### A Pluto.jl notebook ###
# v0.17.7

using Markdown
using InteractiveUtils

# ╔═╡ 6cb3b25b-701f-4eca-bde1-03c7910ec523
begin
	using Pkg
	Pkg.activate("../Project.toml")

	using PolyChaos
	using LinearAlgebra
	using DelimitedFiles
	using StatsPlots

	using Plots.PlotMeasures
	using Printf

	using CSV
	using DataFrames

	using NetCDF
	using Dates
	using LaTeXStrings
	using PlutoUI

	using JLD
end

# ╔═╡ 7f07c44a-3cad-40bc-bb4b-a738e26f1b95
begin
	using Revise
	include("./gsa_utils.jl")
end

# ╔═╡ 76c3e251-4be0-4419-81c5-fbfa32250b8f
## INCREASE THE CELL WIDTH FOR BIGGER FIGURES
html"""
<style>
	main {
		margin: 0 auto;
		max-width: 2000px;
    	padding-left: max(160px, 10%);
    	padding-right: max(160px, 10%);
	}
  svg {
    width: 100%;
  }
</style>
"""

# ╔═╡ e0bb2c6d-9618-4f7b-bf5d-7142e673fa23
md"""
Load required packages (dependencies specified in the `Project.toml` file)
"""

# ╔═╡ 3e140602-f9e6-4c4c-89bd-fc051b505e33
md"""
Include script with different helper functions. If script is changed, this cell will have to be rerun for changes to reflect.
"""

# ╔═╡ cae9da64-c6d0-45ae-9528-ce8cb46969f8
md"""
Note: Figures are plotted here in the order that they appear in the manuscript.
"""

# ╔═╡ f6cf52cf-c677-40eb-8af6-fceb04e32339
md"""
### Preprocessing

"""

# ╔═╡ 844a7d5e-71ef-4e76-85ac-db90d1a581bb
begin
	X_design = CSV.read("./data/design/X_background_CR2208_updated.csv", DataFrame)
	pfss = replace(X_design.PFSS, 1=>"HARMONICS", 2=>"FDIPS")
	surfaceWaveRefl = replace(X_design.UseSurfaceWaveRefl, 1=>"true", 2=>"false")
	select!(X_design, Not([:PFSS, :UseSurfaceWaveRefl]))
	insertcols!(X_design, :PFSS=>pfss, :UseSurfaceWaveRefl=>surfaceWaveRefl)
end

# ╔═╡ 62fff0a4-82ad-4643-b7f1-561c5644d7e0
md"""
Make selected scatterplots - figure 2 (others can be made in the same fashion by selecting appropriate column names in the plotting arguments)
"""

# ╔═╡ 34f42c83-22ad-414a-ab65-be93a5c9be14
begin
	FactorB0PF = scatter(X_design[:, "FactorB0"], X_design[:, "PoyntingFluxPerBSi"], 
								# zcolor=shiftWLRMSE.PTRMSE, 
								marker=(:black, :circle, 4), 
								xlabel="FactorB0", 
								ylabel="PoyntingFluxPerBSi",
								markerstrokewidth=0,
								label="",
								dpi = 300,
								grid=false
								)
	plot!(sort(X_design.FactorB0), 1.2e6 ./ (sort(X_design.FactorB0)), line=(:violet, 3.1), label="")
	plot!(xlims=(0.54, 2.7))
	plot!(ylims=(0.3e6, 1.1e6))
	plot!(guidefontsize=30)
	plot!(tickfontsize=25)
	plot!(framestyle=:box)
	plot!(left_margin=5mm)
	plot!(bottom_margin=5mm)
	plot!(right_margin=8mm)
	plot!(yticks=([4e5, 6e5, 8e5, 1e6], ["4e5", "6e5", "8e5", "1e6"]))
	plot!(size=(800, 600))
end

