v0.10.5 (#93)

* fix for documentatio script / examples

* modified tutotial

* workshop enhanced

* tutorial

* final workshop

* tutorial juliacon

.github/workflows/Example.yml

@@ -44,7 +44,7 @@ jobs:
         env:
           FILE: examples/src/${{ matrix.file-name }}.ipynb
         run: |
-          jupyter nbconvert --ExecutePreprocessor.kernel_name="julia-1.8" --to notebook --inplace --execute ${{ env.FILE }}
+          jupyter nbconvert --ExecutePreprocessor.kernel_name="julia-1.9" --to notebook --inplace --execute ${{ env.FILE }}
           jupyter nbconvert --to script ${{ env.FILE }}
           jupyter nbconvert --to markdown ${{ env.FILE }}
 

Project.toml

@@ -1,6 +1,6 @@
 name = "FMIFlux"
 uuid = "fabad875-0d53-4e47-9446-963b74cae21f"
-version = "0.10.4"
+version = "0.10.5"
 
 [deps]
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"

examples/src/.gitignore

@@ -1,2 +1,3 @@
 params/
-*.png
+*.png
+*.gif

examples/src/juliacon_2023_distributedhyperopt.jl

@@ -16,13 +16,9 @@ nprocs()
 workers = addprocs(5) 
 @everywhere include(joinpath(@__DIR__, "workshop_module.jl"))
 
-# set the current hyperparameter optimization run
-@everywhere NODE_Training.HPRUN = 1
-@info "Run: $(NODE_Training.HPRUN)"
-
 # creating paths for log files (logs), parameter sets (params) and hyperparameter plots (plots) 
-for dir ∈ ("logs", "params", "plots", "results") 
-    path = joinpath(@__DIR__, dir, "$(NODE_Training.HPRUN)")
+for dir ∈ ("logs", "params", "plots") 
+    path = joinpath(@__DIR__, dir)
     @info "Creating (if not already) path: $(path)"
     mkpath(path)
 end 
@@ -43,8 +39,8 @@ DistributedHyperOpt.optimize(optimization;
                           sampler=sampler, 
                           plot=true, 
                           plot_ressources=true,
-                          save_plot=joinpath(@__DIR__, "plots", "$(NODE_Training.HPRUN)", "hyperoptim.png"),
-                          redirect_worker_io_dir=joinpath(@__DIR__, "logs", "$(NODE_Training.HPRUN)"))
+                          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)

examples/src/juliacon_2023_helpers.jl

@@ -5,12 +5,32 @@
 using LaTeXStrings
 
 import FMIFlux: roundToLength
-import FMIZoo:movavg
+import FMIZoo: movavg
 
 import FMI: FMU2Solution
 import FMIZoo: VLDM, VLDM_Data
 
-function plotANNError(neuralFMU::NeuralFMU, data::FMIZoo.VLDM_Data; reductionFactor::Int=10, field=:consumption, mov_avg::Int=100, filename=nothing)
+function fmiSingleInstanceMode(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.FMU2_EXECUTION_CONFIGURATION_NOTHING
+        c, _ = FMIFlux.prepareSolveFMU(fmu, nothing, fmu.type, true, false, false, false, true, data.params; x0=x0)
+    else
+        c = FMI.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 = FMI.FMIImport.FMU2_EXECUTION_CONFIGURATION_NO_RESET
+        FMIFlux.finishSolveFMU(fmu, c, false, 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
@@ -19,7 +39,8 @@ function plotANNError(neuralFMU::NeuralFMU, data::FMIZoo.VLDM_Data; reductionFac
     tStart = data.consumption_t[1]
     tStop = data.consumption_t[end]
     x0 = FMIZoo.getStateVector(data, tStart)
-    result = neuralFMU(x0, (tStart, tStop); parameters=data.params, showProgress=true, recordValues=:derivatives) 
+    resultNFMU = neuralFMU(x0, (tStart, tStop); parameters=data.params, showProgress=false, recordValues=:derivatives, saveat=data.consumption_t)
+    resultFMU = fmiSimulate(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]
@@ -28,40 +49,38 @@ function plotANNError(neuralFMU::NeuralFMU, data::FMIZoo.VLDM_Data; reductionFac
     acceleration_dev = (data.speed_dev[2:end] - data.speed_dev[1:end-1]) / dt
     acceleration_dev = [acceleration_dev..., 0.0]
 
