ "MIT" ]
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# [Library Functions](@id library) ```@index ``` ## FMIFlux functions ```@docs CS_NeuralFMU ME_NeuralFMU NeuralFMU ``` ## FMI 2 version dependent functions ```@docs fmi2DoStepCS fmi2EvaluateME fmi2InputDoStepCSOutput ``` ## FMI version independent functions ```@docs fmiDoStepCS fmiEvaluateME fmiInputDoStepCSOutput ``` ## Additional functions ```@docs mse_interpolate transferParams! ```
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
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# Related Publications Thummerer T, Kircher J and Mikelsons L: __Neural FMU: Towards structual integration of FMUs into neural networks__ (Preprint, accepted 14th International Modelica Conference) [pdf](https://arxiv.org/abs/2109.04351)|DOI Thummerer T, Tintenherr J, Mikelsons L: __Hybrid modeling of the human cardiovascular system using NeuralFMUs__ (Preprint, accepted 10th International Conference on Mathematical Modeling in Physical Sciences) [pdf](https://arxiv.org/abs/2109.04880)|DOI
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
[ "MIT" ]
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docs
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# Examples - Overview This section discusses the included examples of the FMIFlux.jl library. You can execute them on your machine and get detailed information about all of the steps. If you require further information about the function calls, see [library functions](https://thummeto.github.io/FMIFlux.jl/dev/library/) section. For more information related to the setup and simulation of an FMU see [FMI.jl library](https://thummeto.github.io/FMI.jl/dev/). The examples are intended for users who work in the field of first principle and/or data driven modeling and are further interested in hybrid model building. The examples show how to combine FMUs with machine learning ("NeuralFMU") and illustrates the advantages of this approach. ## Examples - [__Simple CS-NeuralFMU__](https://thummeto.github.io/FMIFlux.jl/dev/examples/simple_hybrid_CS/): Showing how to train a NeuralFMU in Co-Simulation-Mode. - [__Simple ME-NeuralFMU__](https://thummeto.github.io/FMIFlux.jl/dev/examples/simple_hybrid_ME/): Showing how to train a NeuralFMU in Model-Exchange-Mode. ## Advanced examples: Demo applications - [__JuliaCon 2023: Using NeuralODEs in real life applications__](https://thummeto.github.io/FMIFlux.jl/dev/examples/juliacon_2023/): An example for a NeuralODE in a real world engineering scenario. - [__Modelica Conference 2021: NeuralFMUs__](https://thummeto.github.io/FMIFlux.jl/dev/examples/modelica_conference_2021/): Showing basics on how to train a NeuralFMU (Contribution for the *Modelica Conference 2021*). ## Workshops [Pluto](https://plutojl.org/) based notebooks, that can easily be executed on your own Pluto-Setup. - [__Scientific Machine Learning using Functional Mock-up Units__](../pluto-src/SciMLUsingFMUs/SciMLUsingFMUs.html): Workshop at JuliaCon 2024 (Eindhoven University, Netherlands) ## Archived - [__MDPI 2022: Physics-enhanced NeuralODEs in real-world applications__](https://thummeto.github.io/FMIFlux.jl/dev/examples/mdpi_2022/): An example for a NeuralODE in a real world modeling scenario (Contribution in *MDPI Electronics 2022*). - [__Growing Horizon ME-NeuralFMU__](https://thummeto.github.io/FMIFlux.jl/dev/examples/growing_horizon_ME/): Growing horizon training technique for a ME-NeuralFMU. - [__HybridModelingUsingFMI__](../pluto-src/HybridModelingUsingFMI/HybridModelingUsingFMI.html): Workshop at MODPROD 2024 (Linköping University, Sweden)
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
[ "MIT" ]
0.13.0
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docs
197
[Pluto](https://plutojl.org/) based notebooks, that can easyly be executed on your own Pluto-Setup. ```@raw html <iframe src="../pluto-src/index.html" style="height:500px;width:100%;"></iframe> ```
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
[ "MIT" ]
0.13.0
7fec0fac72076ea32823fb59efcc0e280cd28162
docs
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![FMIFlux.jl Logo](https://github.com/ThummeTo/FMIFlux.jl/blob/main/logo/dark/fmifluxjl_logo_640_320.png?raw=true "FMIFlux.jl Logo") # Structure Examples can be found in the [examples folder in the examples branch](https://github.com/ThummeTo/FMIFlux.jl/tree/examples/examples) or the [examples section of the documentation](https://thummeto.github.io/FMIFlux.jl/dev/examples/overview/). All examples are available as Julia-Script (*.jl*), Jupyter-Notebook (*.ipynb*) and Markdown (*.md*). # Getting Started ## Install Jupyter in Visual Studio Code The Jupyter Notebooks extension for Visual Studio Code can be [here](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter). ## Add Julia Kernel to Jupyter To run Julia as kernel in a jupyter notebook it is necessary to add the **IJulia** package. 1. Start the Julia REPL. ``` julia ``` 2. Select your environment. ```julia using Pkg Pkg.activate("Your Env") ``` 3. Add and build the IJulia package by typing inside the Julia REPL. ```julia using Pkg Pkg.add("IJulia") Pkg.build("IJulia") ``` 4. Now you should be able to choose a Julia kernel in a Jupyter notebook. More information can be found [here](https://towardsdatascience.com/how-to-best-use-julia-with-jupyter-82678a482677).
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
[ "MIT" ]
0.13.0
7fec0fac72076ea32823fb59efcc0e280cd28162
docs
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![FMIFlux.jl Logo](https://github.com/ThummeTo/FMIFlux.jl/blob/main/logo/fmifluxjl_logo_640_320.png "FMIFlux.jl Logo") # Acknowledgement We thank *Florian Schläffer* for designing the beautiful library logo.
FMIFlux
https://github.com/ThummeTo/FMIFlux.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
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using Documenter using NATS using NATS.JetStream makedocs( sitename = "NATS", format = Documenter.HTML(), modules = [NATS], pages = [ "index.md", "Core NATS" => [ "examples.md", "connect.md", "pubsub.md", "reqreply.md", "custom-data.md", "scoped_connection.md", "debugging.md", ], "JetStream" => [ "jetstream/stream.md" "jetstream/consumer.md" "jetstream/keyvalue.md" "jetstream/jetdict.md" "jetstream/jetchannel.md" ], "Internals" => [ "protocol.md", "interrupt_handling.md", "benchmarks.md", ] ] ) # Documenter can also automatically deploy documentation to gh-pages. # See "Hosting Documentation" and deploydocs() in the Documenter manual # for more information. deploydocs( repo = "github.com/jakubwro/NATS.jl" )
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
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using Markdown typemap = Dict("string" => :String, "bool" => :Bool, "int" => :Int, "uint64" => :UInt64, "[string]" => :(Vector{String})) function parse_operations(operations) parse_row(row) = (name = row[1][1].text[1].code) ops = map(parse_row, operations.content[1].rows[2:end]) ops = map(op -> strip(op, ['+', '-']), ops) Expr(:macrocall, Symbol("@enum"), :(), :ProtocolOperation, Symbol.(ops)...) end function parse_row(row) all(isempty, row) && return nothing, nothing parse_row(row) = (name = row[1][1].code, type = row[3][1], presence = row[4][1], desc = Markdown.plaininline(row[2])) (name, type, presence, desc) = parse_row(row) name = replace(name, "-" => "_", " " => "_", "#" => "") prop_type = typemap[type] if presence != "always" && presence != "true" prop_type = Expr(:curly, :Union, prop_type, :Nothing) end desc, Expr(:(::), Symbol(name), prop_type) end function parse_markdown(md::Markdown.MD, struct_name::Symbol) expr = quote struct $struct_name <: ProtocolMessage $(filter(!isnothing, collect(Iterators.flatten(map(parse_row, md.content[2].rows[2:end]))))...) end end doc = Markdown.plaininline(md.content[1].content) expr = Base.remove_linenums!(expr) doc, expr.args[1] end operations = md""" | OP Name | Sent By | Description | |-------------------------|---------|------------------------------------------------------------------------------------| | [`INFO`](./#info) | Server | Sent to client after initial TCP/IP connection | | [`CONNECT`](./#connect) | Client | Sent to server to specify connection information | | [`PUB`](./#pub) | Client | Publish a message to a subject, with optional reply subject | | [`HPUB`](./#hpub) | Client | Publish a message to a subject including NATS headers, with optional reply subject | | [`SUB`](./#sub) | Client | Subscribe to a subject (or subject wildcard) | | [`UNSUB`](./#unsub) | Client | Unsubscribe (or auto-unsubscribe) from subject | | [`MSG`](./#msg) | Server | Delivers a message payload to a subscriber | | [`HMSG`](./#hmsg) | Server | Delivers a message payload to a subscriber with NATS headers | | [`PING`](./#pingpong) | Both | PING keep-alive message | | [`PONG`](./#pingpong) | Both | PONG keep-alive response | | [`+OK`](./#okerr) | Server | Acknowledges well-formed protocol message in `verbose` mode | | [`-ERR`](./#okerr) | Server | Indicates a protocol error. May cause client disconnect. | """ # FIXME: duplicated client_id, report bug to nats docs. # | `client_id` | The ID of the client. | string | optional | info = md""" A client will need to start as a plain TCP connection, then when the server accepts a connection from the client, it will send information about itself, the configuration and security requirements necessary for the client to successfully authenticate with the server and exchange messages. When using the updated client protocol (see CONNECT below), INFO messages can be sent anytime by the server. This means clients with that protocol level need to be able to asynchronously handle INFO messages. | name | description | type | presence | |-------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------|----------| | `server_id` | The unique identifier of the NATS server. | string | always | | `server_name` | The name of the NATS server. | string | always | | `version` | The version of NATS. | string | always | | `go` | The version of golang the NATS server was built with. | string | always | | `host` | The IP address used to start the NATS server, by default this will be `0.0.0.0` and can be configured with `-client_advertise host:port`. | string | always | | `port` | The port number the NATS server is configured to listen on. | int | always | | `headers` | Whether the server supports headers. | bool | always | | `max_payload` | Maximum payload size, in bytes, that the server will accept from the client. | int | always | | `proto` | An integer indicating the protocol version of the server. The server version 1.2.0 sets this to `1` to indicate that it supports the "Echo" feature. | int | always | | `client_id` | The internal client identifier in the server. This can be used to filter client connections in monitoring, correlate with error logs, etc... | uint64 | optional | | `auth_required` | If this is true, then the client should try to authenticate upon connect. | bool | optional | | `tls_required` | If this is true, then the client must perform the TLS/1.2 handshake. Note, this used to be `ssl_required` and has been updated along with the protocol from SSL to TLS.| bool | optional | | `tls_verify` | If this is true, the client must provide a valid certificate during the TLS handshake. | bool | optional | | `tls_available` | If this is true, the client can provide a valid certificate during the TLS handshake. | bool | optional | | `connect_urls` | List of server urls that a client can connect to. | [string] | optional | | `ws_connect_urls` | List of server urls that a websocket client can connect to. | [string] | optional | | `ldm` | If the server supports _Lame Duck Mode_ notifications, and the current server has transitioned to lame duck, `ldm` will be set to `true`. | bool | optional | | `git_commit` | The git hash at which the NATS server was built. | string | optional | | `jetstream` | Whether the server supports JetStream. | bool | optional | | `ip` | The IP of the server. | string | optional | | `client_ip` | The IP of the client. | string | optional | | `nonce` | The nonce for use in CONNECT. | string | optional | | `cluster` | The name of the cluster. | string | optional | | `domain` | The configured NATS domain of the server. | string | optional | """ connect = md""" The CONNECT message is the client version of the `INFO` message. Once the client has established a TCP/IP socket connection with the NATS server, and an `INFO` message has been received from the server, the client may send a `CONNECT` message to the NATS server to provide more information about the current connection as well as security information. | name | description | type | required | |-----------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------|------------------------------| | `verbose` | Turns on [`+OK`](./#NATS.Ok) protocol acknowledgements. | bool | true | | `pedantic` | Turns on additional strict format checking, e.g. for properly formed subjects. | bool | true | | `tls_required` | Indicates whether the client requires SSL connection. | bool | true | | `auth_token` | Client authorization token. | string | if `auth_required` is `true` | | `user` | Connection username. | string | if `auth_required` is `true` | | `pass` | Connection password. | string | if `auth_required` is `true` | | `name` | Client name. | string | false | | `lang` | The implementation language of the client. | string | true | | `version` | The version of the client. | string | true | | `protocol` | Sending `0` (or absent) indicates client supports original protocol. Sending `1` indicates that the client supports dynamic reconfiguration of cluster topology changes by asynchronously receiving [`INFO`](./#NATS.Info) messages with known servers it can reconnect to. | int | false | | `echo` | If set to `false`, the server (version 1.2.0+) will not send originating messages from this connection to its own subscriptions. Clients should set this to `false` only for server supporting this feature, which is when `proto` in the `INFO` protocol is set to at least `1`. | bool | false | | `sig` | In case the server has responded with a `nonce` on `INFO`, then a NATS client must use this field to reply with the signed `nonce`. | string | if `nonce` received | | `jwt` | The JWT that identifies a user permissions and account. | string | false | | `no_responders` | Enable quick replies for cases where a request is sent to a topic with no responders. | bool | false | | `headers` | Whether the client supports headers. | bool | false | | `nkey` | The public NKey to authenticate the client. This will be used to verify the signature (`sig`) against the `nonce` provided in the `INFO` message. | string | false | """ pub = md""" The PUB message publishes the message payload to the given subject name, optionally supplying a reply subject. If a reply subject is supplied, it will be delivered to eligible subscribers along with the supplied payload. Note that the payload itself is optional. To omit the payload, set the payload size to 0, but the second CRLF is still required. | name | description | type | required | |------------|-----------------------------------------------------------------------------------------------|--------|----------| | `subject` | The destination subject to publish to. | string | true | | `reply-to` | The reply subject that subscribers can use to send a response back to the publisher/requestor.| string | false | | `#bytes` | The payload size in bytes. | int | true | | `payload` | The message payload data. | string | optional | """ hpub = md""" The HPUB message is the same as PUB but extends the message payload to include NATS headers. Note that the payload itself is optional. To omit the payload, set the total message size equal to the size of the headers. Note that the trailing CR+LF is still required. | name | description | type | required | |-----------------|-------------------------------------------------------------------------------------------------|--------|----------| | `subject` | The destination subject to publish to. | string | true | | `reply-to` | The reply subject that subscribers can use to send a response back to the publisher/requestor. | string | false | | `#header bytes` | The size of the headers section in bytes including the `␍␊␍␊` delimiter before the payload. | int | true | | `#total bytes` | The total size of headers and payload sections in bytes. | int | true | | `headers` | Header version `NATS/1.0␍␊` followed by one or more `name: value` pairs, each separated by `␍␊`.