# ╔═╡ 16adf9b7-76ad-4902-81d7-9269318ca26b
begin
	LperpStoch = scatter(X_design[:, "LperpTimesSqrtBSi"], X_design[:, "StochasticExponent"], 
									# zcolor=shiftWLRMSE.PTRMSE, 
									marker=(:black, :circle, 4), 
									xlabel="LperpTimesSqrtBSi", 
									ylabel="StochasticExponent",
									markerstrokewidth=0,
									label="",
									dpi = 300,
									grid=false
									)
	plot!(guidefontsize=30)
	plot!(tickfontsize=25)
	plot!(framestyle=:box)
	plot!(left_margin=5mm)
	plot!(bottom_margin=5mm)
	plot!(right_margin=8mm)
	plot!(xticks=([1e5, 2e5, 3e5], ["1e5", "2e5", "3e5"]))
	plot!(size=(800, 600))
end

# ╔═╡ a659f1da-1b23-44e8-a9db-c5b8953b3e82
begin
	chromoLperp = scatter(X_design[:, "nChromoSi_AWSoM"], X_design[:, "LperpTimesSqrtBSi"], 
									# zcolor=shiftWLRMSE.PTRMSE, 
									marker=(:black, :circle, 4), 
									xlabel="nChromoSi_AWSoM", 
									ylabel="LperpTimesSqrtBSi",
									markerstrokewidth=0,
									label="",
									dpi = 300,
									grid=false
									)
	plot!(guidefontsize=30)
	plot!(tickfontsize=25)
	plot!(framestyle=:box)
	plot!(left_margin=5mm)
	plot!(bottom_margin=5mm)
	plot!(right_margin=8mm)
	plot!(xticks=([1e18, 2e18, 3e18, 4e18, 5e18], ["1e18", "2e18", "3e18", "4e18", "5e18"]))
	plot!(yticks=([1e5, 2e5, 3e5], ["1e5", "2e5", "3e5"]))
	plot!(size=(800, 600))
end

# ╔═╡ f4ef705e-a7e0-49ef-96e1-48eb87df9340
begin
	LperpRMin = scatter(X_design[:, "LperpTimesSqrtBSi"], X_design[:, "rMinWaveReflection"], 
									# zcolor=shiftWLRMSE.PTRMSE, 
									marker=(:black, :circle, 4), 
									xlabel="LperpTimesSqrtBSi", 
									ylabel="rMinWaveReflection",
									markerstrokewidth=0,
									label="",
									dpi = 300,
									grid=false
									)
	plot!(guidefontsize=30)
	plot!(tickfontsize=25)
	plot!(framestyle=:box)
	plot!(left_margin=5mm)
	plot!(bottom_margin=5mm)
	plot!(right_margin=8mm)
	plot!(xticks=([1e5, 2e5, 3e5], ["1e5", "2e5", "3e5"]))
	plot!(size=(800, 600))
end

# ╔═╡ 902b0932-1841-4607-8f4c-b8140edb5cf3


# ╔═╡ bea921ff-cc8e-48e3-806d-78bd52334b8f


# ╔═╡ 2acdc135-7a5a-44a1-9390-464307e177a7


# ╔═╡ d7409613-525d-417f-9d66-7bf3abfec64c
md"""
Scale input parameters to [0-1] range
"""

# ╔═╡ c93a9dbf-95c9-420c-b5f6-6efc28e66eec
begin
		lbBg = [0.54, 2e17, 0.3e6, 0.3e5, 0.1, 1]
		ubBg = [2.7, 5e18, 1.1e6, 3e5, 0.34, 1.2]
		
