fixed examples

ThummeTo · Nov 8, 2023 · e71b70a · e71b70a
1 parent 5a4a001
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Showing 7 changed files with 23 additions and 21 deletions.
diff --git a/examples/src/advanced_hybrid_ME.ipynb b/examples/src/advanced_hybrid_ME.ipynb
@@ -554,7 +554,7 @@
     "numAdditionalVRs = length(additionalVRs)\n",
     "\n",
     "net = Chain(\n",
-    "    x -> simpleFMU(x=x, y_refs=additionalVRs),\n",
+    "    x -> simpleFMU(x=x, dx_refs=:all, y_refs=additionalVRs),\n",
     "    preProc!,\n",
     "    Dense(numStates+numAdditionalVRs, 16, tanh),\n",
     "    postProc!,\n",

diff --git a/examples/src/juliacon_2023.ipynb b/examples/src/juliacon_2023.ipynb
@@ -451,7 +451,7 @@
     "    gates = ScaleSum([1.0, 1.0, 0.0, 0.0], [[1,3], [2,4]]) # gates with sum\n",
     "\n",
     "    # setup the NeuralFMU topology\n",
-    "    model = Chain(x -> f(; x=x),                  # take `x`, put it into the FMU, retrieve `dx`\n",
+    "    model = Chain(x -> f(; x=x, dx_refs=:all),        # take `x`, put it into the FMU, retrieve `dx`\n",
     "                  dx -> cache(dx),                    # cache `dx`\n",
     "                  dx -> dx[4:6],                      # forward only dx[4, 5, 6]\n",
     "                  preProcess,                         # pre-process `dx`\n",

diff --git a/examples/src/mdpi_2022.ipynb b/examples/src/mdpi_2022.ipynb
@@ -457,7 +457,7 @@
         "gates = ScaleSum([1.0, 0.0]) # signal from FMU (#1 = 1.0), signal from ANN (#2 = 0.0)\n",
         "\n",
         "# setup the NeuralFMU topology\n",
-        "net = Chain(x -> fmu(; x=x),                    # take `x`, put it into the FMU, retrieve `dx`\n",
+        "net = Chain(x -> fmu(; x=x, dx_refs=:all),      # take `x`, put it into the FMU, retrieve `dx`\n",
         "            dx -> cache(dx),                    # cache `dx`\n",
         "            dx -> dx[4:6],                      # forward only dx[4, 5, 6]\n",
         "            preProcess,                         # pre-process `dx`\n",

diff --git a/examples/src/modelica_conference_2021.ipynb b/examples/src/modelica_conference_2021.ipynb
@@ -777,7 +777,7 @@
     "end\n",
     "\n",
     "net = Chain(Dense(numStates, numStates,  identity),\n",
-    "            x -> simpleFMU(x=x),\n",
+    "            x -> simpleFMU(x=x, dx_refs=:all),\n",
     "            Dense(numStates, 8, identity),\n",
     "            Dense(8, 8, tanh),\n",
     "            Dense(8, numStates))"

diff --git a/examples/src/simple_hybrid_ME.ipynb b/examples/src/simple_hybrid_ME.ipynb
@@ -413,7 +413,7 @@
     "# NeuralFMU setup\n",
     "numStates = fmiGetNumberOfStates(simpleFMU)\n",
     "\n",
-    "net = Chain(x -> simpleFMU(x=x),\n",
+    "net = Chain(x -> simpleFMU(x=x, dx_refs=:all),\n",
     "            Dense(numStates, 16, tanh),\n",
     "            Dense(16, 16, tanh),\n",
     "            Dense(16, numStates))"

diff --git a/test/runtests.jl b/test/runtests.jl
@@ -7,6 +7,7 @@ using FMIFlux
 using Test
 using FMIZoo
 using FMIFlux.FMIImport
+using FMIFlux.FMIImport.FMICore
 
 import FMIFlux.FMISensitivity: FiniteDiff, ForwardDiff, ReverseDiff
 
@@ -50,7 +51,7 @@ function runtests(exportingTool)
         @testset "Solution Gradients" begin
             include("solution_gradients.jl")
         end
-        
+
         for _GRADIENT ∈ (:ReverseDiff, :ForwardDiff) # , :FiniteDiff)
 
