fix (#137) missing problem in forward

Author: BernhardAhrens

projects/Respiration_Fluxnet/Project.toml

@@ -6,5 +6,6 @@ Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
 EasyHybrid = "61bb816a-e6af-4913-ab9e-91bff2e122e3"
 NCDatasets = "85f8d34a-cbdd-5861-8df4-14fed0d494ab"
 OhMyThreads = "67456a42-1dca-4109-a031-0a68de7e3ad5"
+RetryManagers = "7a8cfeaa-437c-4519-b75e-06295593f538"
 TidierPlots = "337ecbd1-5042-4e2a-ae6f-ca776f97570a"
 WGLMakie = "276b4fcb-3e11-5398-bf8b-a0c2d153d008"

projects/Respiration_Fluxnet/script.jl

@@ -5,8 +5,7 @@
 using Pkg
 project_path = "projects/Respiration_Fluxnet"
 Pkg.activate(project_path)
-
-#Pkg.develop(path=pwd())
+Pkg.develop(path=pwd())
 #Pkg.instantiate()
 
 # start using the package
@@ -25,6 +24,8 @@ include("Data/load_data.jl")
 
 site = "US-SRG"
 
+fluxnet_data = load_fluxnet_nc(joinpath(project_path, "Data", "data20240123", "$site.nc"), timevar="date")
+
 # explore data structure
 println(names(fluxnet_data.timeseries))
 println(fluxnet_data.scalars)
@@ -77,11 +78,10 @@ parameters = (
 target_FluxPartModel = [:NEE]
 forcing_FluxPartModel = [:SW_IN, :TA]
 
-predictors = (Rb = [:SWC_shallow, :P, :WS], 
+predictors = (Rb = [:SWC_shallow, :P, :WS, :cos_dayofyear, :sin_dayofyear], 
               RUE = [:TA, :P, :WS, :SWC_shallow, :VPD, :SW_IN_POT, :dSW_IN_POT, :dSW_IN_POT_DAY])
 
 global_param_names = [:Q10]
-
 hybrid_model = constructHybridModel(
     predictors,
     forcing_FluxPartModel,
@@ -91,7 +91,7 @@ hybrid_model = constructHybridModel(
     global_param_names,
     scale_nn_outputs=true,
     hidden_layers = [32, 32],
-    activation = tanh,
+    activation = sigmoid,
     input_batchnorm = true,
     start_from_default = false
 )
@@ -178,7 +178,7 @@ using TidierPlots
 using WGLMakie
 beautiful_makie_theme = Attributes(fonts=(;regular="CMU Serif"))
 
-ggplot(forward_run, aes(x=:GPP_NT, y=:GPP_pred)) + geom_point() + beautiful_makie_theme
+ggplot(forward_run, @aes(x=GPP_NT, y=GPP_pred)) + geom_point() + beautiful_makie_theme
 
 idx = .!isnan.(forward_run.GPP_NT) .& .!isnan.(forward_run.GPP_pred)
 EasyHybrid.poplot(forward_run.GPP_NT[idx], forward_run.GPP_pred[idx], "GPP", xlabel = "Nighttime GPP", ylabel = "Hybrid GPP")

src/models/GenericHybridModel.jl

@@ -382,6 +382,15 @@ end
 function (m::SingleNNHybridModel)(df::DataFrame, ps, st)
     @warn "Only makes sense in test mode, not training!"
 
+    
+    # Process numeric or missing-containing columns
+    for col in names(df)
+        what_type = eltype(df[!, col])
+        if what_type <: Union{Missing, Real} || what_type <: Real
+            df[!, col] = Float64.(coalesce.(df[!, col], NaN))
+        end
+    end
+
     all_data = to_keyedArray(df)
     x, _ = prepare_data(m, all_data)
     out, _ = m(x, ps, LuxCore.testmode(st))
@@ -470,8 +479,18 @@ end
 function (m::MultiNNHybridModel)(df::DataFrame, ps, st)
     @warn "Only makes sense in test mode, not training!"
 
+    # Process numeric or missing-containing columns
+    for col in names(df)
+        what_type = eltype(df[!, col])
+        if what_type <: Union{Missing, Real} || what_type <: Real
+            df[!, col] = Float64.(coalesce.(df[!, col], NaN))
+        end
+    end
+
     all_data = to_keyedArray(df)
+
     x, _ = prepare_data(m, all_data)
+    @show typeof(x)
     out, _ = m(x, ps, LuxCore.testmode(st))
     dfnew = copy(df)
     for k in keys(out)