		paramsBgScaled = (X_design[!, 1:6] .- lbBg') ./ (ubBg' - lbBg')
end

# ╔═╡ 195f5770-2391-4101-893a-1c367abff704
inputNames = names(paramsBgScaled)

# ╔═╡ 189dc329-5414-4b48-8bf9-94ec4579048e
md"""
Filter out the single run that failed.
"""

# ╔═╡ e89f5564-75ed-40e4-a26f-b9e91dc65d8c
excludeRunIdx = [159]

# ╔═╡ 40222bdc-5e2a-4ca5-8bcb-3adf27cabad5
md"""
List all Parameter Combinations for AWSoM that are not used by us.
"""

# ╔═╡ 8d0da409-7706-4a4c-90e0-18c41e747f7b
X_design[excludeRunIdx, :]

# ╔═╡ 42145a1f-b99b-4a70-8a37-923682dee2ab
finalRuns = setdiff(1:200, excludeRunIdx)

# ╔═╡ 2ffe286e-d361-4c75-8529-d148e5f896d7
md"""
Extract QoIs and observations
"""

# ╔═╡ 6035afb9-31da-43cf-816a-36962d3a996c
begin
	fn = "./data/background_sims/bg_CR2208.nc"
	UrSim = ncread(fn, "UrSim")
	NpSim = ncread(fn, "NpSim")
	TSim  = ncread(fn, "TSim")
	BSim  = ncread(fn, "BSim")	

	UrObs = ncread(fn, "UrObs")
	NpObs = ncread(fn, "NpObs")
	TObs  = ncread(fn, "TObs")
	BObs  = ncread(fn, "BObs")	
	
	startTime = ncgetatt(fn, "time", "startTime")
	timeElapsed = Dates.Hour.(ncread(fn, "time"))
	times = timeElapsed .+ Dates.DateTime(startTime, "yyyy_mm_ddTHH:MM:SS")
end

# ╔═╡ dc675b70-a7d2-435d-afdc-e05cc32a061f
md"""
Plot the QoIs (figure 3). If we want to plot observations, supply `plotObs=true` as a keyword.
"""

# ╔═╡ e0a4514a-83fc-4c21-a555-d89407cd436d
md"""
### UQ and GSA
"""

# ╔═╡ 6a8503ed-e59a-4391-9f9a-88b761976e36
md"""
Build surrogate
"""

# ╔═╡ b4f5a355-3625-4f9a-b01b-4355d49ece98
begin
	XTrainFinal = Matrix(paramsBgScaled[finalRuns, :])
	YTrainUr = Array{Float64, 2}(UrSim[:, finalRuns]')
	YTrainNp = Array{Float64, 2}(NpSim[:, finalRuns]')
end

# ╔═╡ b4faacd2-4df5-4b30-b928-aeb160049678
XTrainFinal

# ╔═╡ de3f0b4e-2d49-4ba5-9b22-799314cc5076
# build coefficient matrix!
ATrainFinal = buildCoefficientMatrix(XTrainFinal; pceDegree=2)

# ╔═╡ 7b049137-6ec2-43b1-8fb2-95c6799a3cfb
begin
	# set regularization coefficients
	lambdaUrFinal = 0.4
	lambdaNpFinal = 5
end

# ╔═╡ 55487f5c-2542-4ecd-9091-92c618a9b3cd
begin
	# Perform ridge regression
	betaUrFinal = solveRegPCE(ATrainFinal, YTrainUr; λ=lambdaUrFinal)
	betaNpFinal = solveRegPCE(ATrainFinal, YTrainNp; λ=lambdaNpFinal)
end

# ╔═╡ cd85f891-6f1e-4cea-9307-bf4231f4a446
md"""
Load matrix of test points
"""

# ╔═╡ 79ca120b-3810-4e8b-9237-c98119aaa736
XTestFinal = load("./data/design/CR2208TestFinal.jld", "XTestFinal")[:, 1:6]

# ╔═╡ 232f5599-6d41-4379-9e5f-c6922b1ac2c9
ATest = buildCoefficientMatrix(XTestFinal[:, 1:6]; pceDegree=2);

# ╔═╡ 281b69ad-e061-4fd5-9f85-9aae93e280ff
begin
	ATestFinalCoeffs = ATest[:, 2:end]
	ATestFinalFiltered = ATestFinalCoeffs .- mean(ATestFinalCoeffs, dims=1)
	yPredNpFinal = betaNpFinal[1, :] .+ Matrix((ATestFinalFiltered * betaNpFinal[2:end, :])');
	yPredUrFinal = betaUrFinal[1, :] .+ Matrix((ATestFinalFiltered * betaUrFinal[2:end, :])');
end