-    ANNInputs = fmiGetSolutionValue(result, :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 = fmiGetSolutionValue(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 in 1:length(ANNInputs[1]))
-    ANNOutputs = fmiGetSolutionDerivative(result, 5:6; isIndex=true)
+
+    ANNOutputs = fmiGetSolutionDerivative(resultNFMU, 5:6; isIndex=true)
     ANNOutputs = collect([ANNOutputs[1][i], ANNOutputs[2][i]] for i in 1:length(ANNOutputs[1]))
 
-    ANN_error = nothing 
-
-    if field == :consumption
-        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 in 1:length(data.consumption_t))
-
-        label = L"consumption [W]"
-        colorMin=-610.0
-        colorMax=610.0
-    else # :acceleration 
-        ANN_acceleration = collect(o[1] for o in ANNOutputs)
-        ANN_error = ANN_acceleration - acceleration_val
-        ANN_error = collect(ANN_error[i] > 0.0 ? max(0.0, ANN_error[i]-acceleration_dev[i]) : min(0.0, ANN_error[i]+acceleration_dev[i]) for i in 1:length(data.consumption_t))
-
-        label = L"acceleration [m/s^2]"
-        colorMin=-0.04
-        colorMax=0.04
-    end
+    FMUOutputs = fmiGetSolutionDerivative(resultFMU, 5:6; isIndex=true)
+    FMUOutputs = collect([FMUOutputs[1][i], FMUOutputs[2][i]] for i in 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 in 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 in 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 = -colorMin/(colorMax-colorMin) # -_min/(_max-_min)
+    neutral = 0.5 
 
     if _max > colorMax
         @warn "max value ($(_max)) is larger than colorMax ($(colorMax)) - values will be cut"
@@ -71,31 +90,27 @@ function plotANNError(neuralFMU::NeuralFMU, data::FMIZoo.VLDM_Data; reductionFac
         @warn "min value ($(_min)) is smaller than colorMin ($(colorMin)) - values will be cut"
     end
 
-    ANN_error = collect(min(max(e, colorMin), colorMax) for e in ANN_error)
-
-    @info "$(_min) $(_max) $(neutral)"
-
     anim = @animate for ang in 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([:orange, :white, :blue], [0.0, 0.5, 1.0]) # , scale = :log)
-
+    
+        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=L"velocity [m/s]", ylabel=L"acceleration [m/s^2]",
+                    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=L"acceleration [m/s^2]", ylabel=L"consumption [W]",
+                    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=L"velocity [m/s]", ylabel=L"consumption [W]",
+                    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=L"velocity [m/s]", ylabel=L"acceleration [m/s^2]", zlabel=L"consumption [W]",
-                    color=colorgrad, zcolor=ANN_error[1:reductionFactor:end], markersize=8, label=:none, camera=(ang,20), colorbar_title=" \n\n\n\n" * L"Δ" * label * " (smoothed)") # 
+                    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 in 1:3 
@@ -110,7 +125,11 @@ function plotANNError(neuralFMU::NeuralFMU, data::FMIZoo.VLDM_Data; reductionFac
         end
     end
 
-    return gif(anim, filename; fps=10)
+    if !isnothing(filename)
+        return gif(anim, filename; fps=10)
+    else
+        return gif(anim; fps=10)
+    end
 end
 
 function plotCumulativeConsumption(solutionNFMU::FMU2Solution, solutionFMU::FMU2Solution, data::FMIZoo.VLDM_Data; range=(0.0,1.0), filename=nothing)
@@ -159,24 +178,4 @@ function simPlotCumulativeConsumption(cycle::Symbol, filename=nothing; kwargs...
         savefig(fig, filename)
     end
     return fig
-end
-
-function checkMSE(cycle; init::Bool=false)
-
-    data = FMIZoo.VLDM(cycle)
-    tStart = data.consumption_t[1]
-    tStop = data.consumption_t[end]
-    tSave = data.consumption_t
-
-    if init
-        c = FMI.FMIImport.getCurrentComponent(fmu)
-        FMI.FMIImport.fmi2SetFMUstate(c, batch[1].initialState)
-        c.eventInfo = deepcopy(batch[1].initialEventInfo)
-        c.t = batch[1].tStart
-    end
-    resultNFMU = neuralFMU(x0, (tStart, tStop); parameters=data.params, showProgress=true, maxiters=1e7, saveat=tSave) 
-
-    mse_NFMU = FMIFlux.Losses.mse_dev(data.cumconsumption_val, fmiGetSolutionState(resultNFMU, 6; isIndex=true), data.cumconsumption_dev)
-
-    return mse_NFMU
-end
+end