| string | false | | `payload` | The message payload data. | string | false | """ sub = md""" `SUB` initiates a subscription to a subject, optionally joining a distributed queue group. | name | description | type | required | |---------------|----------------------------------------------------------------|--------|----------| | `subject` | The subject name to subscribe to. | string | true | | `queue group` | If specified, the subscriber will join this queue group. | string | false | | `sid` | A unique alphanumeric subscription ID, generated by the client.| string | true | """ unsub = md""" `UNSUB` unsubscribes the connection from the specified subject, or auto-unsubscribes after the specified number of messages has been received. | name | description | type | required | |------------|----------------------------------------------------------------------------|--------|----------| | `sid` | The unique alphanumeric subscription ID of the subject to unsubscribe from.| string | true | | `max_msgs` | A number of messages to wait for before automatically unsubscribing. | int | false | """ msg = md""" The `MSG` protocol message is used to deliver an application message to the client. | name | description | type | presence | |------------|---------------------------------------------------------------|--------|----------| | `subject` | Subject name this message was received on. | string | always | | `sid` | The unique alphanumeric subscription ID of the subject. | string | always | | `reply-to` | The subject on which the publisher is listening for responses.| string | optional | | `#bytes` | Size of the payload in bytes. | int | always | | `payload` | The message payload data. | string | optional | """ hmsg = md""" The HMSG message is the same as MSG, but extends the message payload with headers. See also [ADR-4 NATS Message Headers](https://github.com/nats-io/nats-architecture-and-design/blob/main/adr/ADR-4.md). | name | description | type | presence | |-----------------|-------------------------------------------------------------------------------------------------|--------|----------| | `subject` | Subject name this message was received on. | string | always | `sid` | The unique alphanumeric subscription ID of the subject. | string | always | | `reply-to` | The subject on which the publisher is listening for responses. | string | optional | | `#header bytes` | The size of the headers section in bytes including the `␍␊␍␊` delimiter before the payload. | int | always | | `#total bytes` | The total size of headers and payload sections in bytes. | int | always | | `headers` | Header version `NATS/1.0␍␊` followed by one or more `name: value` pairs, each separated by `␍␊`.| string | optional | | `payload` | The message payload data. | string | optional | """ ping = md""" `PING` and `PONG` implement a simple keep-alive mechanism between client and server. | name | description | type | presence | |------|--------------|--------|----------| | | | | | """ pong = md""" `PING` and `PONG` implement a simple keep-alive mechanism between client and server. | name | description | type | presence | |------|--------------|--------|----------| | | | | | """ err = md""" The `-ERR` message is used by the server indicate a protocol, authorization, or other runtime connection error to the client. Most of these errors result in the server closing the connection. | name | description | type | presence | |-----------|----------------|--------|----------| | `message` | Error message. | string | always | """ ok = md""" When the `verbose` connection option is set to `true` (the default value), the server acknowledges each well-formed protocol message from the client with a `+OK` message. | name | description | type | presence | |------|--------------|--------|----------| | | | | | """ docs = [info, connect, pub, hpub, sub, unsub, msg, hmsg, ping, pong, err, ok] structs = [:Info, :Connect, :Pub, :HPub, :Sub, :Unsub, :Msg, :HMsg, :Ping, :Pong, :Err, :Ok] open("../src/protocol/structs.jl", "w") do f; println(f, "# This file is autogenerated by `$(relpath(@__FILE__, dirname(@__DIR__)))`. Maunal changes will be lost.") println(f) # println(f, parse_operations(operations)) # println(f) println(f, "abstract type ProtocolMessage end") for (doc, struct_def) in parse_markdown.(docs, structs) println(f) println(f, "\"\"\"") println(f, doc) println(f) println(f, "\$(TYPEDFIELDS)") println(f, "\"\"\"") println(f, struct_def) end end
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
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### NATS.jl # # Copyright (C) 2023 Jakub Wronowski. # # Maintainer: Jakub Wronowski <jakubwro@users.noreply.github.com> # Keywords: nats, nats-client, julia # # This file is a part of NATS.jl. # # License is MIT. # ### Commentary: # # This file aggregates all files of NATS.jl package. # ### Code: module NATS using Random using Sockets using StructTypes using JSON3 using MbedTLS using DocStringExtensions using BufferedStreams using Sodium using CodecBase using ScopedValues using URIs import Base: show, convert export NATSError export connect, reconnect, ping, drain export payload, header, headers export publish, subscribe, unsubscribe, next export request, reply export with_connection export JetStream const DEFAULT_HOST = "localhost" const DEFAULT_PORT = "4222" const DEFAULT_CONNECT_URL = "nats://$(DEFAULT_HOST):$(DEFAULT_PORT)" const CLIENT_VERSION = "0.1.0" const CLIENT_LANG = "julia" const MIME_PROTOCOL = MIME"application/nats" const MIME_PAYLOAD = MIME"application/nats-payload" const MIME_HEADERS = MIME"application/nats-headers" # Granular reconnect retries configuration #TODO: ADR-40 says it should be 3. const DEFAULT_RECONNECT_RETRIES = 220752000000000000 # 7 bilion years. const DEFAULT_RECONNECT_FIRST_DELAY = 0.1 const DEFAULT_RECONNECT_MAX_DELAY = 5.0 const DEFAULT_RECONNECT_FACTOR = 5.0 const DEFAULT_RECONNECT_JITTER = 0.1 const DEFAULT_SEND_BUFFER_LIMIT_BYTES = 2 * 2^20 # 2 MB const DEFAULT_PING_INTERVAL_SECONDS = 2.0 * 60.0 const DEFAULT_MAX_PINGS_OUT = 2 const DEFAULT_RETRY_ON_INIT_FAIL = false const DEFAULT_IGNORE_ADVERTISED_SERVERS = false const DEFAULT_RETAIN_SERVERS_ORDER = false const DEFAULT_ENQUEUE_WHEN_DISCONNECTED = true # If set to true messages will be enqueued when connection lost, otherwise exception will be thrown. const DEFAULT_SUBSCRIPTION_CHANNEL_SIZE = 512 * 1024 const DEFAULT_SUBSCRIPTION_ERROR_THROTTLING_SECONDS = 5.0 const DEFAULT_REQUEST_TIMEOUT_SECONDS = 5.0 const DEFAULT_DRAIN_TIMEOUT_SECONDS = 5.0 const DEFAULT_DRAIN_POLL_INTERVAL_SECONDS = 0.2 const INVOKE_LATEST_CONVERSIONS = false # TODO: use this in code include("protocol/protocol.jl") include("connection/connection.jl") include("pubsub/pubsub.jl") include("reqreply/reqreply.jl") include("experimental/experimental.jl") include("jetstream/JetStream.jl") end
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
19009
### connect.jl # # Copyright (C) 2023 Jakub Wronowski. # # Maintainer: Jakub Wronowski <jakubwro@users.noreply.github.com> # Keywords: nats, nats-client, julia # # This file is a part of NATS.jl. # # License is MIT. # ### Commentary: # # This file contains functions for estabilishing connection and maintaining connectivity when TCP connection fails. # ### Code: function default_connect_options() ( # Options that are defined in the protocol, see Connect struct. verbose= parse(Bool, get(ENV, "NATS_VERBOSE", "false")), pedantic = parse(Bool, get(ENV, "NATS_PEDANTIC", "false")), tls_required = parse(Bool, get(ENV, "NATS_TLS_REQUIRED", "false")), auth_token = get(ENV, "NATS_AUTH_TOKEN", nothing), user = get(ENV, "NATS_USER", nothing), pass = get(ENV, "NATS_PASS", nothing), name = nothing, lang = CLIENT_LANG, version = CLIENT_VERSION, protocol = 1, echo = nothing, sig = nothing, jwt = get(ENV, "NATS_JWT", nothing), no_responders = true, headers = true, nkey = get(ENV, "NATS_NKEY", nothing), # Options used only on client side, never sent to server. nkey_seed = get(ENV, "NATS_NKEY_SEED", nothing), tls_ca_path = get(ENV, "NATS_TLS_CA_PATH", nothing), tls_cert_path = get(ENV, "NATS_TLS_CERT_PATH", nothing), tls_key_path = get(ENV, "NATS_TLS_KEY_PATH", nothing), ping_interval = parse(Float64, get(ENV, "NATS_PING_INTERVAL", string(DEFAULT_PING_INTERVAL_SECONDS))), max_pings_out = parse(Int64, get(ENV, "NATS_MAX_PINGS_OUT", string(DEFAULT_MAX_PINGS_OUT))), retry_on_init_fail = parse(Bool, get(ENV, "NATS_RETRY_ON_INIT_FAIL", string(DEFAULT_RETRY_ON_INIT_FAIL))), ignore_advertised_servers = parse(Bool, get(ENV, "NATS_IGNORE_ADVERTISED_SERVERS", string(DEFAULT_IGNORE_ADVERTISED_SERVERS))), retain_servers_order = parse(Bool, get(ENV, "NATS_RETAIN_SERVERS_ORDER", string(DEFAULT_RETAIN_SERVERS_ORDER))), send_enqueue_when_disconnected = parse(Bool, get(ENV, "NATS_ENQUEUE_WHEN_DISCONNECTED", string(DEFAULT_ENQUEUE_WHEN_DISCONNECTED))), reconnect_delays = default_reconnect_delays(), send_buffer_limit = parse(Int, get(ENV, "NATS_SEND_BUFFER_LIMIT_BYTES", string(DEFAULT_SEND_BUFFER_LIMIT_BYTES))), send_retry_delays = SEND_RETRY_DELAYS, drain_timeout = parse(Float64, get(ENV, "NATS_DRAIN_TIMEOUT_SECONDS", string(DEFAULT_DRAIN_TIMEOUT_SECONDS))), drain_poll = parse(Float64, get(ENV, "NATS_DRAIN_POLL_INTERVAL_SECONDS", string(DEFAULT_DRAIN_POLL_INTERVAL_SECONDS))), ) end function default_reconnect_delays() ExponentialBackOff( n = parse(Int64, get(ENV, "NATS_RECONNECT_RETRIES", string(DEFAULT_RECONNECT_RETRIES))), first_delay = parse(Float64, get(ENV, "NATS_RECONNECT_FIRST_DELAY", string(DEFAULT_RECONNECT_FIRST_DELAY))), max_delay = parse(Float64, get(ENV, "NATS_RECONNECT_MAX_DELAY", string(DEFAULT_RECONNECT_MAX_DELAY))), factor = parse(Float64, get(ENV, "NATS_RECONNECT_FACTOR", string(DEFAULT_RECONNECT_FACTOR))), jitter = parse(Float64, get(ENV, "NATS_RECONNECT_JITTER", string(DEFAULT_RECONNECT_JITTER)))) end function validate_connect_options(server_info::Info, options) # TODO: check if proto is 1 when `echo` flag is set # TODO: maybe better to rely on server side validation. Grab Err messages and decide if conn should be terminated. server_info.proto > 0 || error("Server supports too old protocol version.") server_info.headers || error("Server does not support headers.") # TODO: maybe this can be relaxed. # Check TLS requirements if get(options, :tls_required, false) !isnothing(server_info.tls_available) && server_info.tls_available || error("Client requires TLS but it is not available for the server.") end end function host_port(url::AbstractString) if !contains(url, "://") url = "nats://$url" end uri = URI(url) host, port, scheme, userinfo = uri.host, uri.port, uri.scheme, uri.userinfo if isempty(host) error("Host not specified in url `$url`.") end if isempty(port) port = DEFAULT_PORT end host, parse(Int, port), scheme, userinfo end function connect_urls(nc::Connection, url; ignore_advertised_servers::Bool) info_msg = info(nc) if ignore_advertised_servers || isnothing(info_msg) || isnothing(info_msg.connect_urls) || isempty(info_msg.connect_urls) split(url, ",") else info_msg.connect_urls end end function init_protocol(nc, url, options) @atomic nc.connect_init_count += 1 urls = connect_urls(nc, url; options.ignore_advertised_servers) if options.retain_servers_order idx = mod((@atomic nc.connect_init_count) - 1, length(urls)) + 1 url = urls[idx] else url = rand(urls) end host, port, scheme, userinfo = host_port(url) if scheme == "tls" # Due to ADR-40 url schema can enforce TLS. options = merge(options, (tls_required = true,)) end if !isnothing(userinfo) && !isempty(userinfo) user, pass = split(userinfo, ":"; limit = 2) if !haskey(options, :user) || isnothing(options.user) options = merge(options, (user = user,)) end if !haskey(options, :pass) || isnothing(options.pass) options = merge(options, (pass = pass,)) end end sock = Sockets.connect(host, port) try info_msg = next_protocol_message(sock) info_msg isa Info || error("Expected INFO, received $info_msg") validate_connect_options(info_msg, options) read_stream, write_stream = sock, sock if !isnothing(info_msg.tls_required) && info_msg.tls_required tls_options = options[(:tls_ca_path, :tls_cert_path, :tls_key_path)] (read_stream, write_stream) = upgrade_to_tls(sock, tls_options...) @debug "Socket upgraded" end if !isnothing(info_msg.nonce) isnothing(options.nkey_seed) && error("Server requires signature but no `nkey_seed` provided.") isnothing(options.nkey) && error("Missing `nkey` parameter.") sig = sign(info_msg.nonce, options.nkey_seed) options = merge(options, (sig = sig,)) end defaults = default_connect_options() known_options = keys(defaults) provided_keys = keys(options) keys_df = setdiff(provided_keys, known_options) !isempty(keys_df) && error("Unknown `connect` options: $(join(keys_df, ", "))") connect_msg = StructTypes.constructfrom(Connect, options) show(write_stream, MIME_PROTOCOL(), connect_msg) flush(write_stream) show(write_stream, MIME_PROTOCOL(), Ping()) flush(write_stream) msg = next_protocol_message(read_stream) msg isa Union{Ok, Err, Pong, Ping} || error("Expected +OK, -ERR, PING or PONG , received $msg") while true if msg isa Ping show(write_stream, MIME_PROTOCOL(), Pong()) elseif msg isa Err error(msg.message) elseif msg isa Pong break # This is what we waiting for. elseif msg isa Ok # Do nothing, verbose protocol. else error("Unexpected message received $msg") end msg = next_protocol_message(read_stream) end if !isnothing(nc) nc.url = url end sock, read_stream, write_stream, info_msg catch err close(sock) rethrow() end end function receiver(nc::Connection, io::IO) # @show Threads.threadid() parser_loop(io) do msg process(nc, msg) end end function ping_loop(nc::Connection, ping_interval::Float64, max_pings_out::Int64) pings_out = 0 reconnects = (@atomic nc.reconnect_count) while status(nc) == CONNECTED && reconnects == (@atomic nc.reconnect_count) sleep(ping_interval) if !