# ╔═╡ bf6e9034-3eae-4875-8f36-2cc7b31bf3b1
md"""
Get mean and standard deviations of Ur and Np
"""

# ╔═╡ 2017c596-f6dd-4b4b-bb9c-bf05e9980432
begin
	meanEmpiricalNpFinal = mean(yPredNpFinal; dims=2)
	stdEmpiricalNpFinal = std(yPredNpFinal; dims=2)[:]

	meanEmpiricalUrFinal = mean(yPredUrFinal; dims=2)
	stdEmpiricalUrFinal = std(yPredUrFinal; dims=2)[:]
end

# ╔═╡ b9ac0fc0-076c-448b-ad2f-18da9ab8e9fa
UrSim

# ╔═╡ 43d86334-5128-4519-8bc7-d3a47ff8a5f0
md"""
Plot predictive uncertainty (figure 5)
"""

# ╔═╡ 4d4f206c-9e87-4be4-9b14-acc3dd92f4b3
begin
	plotUncertainty(yPredUrFinal, meanEmpiricalUrFinal, stdEmpiricalUrFinal, UrObs, ylabel="Uᵣ [km/s]", times, nSTD=2, trimIndices=(1, 577))
	plot!(guidefontsize=20)
	plot!(tickfontsize=15)
	plot!(legend=:topleft)
	plot!(left_margin=3mm)
	plot!(bottom_margin=3mm)
end

# ╔═╡ eb7349b5-c681-436b-8295-48ca47b9af09
begin
	plotUncertainty(yPredNpFinal, meanEmpiricalNpFinal, stdEmpiricalNpFinal, NpObs, ylabel="Nₚ [cm⁻³]", ylims=(-20, 120), times, nSTD=2, trimIndices=(1, 577))
	plot!(guidefontsize=20)
	plot!(tickfontsize=15)
	plot!(legend=:topleft)
	plot!(left_margin=3mm)
	plot!(bottom_margin=3mm)
end

# ╔═╡ 656871e0-1b36-4965-8b4d-6da2191d698b
md"""
Sensitivities
"""

# ╔═╡ 88d080a3-ad9c-496f-ac24-794e2026c906
begin
	gsaUrFinal = gsa(XTrainFinal, YTrainUr; regularize=true, pceDegree=2, lambda=lambdaUrFinal)
	gsaNpFinal = gsa(XTrainFinal, YTrainNp; regularize=true, pceDegree=2, lambda=lambdaNpFinal)
end

# ╔═╡ aeac3a5e-1d40-4f14-8c79-5488a36e871f
# Extract main effects
begin
	gsaMainUrFinal = processMainEffects(gsaUrFinal)
	gsaMainNpFinal = processMainEffects(gsaNpFinal)
end

# ╔═╡ 35e569be-5926-4ec6-9cb7-a31be6bee6c3
md"""
Plot sensitivities (figures 6 and 7)
"""

# ╔═╡ 54444b2b-0b62-4bf5-a4e5-de0b274326e1
begin
	pMainUrFinal = plotMainEffects(gsaMainUrFinal, times, inputNames; title = "", dpi=300, actualStartTime=startTime, tickFormat="dd-m")
	plot!(grid=false)
	plot!(ylabel="Sᵢᵗ")
	plot!(titlefontsize=17)
	plot!(xtickfontsize=20)
	plot!(ytickfontsize=20)
	plot!(guidefontsize=23)
	plot!(leftmargin=7mm)
	plot!(rightmargin=5mm)
	plot!(topmargin=2mm)
	plot!(bottommargin=8mm)
	plot!(size=(1200, 600))
end

# ╔═╡ 2dbf33e2-14fc-446a-82df-4308307a8922
begin
	pMainNpFinal = plotMainEffects(gsaMainNpFinal, times, inputNames; title="", dpi=300, actualStartTime=startTime, tickFormat="dd-m")
	plot!(grid=false)
	plot!(ylabel="Sᵢᵗ")
	plot!(titlefontsize=17)
	plot!(xtickfontsize=20)
	plot!(ytickfontsize=20)
	plot!(guidefontsize=23)
	plot!(leftmargin=7mm)
	plot!(rightmargin=5mm)
	plot!(topmargin=2mm)
	plot!(bottommargin=8mm)
	plot!(size=(1200, 600))
end

