text:
### A Pluto.jl notebook ###
# v0.18.1

using Markdown
using InteractiveUtils

# ╔═╡ 1609ce3c-4066-45ca-acce-8dbf081bfb89
begin
	using DataFrames
	using Plots
	using RCall
	using CSV
	using PlantBiophysics
	using Statistics
	using Revise
	constants = Constants()
	using Makie
	using WGLMakie
	using Dates
	using MonteCarloMeasurements
	unsafe_comparisons(true)
	using LeafGasExchange
	using Cropbox
end

# ╔═╡ 1100f4ba-d45e-11ec-25f5-b79c9d3b9851
md"# Global simulation with _PlantBiophysics.jl_

This Pluto notebook computes the Fig. .. from PlantBiophysics.jl paper. You can run the fitting and simulation parts in a classic Julia file apart.
"

# ╔═╡ c2e2c3c8-288c-491a-a8c7-8adf4517103c
md"""
## Global simulation parameters
"""

# ╔═╡ f3e741d1-ba01-4ec1-9cdb-f61611679d17
begin
	path="../data/"
	fitted=true
	simulated=true
end

# ╔═╡ 98dcfd9e-e85f-4b38-a546-e9932c7360b0
begin
	function RMSE(obs,sim)
	    return sqrt(sum((obs .- sim).^2)/length(obs))
	end

	function EF(obs,sim)
	    SSres = sum((obs - sim).^2)
	    SStot = sum((obs .- mean(obs)).^2)
	    return 1-SSres/SStot
	end

	# Loading R packages
	#R"""
	#library(plantecophys)
	#library(dplyr)
	#library(readr)
	#library(microbenchmark)
	#"""

end

# ╔═╡ e8961d19-85d4-484b-a029-359a9dbea800
md"""
## Fitting
Photosynthesis and stomatal conductance parameters are fitted using plantecophys for plantecophys and LeafGasExchange.jl packages, and with own fitting module for PlantBiophysics.jl.
"""

# ╔═╡ 3592d0a1-d658-4137-8ea8-e8ea9ff3dc91
md"""
## Simulating

Here we set wind velocity to 20 m/s (the Licor6400 chamber is well ventilated) and the characteristic length to the square root of the chamber area in $m^2$.
"""

# ╔═╡ 2c7870e2-1601-4c5a-adbf-200d8ae66c09
begin
	if simulated
		df = CSV.File(path*"../tutorials/Medlyn_ACis_simulations.csv", dateformat="yyyy/mm/dd") |> DataFrame
	end
end

# ╔═╡ f9be64a3-b788-48d8-a925-5954e532effb
begin
	if !fitted
    	dfParam = read_licor6400(path*"TumbarumbaGasex_ACis_Medlyn.csv")
	    dfParam.Date = Date.(dfParam.Date, Dates.DateFormat("yyyy/mm/dd"))

	    dfParam.VcMaxRef .= dfParam.JMaxRef .= dfParam.RdRef .= dfParam.TPURef .= dfParam.g0 .= dfParam.g1 .= 0.
	    dfParam.VcMaxRefPE .= dfParam.JMaxRefPE .= dfParam.RdRefPE .= dfParam.TPURefPE .= dfParam.g0PE .= dfParam.g1PE .= 0.
	    for i in unique(dfParam.Curve)
	        dfi = filter(x -> x.Curve == i, dfParam)
	        sort!(dfi,:Cᵢ)

	        g0,g1 = PlantBiophysics.fit(Medlyn,dfi)
	        dfParam.g0[dfParam.Curve.==i] .= g0
	        dfParam.g1[dfParam.Curve.==i] .= g1

	        filter!(x->x.PPFD > 1400., dfi)

	        VcMaxRef,JMaxRef,RdRef,TPURef = PlantBiophysics.fit(Fvcb,dfi)
	        df.VcMaxRef[df.Curve.==i] .= VcMaxRef
	        df.JMaxRef[df.Curve.==i] .= JMaxRef
	        df.RdRef[df.Curve.==i] .= RdRef
	        df.TPURef[df.Curve.==i] .= TPURef