(status(nc) == CONNECTED && reconnects == (@atomic nc.reconnect_count)) # In case if connection is broken new task will be spawned. # If another reconnect occured in meanwhile, stop this task cause another was already spawned. break end try _, tm = @timed ping(nc) pings_out = 0 @debug "PONG received after $tm seconds" catch @debug "No PONG received." pings_out += 1 end if pings_out > max_pings_out @warn "No pong received after $pings_out attempts." break end end end #TODO: restore link #NATS.Connect """ $(SIGNATURES) Connect to NATS server. The function is blocking until connection is initialized. In case of error during initialization process `connect` will throw exception if `retry_on_init_fail` is set to `false` (what is default). Otherwise handle will be returned and reconnect will continue in background. Options are: - `verbose`: turns on protocol acknowledgements - `pedantic`: turns on additional strict format checking, e.g. for properly formed subjects - `tls_required`: indicates whether the client requires SSL connection - `tls_ca_path`: CA certuficate file path - `tls_cert_path`: client public certificate file - `tls_key_path`: client private certificate file - `auth_token`: client authorization token - `user`: connection username - `pass`: connection password - `name`: client name - `echo`: if set to `false`, the server will not send originating messages from this connection to its own subscriptions - `jwt`: the JWT that identifies a user permissions and account. - `no_responders`: enable quick replies for cases where a request is sent to a topic with no responders. - `nkey`: the public NKey to authenticate the client - `nkey_seed`: the private NKey to authenticate the client - `ping_interval`: interval in seconds how often server should be pinged to check connection health. Default is $DEFAULT_PING_INTERVAL_SECONDS seconds - `max_pings_out`: how many pings in a row might fail before connection will be restarted. Default is `$DEFAULT_MAX_PINGS_OUT` - `retry_on_init_fail`: if set connection handle will be returned even if initial connect fails. Otherwise error causing failure will be trown. Default is `$DEFAULT_RETRY_ON_INIT_FAIL` - `ignore_advertised_servers`: ignores other cluster servers returned by server. Default is `$DEFAULT_IGNORE_ADVERTISED_SERVERS` - `retain_servers_order`: try to connect server in order specified in `url` or list returned by the server. Defaylt is `$DEFAULT_RETAIN_SERVERS_ORDER` - `send_enqueue_when_disconnected`: allows buffering outgoing messages during disconnection. Default is `$DEFAULT_ENQUEUE_WHEN_DISCONNECTED` - `reconnect_delays`: vector of delays that reconnect is performed until connected again, by default it will try to reconnect every second without time limit. - `send_buffer_limit`: soft limit for buffer of messages pending. Default is `$DEFAULT_SEND_BUFFER_LIMIT_BYTES` bytes, if too small operations that send messages to server (e.g. `publish`) may throw an exception - `drain_timeout`: Timeout for drain process. After timeout in case of not everyting is processed drain will stop and error will be reported. - `drain_poll`: Interval for `drain` to check if all messages in buffers are processed. """ function connect( url::String = get(ENV, "NATS_CONNECT_URL", DEFAULT_CONNECT_URL); options... ) options = merge(default_connect_options(), options) nc = Connection(; url, info = nothing, reconnect_count = 0, connect_init_count = 0, send_buffer_flushed = true, options.send_buffer_limit, options.send_retry_delays, options.send_enqueue_when_disconnected, options.drain_timeout, options.drain_poll) sock = nothing read_stream = nothing write_stream = nothing info_msg = nothing try sock, read_stream, write_stream, info_msg = init_protocol(nc, url, options) info(nc, info_msg) status(nc, CONNECTED) catch if !options.retry_on_init_fail rethrow() end end # This task just waits for `drain_even`, to wake up `reconnect_task` that there is cleanup to do. drain_await_task = Threads.@spawn :interactive disable_sigint() do wait(nc.drain_event) end # This works as controller for connection state. It spawns other task and listens for their completion to do # reconnect logic. reconnect_task = Threads.@spawn :interactive disable_sigint() do # @show Threads.threadid() while true if status(nc) == CONNECTING start_time = time() # TODO: handle repeating server Err messages. start_reconnect_time = time() function check_errors(s, e) total_retries = length(options.reconnect_delays) current_retries = total_retries - s[1] current_time = time() - start_reconnect_time mod(current_retries, 10) == 0 && @warn "Reconnect to $(clustername(nc)) cluster failed $current_retries times in $current_time seconds." e (@atomic nc.drain_event.set) == false # Stop on drain end retry_init_protocol = retry(init_protocol, delays=options.reconnect_delays, check = check_errors) try sock, read_stream, write_stream, info_msg = retry_init_protocol(nc, url, options) status(nc, CONNECTED) catch err time_diff = time() - start_reconnect_time @error "Connection disconnected after $(nc.connect_init_count) reconnect retries, it took $time_diff seconds." err if (@atomic nc.drain_event.set) == true status(nc, DRAINING) _do_drain(nc, false) status(nc, DRAINED) else status(nc, DISCONNECTED) end end if status(nc) == CONNECTED @atomic nc.reconnect_count += 1 info(nc, info_msg) @info "Reconnected to $(clustername(nc)) cluster on `$(nc.url)` after $(time() - start_time) seconds." elseif status(nc) == DISCONNECTED wait(nc.reconnect_event) @debug "Reconnect requested" if (@atomic nc.drain_event.set) == true status(nc, DRAINING) _do_drain(nc, false) status(nc, DRAINED) break else status(nc, CONNECTING) continue end elseif status(nc) == DRAINED break end end receiver_task = Threads.@spawn :interactive disable_sigint() do; receiver(nc, read_stream) end sender_task = Threads.@spawn :interactive disable_sigint() do; sendloop(nc, write_stream) end ping_task = Threads.@spawn :interactive disable_sigint() do; ping_loop(nc, options.ping_interval, options.max_pings_out) end reconnect_await_task = Threads.@spawn :interactive disable_sigint() do; wait(nc.reconnect_event) end tasks = [receiver_task, sender_task, ping_task, reconnect_await_task, drain_await_task] names = ["receiver", "sender", "ping", "reconnect", "drain"] err_channel = Channel() for task in tasks bind(err_channel, task) end try wait(err_channel) catch err if !(err isa InvalidStateException) @debug "Error caused wake up" err end end cleanup_start = time() reason = join(names[istaskdone.(tasks)], ", ") @debug "Controller task woken by: $reason" if istaskdone(drain_await_task) status(nc, DRAINING) # Check if there is a chance for send buffer flush. is_connected = !(istaskdone(sender_task) || istaskdone(receiver_task)) _do_drain(nc, is_connected) status(nc, DRAINED) reopen_send_buffer(nc) close(sock) break end notify(nc.reconnect_event) # Finish reconnect_await_task. # TODO: maybe `autoreset` should be used, but special care needs to be taken to not consume it anywhere else. reset(nc.reconnect_event) # Reset event to prevent forever reconnect. reopen_send_buffer(nc) # Finish sender_task. close(sock) # Finish receiver_task. #TODO: maybe in some case waiting for tasks to finish is not needed, it will shortned reconnect time 10x try wait(sender_task) catch end try wait(receiver_task) catch end try wait(reconnect_await_task) catch end # `ping_task` will complete eventually seeing `reconnect_count` increased. @assert istaskdone(receiver_task) @assert istaskdone(sender_task) @assert istaskdone(reconnect_await_task) @warn "Connection to $(clustername(nc)) cluster on `$(nc.url)` lost, trynig to reconnect." status(nc, CONNECTING) @atomic nc.connect_init_count = 0 @debug "Cleanup time: $(time() - cleanup_start) seconds" end end errormonitor(reconnect_task) @lock state.lock push!(state.connections, nc) nc end """ $(SIGNATURES) Force a connection reconnect. If connection is `CONNECTED` this will close it and reopen again resubscribing all existing subscriptions. If connection is `DISCONNECTED` it will try to connect with all previously existing subscription restored. In case connection is already `CONNECTING` this method have no effect. If called on connection that is `DRAINING` or `DRAINED` error will be thrown. During reconnect period some messages both published and received by the connection might be lost. Optional keyword aruguments: - `should_wait`: If `true` method will block until reconnection process is started, default is `true`. """ function reconnect(connection::NATS.Connection; should_wait::Bool = true) @lock connection.status_change_cond begin if connection.status == DRAINING || connection.status == DRAINED error("Cannot reconnect a drained connection.") end notify(connection.reconnect_event) while should_wait && connection.status != CONNECTING wait(connection.status_change_cond) end end end
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
4796
### connection.jl # # Copyright (C) 2023 Jakub Wronowski. # # Maintainer: Jakub Wronowski <jakubwro@users.noreply.github.com> # Keywords: nats, nats-client, julia # # This file is a part of NATS.jl. # # License is MIT. # ### Commentary: # # This file contains data structure definitions and aggregates utilities for handling connection to NATS server. # ### Code: @enum ConnectionStatus CONNECTING CONNECTED DISCONNECTED DRAINING DRAINED include("stats.jl") struct SubscriptionData sub::Sub channel::Channel stats::Stats is_async::Bool lock::ReentrantLock end @kwdef mutable struct Connection url::String status::ConnectionStatus = CONNECTING stats::Stats = Stats() info::Union{Info, Nothing} sub_data::Dict{Int64, SubscriptionData} = Dict{Int64, SubscriptionData}() unsubs::Dict{Int64, Int64} = Dict{Int64, Int64}() lock::ReentrantLock = ReentrantLock() rng::AbstractRNG = MersenneTwister() last_sid::Int64 = 0 send_buffer::IO = IOBuffer() send_buffer_cond::Threads.Condition = Threads.Condition() send_buffer_limit::Int64 = DEFAULT_SEND_BUFFER_LIMIT_BYTES send_retry_delays::Any = SEND_RETRY_DELAYS send_enqueue_when_disconnected::Bool reconnect_event::Threads.Event = Threads.Event() drain_event::Threads.Event = Threads.Event() pong_received_cond::Threads.Condition = Threads.Condition() status_change_cond::Threads.Condition = Threads.Condition() @atomic connect_init_count::Int64 # How many tries of protocol init was done on last reconnect. @atomic reconnect_count::Int64 @atomic send_buffer_flushed::Bool drain_timeout::Float64 drain_poll::Float64 allow_direct::Dict{String, Bool} = Dict{String, Bool}() # Cache for jetstream for fast lookup of streams that have direct access. allow_direct_lock = ReentrantLock() "Handles messages for which handler was not found." fallback_handlers::Vector{Function} = Function[] end info(c::Connection)::Union{Info, Nothing} = @lock c.lock c.info info(c::Connection, info::Info) = @lock c.lock c.info = info status(c::Connection)::ConnectionStatus = @lock c.status_change_cond c.status function status(c::Connection, status::ConnectionStatus) @lock c.status_change_cond begin c.status = status notify(c.status_change_cond) end end function clustername(c::Connection) info_msg = info(c) if isnothing(info_msg) "unknown" else @something info(c).cluster "unnamed" end end function new_inbox(connection::Connection, prefix::String = "inbox.") random_suffix = @lock connection.lock randstring(connection.rng, 10) "inbox.$random_suffix" end function new_sid(connection::Connection) @lock connection.lock begin connection.last_sid += 1 connection.last_sid end end include("state.jl") include("utils.jl") include("tls.jl") include("send.jl") include("handlers.jl") include("drain.jl") include("connect.jl") function status() println("=== Connection status ====================") println("connections: $(length(state.connections)) ") for (i, nc) in enumerate(state.connections) print(" [#$i]: ") print(status(nc), ", " , length(nc.sub_data)," subs, ", length(nc.unsubs)," unsubs ") println() end # println("subscriptions: $(length(state.handlers)) ") println("msgs_handled: $(state.stats.msgs_handled) ") println("msgs_errored: $(state.stats.msgs_errored) ") println("==========================================") end show(io::IO, nc::Connection) = print(io, typeof(nc), "(", clustername(nc), " cluster", ", " , status(nc), ", " , length(nc.sub_data)," subs, ", length(nc.unsubs)," unsubs)") function ping(nc; timer = Timer(1.0)) pong_ch = Channel{Pong}(1) ping_task = @async begin @async send(nc, Ping()) @lock nc.pong_received_cond wait(nc.pong_received_cond) put!(pong_ch, Pong()) end @async begin try wait(timer) catch end close(pong_ch) end try take!(pong_ch) catch error("No PONG received.") end end function stats(connection::Connection) connection.stats end function stats(connection::Connection, sid::Int64) sub_data = @lock connection.lock get(connection.sub_data, sid, nothing) isnothing(sub_data) && return sub_data.stats end function stats(connection::Connection, sub::Sub) stats(connection, sub.sid) end function status_change(f, nc::Connection) while true st = status(nc) if st == DRAINED break end @lock nc.status_change_cond begin wait(nc.status_change_cond) st = nc.status end f(st) end end
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
3112
### drain.jl # # Copyright (C) 2023 Jakub Wronowski. # # Maintainer: Jakub Wronowski <jakubwro@users.noreply.github.com> # Keywords: nats, nats-client, julia # # This file is a part of NATS.jl. # # License is MIT. # ### Commentary: # # This file contains implementation of connection draining what means # to close it with processing all messages that are in buffers already. # ### Code: # Actual drain logic, for thread safety executed in connection controller task. function _do_drain(nc::Connection, is_connected; timeout = Timer(nc.drain_timeout)) sids = @lock nc.lock copy(keys(nc.sub_data)) for sid in sids send(nc, Unsub(sid, 0)) end sleep(nc.drain_poll) conn_stats = stats(nc) while !is_every_message_handled(conn_stats) if !isopen(timeout) @error "Timeout for drain exceeded, not all subs might be drained." # TODO: add log about count of messages not handled. break end sleep(nc.drain_poll) end for sid in sids cleanup_sub_resources(nc, sid) end # At this point no more publications can be done. Wait for `send_buffer` flush. while !is_send_buffer_flushed(nc) if !isopen(timeout) @error "Timeout for drain exceeded, some publications might be lost." # TODO: add log about count of messages undelivered. break end if !is_connected @error "Cannot flush send buffer as connection is disconnected from server, some publications might be lost." # TODO: add log about count of messages undelivered. break end sleep(nc.drain_poll) end @lock nc.lock empty!(nc.sub_data) end """ $SIGNATURES Unsubscribe all subscriptions, wait for precessing all messages in buffers, then close connection. Drained connection is no more usable. This method is used to gracefuly stop the process. Underneeth it periodicaly checks for state of all buffers, interval for checks is configurable per connection with `drain_poll` parameter of `connect` method. It can also be set globally with `NATS_DRAIN_POLL_INTERVAL_SECONDS` environment variable. If not set explicitly default polling interval is `$DEFAULT_DRAIN_POLL_INTERVAL_SECONDS` seconds. Error will be written to log if drain not finished until timeout expires. Default timeout value is configurable per connection on `connect` with `drain_timeout`. Can be also set globally with `NATS_DRAIN_TIMEOUT_SECONDS` environment variable. If not set explicitly default drain timeout is `$DEFAULT_DRAIN_TIMEOUT_SECONDS` seconds. """ function drain(connection::Connection) @lock connection.status_change_cond begin notify(connection.drain_event) # There is a chance that connection is DISCONNECTED or is in CONNECTING # state and became DISCONNECTED before drain event is handled. Wake it up # to force reconnect that will promptly do drain. notify(connection.reconnect_event) while connection.status != DRAINED wait(connection.status_change_cond) end end end
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
3238
### handlers.jl # # Copyright (C) 2023 Jakub Wronowski. # # Maintainer: Jakub Wronowski <jakubwro@users.noreply.github.com> # Keywords: nats, nats-client, julia # # This file is a part of NATS.jl. # # License is MIT. # ### Commentary: # # This file contains logic related to processing messages received from NATS server. # ### Code: function process(nc::Connection, msg::Info) @debug "New INFO received: ." msg info(nc, msg) if !isnothing(msg.ldm) && msg.ldm @warn "Server is in Lame Duck Mode, forcing reconnect to other server" @debug "Connect urls are: $(msg.connect_urls)" # TODO: do not reconnect if there are no urls provided reconnect(nc, should_wait = false) end end function process(nc::Connection, ::Ping) @debug "Sending PONG." send(nc, Pong()) end function process(nc::Connection, ::Pong) @debug "Received pong." @lock nc.pong_received_cond notify(nc.pong_received_cond) end function process(nc::Connection, batch::Vector{ProtocolMessage}) groups = Dict{Int64, Vector{MsgRaw}}() for msg in batch if msg isa MsgRaw arr = get(groups, msg.sid, nothing) if isnothing(arr) arr = MsgRaw[] groups[msg.sid] = arr end push!(arr, msg) else process(nc, msg) end end fallbacks = nothing for (sid, msgs) in groups n_received = length(msgs) n = n_received sub_data = @lock nc.lock get(nc.sub_data, sid, nothing) if !isnothing(sub_data) sub_stats = sub_data.stats max_msgs = sub_data.channel.sz_max n_dropped = max(0, sub_stats.msgs_pending + n_received - max_msgs) if n_dropped > 0 inc_stats(:msgs_dropped, n_dropped, state.stats, nc.stats, sub_stats) n -= n_dropped msgs = first(msgs, n) # TODO: send NAK for dropped messages end if n > 0 try inc_stats(:msgs_pending, n, state.stats, nc.stats, sub_stats) put!(sub_data.channel, msgs) inc_stats(:msgs_received, n, state.stats, nc.stats, sub_stats) catch # TODO: if msg needs ack send nak here # Channel was closed by `unsubscribe`. dec_stats(:msgs_pending, n, state.stats, nc.stats, sub_stats) inc_stats(:msgs_dropped, n, state.stats, nc.stats, sub_stats) end end cleanup_sub_resources_if_all_msgs_received(nc, sid, n_received) else if isnothing(fallbacks) fallbacks = lock(nc.lock) do collect(nc.fallback_handlers) end end for f in fallbacks for msg in msgs Base.invokelatest(f, nc, msg) end end inc_stats(:msgs_dropped, n, state.stats, nc.stats) end end end function process(nc::Connection, ok::Ok) @debug "Received OK." end function process(nc::Connection, err::Err) @error "NATS protocol error!" err end
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