# ╔═╡ 623254e2-2d04-48f1-aaf2-e2a43102a34d
begin
	savefig(pMainUrFinal, "/Users/ajivani/Downloads/pMainUrFinal2208.png")
	savefig(pMainNpFinal, "/Users/ajivani/Downloads/pMainNpFinal2208.png")

end

# ╔═╡ 1e87d6de-a83a-4fbd-b449-8fa7afcc4782
begin
	pUrIntMeanFinal = plotInteractionEffects(gsaUrFinal, times, inputNames, dpi=300) # symmetric matrix (interactions are read from either the upper triangle or the lower triangle)
	plot!(tickfontsize=12)
	plot!(bottom_margin=9mm)
	plot!(right_margin=3mm)
end

# ╔═╡ b4fe4583-7275-494e-a7dd-db1ed034dbf5
begin
	pNpIntMeanFinal = plotInteractionEffects(gsaNpFinal, times, inputNames, dpi=300)
	plot!(tickfontsize=12)
	plot!(bottom_margin=9mm)
	plot!(right_margin=3mm)
end

# ╔═╡ 1d80aaf8-f44c-477f-b172-388aeca62e74


# ╔═╡ d790df8c-e0f5-4699-a711-df8a9115cdda
md"""
Bootstrapping
"""

# ╔═╡ 5e35ff10-728b-4de7-b006-854f86b00ebb
md"""
Load saved bootstrap data. Performing the bootstrap can be quite slow, so the actual command for getting the data is commented out.
"""

# ╔═╡ 903609fb-93b0-464a-93a0-d109b7c99f83
# UrBootstrap = bootstrapGSA(XTrainFinal, YTrainUr; regularize=false, nStart=20, nEnd=140, nStep=20)
# NpBootstrap = bootstrapGSA(XTrainFinal, YTrainNp; regularize=false, nStart=20, nEnd=140, nStep=20)

# ╔═╡ 0fb17118-ed57-4871-aded-4e71c4688f56
begin
	UrBootstrap = load("./data/bootstrapping/bootstrapUr2208.jld", "UrBootstrap")
	avgBootstrapUr = mean(UrBootstrap; dims=2)[:, 1, :, :]
	avgBootstrapRepsUr = mean(avgBootstrapUr; dims=2)[:, 1, :]
	stdBootstrapRepsUr = std(avgBootstrapUr; dims=2)[:, 1, :]
end

# ╔═╡ 97400496-6816-4214-9bbc-bc577cf23097
begin
	NpBootstrap = load("./data/bootstrapping/bootstrapNp2208.jld", "NpBootstrap")
	avgBootstrapNp = mean(NpBootstrap; dims=2)[:, 1, :, :]
	avgBootstrapRepsNp = mean(avgBootstrapNp; dims=2)[:, 1, :]
	stdBootstrapRepsNp = std(avgBootstrapNp; dims=2)[:, 1, :]
end

# ╔═╡ d78c1a5e-3ecd-4fc2-b03a-5cb81a94b963
summaryColors = palette(:tab10, rev=true)[6:-1:1]

# ╔═╡ 0f8d8031-56c0-4973-b378-310745f5731b
md"""
Plot summary of bootstrapping results. (figure 9)
"""

# ╔═╡ 9b95b6dc-5a09-4ed3-aa67-789ed7ce6072
begin
	pLineSummaryUr = plot()
	for (idx, eachName) in enumerate(inputNames)
		meanTrend = avgBootstrapRepsUr[idx, :]
		errTrend  = stdBootstrapRepsUr[idx, :] 
		plot!(20:20:140, meanTrend, 
			yerr=errTrend,
			linewidth=2.5,
			linecolor=summaryColors[idx], 
			marker=:circle, 
			markercolor=summaryColors[idx],
			markerstrokecolor=summaryColors[idx],
			markerstrokewidth=1.5,
			label=eachName)
	end
	plot!(legend=:topleft)
	plot!(xticks=(20:20:140, string.(20:20:140)))
	plot!(xlabel="Sample size n")
	plot!(ylabel="Sᵢ")
	plot!(guidefontsize=20)
	plot!(tickfontsize=16)
	plot!(leftmargin=5mm)
	plot!(bottommargin=5mm)
	plot!(framestyle=:box)
	plot!(fg_legend=nothing)
	plot!(bg_legend=nothing)
	plot!(size=(750, 500))
	plot!(ylims=(0, 0.5))
end