	        dfiPE = dfi[:,3:end]
	        P = mean(dfi.P)
	        rename!(dfiPE, :Tₗ=>:Tleaf, :Cᵢ=>:Ci, :Cₐ=>:Ca, :Gₛ=>:gs)
	        @rput P
	        @rput dfiPE
	        R"""
	        fit = fitaci(
	            dfiPE,
	            varnames = list(ALEAF = "A", Tleaf = "Tleaf", Ci = "Ci", PPFD = "PPFD"),
	            Tcorrect = TRUE,
	            Patm = P,
	            citransition = NULL,
	            quiet = TRUE,
	            startValgrid = TRUE,
	            fitmethod = "bilinear",
	            algorithm = "nls",
	            fitTPU = TRUE,
	            alphag = 0,
	            useRd = FALSE,
	            PPFD = dfiPE$PPFD,
	            Tleaf = dfiPE$Tleaf,
	            alpha = 0.24,
	            theta = 0.7,
	            gmeso = NULL,
	            EaV = 58550.0,
	            EdVC = 2e+05,
	            delsC = 629.26,
	            EaJ = 29680.0,
	            EdVJ = 2e+05,
	            delsJ = 631.88,
	            #GammaStar = NULL,
	            #Km = NULL,
	            id = NULL)
	        coefs = coef(fit)
	        """
	        @rget coefs
	        dfi = filter(x -> x.Curve == i, df)
	        dfiPE = dfi[:,3:end]
	        rename!(dfiPE, :Tₗ=>:Tleaf, :Cᵢ=>:Ci, :Cₐ=>:Ca, :Gₛ=>:gs, :Dₗ=>:VpdL)
	        filter!(x->x.A ./ (x.Ca)>0.,dfiPE)
	        @rput dfiPE
	        R"""
	        dfiPE$Rh = dfiPE$Rh*100
	        myfit = fitBB(
	            dfiPE,
	            varnames = list(ALEAF = "A", GS = "gs", VPD = "VpdL", Ca = "Ca", RH = "Rh"),
	            gsmodel = c("BBOpti"),
	            fitg0 = TRUE,
	            D0 = NULL
	            )
	        gP = coef(myfit)
	        """
	        @rget gP
	        dfParam.VcMaxRefPE[dfParam.Curve.==i] .= coefs[1]
	        dfParam.JMaxRefPE[dfParam.Curve.==i] .= coefs[2]
	        dfParam.RdRefPE[dfParam.Curve.==i] .= coefs[3]
	        if !ismissing(coefs[4])
	            dfParam.TPURefPE[dfParam.Curve.==i] .= coefs[4]
	        else
	            dfParam.TPURefPE[dfParam.Curve.==i] .= 1000000.
	        end
	        dfParam.g0PE[dfParam.Curve.==i] .= gP[1]
	        dfParam.g1PE[dfParam.Curve.==i] .= gP[2]
	    end
	    CSV.write(path*"../tutorials/Medlyn_ACis_param.csv",dfParam)

	else
	    dfParam  = CSV.File(path*"../tutorials/Medlyn_ACis_param.csv", dateformat="yyyy/mm/dd") |> DataFrame
	end
end

# ╔═╡ a65d0175-f6cf-4bc9-8a2f-fbde4d2692b6
begin
	d = sqrt(df.Area[1])/100
	Wind = 20.
	#@rput Wind
	#@rput d
end

# ╔═╡ 4e9068e2-3bc4-4fe3-92d2-3241f15b259b
md"### With PlantBiophysics.jl"

# ╔═╡ 44db3882-e5b3-4427-b781-e5185e2d70af
begin
	if !simulated
		df.AsimPB .= df.EsimPB .= df.TlsimPB .= df.GssimPB .= 0.
		for i in unique(df.Curve)
		    dfi = filter(x->x.Curve == i,df)

		    cols = fieldnames(Atmosphere)
		    dfiMeteo = select(dfi, names(dfi, x -> Symbol(x) in cols))
		    dfiMeteo.Wind .= Wind
		    # Note that as we only use A-Ci curves, there is no NIR in the Licor6400
		    dfiMeteo.Ri_SW_f .= dfi.PPFD ./ (4.57)
		    meteo = Weather(dfiMeteo)

		    leaf = LeafModels(
		            energy = Monteith(aₛₕ = 2, aₛᵥ = 1,ε = 0.955 ,maxiter=100),
		            photosynthesis = Fvcb(VcMaxRef = dfi.VcMaxRef[1], JMaxRef = dfi.JMaxRef[1], RdRef = dfi.RdRef[1], TPURef = dfi.TPURef[1]),
		            stomatal_conductance = Medlyn(dfi.g0[1], dfi.g1[1]),
		            Rₛ = meteo[:Ri_SW_f],
		            sky_fraction = 1.0,
		            PPFD = dfi.PPFD,
		            d = d
		    )

		    energy_balance!(leaf,meteo)
		    df.AsimPB[df.Curve .== i, :] = DataFrame(leaf).A
		    df.EsimPB[df.Curve .== i, :] = DataFrame(leaf).λE ./(meteo[:λ] * constants.Mₕ₂ₒ)*1000
		    df.TlsimPB[df.Curve .== i, :] = DataFrame(leaf).Tₗ
		    df.GssimPB[df.Curve .== i, :] = DataFrame(leaf).Gₛ
		end
	end
end

# ╔═╡ 92deadb1-537b-4c3d-b0d2-3fd3cba171e4
md"### With plantecophys"

# ╔═╡ 8fe5544c-8f61-4306-b424-01d62e25fbcc
begin
	if !simulated

		R"""
		A_sim = c()
		E_sim = c()
		Tl_sim = c()
		Gs_sim = c()
		failed =c()
		"""