5102
### send.jl # # Copyright (C) 2023 Jakub Wronowski. # # Maintainer: Jakub Wronowski <jakubwro@users.noreply.github.com> # Keywords: nats, nats-client, julia # # This file is a part of NATS.jl. # # License is MIT. # ### Commentary: # # This file contains logic related to sending messages to NATS server. # ### Code: const SEND_RETRY_DELAYS = Base.ExponentialBackOff(n=53, first_delay=0.01, max_delay=0.1) function can_send(nc::Connection, ::ProtocolMessage) # Drained conection is not usable, otherwise allow ping and pong and unsubs. status(nc) != DRAINED end function can_send(nc::Connection, ::Union{Ping, Pong}) # Do not let PING and PONG polute send buffer during drain. conn_status = status(nc) conn_status != DRAINED && conn_status != DRAINING end function can_send(nc::Connection, ::Union{Pub, Vector{Pub}}) conn_status = status(nc) if conn_status == CONNECTED true elseif conn_status == CONNECTING true # TODO: or nc.send_enqueue_when_disconnected? elseif conn_status == DISCONNECTED nc.send_enqueue_when_disconnected elseif conn_status == DRAINING # Allow handlers to publish results during drain sub_stats = ScopedValues.get(scoped_subscription_stats) is_called_from_subscription_handler = !isnothing(sub_stats) is_called_from_subscription_handler elseif conn_status == DRAINED false end end function can_send(nc::Connection, ::Sub) conn_status = status(nc) if conn_status == CONNECTED true elseif conn_status == CONNECTING true elseif conn_status == DISCONNECTED true elseif conn_status == DRAINING # No new subs allowed during drain. false elseif conn_status == DRAINED false end end function try_send(nc::Connection, msgs::Vector{Pub})::Bool can_send(nc, msgs) || error("Cannot send on connection with status $(status(nc))") @lock nc.send_buffer_cond begin if nc.send_buffer.size < nc.send_buffer_limit for msg in msgs show(nc.send_buffer, MIME_PROTOCOL(), msg) end notify(nc.send_buffer_cond) true else false end end end function try_send(nc::Connection, msg::ProtocolMessage) can_send(nc, msg) || error("Cannot send on connection with status $(status(nc))") @lock nc.send_buffer_cond begin if msg isa Pub && nc.send_buffer.size > nc.send_buffer_limit # Apply limits only for publications, to allow unsubs and subs be done with higher priority. false else show(nc.send_buffer, MIME_PROTOCOL(), msg) notify(nc.send_buffer_cond) true end end end function send(nc::Connection, message::Union{ProtocolMessage, Vector{Pub}}) if try_send(nc, message) return end for d in nc.send_retry_delays sleep(d) if try_send(nc, message) return end end error("Cannot send, send buffer too large.") end # Calling this function is an easy way to force crash of sender task what will force reconnect. function reopen_send_buffer(nc::Connection) @lock nc.send_buffer_cond begin new_send_buffer = IOBuffer() data = take!(nc.send_buffer) for (sid, sub_data) in pairs(nc.sub_data) show(new_send_buffer, MIME_PROTOCOL(), sub_data.sub) unsub_max_msgs = get(nc.unsubs, sid, nothing) # TODO: lock on connection may be needed isnothing(unsub_max_msgs) || show(new_send_buffer, MIME_PROTOCOL(), Unsub(sid, unsub_max_msgs)) end @debug "Restored subs buffer length $(length(data))" write(new_send_buffer, data) @debug "Total restored buffer length $(length(data))" close(nc.send_buffer) nc.send_buffer = new_send_buffer notify(nc.send_buffer_cond) end end # Tells if send buffer if flushed what means no protocol messages are waiting # to be delivered to the server. function is_send_buffer_flushed(nc::Connection) @lock nc.send_buffer_cond begin nc.send_buffer.size == 0 && (@atomic nc.send_buffer_flushed) end end function sendloop(nc::Connection, io::IO) # @show Threads.threadid() send_buffer = nc.send_buffer while isopen(send_buffer) # @show !eof(io) && !isdrained(nc) buf = @lock nc.send_buffer_cond begin # TODO: check for eof on io taken = take!(send_buffer) if isempty(taken) wait(nc.send_buffer_cond) if !isopen(send_buffer) break end # TODO: check for eof on io @atomic nc.send_buffer_flushed = false take!(send_buffer) else @atomic nc.send_buffer_flushed = false taken end end write(io, buf) flush(io) @atomic nc.send_buffer_flushed = true end @debug "Sender task finished. $(send_buffer.size) bytes in send buffer." end
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
2218
### state.jl # # Copyright (C) 2023 Jakub Wronowski. # # Maintainer: Jakub Wronowski <jakubwro@users.noreply.github.com> # Keywords: nats, nats-client, julia # # This file is a part of NATS.jl. # # License is MIT. # ### Commentary: # # This file contains utilities for maintaining global state of NATS.jl package. # ### Code: @kwdef mutable struct State connections::Vector{Connection} = Connection[] lock::ReentrantLock = ReentrantLock() stats::Stats = Stats() end const state = State() # Allow other packages to handle unexpected messages. # JetStream might want to `nak` messages that need acknowledgement. function install_fallback_handler(f, nc::Connection) @lock nc.lock begin if !(f in nc.fallback_handlers) push!(nc.fallback_handlers, f) end end end function connection(id::Integer) if id in 1:length(state.connections) state.connections[id] else error("Connection #$id does not exists.") end end # """ # Cleanup subscription data when no more messages are expected. # """ function cleanup_sub_resources(nc::Connection, sid::Int64) @lock nc.lock begin sub_data = get(nc.sub_data, sid, nothing) if isnothing(sub_data) # Already cleaned up by other task. return end close(sub_data.channel) if sub_data.is_async == true || Base.n_avail(sub_data.channel) == 0 # `next` rely on lookup of sub data, in this case let sub data stay and do cleanup # when `next` gets the last message of a closed channel. delete!(nc.sub_data, sid) delete!(nc.unsubs, sid) end end end # """ # Update state on message received and conditionaly do cleanup. # Check if previously unsubscribed sub needs cleanup when no more messages are expected. # """ function cleanup_sub_resources_if_all_msgs_received(nc::Connection, sid::Int64, n::Int64) lock(nc.lock) do count = get(nc.unsubs, sid, nothing) if !isnothing(count) count -= n if count <= 0 cleanup_sub_resources(nc, sid) else nc.unsubs[sid] = count end end end end
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
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### stats.jl # # Copyright (C) 2023 Jakub Wronowski. # # Maintainer: Jakub Wronowski <jakubwro@users.noreply.github.com> # Keywords: nats, nats-client, julia # # This file is a part of NATS.jl. # # License is MIT. # ### Commentary: # # This file contains utilities for collecting statistics connections, subscrptions and NATS.jl package. # ### Code: const NATS_STATS_MAX_RECENT_ERRORS = 100 mutable struct Stats "Count of msgs received but maybe not yet handled by subscription." @atomic msgs_received::Int64 "Count of msgs received that was not pickuped by subscription handler yet." @atomic msgs_pending::Int64 "Count of msgs handled without error." @atomic msgs_handled::Int64 "Count of msgs that caused handler function error." @atomic msgs_errored::Int64 "Msgs that was not put to a subscription channel because it was full or `sid` was not known." @atomic msgs_dropped::Int64 "Msgs published count." @atomic msgs_published::Int64 "Subscription handlers running at the moment count." @atomic handlers_running::Int64 "Recent errors." errors::Channel{Exception} function Stats() new(0, 0, 0, 0, 0, 0, 0, Channel{Exception}(NATS_STATS_MAX_RECENT_ERRORS)) end end const scoped_subscription_stats = ScopedValue{Stats}() function show(io::IO, stats::Stats) print(io, "published: $(stats.msgs_published) \n") print(io, " received: $(stats.msgs_received) \n") print(io, " pending: $(stats.msgs_pending) \n") print(io, " active: $(stats.handlers_running) \n") print(io, " handled: $(stats.msgs_handled) \n") print(io, " errored: $(stats.msgs_errored) \n") print(io, " dropped: $(stats.msgs_dropped) \n") end function inc_stats(field, value, stats...) for stat in stats inc_stat(stat, field, value) end end function dec_stats(field, value, stats...) for stat in stats dec_stat(stat, field, value) end end function inc_stat(stat, field, value) Base.modifyproperty!(stat, field, +, value, :sequentially_consistent) end function dec_stat(stat, field, value) Base.modifyproperty!(stat, field, -, value, :sequentially_consistent) end # Tell if all received messages are delivered to subscription and handlers finished. function is_every_message_handled(stats::Stats) (@atomic stats.msgs_pending) == 0 && (@atomic stats.handlers_running) == 0 && (@atomic stats.msgs_received) == (@atomic stats.msgs_handled) + (@atomic stats.msgs_errored) end
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
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### tls.jl # # Copyright (C) 2023 Jakub Wronowski. # # Maintainer: Jakub Wronowski <jakubwro@users.noreply.github.com> # Keywords: nats, nats-client, julia # # This file is a part of NATS.jl. # # License is MIT. # ### Commentary: # # This file contains utilities for handling TLS handshake. # ### Code: function upgrade_to_tls(sock::Sockets.TCPSocket, ca_cert_path::Union{String, Nothing}, client_cert_path::Union{String, Nothing}, client_key_path::Union{String, Nothing}) entropy = MbedTLS.Entropy() rng = MbedTLS.CtrDrbg() MbedTLS.seed!(rng, entropy) ctx = MbedTLS.SSLContext() conf = MbedTLS.SSLConfig() MbedTLS.config_defaults!(conf) MbedTLS.authmode!(conf, MbedTLS.MBEDTLS_SSL_VERIFY_REQUIRED) MbedTLS.rng!(conf, rng) # function show_debug(level, filename, number, msg) # @show level, filename, number, msg # end # MbedTLS.dbg!(conf, show_debug) if !isnothing(ca_cert_path) MbedTLS.ca_chain!(conf, MbedTLS.crt_parse_file(ca_cert_path)) end MbedTLS.setup!(ctx, conf) MbedTLS.set_bio!(ctx, sock) if !isnothing(client_key_path) && !isnothing(client_key_path) cert = MbedTLS.crt_parse_file(client_cert_path) key = MbedTLS.parse_keyfile(client_key_path) MbedTLS.own_cert!(conf, cert, key) end MbedTLS.handshake(ctx) get_tls_input_buffered(ctx), ctx end function get_tls_input_buffered(ssl) io = Base.BufferStream() t = Threads.@spawn :interactive disable_sigint() do try while !eof(ssl) av = readavailable(ssl) write(io, av) end finally close(io) end end errormonitor(t) BufferedInputStream(io, 1) end
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
2648
### utils.jl # # Copyright (C) 2023 Jakub Wronowski. # # Maintainer: Jakub Wronowski <jakubwro@users.noreply.github.com> # Keywords: nats, nats-client, julia # # This file is a part of NATS.jl. # # License is MIT. # ### Commentary: # # This file contains utilities for managing NATS connection that do not fit anywhere else. # ### Code: function argtype(handler) handler_methods = methods(handler) if length(handler_methods) > 1 error("Multimethod functions not suported as subscription handler.") end signature = first(methods(handler)).sig # TODO: handle multi methods. if length(signature.parameters) == 1 Nothing elseif length(signature.parameters) == 2 signature.parameters[2] else Tuple{signature.parameters[2:end]...} end end function find_msg_conversion_or_throw(T::Type) if T != Any && !hasmethod(Base.convert, (Type{T}, Msg)) error("""Conversion of NATS message into type $T is not defined. Example how to define it: ``` import Base: convert function convert(::Type{$T}, msg::NATS.Msg) # Implement conversion logic here. # For example: field1, field2 = split(payload(msg), ",") $T(field1, field2) end ``` """) end end function find_data_conversion_or_throw(T::Type) if T != Any && !hasmethod(Base.show, (IO, NATS.MIME_PAYLOAD, T)) error("""Conversion of type $T to NATS payload is not defined. Example how to define it: ``` import Base: show function Base.show(io::IO, ::NATS.MIME_PAYLOAD, x::$T) # Write content to `io` here, it can be UTF-8 string or byte array. end ``` Optionally you might want to attach headers to a message: ``` function Base.show(io::IO, ::NATS.MIME_HEADERS, x::$T) # Create vector of pairs of strings hdrs = ["header_key" => "header_value"] # Write them to the buffer show(io, ::NATS.MIME_HEADERS, hdrs) end ``` """) end end # """ # Return lambda that avoids type conversions for certain types. # Also allows for use of parameterless handlers for subs that do not need look into msg payload. # """ function _fast_call(f::Function) arg_t = argtype(f) if arg_t === Any || arg_t == NATS.Msg f elseif arg_t == Nothing _ -> f() else find_msg_conversion_or_throw(arg_t) msg -> f(convert(arg_t, msg)) end end
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
353
### experimental.jl # # Copyright (C) 2023 Jakub Wronowski. # # Maintainer: Jakub Wronowski <jakubwro@users.noreply.github.com> # Keywords: nats, nats-client, julia # # This file is a part of NATS.jl. # # License is MIT. # ### Commentary: # # This file contains aggregates experimantal NATS client features. # ### Code: include("scoped_connection.jl")
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
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### scoped_connection.jl # # Copyright (C) 2023 Jakub Wronowski. # # Maintainer: Jakub Wronowski <jakubwro@users.noreply.github.com> # Keywords: nats, nats-client, julia # # This file is a part of NATS.jl. # # License is MIT. # ### Commentary: # # This file contains implementation of simplified interface utilizing connection as dynamically scoped variable. # ### Code: const sconnection = ScopedValue{Connection}() function scoped_connection() conn = ScopedValues.get(sconnection) if isnothing(conn) error("""No scoped connection. To use methods without explicit `connection` parameter you need to wrap your logic into `with_connection` function. Example: ``` nc = NATS.connect() with_connection(nc) do publish("some_subject", "Some payload") end ``` Or pass `connection` explicitly: ``` nc = NATS.connect() publish(nc, "some_subject", "Some payload") ``` """) end conn.value end """ $(SIGNATURES) Create scope with ambient context connection, in which connection argument might be skipped during invocation of functions. Usage: ``` nc = NATS.connect() with_connection(nc) do publish("some.subject") # No `connection` argument. end ``` """ function with_connection(f, nc::Connection) with(f, sconnection => nc) end function subscribe( subject::String; queue_group::Union{String, Nothing} = nothing, channel_size = parse(Int64, get(ENV, "NATS_SUBSCRIPTION_CHANNEL_SIZE", string(DEFAULT_SUBSCRIPTION_CHANNEL_SIZE))), monitoring_throttle_seconds = parse(Float64, get(ENV, "NATS_SUBSCRIPTION_ERROR_THROTTLING_SECONDS", string(DEFAULT_SUBSCRIPTION_ERROR_THROTTLING_SECONDS))) ) subscribe(scoped_connection(), subject; queue_group, channel_size, monitoring_throttle_seconds) end function subscribe( f, subject::String; queue_group::Union{String, Nothing} = nothing, spawn = false, channel_size = parse(Int64, get(ENV, "NATS_SUBSCRIPTION_CHANNEL_SIZE", string(DEFAULT_SUBSCRIPTION_CHANNEL_SIZE))), monitoring_throttle_seconds = parse(Float64, get(ENV, "NATS_SUBSCRIPTION_ERROR_THROTTLING_SECONDS", string(DEFAULT_SUBSCRIPTION_ERROR_THROTTLING_SECONDS))) ) subscribe(f, scoped_connection(), subject; queue_group, spawn, channel_size, monitoring_throttle_seconds) end function unsubscribe( sub::Sub; max_msgs::Union{Int, Nothing} = nothing ) unsubscribe(scoped_connection(), sub; max_msgs) end function unsubscribe( sid::Int64; max_msgs::Union{Int, Nothing} = nothing ) unsubscribe(scoped_connection(), sid; max_msgs) end function drain(sub::Sub) drain(scoped_connection(), sub) end function publish( subject::String, data = nothing; reply_to::Union{String, Nothing} = nothing ) publish(scoped_connection(), subject, data; reply_to) end function reply( f, subject::String; queue_group::Union{Nothing, String} = nothing, spawn = false ) reply(f, scoped_connection(), subject; queue_group, spawn) end function request( subject::String, data = nothing; timer::Timer = Timer(parse(Float64, get(ENV, "NATS_REQUEST_TIMEOUT_SECONDS", string(DEFAULT_REQUEST_TIMEOUT_SECONDS)))) ) request(scoped_connection(), subject, data; timer) end function request( nreplies::Integer, subject::String, data = nothing; timer::Timer = Timer(parse(Float64, get(ENV, "NATS_REQUEST_TIMEOUT_SECONDS", string(DEFAULT_REQUEST_TIMEOUT_SECONDS)))) ) request(scoped_connection(), nreplies, subject, data; timer) end function request( T::Type, subject::String, data = nothing; timer::Timer = Timer(parse(Float64, get(ENV, "NATS_REQUEST_TIMEOUT_SECONDS", string(DEFAULT_REQUEST_TIMEOUT_SECONDS)))) ) request(T, scoped_connection(), subject, data; timer) end function next(sub::Sub; no_wait = false, no_throw = false)::Union{Msg, Nothing} next(scoped_connection(), sub::Sub; no_wait = false, no_throw = false) end function next(T::Type, sub::Sub; no_wait = false, no_throw = false)::Union{T, Nothing} next(T, scoped_connection(), sub::Sub; no_wait = false, no_throw = false) end function next(sub::Sub, batch::Integer; no_wait = false, no_throw = false)::Vector{Msg} next(scoped_connection(), sub, batch; no_wait = false, no_throw = false) end function next(T::Type, sub::Sub, batch::Integer; no_wait = false, no_throw = false)::Vector{T} next(T, scoped_connection(), sub, batch; no_wait = false, no_throw = false) end