# ╔═╡ 5ff32208-b48c-4592-826e-a445efdacb1a
begin
	pLineSummaryNp = plot()
	for (idx, eachName) in enumerate(inputNames)
		meanTrend = avgBootstrapRepsNp[idx, :]
		errTrend  = stdBootstrapRepsNp[idx, :] 
		plot!(20:20:140, meanTrend, 
			yerr=errTrend,
			linewidth=2.5,
			linecolor=summaryColors[idx], 
			marker=:circle, 
			markercolor=summaryColors[idx],
			markerstrokecolor=summaryColors[idx],
			markerstrokewidth=1.5,
			label=eachName)
	end
	plot!(legend=:topleft)
	plot!(xticks=(20:20:140, string.(20:20:140)))
	plot!(xlabel="Sample size n")
	plot!(ylabel="Sᵢ")
	plot!(guidefontsize=20)
	plot!(tickfontsize=16)
	plot!(leftmargin=5mm)
	plot!(bottommargin=5mm)
	plot!(framestyle=:box)
	plot!(fg_legend=nothing)
    plot!(bg_legend=nothing)
	plot!(size=(750, 500))
	plot!(ylims=(0, 0.5))
end

# ╔═╡ 36b6cc07-fa03-4155-934c-16561bd0d741
md"""
Miscellaneous
"""

# ╔═╡ 5a63d30b-e0a3-4270-93ba-f5e37b060f0f
begin
		plotArgsUr = Dict(:palette=>:seaborn_bright,
					   :dateFormat=>"dd-mm HH:MM",
					   :tickInterval=>108,
					   # :simIdx => parse.(Int, EachSimID),
					   :simAlpha=>0.6, 
					   :simWidth=>1.2,
					   :ylabel=>"Uᵣ[km/s]",
					   :title=>"",
					   :startTime=>startTime,
					   :dpi=>200,
					   :plotLabels=>:false,
					   
	                  )
	
		plotArgsNp = Dict(:palette=>:seaborn_bright,
						   :dateFormat=>"dd-mm HH:MM",
						   :tickInterval=>108,
						   # :simIdx => parse.(Int, EachSimID),
						   :simAlpha=>0.6, 
						   :simWidth=>1.2,
						   :ylabel=>"Nₚ[cm⁻³]",
						   # :ylabel=>"Np [cm" * L"^{-3}" * "]",
						   :title=>"",
						   :startTime=>startTime,
						   :dpi=>200,
						   :ylims=>(0, 80),
						   :plotLabels=>:false
						  )
	
		plotArgsB = Dict(:palette=>:seaborn_bright,
						   :dateFormat=>"dd-mm HH:MM",
						   :tickInterval=>108,
						   # :simIdx => parse.(Int, EachSimID),
						   :simAlpha=>0.6, 
						   :simWidth=>1.2,
						   :ylabel=>"B[nT]",
						   # :ylabel=>"Np [cm" * L"^{-3}" * "]",
						   :title=>"",
						   :startTime=>startTime,
						   :dpi=>200,
						   :ylims=>(0, 20),
						   :plotLabels=>:false,
						   :subtractFactor=>0
		                  )
	
		plotArgsT = Dict(:palette=>:seaborn_bright,
					   :dateFormat=>"dd-mm HH:MM",
					   :tickInterval=>108,
					   # :simIdx => parse.(Int, EachSimID),
					   :simAlpha=>0.6, 
					   :simWidth=>1.5,
					   :ylabel=>"T[K]",
					   # :ylabel=>"Np [cm" * L"^{-3}" * "]",
					   :title=>"",
					   :startTime=>startTime,
					   :dpi=>200,
					   :ylims=>(0, 9e5),
					   :plotLabels=>:false,
					   :subtractFactor=>0
					  )
end