		df.AsimPE .= df.EsimPE .= df.TlsimPE .= df.GssimPE .= df.PEfailed .= 0.
		for i in unique(df.Curve)
		    dfi = filter(x->x.Curve == i,df)
		    dfi = dfi[:,3:end]
		    @rput dfi
		    Ca = dfi.Cₐ
		    @rput Ca
		    R"""
		    VcMaxRef = dfi$VcMaxRefPE[1]
		    JMaxRef = dfi$JMaxRefPE[1]
		    TPURef = dfi$TPURefPE[1]
		    RdRef = dfi$RdRefPE[1]
		    g0 = dfi$g0PE[1]
		    g1 = dfi$g1PE[1]
		    res = PhotosynEB(Tair = dfi$T,Wind = Wind,VPD=dfi$VPD,
		             Wleaf = d,Ca = Ca,  StomatalRatio = 1,
		             LeafAbs = 1.,gsmodel = "BBOpti",g0 = g0, g1 = g1,
		             EaV = 58550.0,EdVC = 2e+05, delsC = 629.26,
		             EaJ = 29680.0,EdVJ = 2e+05,delsJ = 631.88,
		             alpha = 0.24,theta = 0.7, Jmax = JMaxRef,
		             Vcmax = VcMaxRef, TPU = TPURef,Rd = RdRef,
		             RH = dfi$Rh*100,PPFD=dfi$PPFD,
		             Patm = dfi$P,gk=0.,
		             Tcorrect = FALSE)
		    A_sim = append(A_sim,res$ALEAF)
		    Gs_sim = append(Gs_sim,res$GS)
		    failed = append(failed,res$failed)
		    Tl_sim = append(Tl_sim,res$Tleaf)
		    E_sim = append(E_sim,res$ELEAF)
		    """
		end
		@rget A_sim

		@rget failed
		@rget Gs_sim
		@rget E_sim
		@rget Tl_sim

		df.AsimPE .= A_sim
		df.EsimPE .= E_sim
		df.TlsimPE .= Tl_sim
		df.GssimPE .= Gs_sim
		df.PEfailed .= failed
	end
end

# ╔═╡ 96af7e3e-38ce-4a8a-b503-163e88ddfb8f
md"### With LeafGasExchange.jl"

# ╔═╡ 232de02a-9440-4df5-8fbc-1e2eb6dae24a
begin
	if !simulated
		df.AsimLG .= df.EsimLG .= df.TlsimLG .= df.GssimLG .= 0.
		for i in unique(df.Curve)
		    dfi = filter(x->x.Curve == i,df)
		    #dfi = filter(x->x.PPFD > 1400.,dfi)
		    #dfi = filter(x->x.Cₐ > 150.,dfi)

		    configs = []
		    for i in 1:length(dfi.T)
		        config = :Weather => (
		                PFD = dfi.PPFD[i],
		                CO2 = dfi.Cₐ[i],
		                RH = dfi.Rh[i] * 100,
		                T_air = dfi.T[i],
		                wind = Wind,
		                P_air = dfi.P[i],
		                g0 = dfi.g0PE[i],
		                g1 = 1.57*dfi.g1PE[i],
		                Vc25 = dfi.VcMaxRefPE[i],
		                Jm25 = dfi.JMaxRefPE[i],
		                Rd25 = dfi.RdRefPE[i],
		                Tp25 = dfi.TPURefPE[i],
		                Ear = 46.39,
		                Haj = 29.68,
		                w = d,
		                d = d,
		                EaVc = 58.55,
		                ϵ = 0.955,
		                Dh = 21.5
		        )
		        push!(configs,config)
		    end
		    res = simulate(ModelC3MD;configs=configs,nounit=true)
		    df.AsimLG[df.Curve .== i, :] = res.A_net
		    df.EsimLG[df.Curve .== i, :] = res.E
		    df.TlsimLG[df.Curve .== i, :] = res.T
		    df.GssimLG[df.Curve .== i, :] = res.gsc
		end
	end
end

# ╔═╡ e8171ee6-36fa-4227-8f78-2b0bd8675cdf
md"""
## Plotting

Plotting $E$, $A_s$, $T_l$ and $G_s$ using Makie.
"""

# ╔═╡ b80a65bf-d754-4f66-85d1-fda258ee2228
begin
	noto_sans = assetpath("fonts", "NotoSans-Regular.ttf")
	fig = Makie.Figure(backgroundcolor = RGBf(1, 1, 1),resolution = (1000, 1000),font=noto_sans,dpi=600,size=(600,600),xminorgridstyle=true)
	sideinfo1 = Label(fig[1:2, 0], "Simulations", rotation = pi/2,labelsize=25)
	sideinfo2 = Label(fig[3,2:3], "Observations")
	n=" "
end

# ╔═╡ b040102a-bf17-47d7-acfd-3a80b843fc4b
begin
	########################################################################
	# Assimilation
	axa = Axis(fig[1,2],title="(a) Net CO₂ assimilation Aₙ")
	indexA = 1:length(df.PPFD)
	indexA = ifelse.(df.PPFD .> 1400, indexA, 9999.)
	filter!(x->x!=9999.,indexA)
	indexPEA = 1:length(df.PPFD)
	indexPEA = ifelse.((df.PPFD .> 1400).&(df.PEfailed .==0.), indexPEA, 9999.)
	filter!(x->x!=9999.,indexPEA)
	RMSEA=DataFrame("RMSPB"=>round(RMSE(df.A[indexA],df.AsimPB[indexA]),digits=3),"RMSLG"=>round(RMSE(df.A[indexA],df.AsimLG[indexA]),digits=3),"RMSPE"=>round(RMSE(df.A[indexPEA],df.AsimPE[indexPEA]),digits=3))
	EFA=DataFrame("EFPB"=>EF(df.A[indexA],df.AsimPB[indexA]),"EFLG"=>EF(df.A[indexA],df.AsimLG[indexA]),"EFPE"=>EF(df.A[indexPEA],df.AsimPE[indexPEA]))
	Makie.lines!(axa,df.AsimPB[indexA],df.AsimPB[indexA],color=(:grey,0.4),linewidth=4)
	LGA = Makie.scatter!(axa,df.A[indexA],df.AsimLG[indexA],color=(:black,0.5),markersize=8,marker='□')
	PEA = Makie.scatter!(axa,df.A[indexPEA],df.AsimPE[indexPEA],color=(:blue,0.5),markersize=8,marker='∆')
	PBA = Makie.scatter!(axa,df.A[indexA],df.AsimPB[indexA],color=(:red,0.8),markersize=8,marker='o')
	axislegend(axa, [PBA,PEA,LGA], ["PlantBiophysics.jl (RMSE="*string(RMSEA.RMSPB)*")","plantecophys (RMSE="*string(RMSEA.RMSPE)*")","LeafGasExchange.jl (RMSE="*string(RMSEA.RMSLG)*")"], "", position = :rb,
	    orientation = :vertical,labelsize=13)
end