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
1483
module JetStream using Dates using NanoDates using StructTypes using Random using JSON3 using DocStringExtensions using ScopedValues using Base64 using CodecBase import NATS import Base: show, showerror import Base: setindex!, getindex, empty!, delete!, iterate, length import Base: IteratorSize import Base: put!, take! export StreamConfiguration, Republish, StreamConsumerLimit, StreamSource export stream_create, stream_update, stream_update_or_create, stream_purge, stream_delete export stream_publish, stream_subscribe, stream_unsubscribe export stream_message_get, stream_message_delete export ConsumerConfiguration export consumer_create, consumer_update, consumer_delete export consumer_next, consumer_ack export keyvalue_stream_info, keyvalue_buckets export keyvalue_stream_create, keyvalue_stream_purge, keyvalue_stream_delete export keyvalue_get, keyvalue_put, keyvalue_delete, keyvalue_watch export JetDict, watch, with_optimistic_concurrency export JetChannel, destroy! const STREAM_RETENTION_OPTIONS = [:limits, :interest, :workqueue] const STREAM_STORAGE_OPTIONS = [:file, :memory] const STREAM_COMPRESSION_OPTIONS = [:none, :s2] const CONSUMER_ACK_POLICY_OPTIONS = [:none, :all, :explicit] const CONSUMER_REPLAY_POLICY_OPTIONS = [:instant, :original] include("api/api.jl") include("stream/stream.jl") include("consumer/consumer.jl") include("keyvalue/keyvalue.jl") include("jetdict/jetdict.jl") include("jetchannel/jetchannel.jl") end
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
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abstract type ApiResponse end @kwdef struct ApiError <: Exception "HTTP like error code in the 300 to 500 range" code::Int64 "A human friendly description of the error" description::Union{String, Nothing} = nothing "The NATS error code unique to each kind of error" err_code::Union{Int64, Nothing} = nothing end struct ApiResult <: ApiResponse success::Bool end struct IterableResponse total::Int64 offset::Int64 limit::Int64 end include("stream.jl") include("consumer.jl") include("errors.jl") include("validate.jl") include("show.jl") include("convert.jl") include("call.jl")
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
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const DEFAULT_API_CALL_DELAYS = ExponentialBackOff(n = 7, first_delay = 0.1, max_delay = 0.5) function check_api_call_error(s, e) e isa Union{NATS.NATSError, ApiError} && e.code == 503 end function jetstream_api_call(T, connection::NATS.Connection, subject, data = nothing; delays = DEFAULT_API_CALL_DELAYS) call_retry = retry(NATS.request; delays, check = check_api_call_error) call_retry(T, connection, subject, data) end
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
5162
""" Configuration options for a consumer. $(TYPEDFIELDS) """ @kwdef struct ConsumerConfiguration "A unique name for a durable consumer" durable_name::Union{String, Nothing} = nothing "A unique name for a consumer" name::Union{String, Nothing} = nothing "A short description of the purpose of this consumer" description::Union{String, Nothing} = nothing deliver_subject::Union{String, Nothing} = nothing ack_policy::Symbol = :none "How long (in nanoseconds) to allow messages to remain un-acknowledged before attempting redelivery" ack_wait::Union{Int64, Nothing} = 30000000000 "The number of times a message will be redelivered to consumers if not acknowledged in time" max_deliver::Union{Int64, Nothing} = 1000 # This one is only for NATS 2.9 and older # "Filter the stream by a single subjects" # filter_subject::Union{String, Nothing} = nothing "Filter the stream by multiple subjects" filter_subjects::Union{Vector{String}, Nothing} = nothing replay_policy::Symbol = :instant sample_freq::Union{String, Nothing} = nothing "The rate at which messages will be delivered to clients, expressed in bit per second" rate_limit_bps::Union{UInt64, Nothing} = nothing "The maximum number of messages without acknowledgement that can be outstanding, once this limit is reached message delivery will be suspended" max_ack_pending::Union{Int64, Nothing} = nothing "If the Consumer is idle for more than this many nano seconds a empty message with Status header 100 will be sent indicating the consumer is still alive" idle_heartbeat::Union{Int64, Nothing} = nothing "For push consumers this will regularly send an empty mess with Status header 100 and a reply subject, consumers must reply to these messages to control the rate of message delivery" flow_control::Union{Bool, Nothing} = nothing "The number of pulls that can be outstanding on a pull consumer, pulls received after this is reached are ignored" max_waiting::Union{Int64, Nothing} = nothing "Delivers only the headers of messages in the stream and not the bodies. Additionally adds Nats-Msg-Size header to indicate the size of the removed payload" headers_only::Union{Bool, Nothing} = nothing "The largest batch property that may be specified when doing a pull on a Pull Consumer" max_batch::Union{Int64, Nothing} = nothing "The maximum expires value that may be set when doing a pull on a Pull Consumer" max_expires::Union{Int64, Nothing} = nothing "The maximum bytes value that maybe set when dong a pull on a Pull Consumer" max_bytes::Union{Int64, Nothing} = nothing "Duration that instructs the server to cleanup ephemeral consumers that are inactive for that long" inactive_threshold::Union{Int64, Nothing} = nothing "List of durations in Go format that represents a retry time scale for NaK'd messages" backoff::Union{Vector{Int64}, Nothing} = nothing "When set do not inherit the replica count from the stream but specifically set it to this amount" num_replicas::Union{Int64, Nothing} = nothing "Force the consumer state to be kept in memory rather than inherit the setting from the stream" mem_storage::Union{Bool, Nothing} = nothing # "Additional metadata for the Consumer" # metadata::Union{Any, Nothing} = nothing end @kwdef struct SequenceInfo "The sequence number of the Consumer" consumer_seq::UInt64 "The sequence number of the Stream" stream_seq::UInt64 "The last time a message was delivered or acknowledged (for ack_floor)" last_active::Union{NanoDate, Nothing} = nothing end @kwdef struct ConsumerInfo <: ApiResponse "The Stream the consumer belongs to" stream_name::String "A unique name for the consumer, either machine generated or the durable name" name::String "The server time the consumer info was created" ts::Union{NanoDate, Nothing} = nothing config::ConsumerConfiguration "The time the Consumer was created" created::NanoDate "The last message delivered from this Consumer" delivered::SequenceInfo "The highest contiguous acknowledged message" ack_floor::SequenceInfo "The number of messages pending acknowledgement" num_ack_pending::Int64 "The number of redeliveries that have been performed" num_redelivered::Int64 "The number of pull consumers waiting for messages" num_waiting::Int64 "The number of messages left unconsumed in this Consumer" num_pending::UInt64 cluster::Union{ClusterInfo, Nothing} = nothing "Indicates if any client is connected and receiving messages from a push consumer" push_bound::Union{Bool, Nothing} = nothing end @kwdef struct StoredMessage "The subject the message was originally received on" subject::String "The sequence number of the message in the Stream" seq::UInt64 "The base64 encoded payload of the message body" data::Union{String, Nothing} = nothing "The time the message was received" time::String "Base64 encoded headers for the message" hdrs::Union{String, Nothing} = nothing end
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
945
import Base.convert # const api_type_map = Dict( # "io.nats.jetstream.api.v1.consumer_create_response" => ConsumerInfo, # "io.nats.jetstream.api.v1.stream_create_response" => StreamInfo, # "io.nats.jetstream.api.v1.stream_delete_response" => ApiResult, # "io.nats.jetstream.api.v1.stream_info_response" => StreamInfo # ) function convert(::Type{T}, msg::NATS.Msg) where { T <: ApiResponse } # TODO: check headers response = JSON3.read(@view msg.payload[(begin + msg.headers_length):end]) throw_on_api_error(response) StructTypes.constructfrom(T, response) end function convert(::Type{Union{T, ApiError}}, msg::NATS.Msg) where { T <: ApiResponse } # TODO: check headers response = JSON3.read(@view msg.payload[begin+msg.headers_length:end]) if haskey(response, :error) StructTypes.constructfrom(ApiError, response.error) else StructTypes.constructfrom(T, response) end end
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
540
function throw_on_api_error(response::JSON3.Object) if haskey(response, :error) throw(StructTypes.constructfrom(ApiError, response.error)) end end function throw_on_api_error(response::ApiError) throw(response) end function throw_on_api_error(response::ApiResponse) # Nothing to do end function Base.showerror(io::IO, err::ApiError) print(io, "JetStream ") printstyled(io, "$(err.code)"; color=Base.error_color()) if !isnothing(err.description) print(io, ": $(err.description).") end end
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
963
StructTypes.omitempties(::Type{SubjectTransform}) = true StructTypes.omitempties(::Type{Placement}) = true StructTypes.omitempties(::Type{ExternalStreamSource}) = true StructTypes.omitempties(::Type{StreamSource}) = true StructTypes.omitempties(::Type{Republish}) = true StructTypes.omitempties(::Type{StreamConsumerLimit}) = true StructTypes.omitempties(::Type{StreamConfiguration}) = true StructTypes.omitempties(::Type{ConsumerConfiguration}) = true show(io::IO, st::SubjectTransform) = JSON3.pretty(io, st) show(io::IO, st::Placement) = JSON3.pretty(io, st) show(io::IO, st::ExternalStreamSource) = JSON3.pretty(io, st) show(io::IO, st::StreamSource) = JSON3.pretty(io, st) show(io::IO, st::Republish) = JSON3.pretty(io, st) show(io::IO, st::StreamConsumerLimit) = JSON3.pretty(io, st) show(io::IO, st::StreamConfiguration) = JSON3.pretty(io, st) # function show(io::IO, ::MIME"text/plain", response::ApiResponse) # JSON3.pretty(io, response) # end
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
9821
@kwdef struct SubjectTransform "The subject transform source" src::String "The subject transform destination" dest::String end @kwdef struct Placement "The desired cluster name to place the stream" cluster::Union{String, Nothing} "Tags required on servers hosting this stream" tags::Union{Vector{String}, Nothing} = nothing end @kwdef struct ExternalStreamSource "The subject prefix that imports the other account/domain $JS.API.CONSUMER.> subjects" api::String "The delivery subject to use for the push consumer" deliver::Union{String, Nothing} = nothing end @kwdef struct StreamSource "Stream name" name::String "Sequence to start replicating from" opt_start_seq::Union{UInt64, Nothing} = nothing "Time stamp to start replicating from" opt_start_time::Union{NanoDate, Nothing} = nothing "Replicate only a subset of messages based on filter" filter_subject::Union{String, Nothing} = nothing "The subject filtering sources and associated destination transforms" subject_transforms::Union{Vector{SubjectTransform}, Nothing} = nothing external::Union{ExternalStreamSource, Nothing} = nothing end @kwdef struct Republish "The source subject to republish" src::String "The destination to publish to" dest::String "Only send message headers, no bodies" headers_only::Union{Bool, Nothing} = nothing end @kwdef struct StreamConsumerLimit "Maximum value for inactive_threshold for consumers of this stream. Acts as a default when consumers do not set this value." inactive_threshold::Union{Int64, Nothing} = nothing "Maximum value for max_ack_pending for consumers of this stream. Acts as a default when consumers do not set this value." max_ack_pending::Union{Int64, Nothing} = nothing end """ Configuration options for a stream. $(TYPEDFIELDS) """ @kwdef struct StreamConfiguration "A unique name for the Stream." name::String "A short description of the purpose of this stream" description::Union{String, Nothing} = nothing "A list of subjects to consume, supports wildcards. Must be empty when a mirror is configured. May be empty when sources are configured." subjects::Union{Vector{String}, Nothing} = nothing "Subject transform to apply to matching messages" subject_transform::Union{SubjectTransform, Nothing} = nothing "How messages are retained in the Stream, once this is exceeded old messages are removed." retention::Symbol = :limits "How many Consumers can be defined for a given Stream. -1 for unlimited." max_consumers::Int64 = -1 "How many messages may be in a Stream, oldest messages will be removed if the Stream exceeds this size. -1 for unlimited." max_msgs::Int64 = -1 "For wildcard streams ensure that for every unique subject this many messages are kept - a per subject retention limit" max_msgs_per_subject::Union{Int64, Nothing} = nothing "How big the Stream may be, when the combined stream size exceeds this old messages are removed. -1 for unlimited." max_bytes::Int64 = -1 "Maximum age of any message in the stream, expressed in nanoseconds. 0 for unlimited." max_age::Int64 = 0 "The largest message that will be accepted by the Stream. -1 for unlimited." max_msg_size::Union{Int32, Nothing} = nothing "The storage backend to use for the Stream." storage::Symbol = :file "Optional compression algorithm used for the Stream." compression::Symbol = :none "A custom sequence to use for the first message in the stream" first_seq::Union{UInt64, Nothing} = nothing "How many replicas to keep for each message." num_replicas::Int64 = 1 "Disables acknowledging messages that are received by the Stream." no_ack::Union{Bool, Nothing} = nothing "When a Stream reach it's limits either old messages are deleted or new ones are denied" discard::Union{Symbol, Nothing} = nothing "The time window to track duplicate messages for, expressed in nanoseconds. 