             global GRADIENT = _GRADIENT

diff --git a/test/solution_gradients.jl b/test/solution_gradients.jl
@@ -28,6 +28,7 @@ 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]
 
 numStates = 2
 solver = Tsit5()
@@ -48,7 +49,7 @@ net_bb = Chain(#Dense([1.0 0.0; 0.0 1.0], [0.0, 0.0], identity),
 p_net_bb, re_bb = Flux.destructure(net_bb)
 
 ff = ODEFunction{true}(fx_bb) 
-prob_bb = ODEProblem{true}(ff, x0, (t_start, t_stop), p_net_bb)
+prob_bb = ODEProblem{true}(ff, x0_bb, (t_start, t_stop), p_net_bb)
 
 function condition(out, x, t, integrator)
     out[1] = x[1]-RADIUS
@@ -116,10 +117,10 @@ end
 
 function mysolve(p; sensealg=nothing)
     global solution, events # write
-    global prob, x0, posData, solver # read-only
+    global prob, x0_bb, posData, solver # read-only
     events = 0
 
-    solution = prob(x0; p=p, solver=solver, saveat=tData)
+    solution = prob(x0_bb; p=p, solver=solver, saveat=tData)
 
     return collect(u[1] for u in solution.states.u)
 end
@@ -155,7 +156,7 @@ using FMIFlux.FMISensitivity.SciMLSensitivity
 sensealg = ReverseDiffAdjoint() 
 
 c = nothing
-c, x0 = FMIFlux.prepareSolveFMU(prob.fmu, c, fmi2TypeModelExchange, nothing, nothing, nothing, nothing, nothing, prob.parameters, prob.tspan[1], prob.tspan[end], nothing; x0=prob.x0, handleEvents=FMIFlux.handleEvents, cleanup=true)
+c, _ = FMIFlux.prepareSolveFMU(prob.fmu, c, fmi2TypeModelExchange, nothing, nothing, nothing, nothing, nothing, prob.parameters, prob.tspan[1], prob.tspan[end], nothing; x0=prob.x0, handleEvents=FMIFlux.handleEvents, cleanup=true)
 
 ### START CHECK CONDITIONS 
 
@@ -169,14 +170,14 @@ function condition_nfmu_check(x)
     FMIFlux.condition!(prob, FMIFlux.getComponent(prob), buffer, x, t_start, nothing, [UInt32(1)])
     return buffer 
 end
-jac_fwd1 = ForwardDiff.jacobian(condition_bb_check, x0)
-jac_fwd2 = ForwardDiff.jacobian(condition_nfmu_check, x0)
+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)
-jac_rwd2 = ReverseDiff.jacobian(condition_nfmu_check, x0)
+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)
-jac_fin2 = FiniteDiff.finite_difference_jacobian(condition_nfmu_check, x0)
+jac_fin1 = FiniteDiff.finite_difference_jacobian(condition_bb_check, x0_bb)
+jac_fin2 = FiniteDiff.finite_difference_jacobian(condition_nfmu_check, x0_bb)
 
 atol = 1e-8
 @test isapprox(jac_fin1, jac_fwd1; atol=atol)
@@ -206,17 +207,17 @@ atol = 1e-8
 # t_first_event_time = 0.451523640985728
 # x_first_event_right = [2.2250738585072014e-308, 3.1006128426489954]
 
-# jac_con1 = ForwardDiff.jacobian(affect_bb_check, x0)
-# jac_con2 = ForwardDiff.jacobian(affect_nfmu_check, x0)
+# jac_con1 = ForwardDiff.jacobian(affect_bb_check, x0_bb)
+# jac_con2 = ForwardDiff.jacobian(affect_nfmu_check, x0_bb)
 
-# jac_con1 = ReverseDiff.jacobian(affect_bb_check, x0)
-# jac_con2 = ReverseDiff.jacobian(affect_nfmu_check, x0)
+# jac_con1 = ReverseDiff.jacobian(affect_bb_check, x0_bb)
+# jac_con2 = ReverseDiff.jacobian(affect_nfmu_check, x0_bb)
 
 ###
 
 # Solution (plain)
 losssum(p_net; sensealg=sensealg) 
-#@test length(solution.events) == NUMEVENTS
+@test length(solution.events) == NUMEVENTS
 
 losssum_bb(p_net_bb; sensealg=sensealg) 
 @test events == NUMEVENTS