# ╔═╡ 0620028a-5c1a-43ff-a0e1-abdbfabe313b
begin
	########################################################################
	# Transpiration
	indexE = 1:length(df.PPFD)
	indexE = ifelse.(df.Cₐ .> 140., indexE, 9999.)
	filter!(x->x!=9999.,indexE)
	indexPEE = 1:length(df.PPFD)
	indexPEE = ifelse.((df.Cₐ .> 140.).&(df.PEfailed .==0.), indexPEE, 9999.)
	filter!(x->x!=9999.,indexPEE)

	axb = Axis(fig[1,3],title="(b) Transpiration rate E")

	RMSEE=DataFrame("RMSPB"=>round(RMSE(df.Trmmol[indexE],df.EsimPB[indexE]),digits=3),"RMSLG"=>round(RMSE(df.Trmmol[indexE],df.EsimLG[indexE]),digits=3),"RMSPE"=>round(RMSE(df.Trmmol[indexPEE],df.EsimPE[indexPEE]),digits=3))
	EFE=DataFrame("EFPB"=>EF(df.Trmmol[indexE],df.EsimPB[indexE]),"EFLG"=>EF(df.Trmmol[indexE],df.EsimLG[indexE]),"EFPE"=>EF(df.Trmmol[indexPEE],df.EsimPE[indexPEE]))
	Makie.lines!(axb,df.EsimPB[indexE],df.EsimPB[indexE],linewidth=4,color=(:grey,0.4))
	LGE = Makie.scatter!(axb,df.Trmmol[indexE],df.EsimLG[indexE],color=(:black,0.5),markersize=8,marker='□')
	PEE = Makie.scatter!(axb,df.Trmmol[indexPEE],df.EsimPE[indexPEE],color=(:blue,0.5),markersize=8,marker='∆')
	PBE = Makie.scatter!(axb,df.Trmmol[indexE],df.EsimPB[indexE],color=(:red,0.8),markersize=8,marker='o')
	axislegend(axb, [PBE,PEE,LGE], ["PlantBiophysics.jl (RMSE="*string(RMSEE.RMSPB)*")","plantecophys (RMSE="*string(RMSEE.RMSPE)*")","LeafGasExchange.jl (RMSE="*string(RMSEE.RMSLG)*")"], "", position = :rb,
	    orientation = :vertical,labelsize=13)
end

# ╔═╡ 3a477693-a953-475e-8731-a14a24966ba1
begin
	########################################################################
	# Leaf temperature
	indexPEL = 1:length(df.PPFD)
	indexPEL = ifelse.((df.PEfailed .==0.), indexPEL, 9999.)
	filter!(x->x!=9999.,indexPEL)

	axd = Axis(fig[2,3],title="(d) Leaf temperature Tₗ")

	RMSEL=DataFrame("RMSPB"=>round(RMSE(df.Tₗ,df.TlsimPB),digits=3),"RMSLG"=>round(RMSE(df.Tₗ,df.TlsimLG),digits=3),"RMSPE"=>round(RMSE(df.Tₗ[indexPEL],df.TlsimPE[indexPEL]),digits=3))
	EFL=DataFrame("EFPB"=>EF(df.Tₗ,df.TlsimPB),"EFLG"=>EF(df.Tₗ,df.TlsimLG),"EFPE"=>EF(df.Tₗ[indexPEL],df.TlsimPE[indexPEL]))
	Makie.lines!(axd,df.TlsimPB,df.TlsimPB,linewidth=4,color=(:grey,0.4))
	LGL = Makie.scatter!(axd,df.Tₗ,df.TlsimLG,color=(:black,0.5),markersize=8,marker='□')
	PEL = Makie.scatter!(axd,df.Tₗ[indexPEL],df.TlsimPE[indexPEL],color=(:blue,0.5),markersize=8,marker='∆')
	PBL = Makie.scatter!(axd,df.Tₗ,df.TlsimPB,color=(:red,0.8),markersize=8,marker='o')
	axislegend(axd, [PBL,PEL,LGL], ["PlantBiophysics.jl (RMSE="*string(RMSEL.RMSPB)*")","plantecophys (RMSE="*string(RMSEL.RMSPE)*")","LeafGasExchange.jl (RMSE="*string(RMSEL.RMSLG)*")"], "", position = :rb,
	    orientation = :vertical,labelsize=13)
end