0 for default" duplicate_window::Union{Int64, Nothing} = nothing "Placement directives to consider when placing replicas of this stream, random placement when unset" placement::Union{Placement, Nothing} = nothing "Maintains a 1:1 mirror of another stream with name matching this property. When a mirror is configured subjects and sources must be empty." mirror::Union{StreamSource, Nothing} = nothing "List of Stream names to replicate into this Stream" sources::Union{Vector{StreamSource}, Nothing} = nothing "Sealed streams do not allow messages to be deleted via limits or API, sealed streams can not be unsealed via configuration update. Can only be set on already created streams via the Update API" sealed::Union{Bool, Nothing} = nothing "Restricts the ability to delete messages from a stream via the API. Cannot be changed once set to true" deny_delete::Union{Bool, Nothing} = nothing "Restricts the ability to purge messages from a stream via the API. Cannot be change once set to true" deny_purge::Union{Bool, Nothing} = nothing "Allows the use of the Nats-Rollup header to replace all contents of a stream, or subject in a stream, with a single new message" allow_rollup_hdrs::Union{Bool, Nothing} = nothing "Allow higher performance, direct access to get individual messages" allow_direct::Union{Bool, Nothing} = nothing "Allow higher performance, direct access for mirrors as well" mirror_direct::Union{Bool, Nothing} = nothing republish::Union{Republish, Nothing} = nothing "When discard policy is new and the stream is one with max messages per subject set, this will apply the new behavior to every subject. Essentially turning discard new from maximum number of subjects into maximum number of messages in a subject." discard_new_per_subject::Union{Bool, Nothing} = nothing "Additional metadata for the Stream" metadata::Union{Dict{String, String}, Nothing} = nothing # TODO: what is this for? "Limits of certain values that consumers can set, defaults for those who don't set these settings" consumer_limits::Union{StreamConsumerLimit, Nothing} = nothing end @kwdef struct StreamState "Number of messages stored in the Stream" messages::UInt64 "Combined size of all messages in the Stream" bytes::UInt64 "Sequence number of the first message in the Stream" first_seq::UInt64 "The timestamp of the first message in the Stream" first_ts::Union{NanoDate, Nothing} = nothing "Sequence number of the last message in the Stream" last_seq::UInt64 "The timestamp of the last message in the Stream" last_ts::Union{NanoDate, Nothing} = nothing "IDs of messages that were deleted using the Message Delete API or Interest based streams removing messages out of order" deleted::Union{Vector{UInt64}, Nothing} = nothing # "Subjects and their message counts when a subjects_filter was set" # subjects::Union{Any, Nothing} = nothing "The number of unique subjects held in the stream" num_subjects::Union{Int64, Nothing} = nothing "The number of deleted messages" num_deleted::Union{Int64, Nothing} = nothing # lost::Union{LostStreamData, Nothing} = nothing "Number of Consumers attached to the Stream" consumer_count::Int64 end @kwdef struct PeerInfo "The server name of the peer" name::String "Indicates if the server is up to date and synchronised" current::Bool = false "Nanoseconds since this peer was last seen" active::Int64 "Indicates the node is considered offline by the group" offline::Union{Bool, Nothing} = nothing "How many uncommitted operations this peer is behind the leader" lag::Union{Int64, Nothing} = nothing end @kwdef struct ClusterInfo "The cluster name" name::Union{String, Nothing} = nothing "The server name of the RAFT leader" leader::Union{String, Nothing} = nothing "The members of the RAFT cluster" replicas::Union{Vector{PeerInfo}, Nothing} = nothing end @kwdef struct StreamSourceInfo "The name of the Stream being replicated" name::String "The subject filter to apply to the messages" filter_subject::Union{String, Nothing} = nothing "The subject filtering sources and associated destination transforms" subject_transforms::Union{Vector{SubjectTransform}, Nothing} = nothing "How many messages behind the mirror operation is" lag::UInt64 "When last the mirror had activity, in nanoseconds. Value will be -1 when there has been no activity." active::Int64 external::Union{ExternalStreamSource, Nothing} = nothing error::Union{ApiError, Nothing} = nothing end @kwdef struct StreamAlternate "The mirror stream name" name::String "The name of the cluster holding the stream" cluster::String "The domain holding the string" domain::Union{String, Nothing} = nothing end @kwdef struct StreamInfo <: ApiResponse "The active configuration for the Stream" config::StreamConfiguration "Detail about the current State of the Stream" state::StreamState "Timestamp when the stream was created" created::NanoDate "The server time the stream info was created" ts::Union{NanoDate, Nothing} = nothing cluster::Union{ClusterInfo, Nothing} = nothing mirror::Union{StreamSourceInfo, Nothing} = nothing "Streams being sourced into this Stream" sources::Union{Vector{StreamSourceInfo}, Nothing} = nothing "List of mirrors sorted by priority" alternates::Union{Vector{StreamAlternate}, Nothing} = nothing end @kwdef struct PubAck <: ApiResponse stream::String seq::Union{Int64, Nothing} duplicate::Union{Bool, Nothing} domain::Union{String, Nothing} end
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
697
function validate_name(name::String) # ^[^.*>]+$ # https://github.com/nats-io/jsm.go/blob/30c85c3d2258321d4a2ded882fe8561a83330e5d/schema_source/jetstream/api/v1/definitions.json#L445 isempty(name) && error("Name is empty.") for c in name if c == '.' || c == '*' || c == '>' error("Name \"$name\" contains invalid character '$c'.") end end true end function validate(stream_configuration::StreamConfiguration) validate_name(stream_configuration.name) stream_configuration.retention in STREAM_RETENTION_OPTIONS || error("Invalid `retention = :$(stream_configuration.retention)`, expected one of $STREAM_RETENTION_OPTIONS") true end
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
737
# https://docs.nats.io/using-nats/developer/develop_jetstream/model_deep_dive#acknowledgement-models const CONSUMER_ACK_OPTIONS = [ "+ACK", "-NAK", "+WPI", "+NXT", "+TERM" ] """ $(SIGNATURES) Confirms message delivery to server. """ function consumer_ack(connection::NATS.Connection, msg::NATS.Msg, ack::String = "+ACK"; delays = DEFAULT_API_CALL_DELAYS) ack in CONSUMER_ACK_OPTIONS || error("Unknown ack type \"$ack\", allowed values: $(join(CONSUMER_ACK_OPTIONS, ", "))") isnothing(msg.reply_to) && error("No reply subject for msg $msg.") !startswith(msg.reply_to, "\$JS.ACK") && @warn "`ack` sent for message that doesn't need acknowledgement." jetstream_api_call(NATS.Msg, connection, msg.reply_to; delays) end
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
77
include("manage.jl") include("list.jl") include("next.jl") include("ack.jl")
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
1628
function consumer_create_or_update(connection::NATS.Connection, config::ConsumerConfiguration, stream::String) consumer_name = @something config.name consumer_config.durable_name randstring(20) subject = "\$JS.API.CONSUMER.CREATE.$stream.$consumer_name" req_data = Dict(:stream_name => stream, :config => config) # if !isnothing(action) #TODO: handle action # req_data[:action] = action # end NATS.request(ConsumerInfo, connection, subject, JSON3.write(req_data)) end function consumer_create_or_update(connection::NATS.Connection, config::ConsumerConfiguration, stream::StreamInfo) consumer_create_or_update(connection, config, stream.config.name) end """ $(SIGNATURES) Create a stream consumer. """ function consumer_create(connection::NATS.Connection, config::ConsumerConfiguration, stream::Union{String, StreamInfo}) consumer_create_or_update(connection, config, stream) end """ $(SIGNATURES) Update stream consumer configuration. """ function consumer_update(connection::NATS.Connection, consumer::ConsumerConfiguration, stream::Union{StreamInfo, String}) consumer_create_or_update(connection, consumer, stream) end """ $(SIGNATURES) Delete a consumer. """ function consumer_delete(connection::NATS.Connection, stream_name::String, consumer_name::String) subject = "\$JS.API.CONSUMER.DELETE.$stream_name.$consumer_name" res = NATS.request(Union{ApiResult, ApiError}, connection, subject) throw_on_api_error(res) res end function consumer_delete(connection, consumer::ConsumerInfo) consumer_delete(connection, consumer.stream_name, consumer.name) end
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
1175
const NEXT_EXPIRES = 500 * 1000 * 1000 # 500 ms, TODO: add to env """ $(SIGNATURES) Get next message for a consumer. """ function consumer_next(connection::NATS.Connection, consumer::ConsumerInfo, batch::Int64; no_wait = false, no_throw = false) req = Dict() req[:no_wait] = no_wait req[:batch] = batch if !no_wait req[:expires] = NEXT_EXPIRES end subject = "\$JS.API.CONSUMER.MSG.NEXT.$(consumer.stream_name).$(consumer.name)" while true msgs = NATS.request(connection, batch, subject, JSON3.write(req)) ok = filter(!NATS.has_error_status, msgs) !isempty(ok) && return ok err = filter(NATS.has_error_status, msgs) critical = filter(m -> NATS.statuscode(m) != 408, err) # 408 indicates timeout if !isempty(critical) # TODO warn other errors if any no_throw || NATS.throw_on_error_status(first(critical)) return critical end end end function consumer_next(connection::NATS.Connection, consumer::ConsumerInfo; no_wait = false, no_throw = false) batch = consumer_next(connection, consumer, 1; no_wait, no_throw) only(batch) end
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
1462
include("manage.jl") const DEFAULT_JETCHANNEL_DELAYS = ExponentialBackOff(n = typemax(Int64), first_delay = 0.2, max_delay = 0.5) struct JetChannel{T} <: AbstractChannel{T} connection::NATS.Connection name::String stream::StreamInfo consumer::ConsumerInfo end const DEFAULT_JETCHANNEL_SIZE = 1 function JetChannel{T}(connection::NATS.Connection, name::String, size::Int64 = DEFAULT_JETCHANNEL_SIZE) where T stream = channel_stream_create(connection, name, size) consumer = channel_consumer_create(connection, name) JetChannel{T}(connection, name, stream, consumer) end function destroy!(jetchannel::JetChannel) channel_stream_delete(jetchannel.connection, jetchannel.name) end function show(io::IO, jetchannel::JetChannel{T}) where T sz = jetchannel.stream.config.max_msgs sz_str = sz == -1 ? "Inf" : string(sz) print(io, "JetChannel{$T}(\"$(jetchannel.name)\", $sz_str)") end function Base.take!(jetchannel::JetChannel{T}) where T msg = consumer_next(jetchannel.connection, jetchannel.consumer) ack = consumer_ack(jetchannel.connection, msg; delays = DEFAULT_JETCHANNEL_DELAYS) @assert ack isa NATS.Msg convert(T, msg) end function Base.put!(jetchannel::JetChannel{T}, v::T) where T subject = channel_subject(jetchannel.name) ack = stream_publish(jetchannel.connection, subject, v; delays = DEFAULT_JETCHANNEL_DELAYS) @assert ack.stream == jetchannel.stream.config.name v end
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
1236
const CHANNEL_STREAM_PREFIX = "JCH" channel_stream_name(channel_name::String) = "$(CHANNEL_STREAM_PREFIX)_$(channel_name)" channel_consumer_name(channel_name::String) = "$(CHANNEL_STREAM_PREFIX)_$(channel_name)_consumer" channel_subject(channel_name::String) = "$(CHANNEL_STREAM_PREFIX)_$(channel_name)" const INFINITE_CHANNEL_SIZE = -1 function channel_stream_create(connection::NATS.Connection, name::String, max_msgs = INFINITE_CHANNEL_SIZE) config = StreamConfiguration( name = channel_stream_name(name), subjects = [ channel_subject(name) ], retention = :workqueue, max_msgs = max_msgs, discard = :new, ) stream_create(connection, config) end function channel_stream_delete(connection::NATS.Connection, channel_name::String) stream_delete(connection, channel_stream_name(channel_name)) end function channel_consumer_create(connection::NATS.Connection, channel_name::String) stream_name = channel_stream_name(channel_name) config = ConsumerConfiguration( ack_policy = :explicit, name = channel_consumer_name(channel_name), durable_name = channel_consumer_name(channel_name) ) consumer_create(connection, config, stream_name) end
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
938
# Encoding must confirm to `validate_key`, so not url safe base64 is not allowed because of '+' struct KeyEncoding{encoding} end const JETDICT_KEY_ENCODING = [:none, :base64url] encodekey(::KeyEncoding{:none}, key::String) = key decodekey(::KeyEncoding{:none}, key::String) = key encodekey(::KeyEncoding{:base64url}, key::String) = String(transcode(Base64Encoder(urlsafe = true), key)) decodekey(::KeyEncoding{:base64url}, key::String) = String(transcode(Base64Decoder(urlsafe = true), key)) function check_encoding_implemented(encoding::Symbol) hasmethod(encodekey, (KeyEncoding{encoding}, String)) || error("No `encodekey` implemented for $encoding encoding, allowed encodings: $(join(NATS.JetStream.JETDICT_KEY_ENCODING, ", "))") hasmethod(decodekey, (KeyEncoding{encoding}, String)) || error("No `decodekey` implemented for $encoding encoding, allowed encodings: $(join(NATS.JetStream.JETDICT_KEY_ENCODING, ", "))") end
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
5751
include("encode.jl") struct JetDict{T} <: AbstractDict{String, T} connection::NATS.Connection bucket::String stream_info::StreamInfo T::DataType revisions::ScopedValue{Dict{String, UInt64}} encoding::KeyEncoding end function get_jetdict_stream_info(connection, bucket, encoding) res = stream_info(connection, "$KV_STREAM_NAME_PREFIX$bucket"; no_throw = true) if res isa ApiError res.code != 404 && throw(res) keyvalue_stream_create(connection, bucket, encoding, 1) else stream_encoding = isnothing(res.config.metadata) ? :none : Symbol(get(res.config.metadata, "encoding", "none")) if encoding != stream_encoding error("Encoding do not match, cannot use :$encoding encoding on stream with :$stream_encoding encoding") end res end end function JetDict{T}(connection::NATS.Connection, bucket::String, encoding::Symbol = :none) where T check_encoding_implemented(encoding) NATS.find_msg_conversion_or_throw(T) NATS.find_data_conversion_or_throw(T) stream = get_jetdict_stream_info(connection, bucket, encoding) JetDict{T}(connection, bucket, stream, T, ScopedValue{Dict{String, UInt64}}(), KeyEncoding{encoding}()) end function setindex!(jetdict::JetDict{T}, value::T, key::String) where T escaped = encodekey(jetdict.encoding, key) validate_key(escaped) revisions = ScopedValues.get(jetdict.revisions) if !isnothing(revisions) revision = get(revisions.value, key, 0) ack = keyvalue_put(jetdict.connection, jetdict.bucket, escaped, value, revision) @assert ack isa PubAck revisions.value[key] = ack.seq else ack = keyvalue_put(jetdict.connection, jetdict.bucket, escaped, value) @assert ack isa PubAck end jetdict end function getindex(jetdict::JetDict, key::String) escaped = encodekey(jetdict.encoding, key) validate_key(escaped) msg = try keyvalue_get(jetdict.connection, jetdict.bucket, escaped) catch err if err isa NATS.NATSError && err.code == 404 throw(KeyError(key)) else rethrow() end end if isdeleted(msg) throw(KeyError(key)) end revisions = ScopedValues.get(jetdict.revisions) if !isnothing(revisions) seq = NATS.header(msg, "Nats-Sequence") revisions.value[key] = parse(UInt64, seq) end convert(jetdict.T, msg) end function delete!(jetdict::JetDict, key::String) escaped = encodekey(jetdict.encoding, key) ack = keyvalue_delete(jetdict.connection, jetdict.bucket, escaped) @assert ack isa PubAck jetdict end # No way to get number of not deleted items fast, also kv can change during iteration. IteratorSize(::JetDict) = Base.SizeUnknown() IteratorSize(::Base.KeySet{String, JetDict{T}}) where {T} = Base.SizeUnknown() IteratorSize(::Base.ValueIterator{JetDict{T}}) where {T} = Base.SizeUnknown() function iterate(jetdict::JetDict) unique_keys = Set{String}() consumer_config = ConsumerConfiguration( name = randstring(20) ) consumer = consumer_create(jetdict.connection, consumer_config, jetdict.stream_info) msg = consumer_next(jetdict.connection, consumer, no_wait = true, no_throw = true) msg_status = NATS.statuscode(msg) msg_status == 404 && return nothing NATS.throw_on_error_status(msg) key = decodekey(jetdict.encoding, replace(msg.subject, "\$KV.$(jetdict.bucket)." => "")) value = convert(jetdict.T, msg) push!(unique_keys, key) (key => value, (consumer, unique_keys)) end function iterate(jetdict::JetDict, (consumer, unique_keys)) msg = consumer_next(jetdict.connection, consumer, no_wait = true, no_throw = true) msg_status = NATS.statuscode(msg) msg_status == 404 && return nothing NATS.throw_on_error_status(msg) key = decodekey(jetdict.encoding, replace(msg.subject, "\$KV.$(jetdict.bucket)." => "")) if key in unique_keys @warn "Key \"$key\" changed during iteration." # skip item iterate(jetdict, (consumer, unique_keys)) elseif isdeleted(msg) # skip item iterate(jetdict, (consumer, unique_keys)) else value = convert(jetdict.T, msg) push!(unique_keys, key) (key => value, (consumer, unique_keys)) end end function length(jetdict::JetDict) # TODO: this is not reliable way to check length, it counts deleted items consumer_config = ConsumerConfiguration( name = randstring(20) ) consumer = consumer_create(jetdict.connection, consumer_config, "KV_$(jetdict.bucket)") msg = consumer_next(jetdict.connection, consumer, no_wait = true, no_throw = true) msg_status = NATS.statuscode(msg) msg_status == 404 && return 0 NATS.throw_on_error_status(msg) remaining = last(split(msg.reply_to, ".")) parse(Int64, remaining) + 1 end function empty!(jetdict::JetDict) keyvalue_stream_purge(jetdict.connection, jetdict.bucket) jetdict end function with_optimistic_concurrency(f, kv::JetDict) with(f, kv.revisions => Dict{String, UInt64}()) end function isdeleted(msg) NATS.header(msg, "KV-Operation") in [ "DEL", "PURGE" ] end function watch(f, jetdict::JetDict, key = ALL_KEYS; skip_deletes = false) keyvalue_watch(jetdict.connection, jetdict.bucket, key) do msg deleted = isdeleted(msg) if !(skip_deletes && isdeleted(msg)) encoded_key = msg.subject[begin + 1 + length(keyvalue_subject_prefix(jetdict.bucket)):end] key = decodekey(jetdict.encoding, encoded_key) value = deleted ? nothing : convert(jetdict.T, msg) f(key => value) end end end