# ╔═╡ 01d0b519-b7b1-4f36-a322-40e4ddbbcfb1
begin
	########################################################################
	# Stomatal conductance
	index = 1:length(df.PPFD)
	index = ifelse.(df.Cₐ .> 140., index, 9999.)
	filter!(x->x!=9999.,index)
	indexPE = 1:length(df.PPFD)
	indexPE = ifelse.((df.Cₐ .> 140.).&(df.PEfailed .==0.), indexPE, 9999.)
	filter!(x->x!=9999.,indexPE)
	axc = Axis(fig[2,2],title="(c) CO₂ stomatal conductance Gₛ")

	# Homogenize Gs after
	RMSLG=round(RMSE(df.gs[index],df.GssimLG[index]),digits=3)
	RMSPB=round(RMSE(df.gs[index],df.GssimPB[index]),digits=3)
	RMSPE=round(RMSE(df.gs[indexPE],df.GssimPE[indexPE]),digits=3)
	EF(df.gs[index],df.GssimPB[index])
	EF(df.gs[index],df.GssimLG[index])
	EF(df.gs[indexPE],df.GssimPE[indexPE])
	Makie.lines!(axc,df.GssimPB,df.GssimPB,linewidth=4,color=(:grey,0.4))
	LG = Makie.scatter!(axc,df.gs[index],df.GssimLG[index],color=(:black,0.5),markersize=8,marker='□')
	PE = Makie.scatter!(axc,df.gs[indexPE],df.GssimPE[indexPE],color=(:blue,0.5),markersize=8,marker='∆')
	PB = Makie.scatter!(axc,df.gs[index],df.GssimPB[index],color=(:red,0.8),markersize=8,marker='o')
	Makie.ylims!(axc,(0.,0.4))
	Makie.xlims!(axc,(0.,0.4))
	axislegend(axc, [PB,PE,LG], ["PlantBiophysics.jl (RMSE="*string(RMSPB)*")","plantecophys (RMSE="*string(RMSPE)*")","LeafGasExchange.jl (RMSE="*string(RMSLG)*")"], "", position = :rb,
	    orientation = :vertical,labelsize=13)
end

# ╔═╡ bbf15f8a-e954-47aa-8944-7b70edb824d7
fig

# ╔═╡ 00000000-0000-0000-0000-000000000001
PLUTO_PROJECT_TOML_CONTENTS = """
[deps]
CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
Cropbox = "a904b226-abf1-11e9-2713-059ba252a964"
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
LeafGasExchange = "eed59905-05f1-464e-afba-94c39a4505fd"
Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a"
MonteCarloMeasurements = "0987c9cc-fe09-11e8-30f0-b96dd679fdca"
PlantBiophysics = "7ae8fcfa-76ad-4ec6-9ea7-5f8f5e2d6ec9"
Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
RCall = "6f49c342-dc21-5d91-9882-a32aef131414"
Revise = "295af30f-e4ad-537b-8983-00126c2a3abe"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
WGLMakie = "276b4fcb-3e11-5398-bf8b-a0c2d153d008"

[compat]
CSV = "~0.10.4"
Cropbox = "~0.3.22"
DataFrames = "~1.3.4"
LeafGasExchange = "~0.1.0"
Makie = "~0.16.6"
MonteCarloMeasurements = "~1.0.8"
PlantBiophysics = "~0.3.0"
Plots = "~1.29.0"
RCall = "~0.13.13"
Revise = "~3.3.3"
WGLMakie = "~0.5.5"
"""

# ╔═╡ 00000000-0000-0000-0000-000000000002
PLUTO_MANIFEST_TOML_CONTENTS = """
# This file is machine-generated - editing it directly is not advised

julia_version = "1.7.1"
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git-tree-sha1 = "caaaecca3dff47c774fb0cbac9d4899fad3d8f18"
uuid = "276b4fcb-3e11-5398-bf8b-a0c2d153d008"
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[[deps.Wayland_jll]]
deps = ["Artifacts", "Expat_jll", "JLLWrappers", "Libdl", "Libffi_jll", "Pkg", "XML2_jll"]
git-tree-sha1 = "3e61f0b86f90dacb0bc0e73a0c5a83f6a8636e23"
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[[deps.Wayland_protocols_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
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[[deps.WeakRefStrings]]
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[[deps.WebIO]]
deps = ["AssetRegistry", "Base64", "Distributed", "FunctionalCollections", "JSON", "Logging", "Observables", "Pkg", "Random", "Requires", "Sockets", "UUIDs", "WebSockets", "Widgets"]
git-tree-sha1 = "c9529be473e97fa0b3b2642cdafcd0896b4c9494"
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[[deps.WebSockets]]
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[[deps.Xorg_libpthread_stubs_jll]]
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[[deps.Xorg_libxkbfile_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "926af861744212db0eb001d9e40b5d16292080b2"
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[[deps.Xorg_xcb_util_image_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "0fab0a40349ba1cba2c1da699243396ff8e94b97"
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[[deps.Xorg_xcb_util_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxcb_jll"]
git-tree-sha1 = "e7fd7b2881fa2eaa72717420894d3938177862d1"
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[[deps.Xorg_xcb_util_keysyms_jll]]
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[[deps.Xorg_xcb_util_renderutil_jll]]
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[[deps.Xorg_xkbcomp_jll]]
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[[deps.Xorg_xkeyboard_config_jll]]
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[[deps.YAML]]
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[[deps.libpng_jll]]
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[[deps.libsixel_jll]]
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[[deps.libvorbis_jll]]
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[[deps.nghttp2_jll]]
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uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"