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
418
function keyvalue_history(connection::NATS.Connection, bucket::String, key::String) subject = "$(keyvalue_subject_prefix(bucket)).$(key)" consumer_config = ConsumerConfiguration(; name = randstring(10), filter_subjects = [ subject ] ) consumer = consumer_create(connection, consumer_config, keyvalue_stream_name(bucket)) consumer_next(connection, consumer, 64; no_wait = true) end
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
561
const KV_STREAM_NAME_PREFIX = "KV_" function validate_key(key::String) length(key) <= 3000 || error("Key is too long.") isempty(key) && error("Key is an empty string.") first(key) == '.' && error("Key \"$key\" starts with '.'") last(key) == '.' && error("Key \"$key\" ends with '.'") for c in key is_valid = isdigit(c) || isletter(c) || c in [ '-', '/', '_', '=', '.' ] !is_valid && error("Key \"$key\" contains invalid character '$c'.") end true end include("manage.jl") include("watch.jl") include("history.jl")
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
2727
function keyvalue_stream_name(bucket::String) "KV_$bucket" end function keyvalue_subject_prefix(bucket::String) "\$KV.$bucket" end const MAX_HISTORY = 64 """ $(SIGNATURES) Create a stream for KV bucket. """ function keyvalue_stream_create(connection::NATS.Connection, bucket::String, encoding::Symbol, history = MAX_HISTORY) history in 1:MAX_HISTORY || error("History must be greater than 0 and cannot be greater than $MAX_HISTORY") stream_config = StreamConfiguration( name = keyvalue_stream_name(bucket), subjects = ["$(keyvalue_subject_prefix(bucket)).>"], allow_rollup_hdrs = true, deny_delete = true, allow_direct = true, max_msgs_per_subject = history, discard = :new, metadata = Dict("encoding" => string(encoding)) ) stream_create(connection::NATS.Connection, stream_config) end function keyvalue_stream_info(connection::NATS.Connection, bucket::String) stream_info(connection, keyvalue_stream_name(bucket)) end """ $(SIGNATURES) Delete a KV stream by bucket name. """ function keyvalue_stream_delete(connection::NATS.Connection, bucket::String) stream_delete(connection, keyvalue_stream_name(bucket)) end """ $(SIGNATURES) Purge a KV stream. """ function keyvalue_stream_purge(connection::NATS.Connection, bucket::String) stream_purge(connection, keyvalue_stream_name(bucket)) end """ $(SIGNATURES) Get a value from KV stream. """ function keyvalue_get(connection::NATS.Connection, bucket::String, key::String)::NATS.Msg validate_key(key) stream = keyvalue_stream_name(bucket) subject = "$(keyvalue_subject_prefix(bucket)).$key" stream_message_get(connection, stream, subject; allow_direct = true) end """ $(SIGNATURES) Put a value to KV stream. """ function keyvalue_put(connection::NATS.Connection, bucket::String, key::String, value, revision = 0)::PubAck validate_key(key) hdrs = NATS.Headers() #TODO: can preserve original headers? if revision > 0 push!(hdrs, "Nats-Expected-Last-Subject-Sequence" => string(revision)) end subject = "$(keyvalue_subject_prefix(bucket)).$key" stream_publish(connection, subject, (value, hdrs)) end """ $(SIGNATURES) Delete a value from KV stream. """ function keyvalue_delete(connection::NATS.Connection, bucket::String, key)::PubAck validate_key(key) hdrs = [ "KV-Operation" => "DEL" ] subject = "$(keyvalue_subject_prefix(bucket)).$key" stream_publish(connection, subject, (nothing, hdrs)) end function keyvalue_buckets(connection::NATS.Connection) map(stream_names(connection::NATS.Connection, "\$KV.>")) do stream_name stream_name[begin+length(KV_STREAM_NAME_PREFIX):end] end end
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
299
const ALL_KEYS = ">" """ $(SIGNATURES) Watch for changes in KV stream. """ function keyvalue_watch(f, connection::NATS.Connection, bucket::String, key = ALL_KEYS) prefix = keyvalue_subject_prefix(bucket) subject = "$prefix.$key" JetStream.stream_subscribe(f, connection, subject) end
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
1911
function stream_info(connection::NATS.Connection, stream_name::String; no_throw = false, deleted_details = false, subjects_filter::Union{String, Nothing} = nothing) validate_name(stream_name) res = NATS.request(Union{StreamInfo, ApiError}, connection, "\$JS.API.STREAM.INFO.$(stream_name)") no_throw || throw_on_api_error(res) res end function iterable_request(f) offset = 0 iterable = f(offset) offset = iterable.offset + iterable.limit while iterable.total > offset iterable = f(offset) offset = iterable.offset + iterable.limit end end function stream_infos(connection::NATS.Connection, subject = nothing) result = StreamInfo[] req = Dict() if !isnothing(subject) req[:subject] = subject end iterable_request() do offset req[:offset] = offset json = NATS.request(JSON3.Object, connection, "\$JS.API.STREAM.LIST", JSON3.write(req)) throw_on_api_error(json) if !isnothing(json.streams) for s in json.streams item = StructTypes.constructfrom(StreamInfo, s) push!(result, item) end end StructTypes.constructfrom(IterableResponse, json) end result end function stream_names(connection::NATS.Connection, subject = nothing; timer = Timer(5)) result = String[] offset = 0 req = Dict() if !isnothing(subject) req[:subject] = subject end iterable_request() do offset req[:offset] = offset json = NATS.request(JSON3.Object, connection, "\$JS.API.STREAM.NAMES", JSON3.write(req); timer) throw_on_api_error(json) if !isnothing(json.streams) append!(result, json.streams) end StructTypes.constructfrom(IterableResponse, json) end result end
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
1962
""" $(SIGNATURES) Create a stream. """ function stream_create(connection::NATS.Connection, config::StreamConfiguration; no_throw = false) validate(config) response = NATS.request(Union{StreamInfo, ApiError}, connection, "\$JS.API.STREAM.CREATE.$(config.name)", JSON3.write(config)) no_throw || throw_on_api_error(response) response end """ $(SIGNATURES) Update a stream. """ function stream_update(connection::NATS.Connection, config::StreamConfiguration; no_throw = false) validate(config) response = NATS.request(Union{StreamInfo, ApiError}, connection, "\$JS.API.STREAM.UPDATE.$(config.name)", JSON3.write(config)) no_throw || throw_on_api_error(response) response end function stream_update_or_create(connection::NATS.Connection, config::StreamConfiguration) res = stream_update(connection, config; no_throw = true) if res isa StreamInfo res elseif res isa ApiError if res.code == 404 stream_create(connection, config) else throw(res) end end end """ $(SIGNATURES) Delete a stream. """ function stream_delete(connection::NATS.Connection, stream::String; no_throw = false) res = NATS.request(Union{ApiResult, ApiError}, connection, "\$JS.API.STREAM.DELETE.$(stream)") no_throw || throw_on_api_error(res) res end function stream_delete(connection::NATS.Connection, stream::StreamInfo; no_throw = false) stream_delete(connection, stream.config.name; no_throw) end """ $(SIGNATURES) Purge a stream. It is equivalent of deleting all messages. """ function stream_purge(connection::NATS.Connection, stream::String; no_throw = false) res = NATS.request(Union{ApiResult, ApiError}, connection, "\$JS.API.STREAM.PURGE.$stream") no_throw || throw_on_api_error(res) res end function stream_purge(connection::NATS.Connection, stream::StreamInfo; no_throw = false) stream_purge(connection, stream.config.name; no_throw) end
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
2430
function check_allow_direct(connection::NATS.Connection, stream_name::String) @lock connection.allow_direct_lock begin cached = get(connection.allow_direct, stream_name, nothing) if !isnothing(cached) cached else stream = stream_info(connection, stream_name) connection.allow_direct[stream_name] = stream.config.allow_direct stream.config.allow_direct end end end """ $(SIGNATURES) Get a message from stream. """ function stream_message_get(connection::NATS.Connection, stream_name::String, subject::String; allow_direct = nothing) allow_direct = @something allow_direct check_allow_direct(connection, stream_name) if allow_direct msg = NATS.request(connection, "\$JS.API.DIRECT.GET.$(stream_name)", "{\"last_by_subj\": \"$subject\"}") NATS.throw_on_error_status(msg) msg else res = NATS.request(connection, "\$JS.API.STREAM.MSG.GET.$(stream_name)", "{\"last_by_subj\": \"$subject\"}") json = JSON3.read(NATS.payload(res)) if haskey(json, :error) throw(StructTypes.constructfrom(ApiError, json.error)) end subject = json.message.subject hdrs = haskey(json.message, :hdrs) ? base64decode(json.message.hdrs) : UInt8[] append!(hdrs, "Nats-Stream: $(stream_name)\r\n") append!(hdrs, "Nats-Subject: $(json.message.subject)\r\n") append!(hdrs, "Nats-Sequence: $(json.message.seq)\r\n") append!(hdrs, "Nats-Time-Stamp: $(json.message.time)\r\n") payload = base64decode(json.message.data) NATS.Msg(res.subject, res.sid, nothing, length(hdrs), vcat(hdrs, payload)) end end """ $(SIGNATURES) Get a message from stream. """ function stream_message_get(connection::NATS.Connection, stream::StreamInfo, subject::String) stream_message_get(connection, stream.config.name, subject; stream.config.allow_direct) end """ $(SIGNATURES) Delete a message from stream. """ function stream_message_delete(connection::NATS.Connection, stream::StreamInfo, msg::NATS.Msg) seq = NATS.header(msg, "Nats-Sequence") isnothing(seq) && error("Message has no `Nats-Sequence` header") # TODO: validate stream name req = Dict() req[:seq] = parse(UInt8, seq) req[:no_erase] = true NATS.request(ApiResult, connection, "\$JS.API.STREAM.MSG.DELETE.$(stream.config.name)", JSON3.write(req)) end
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
331
const DEFAULT_STREAM_PUBLISH_DELAYS = ExponentialBackOff(n = 7, first_delay = 0.1, max_delay = 0.5) """ $(SIGNATURES) Publish a message to stream. """ function stream_publish(connection::NATS.Connection, subject, data; delays = DEFAULT_STREAM_PUBLISH_DELAYS) jetstream_api_call(PubAck, connection, subject, data; delays) end
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
327
struct StreamSub subject::String sub::NATS.Sub consumer::ConsumerInfo end function show(io::IO, stream_sub::StreamSub) print(io, "StreamSub(\"$(stream_sub.subject)\")") end include("manage.jl") include("info.jl") include("message.jl") include("publish.jl") include("subscribe.jl") include("unsubscribe.jl")
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
1042
# Subscribe to a stream by creating a consumer. # Might be more performant to configure republish subject on steram. """ $(SIGNATURES) Subscribe to a stream. """ function stream_subscribe(f, connection::NATS.Connection, subject::String) subject_streams = stream_infos(connection, subject) isempty(subject_streams) && error("No stream found for subject \"$subject\"") length(subject_streams) > 1 && error("Multiple streams found for subject \"$subject\"") stream = only(subject_streams) name = randstring(20) deliver_subject = randstring(8) idle_heartbeat = 1000 * 1000 * 1000 * 3 # 300 ms consumer_config = ConsumerConfiguration(;name, deliver_subject) # TODO: filter subject consumer = consumer_create(connection, consumer_config, stream) f_typed = NATS._fast_call(f) sub = NATS.subscribe(connection, deliver_subject) do msg if NATS.statuscode(msg) == 100 @info "heartbeat" else f_typed(msg) end end StreamSub(subject, sub, consumer) end
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
242
""" $(SIGNATURES) Unsubscribe stream subscription. """ function stream_unsubscribe(connection::NATS.Connection, stream_sub::StreamSub) NATS.unsubscribe(connection, stream_sub.sub) consumer_delete(connection, stream_sub.consumer) end
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
1164
### convert.jl # # Copyright (C) 2023 Jakub Wronowski. # # Maintainer: Jakub Wronowski <jakubwro@users.noreply.github.com> # Keywords: nats, nats-client, julia # # This file is a part of NATS.jl. # # License is MIT. # ### Commentary: # # This file contains deserialization utilities for converting NATS protocol messages into structured data. # ### Code: function convert(::Type{Msg}, msgraw::NATS.MsgRaw) buffer = msgraw.buffer sid = msgraw.sid subject = String(buffer[msgraw.subject_range]) reply_to = isempty(msgraw.reply_to_range) ? nothing : String(buffer[msgraw.reply_to_range]) headers_length = msgraw.header_bytes payload = @view buffer[msgraw.payload_range] Msg(subject, sid, reply_to, headers_length, payload) end function convert(::Type{String}, msg::NATS.Msg) # Default representation on msg content is payload string. # This allows to use handlers that take just a payload string and do not use other fields. payload(msg) end function convert(::Type{JSON3.Object}, msg::NATS.Msg) # TODO: some validation if header has error headers JSON3.read(@view msg.payload[(begin + msg.headers_length):end]) end
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
2693
### crc16.jl # # Copyright (C) 2023 Jakub Wronowski. # # Maintainer: Jakub Wronowski <jakubwro@users.noreply.github.com> # Keywords: nats, nats-client, julia # # This file is a part of NATS.jl. # # License is MIT. # ### Commentary: # # This file contains implementations of CRC16 checksum used by NATS nkeys authentication. # ### Code: const CRC16 = [ 0x0000, 0x1021, 0x2042, 0x3063, 0x4084, 0x50a5, 0x60c6, 0x70e7, 0x8108, 0x9129, 0xa14a, 0xb16b, 0xc18c, 0xd1ad, 0xe1ce, 0xf1ef, 0x1231, 0x0210, 0x3273, 0x2252, 0x52b5, 0x4294, 0x72f7, 0x62d6, 0x9339, 0x8318, 0xb37b, 0xa35a, 0xd3bd, 0xc39c, 0xf3ff, 0xe3de, 0x2462, 0x3443, 0x0420, 0x1401, 0x64e6, 0x74c7, 0x44a4, 0x5485, 0xa56a, 0xb54b, 0x8528, 0x9509, 0xe5ee, 0xf5cf, 0xc5ac, 0xd58d, 0x3653, 0x2672, 0x1611, 0x0630, 0x76d7, 0x66f6, 0x5695, 0x46b4, 0xb75b, 0xa77a, 0x9719, 0x8738, 0xf7df, 0xe7fe, 0xd79d, 0xc7bc, 0x48c4, 0x58e5, 0x6886, 0x78a7, 0x0840, 0x1861, 0x2802, 0x3823, 0xc9cc, 0xd9ed, 0xe98e, 0xf9af, 0x8948, 0x9969, 0xa90a, 0xb92b, 0x5af5, 0x4ad4, 0x7ab7, 0x6a96, 0x1a71, 0x0a50, 0x3a33, 0x2a12, 0xdbfd, 0xcbdc, 0xfbbf, 0xeb9e, 0x9b79, 0x8b58, 0xbb3b, 0xab1a, 0x6ca6, 0x7c87, 0x4ce4, 0x5cc5, 0x2c22, 0x3c03, 0x0c60, 0x1c41, 0xedae, 0xfd8f, 0xcdec, 0xddcd, 0xad2a, 0xbd0b, 0x8d68, 0x9d49, 0x7e97, 0x6eb6, 0x5ed5, 0x4ef4, 0x3e13, 0x2e32, 0x1e51, 0x0e70, 0xff9f, 0xefbe, 0xdfdd, 0xcffc, 0xbf1b, 0xaf3a, 0x9f59, 0x8f78, 0x9188, 0x81a9, 0xb1ca, 0xa1eb, 0xd10c, 0xc12d, 0xf14e, 0xe16f, 0x1080, 0x00a1, 0x30c2, 0x20e3, 0x5004, 0x4025, 0x7046, 0x6067, 0x83b9, 0x9398, 0xa3fb, 0xb3da, 0xc33d, 0xd31c, 0xe37f, 0xf35e, 0x02b1, 0x1290, 0x22f3, 0x32d2, 0x4235, 0x5214, 0x6277, 0x7256, 0xb5ea, 0xa5cb, 0x95a8, 0x8589, 0xf56e, 0xe54f, 0xd52c, 0xc50d, 0x34e2, 0x24c3, 0x14a0, 0x0481, 0x7466, 0x6447, 0x5424, 0x4405, 0xa7db, 0xb7fa, 0x8799, 0x97b8, 0xe75f, 0xf77e, 0xc71d, 0xd73c, 0x26d3, 0x36f2, 0x0691, 0x16b0, 0x6657, 0x7676, 0x4615, 0x5634, 0xd94c, 0xc96d, 0xf90e, 0xe92f, 0x99c8, 0x89e9, 0xb98a, 0xa9ab, 0x5844, 0x4865, 0x7806, 0x6827, 0x18c0, 0x08e1, 0x3882, 0x28a3, 0xcb7d, 0xdb5c, 0xeb3f, 0xfb1e, 0x8bf9, 0x9bd8, 0xabbb, 0xbb9a, 0x4a75, 0x5a54, 0x6a37, 0x7a16, 0x0af1, 0x1ad0, 0x2ab3, 0x3a92, 0xfd2e, 0xed0f, 0xdd6c, 0xcd4d, 0xbdaa, 0xad8b, 0x9de8, 0x8dc9, 0x7c26, 0x6c07, 0x5c64, 0x4c45, 0x3ca2, 0x2c83, 0x1ce0, 0x0cc1, 0xef1f, 0xff3e, 0xcf5d, 0xdf7c, 0xaf9b, 0xbfba, 0x8fd9, 0x9ff8, 0x6e17, 0x7e36, 0x4e55, 0x5e74, 0x2e93, 0x3eb2, 0x0ed1, 0x1ef0, ] function crc16(data::Vector{UInt8})::UInt16 crc::UInt16 = 0 for c in data crc = xor(crc << 8, CRC16[begin + xor(crc>>8, c)]) end crc end
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