[[deps.p7zip_jll]]
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[[deps.x264_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4fea590b89e6ec504593146bf8b988b2c00922b2"
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version = "2021.5.5+0"

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git-tree-sha1 = "ece2350174195bb31de1a63bea3a41ae1aa593b6"
uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd"
version = "0.9.1+5"
"""

# ╔═╡ Cell order:
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# ╠═98dcfd9e-e85f-4b38-a546-e9932c7360b0
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# ╟─00000000-0000-0000-0000-000000000001
# ╟─00000000-0000-0000-0000-000000000002


text:
function actions_mdp_fixture()
    dmodel = NoCrashIDMMOBILModel(3, PhysicalParam(3, lane_length=100.0))
    rmodel = NoCrashRewardModel()
    return NoCrashMDP{typeof(rmodel)}(dmodel, rmodel, 1.0)
end

function test_iteration()
    println("\t\tTesting Action iteration.")
    mdp = actions_mdp_fixture()
    s = MLState(0.0,0.0, CarState[CarState(50.0, 2.0, 30.0, 0.0, Multilane.NORMAL, 0)])
    as = actions(mdp)
    as = actions(mdp, s, as)
    @test length(collect(as)) == length(as.acceptable)+1
end

function test_off_road()
    println("\t\tTesting No Offroad Actions...")
    mdp = actions_mdp_fixture()
    as = actions(mdp)
    right = MLState(0.0,0.0, CarState[CarState(50.0, 1.0, 30.0, 0.0, Multilane.NORMAL, 0)])
    ar = collect(actions(mdp, right, as))
    @test length(ar) > 1
    @test all(a -> a.lane_change >= 0., ar)
    left = MLState(0.0,0.0, CarState[CarState(50.0, 3.0, 30.0, 0.0, Multilane.NORMAL, 0)])
    al = collect(actions(mdp, left, as))
    @test length(al) > 1
    @test all(a -> a.lane_change <= 0., al)
end

function test_all_safe()
    println("\t\tTesting case where all actions are safe")
    mdp = actions_mdp_fixture()
    as = actions(mdp)
    allsafe = MLState(0.0,0.0, CarState[CarState(50.0, 2.0, 30.0, 0.0, Multilane.NORMAL, 0)])
    @test length(collect(actions(mdp, allsafe, as))) == 10
end

function failure_4()
    println("\t\tTesting special failure case changing into new lane")
    mdp = Multilane.MLMDP{Multilane.MLState,Multilane.MLAction,Multilane.NoCrashIDMMOBILModel,Multilane.NoCrashRewardModel}(Multilane.NoCrashIDMMOBILModel(10,Multilane.PhysicalParam(0.75,2.0,4.0,31.0,4.0,35.0,27.0,31.0,35.0,27.0,20.0,4,100.0),Multilane.BehaviorModel[Multilane.IDMMOBILBehavior(Multilane.IDMParam(1.0,1.0,2.0,27.5,4.0,4.0),Multilane.MOBILParam(0.5,4.0,0.2),1),Multilane.IDMMOBILBehavior(Multilane.IDMParam(1.5,1.5,1.4,27.5,4.0,4.0),Multilane.MOBILParam(0.25,4.0,0.2),2),Multilane.IDMMOBILBehavior(Multilane.IDMParam(2.0,2.0,0.8,27.5,4.0,4.0),Multilane.MOBILParam(0.0,4.0,0.2),3),Multilane.IDMMOBILBehavior(Multilane.IDMParam(1.0,1.0,2.0,31.0,4.0,4.0),Multilane.MOBILParam(0.5,4.0,0.2),4),Multilane.IDMMOBILBehavior(Multilane.IDMParam(1.5,1.5,1.4,31.0,4.0,4.0),Multilane.MOBILParam(0.25,4.0,0.2),5),Multilane.IDMMOBILBehavior(Multilane.IDMParam(2.0,2.0,0.8,31.0,4.0,4.0),Multilane.MOBILParam(0.0,4.0,0.2),6),Multilane.IDMMOBILBehavior(Multilane.IDMParam(1.0,1.0,2.0,35.0,4.0,4.0),Multilane.MOBILParam(0.5,4.0,0.2),7),Multilane.IDMMOBILBehavior(Multilane.IDMParam(1.5,1.5,1.4,35.0,4.0,4.0),Multilane.MOBILParam(0.25,4.0,0.2),8),Multilane.IDMMOBILBehavior(Multilane.IDMParam(2.0,2.0,0.8,35.0,4.0,4.0),Multilane.MOBILParam(0.0,4.0,0.2),9)],StatsBase.WeightVec{Float64,Array{Float64,1}}([1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0],9.0),1.0,1.3333333333333333,0.5,20.0,0.5,[1.0,1.0,1.0,1.0],2.0,false),Multilane.NoCrashRewardModel(100.0,10.0,5.0,4),1.0)

    s = MLState(false,0.0,0.0, CarState[CarState(50.0,3.0,31.22970781020354,0.0,Multilane.NORMAL,1),CarState(99.24797069990433,4.0,27.167643727404155,0.0,Multilane.IDMMOBILBehavior(Multilane.IDMParam(2.0,2.0,0.8,27.5,4.0,4.0),Multilane.MOBILParam(0.0,4.0,0.2),3),2)])

    @test Multilane.is_safe(mdp, s, MLAction(0.0, 1.0))
end

function failure_5()
    println("\t\tTesting special failure case allowing dangerous lane change when abs(lane_change) > 1")
    mdp = Multilane.MLMDP{Multilane.MLState,Multilane.MLAction,Multilane.NoCrashIDMMOBILModel,Multilane.NoCrashRewardModel}(Multilane.NoCrashIDMMOBILModel(10,Multilane.PhysicalParam(0.75,2.0,4.0,31.0,4.0,35.0,27.0,31.0,35.0,27.0,20.0,4,100.0),Multilane.BehaviorModel[Multilane.IDMMOBILBehavior(Multilane.IDMParam(1.0,1.0,2.0,27.5,4.0,4.0),Multilane.MOBILParam(0.5,4.0,0.2),1),Multilane.IDMMOBILBehavior(Multilane.IDMParam(1.5,1.5,1.4,27.5,4.0,4.0),Multilane.MOBILParam(0.25,4.0,0.2),2),Multilane.IDMMOBILBehavior(Multilane.IDMParam(2.0,2.0,0.8,27.5,4.0,4.0),Multilane.MOBILParam(0.0,4.0,0.2),3),Multilane.IDMMOBILBehavior(Multilane.IDMParam(1.0,1.0,2.0,31.0,4.0,4.0),Multilane.MOBILParam(0.5,4.0,0.2),4),Multilane.IDMMOBILBehavior(Multilane.IDMParam(1.5,1.5,1.4,31.0,4.0,4.0),Multilane.MOBILParam(0.25,4.0,0.2),5),Multilane.IDMMOBILBehavior(Multilane.IDMParam(2.0,2.0,0.8,31.0,4.0,4.0),Multilane.MOBILParam(0.0,4.0,0.2),6),Multilane.IDMMOBILBehavior(Multilane.IDMParam(1.0,1.0,2.0,35.0,4.0,4.0),Multilane.MOBILParam(0.5,4.0,0.2),7),Multilane.IDMMOBILBehavior(Multilane.IDMParam(1.5,1.5,1.4,35.0,4.0,4.0),Multilane.MOBILParam(0.25,4.0,0.2),8),Multilane.IDMMOBILBehavior(Multilane.IDMParam(2.0,2.0,0.8,35.0,4.0,4.0),Multilane.MOBILParam(0.0,4.0,0.2),9)],StatsBase.WeightVec{Float64,Array{Float64,1}}([1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0],9.0),1.0,2.6666666666666665,0.5,20.0,0.5,[1.0,1.0,1.0,1.0],2.0,false),Multilane.NoCrashRewardModel(100.0,10.0,8.0,1),1.0)
    state = MLState(false,0.0,0.0, CarState[CarState(50.0,2.0,27.0,0.0,Multilane.NORMAL,1),CarState(52.934068808019056,1.0,32.940581978786135,0.0,Multilane.IDMMOBILBehavior(Multilane.IDMParam(1.5,1.5,1.4,35.0,4.0,4.0),Multilane.MOBILParam(0.25,4.0,0.2),8),3),CarState(7.329881331042117,1.5,27.127595226858716,-0.6666666666666666,Multilane.IDMMOBILBehavior(Multilane.IDMParam(1.0,1.0,2.0,35.0,4.0,4.0),Multilane.MOBILParam(0.5,4.0,0.2),7),8),CarState(1.3070886107784006,3.0,27.306814286745862,0.0,Multilane.IDMMOBILBehavior(Multilane.IDMParam(1.0,1.0,2.0,31.0,4.0,4.0),Multilane.MOBILParam(0.5,4.0,0.2),4),10)])
    as = actions(mdp, state, actions(mdp))
    @test all([a.lane_change >= 0.0 for a in as])
end