738
### errors.jl # # Copyright (C) 2024 Jakub Wronowski. # # Maintainer: Jakub Wronowski <jakubwro@users.noreply.github.com> # Keywords: nats, nats-client, julia # # This file is a part of NATS.jl. # # License is MIT. # ### Commentary: # # This file contains implementations for handling NATS protocol errors. # ### Code: struct NATSError <: Exception code::Int64 message::String end function has_error_status(code::Int64) code in 400:599 end function has_error_status(msg::NATS.Msg) has_error_status(statuscode(msg)) end function throw_on_error_status(msg::Msg) msg_status, status_message = statusinfo(msg) if has_error_status(msg_status) throw(NATSError(msg_status, status_message)) end msg end
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
1626
### headers.jl # # Copyright (C) 2023 Jakub Wronowski. # # Maintainer: Jakub Wronowski <jakubwro@users.noreply.github.com> # Keywords: nats, nats-client, julia # # This file is a part of NATS.jl. # # License is MIT. # ### Commentary: # # This file contains implementations headers parsig and serialization. Headers are represented as vector of paris of strings. # ### Code: const Header = Pair{String, String} const Headers = Vector{Header} function headers_str(msg::Msg) String(msg.payload[begin:msg.headers_length]) end function headers(msg::Msg) if msg.headers_length == 0 return Headers() end hdr = split(headers_str(msg), "\r\n"; keepempty = false) @assert startswith(first(hdr), "NATS/1.0") "Missing protocol version." items = hdr[2:end] items = split.(items, ": "; keepempty = false) map(x -> string(first(x)) => string(last(x)) , items) end function headers(m::Msg, key::String) last.(filter(h -> first(h) == key, headers(m))) end function header(m::Msg, key::String) hdrs = headers(m, key) isempty(hdrs) ? nothing : only(hdrs) end function statusinfo(header_str::String) hdr = split(header_str, "\r\n"; keepempty = false, limit = 2) splitted = split(first(hdr), ' ', limit = 3) status = length(splitted) >= 2 ? parse(Int, splitted[2]) : 200 message = length(splitted) >= 3 ? splitted[3] : "" status, message end function statusinfo(msg::Msg)::Tuple{Int64, String} if msg.headers_length == 0 200, "" else statusinfo(headers_str(msg)) end end function statuscode(msg::Msg)::Int64 first(statusinfo(msg)) end
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
2708
### nkeys.jl # # Copyright (C) 2023 Jakub Wronowski. # # Maintainer: Jakub Wronowski <jakubwro@users.noreply.github.com> # Keywords: nats, nats-client, julia # # This file is a part of NATS.jl. # # License is MIT. # ### Commentary: # # This file contains implementations nkeys signingature which is one of methods of authentication used by NATS protocol. # ### Code: const PUBLIC_KEY_LENGTH = Sodium.LibSodium.crypto_sign_ed25519_PUBLICKEYBYTES const SECRET_KEY_LENGTH = Sodium.LibSodium.crypto_sign_ed25519_SECRETKEYBYTES const SIGNATURE_LENGTH = Sodium.LibSodium.crypto_sign_ed25519_BYTES function sign(nonce::String, nkey_seed::String) public_key = Vector{Cuchar}(undef, PUBLIC_KEY_LENGTH) secret_key = Vector{Cuchar}(undef, SECRET_KEY_LENGTH) raw_seed = _decode_seed(nkey_seed) errno = Sodium.LibSodium.crypto_sign_ed25519_seed_keypair(public_key, secret_key, raw_seed) errno == 0 || error("Cannot get key pair from nkey seed.") signed_message_length = Ref{UInt64}(0) signed_message = Vector{Cuchar}(undef, SIGNATURE_LENGTH) errno = Sodium.LibSodium.crypto_sign_ed25519_detached(signed_message, signed_message_length, nonce, sizeof(nonce), secret_key) errno == 0 || error("Cannot sign nonce.") @assert signed_message_length[] == SIGNATURE_LENGTH "Unexpected signature length." signature = transcode(Base64Encoder(urlsafe = true), signed_message) String(rstrip(==('='), String(signature))) end function _decode(encoded::String) padding_length = mod(8 - mod(length(encoded), 8), 8) raw = transcode(Base32Decoder(), encoded * repeat("=", padding_length)) length(raw) < 4 && error("Invalid length of decoded nkey.") crc_bytes = raw[end-1:end] data_bytes = raw[begin:end-2] crc = only(reinterpret(UInt16, crc_bytes)) crc == crc16(data_bytes) || error("Invalid nkey CRC16 sum.") data_bytes end const NKEY_SEED_PREFIXES = ['S'] # seed const NKEY_PUBLIC_PREFIXES = ['N', 'C', 'O', 'A', 'U', 'X'] # server, cluster, operator, account, user, curve const NKEY_PREFIXES = ['S', 'P', 'N', 'C', 'O', 'A', 'U', 'X'] # seed, private, server, cluster, operator, account, user, curve function _decode_seed(seed) # https://github.com/nats-io/nkeys/blob/3e454c8ca12e8e8a15d4c058d380e1ec31399597/strkey.go#L172 seed[1] in NKEY_PUBLIC_PREFIXES && error("Public nkey provided instead of private nkey seed, it should start with character '$(NKEY_SEED_PREFIXES...)'.") seed[1] in NKEY_SEED_PREFIXES || error("Invalid nkey seed prefix, expected one of: $NKEY_SEED_PREFIXES.") seed[2] in NKEY_PUBLIC_PREFIXES || error("Invalid public nkey prefix, expected one of: $NKEY_PUBLIC_PREFIXES.") raw = _decode(seed) raw[3:end] end
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
13867
### parser.jl # # Copyright (C) 2023 Jakub Wronowski. # # Maintainer: Jakub Wronowski <jakubwro@users.noreply.github.com> # Keywords: nats, nats-client, julia # # This file is a part of NATS.jl. # # License is MIT. # ### Commentary: # # This file contains implementations of NATS protocol parsers. # ### Code: @enum ParserState OP_START OP_PLUS OP_PLUS_O OP_PLUS_OK OP_MINUS OP_MINUS_E OP_MINUS_ER OP_MINUS_ERR OP_MINUS_ERR_SPC MINUS_ERR_ARG OP_M OP_MS OP_MSG OP_MSG_SPC MSG_ARG MSG_PAYLOAD MSG_END OP_P OP_H OP_PI OP_PIN OP_PING OP_PO OP_PON OP_PONG OP_I OP_IN OP_INF OP_INFO OP_INFO_SPC INFO_ARG # Parser state and temporary buffers for parsing subscription messages. @kwdef mutable struct ParserData state::ParserState = OP_START has_header::Bool = false subject_range::UnitRange{Int64} = 1:0 sid::Int64 = 0 reply_to_range::UnitRange{Int64} = 1:0 payload_start = 0 header_bytes::Int64 = 0 total_bytes::Int64 = 0 arg_begin::Int64 = -1 arg_no::Int64 = 0 args::Vector{UnitRange{Int64}} = UnitRange{Int64}[0:0, 0:0, 0:0, 0:0, 0:0] payload_buffer::Vector{UInt8} = UInt8[] results::Vector{ProtocolMessage} = ProtocolMessage[] end function parse_error(buffer, pos, data::ParserData) buf = String(buffer[max(begin, pos-100):min(end,pos+100)]) error("Parser error on position $pos: $buf\nBuffer length: $(length(buffer))") end function parser_loop(f, io::IO) data = ParserData() # TODO: ensure eof does not block for TLS connection while !eof(io) # EOF indicates connection is closed, task will be stopped and reconnected. data_read_start = time() buffer = readavailable(io) # Sleeps when no data available. data_ready_time = time() parse_buffer(io, buffer, data) batch_ready_time = time() f(data.results) handler_call_time = time() # @info "Read time $(data_ready_time - data_read_start), parser time: $(batch_ready_time - data_ready_time), handler time: $(handler_call_time - batch_ready_time)" length(buffer) length(data.results) empty!(data.results) end end macro uint8(char::Char) convert(UInt8, char) end @inline function bytes_to_int64(buffer, range)::Int64 ret = Int64(0) for i in range ret = (ret << 3) + (ret << 1) ret += buffer[i] - 0x30 end ret end function parse_buffer(io::IO, buffer::Vector{UInt8}, data::ParserData) pos = 0 len = length(buffer) while pos < len pos += 1 byte = buffer[pos] if data.state == OP_START if byte == (@uint8 'M') || byte == (@uint8 'm') data.state = OP_M data.has_header = false elseif byte == (@uint8 'H') || byte == (@uint8 'h') data.state = OP_H data.has_header = true elseif byte == (@uint8 'P') || byte == (@uint8 'p') data.state = OP_P elseif byte == (@uint8 '+') data.state = OP_PLUS elseif byte == (@uint8 '-') data.state = OP_MINUS elseif byte == (@uint8 'I') || byte == (@uint8 'i') data.state = OP_I else parse_error(buffer, pos, data) end elseif data.state == OP_PLUS if byte == (@uint8 'O') || byte == (@uint8 'o') data.state = OP_PLUS_O else parse_error(buffer, pos, data) end elseif data.state == OP_PLUS_O if byte == (@uint8 'K') || byte == (@uint8 'k') data.state = OP_PLUS_OK else parse_error(buffer, pos, data) end elseif data.state == OP_PLUS_OK if byte == (@uint8 '\r') elseif byte == (@uint8 '\n') push!(data.results, Ok()) data.state = OP_START else parse_error(buffer, pos, data) end elseif data.state == OP_MINUS if byte == (@uint8 'E') || byte == (@uint8 'e') data.state = OP_MINUS_E else parse_error(buffer, pos, data) end elseif data.state == OP_MINUS_E if byte == (@uint8 'R') || byte == (@uint8 'r') data.state = OP_MINUS_ER else parse_error(buffer, pos, data) end elseif data.state == OP_MINUS_ER if byte == (@uint8 'R') || byte == (@uint8 'r') data.state = OP_MINUS_ERR else parse_error(buffer, pos, data) end elseif data.state == OP_MINUS_ERR if byte == (@uint8 ' ') || byte == (@uint8 '\t') data.state = OP_MINUS_ERR_SPC else parse_error(buffer, pos, data) end elseif data.state == OP_MINUS_ERR_SPC if byte == @uint8 ' ' data.state = OP_MINUS_ERR_SPC elseif byte == @uint8 '\'' data.state = MINUS_ERR_ARG else parse_error(buffer, pos, data) end elseif data.state == MINUS_ERR_ARG if byte == (@uint8 '\'') elseif byte == (@uint8 '\r') elseif byte == (@uint8 '\n') push!(data.results, Err(String(data.payload_buffer))) data.state = OP_START else push!(data.payload_buffer, byte) end elseif data.state == OP_M if byte == (@uint8 'S') || byte == (@uint8 's') data.state = OP_MS else parse_error(buffer, pos, data) end elseif data.state == OP_MS if byte == (@uint8 'G') || byte == (@uint8 'g') data.state = OP_MSG else parse_error(buffer, pos, data) end elseif data.state == OP_MSG if byte == (@uint8 ' ') || byte == (@uint8 '\t') data.state = OP_MSG_SPC else parse_error(buffer, pos, data) end elseif data.state == OP_MSG_SPC if byte == (@uint8 ' ') || byte == (@uint8 '\t') # Skip all spaces. else data.arg_begin = pos data.arg_no += 1 if pos == len rest = readuntil(io, "\r\n") append!(buffer, rest, "\r\n") len += length(rest) + 2 end data.state = MSG_ARG end elseif data.state == MSG_ARG if pos == len && byte != (@uint8 '\n') rest = readuntil(io, "\r\n") len += length(rest) + 2 append!(buffer, rest, "\r\n") end if byte == (@uint8 ' ') || byte == (@uint8 '\t') argrange = range(data.arg_begin, pos-1) isempty(argrange) || (data.args[data.arg_no] = argrange) data.arg_begin = pos+1 isempty(argrange) || (data.arg_no += 1) elseif byte == (@uint8 '\r') data.args[data.arg_no] = range(data.arg_begin, (pos-1)) elseif byte == (@uint8 '\n') subject_range = data.args[1] if data.has_header if data.arg_no == 4 data.header_bytes = bytes_to_int64(buffer, data.args[3]) data.total_bytes = bytes_to_int64(buffer, data.args[4]) data.reply_to_range = 1:0 elseif data.arg_no == 5 data.header_bytes = bytes_to_int64(buffer, data.args[4]) data.total_bytes = bytes_to_int64(buffer, data.args[5]) data.reply_to_range = data.args[3] else parse_error(buffer, pos, data) end else if data.arg_no == 3 data.header_bytes = 0 data.total_bytes = bytes_to_int64(buffer, data.args[3]) data.reply_to_range = 1:0 elseif data.arg_no == 4 data.total_bytes = bytes_to_int64(buffer, data.args[4]) data.header_bytes = 0 data.reply_to_range = data.args[3] else parse_error(buffer, pos, data) end end ending = pos + data.total_bytes + 2 if ending > len rest = read(io, ending - len) len += length(rest) append!(buffer, rest) end payload_range = range(pos+1, pos + data.total_bytes) pos = pos + data.total_bytes + 2 msg = MsgRaw(data.sid, buffer, subject_range, data.reply_to_range, data.header_bytes, payload_range) push!(data.results, msg) data.arg_no = 0 data.sid = 0 data.state = OP_START else if data.arg_no == 2 data.sid = data.sid * 10 data.sid += byte - 0x30 end end elseif data.state == OP_P if byte == (@uint8 'I') || byte == (@uint8 'i') data.state = OP_PI elseif byte == (@uint8 'O') || byte == (@uint8 'o') data.state = OP_PO else parse_error(buffer, pos, data) end elseif data.state == OP_H if byte == (@uint8 'M') || byte == (@uint8 'm') data.state = OP_M else parse_error(buffer, pos, data) end elseif data.state == OP_PI if byte == (@uint8 'N') || byte == (@uint8 'n') data.state = OP_PIN else parse_error(buffer, pos, data) end elseif data.state == OP_PIN if byte == (@uint8 'G') || byte == (@uint8 'g') data.state = OP_PING else parse_error(buffer, pos, data) end elseif data.state == OP_PING if byte == (@uint8 '\r') #Do nothing elseif byte == (@uint8 '\n') push!(data.results, Ping()) data.state = OP_START else parse_error(buffer, pos, data) end elseif data.state == OP_PO if byte == (@uint8 'N') || byte == (@uint8 'n') data.state = OP_PON else parse_error(buffer, pos, data) end elseif data.state == OP_PON if byte == (@uint8 'G') || byte == (@uint8 'g') data.state = OP_PONG else parse_error(buffer, pos, data) end elseif data.state == OP_PONG if byte == (@uint8 '\r') #Do nothing elseif byte == (@uint8 '\n') push!(data.results, Pong()) data.state = OP_START else parse_error(buffer, pos, data) end elseif data.state == OP_I if byte == (@uint8 'N') || byte == (@uint8 'n') data.state = OP_IN else parse_error(buffer, pos, data) end elseif data.state == OP_IN if byte == (@uint8 'F') || byte == (@uint8 'f') data.state = OP_INF else parse_error(buffer, pos, data) end elseif data.state == OP_INF if byte == (@uint8 'O') || byte == (@uint8 'o') data.state = OP_INFO else parse_error(buffer, pos, data) end elseif data.state == OP_INFO if byte == (@uint8 ' ') || byte == (@uint8 '\t') data.state = OP_INFO_SPC else parse_error(buffer, pos, data) end elseif data.state == OP_INFO_SPC if byte == (@uint8 ' ') || byte == (@uint8 '\t') # skip else push!(data.payload_buffer, byte) data.state = INFO_ARG end elseif data.state == INFO_ARG if byte == (@uint8 '\r') elseif byte == (@uint8 '\n') push!(data.results, JSON3.read(data.payload_buffer, Info)) empty!(data.payload_buffer) data.state = OP_START else push!(data.payload_buffer, byte) end end end end # Simple interactive parser for protocol initialization. function next_protocol_message(io::IO)::ProtocolMessage headline = readuntil(io, "\r\n") if startswith(uppercase(headline), "+OK") Ok() elseif startswith(uppercase(headline), "PING") Ping() elseif startswith(uppercase(headline), "PONG") Pong() elseif startswith(uppercase(headline), "-ERR") parse_err(headline) elseif startswith(uppercase(headline), "INFO") parse_info(headline) elseif startswith(uppercase(headline), "MSG") error("Parsing MSG not supported") elseif startswith(uppercase(headline), "HMSG") error("Parsing HMSG not supported") else error("Unexpected protocol message: '$headline'.") end end function parse_info(headline::String)::Info json = SubString(headline, ncodeunits("INFO ")) JSON3.read(json, Info) end function parse_err(headline::String)::Err left = ncodeunits("-ERR '") + 1 right = ncodeunits(headline) - ncodeunits("'") Err(headline[left:right]) end
NATS
https://github.com/jakubwro/NATS.jl.git
[ "MIT" ]
0.1.0
d9d9a189fb9155a460e6b5e8966bf6a66737abf8
code
411
### payload.jl # # Copyright (C) 2023 Jakub Wronowski. # # Maintainer: Jakub Wronowski <jakubwro@users.noreply.github.com> # Keywords: nats, nats-client, julia # # This file is a part of NATS.jl. # # License is MIT. # ### Commentary: # # This file contains utilities for NATS messages payload manipulation. # ### Code: payload(msg::Msg) = String(msg.payload[begin+msg.headers_length:end]) # TODO: optimize this
NATS
https://github.com/jakubwro/NATS.jl.git