function failure_6()
    println("\t\tTesting special failure case changing into a car slightly behind.")
    mdp = Multilane.MLMDP{Multilane.MLState,Multilane.MLAction,Multilane.NoCrashIDMMOBILModel,Multilane.TargetLaneReward}(Multilane.NoCrashIDMMOBILModel(10,Multilane.PhysicalParam(0.75,2.0,4.0,31.0,4.0,35.0,27.0,31.0,35.0,27.0,8.0,4,100.0),Multilane.CopulaIDMMOBIL(Multilane.IDMParam(0.7999999999999999,1.0,1.0,27.669999999999998,0.0,4.0),Multilane.IDMParam(2.0,3.0,2.0,38.93,4.0,4.0),Multilane.MOBILParam(0.0,1.0,0.0),Multilane.MOBILParam(1.0,3.0,0.2),Multilane.GaussianCopula([1.0 0.25 0.25 0.25 0.25 0.25 0.25 0.25; 0.25 1.0 0.25 0.25 0.25 0.25 0.25 0.25; 0.25 0.25 1.0 0.25 0.25 0.25 0.25 0.25; 0.25 0.25 0.25 1.0 0.25 0.25 0.25 0.25; 0.25 0.25 0.25 0.25 1.0 0.25 0.25 0.25; 0.25 0.25 0.25 0.25 0.25 1.0 0.25 0.25; 0.25 0.25 0.25 0.25 0.25 0.25 1.0 0.25; 0.25 0.25 0.25 0.25 0.25 0.25 0.25 1.0],[1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0; 0.25 0.968246 0.0 0.0 0.0 0.0 0.0 0.0; 0.25 0.193649 0.948683 0.0 0.0 0.0 0.0 0.0; 0.25 0.193649 0.158114 0.935414 0.0 0.0 0.0 0.0; 0.25 0.193649 0.158114 0.133631 0.92582 0.0 0.0 0.0; 0.25 0.193649 0.158114 0.133631 0.115728 0.918559 0.0 0.0; 0.25 0.193649 0.158114 0.133631 0.115728 0.102062 0.912871 0.0; 0.25 0.193649 0.158114 0.133631 0.115728 0.102062 0.0912871 0.908295]),377605),1.0,0.6666666666666666,1.0,35.0,0.25,false,4.0,Inf),Multilane.TargetLaneReward(4),1.0)
    state = Multilane.MLState(0.0,0.0,Multilane.CarState[Multilane.CarState(50.0,2.0,30.9774,0.0,Multilane.IDMMOBILBehavior(Multilane.IDMParam(1.86615,2.77692,1.11154,37.674,0.446166,4.0),Multilane.MOBILParam(0.111542,2.77692,0.0223083),371338),1),Multilane.CarState(45.9956,3.0,30.9545,0.0,Multilane.IDMMOBILBehavior(Multilane.IDMParam(1.27863,1.79771,1.60114,32.1611,2.40458,4.0),Multilane.MOBILParam(0.601144,1.79771,0.120229),371341),16)],Nullable{Any}())
    as = actions(mdp, state, actions(mdp))
    # if !all([a.lane_change <= 0.0 for a in as])
    #     Gallium.@enter actions(mdp, state)
    # end
    @test all([a.lane_change <= 0.0 for a in as])
end

function test_actions()
    println("\tTesting Action Space...")
    test_iteration()
    test_off_road()
    test_all_safe()
    # failure_4() # removed because of behavior change
    # failure_5() # removed because of behavior change
    failure_6()
end

ext: jl

max_stars_repo_name: zhcao/HighwayExit

max_issues_repo_name: zhcao/HighwayExit

max_forks_repo_name: zhcao/HighwayExit


text:
function chebft(func::Function; a=-1.0, b=1.0, n=3)
    f = Array(Float64, n)
    c = Array(Float64, n)
    bma = 0.5*(b-a)
    bpa = 0.5*(b+a)
    for k=1:n
        y = cos(pi*(k-0.5)/n)
        f[k] = func(y*bma+bpa)
    end
    fac = 2.0/n
    for j=1:n
        sum = 0.0
        for k=1:n
            sum += f[k]*cos(pi*(j-1)*(k-0.5)/n)
        end
        c[j] = fac*sum
    end
    return c
end

function chebev(c::Vector{Float64}, x::Float64; a=-1.0, b=1.0, n=length(c))
    d = dd = 0.0
    (x-a)*(x-b) > 0 && warn("x not in range")
    y2=2.0*(y=(2.0*x-a-b)/(b-a))
    for j=n:-1:2
        sv=d
        d=y2*d-dd+c[j]
        dd=sv
    end
    return y*d-dd+0.5*c[1]
end

function chder(c::Vector{Float64}; a=-1.0, b=1.0, n=length(c))
    cder = similar(c)
    cder[n] = 0.0
    n>1 && (cder[n-1] = 2*(n-1)*c[n])
    for j=(n-2):-1:1
        cder[j] = cder[j+2]+2*j*c[j+1]
    end
    con=2.0/(b-a)
    scale!(con, cder)
end

ftmp(x)=3*x^2+5*x+8
fder(x)=6*x+5

function chebtest(f;n=5,eps=exp(-1),a=-eps,b=eps)
    f0 = chebft(f;n=n,a=a,b=b)
    f1 = chder(f0;n=n,a=a,b=b)
    f2 = chder(f1;n=n,a=a,b=b)
    y0 = chebev(f0,0.0;n=n,a=a,b=b)
    y1 = chebev(f1,0.0;n=n,a=a,b=b)
    y2 = chebev(f2,0.0;n=n,a=a,b=b)
    dx = y2>0 ? -y1/y2 : -y1/0
    df = y2>0 ? -y1*y1/y2 : -Inf
    (:err,y0-f(0),:f1,y1,:f2,y2,:dx,dx,:df,df)
end

function chebtest2(f;n=5,eps=exp(-1))
    f0 = chebft(f;n=n,a=-eps,b=eps)
    @show y0 = chebev(f0,0.0;n=n,a=-eps,b=eps)
    f1 = chder(f0;n=n,a=-eps,b=eps)
    @show y1 = chebev(f1,0.0;n=n,a=-eps,b=eps)
    @show eps = 0.1*abs(y0/y1)
    f0 = chebft(f;n=n,a=-eps,b=eps)
    @show y0 = chebev(f0,0.0;n=n,a=-eps,b=eps)
    f1 = chder(f0;n=n,a=-eps,b=eps)
    @show y1 = chebev(f1,0.0;n=n,a=-eps,b=eps)
    f2 = chder(f1;n=n,a=-eps,b=eps)
    @show y2 = chebev(f2,0.0;n=n,a=-eps,b=eps)
    (:err,y0-f(0),y1,y2)
end