# ╔═╡ a8467e8e-9555-4bda-a0bd-da1164c8b232
begin
	# make plots of the runs that are successful and satisfy the constraints
	pUrSimObsF = plotSimObs(UrSim, UrObs, times, collect(1:200); simIdx=finalRuns, plotArgsUr..., ylims=(100, 1000), dateFormat="dd-mm")
	plot!(size=(920, 470))
	plot!(guidefontsize=24)
	plot!(left_margin=7mm)
	plot!(bottom_margin=7mm)
	plot!(top_margin=5.5mm)
	plot!(ytickfontsize=18)
	plot!(xtickfontsize=18)
	plot!(dpi=300)
end

# ╔═╡ 8d64d55c-0068-4d12-9947-e6b5094835ff
begin
	pNpSimObsF = plotSimObs(NpSim, NpObs, times, collect(1:200); simIdx=finalRuns, plotArgsNp..., ylims=(0, 100), dateFormat="dd-mm")
	plot!(size=(920, 470))
	plot!(guidefontsize=24)
	plot!(left_margin=7mm)
	plot!(bottom_margin=7mm)
	plot!(top_margin=5.5mm)
	plot!(ytickfontsize=18)
	plot!(xtickfontsize=18)
	plot!(dpi=300)
	
end

# ╔═╡ 720d0ca2-5da3-4c28-ab55-20a778adc7e4
begin
	pBSimObsF  = plotSimObs(BSim, BObs, times, collect(1:200); simIdx=finalRuns, plotArgsB..., dateFormat="dd-mm")
	plot!(size=(920, 470))
	plot!(guidefontsize=24)
	plot!(left_margin=7mm)
	plot!(bottom_margin=7mm)
	plot!(top_margin=5.5mm)
	plot!(ytickfontsize=18)
	plot!(xtickfontsize=18)
	plot!(dpi=300)
end

# ╔═╡ 1772d4e8-42b4-4932-8d0c-170aae556f2b
begin
	pTSimObsF = plotSimObs(TSim, TObs, times, collect(1:200); simIdx=finalRuns, plotArgsT..., ylims=(0, 10e5), dateFormat="dd-mm")
	plot!(yticks=(0:2e5:10e5, [0; [@sprintf("%de5", i) for i in 2:2:10]][:]))
	plot!(size=(920, 470))
	plot!(guidefontsize=24)
	plot!(left_margin=7mm)
	plot!(bottom_margin=7mm)
	plot!(top_margin=5.5mm)
	plot!(ytickfontsize=18)
	plot!(xtickfontsize=18)
	plot!(dpi=300)
end

# ╔═╡ Cell order:
# ╠═76c3e251-4be0-4419-81c5-fbfa32250b8f
# ╟─e0bb2c6d-9618-4f7b-bf5d-7142e673fa23
# ╠═6cb3b25b-701f-4eca-bde1-03c7910ec523
# ╟─3e140602-f9e6-4c4c-89bd-fc051b505e33
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# ╟─cae9da64-c6d0-45ae-9528-ce8cb46969f8
# ╟─f6cf52cf-c677-40eb-8af6-fceb04e32339
# ╠═844a7d5e-71ef-4e76-85ac-db90d1a581bb
# ╟─62fff0a4-82ad-4643-b7f1-561c5644d7e0
# ╠═34f42c83-22ad-414a-ab65-be93a5c9be14
# ╠═16adf9b7-76ad-4902-81d7-9269318ca26b
# ╠═a659f1da-1b23-44e8-a9db-c5b8953b3e82
# ╠═f4ef705e-a7e0-49ef-96e1-48eb87df9340
# ╠═902b0932-1841-4607-8f4c-b8140edb5cf3
# ╟─bea921ff-cc8e-48e3-806d-78bd52334b8f
# ╠═2acdc135-7a5a-44a1-9390-464307e177a7
# ╟─d7409613-525d-417f-9d66-7bf3abfec64c
# ╠═c93a9dbf-95c9-420c-b5f6-6efc28e66eec
# ╠═195f5770-2391-4101-893a-1c367abff704
# ╟─189dc329-5414-4b48-8bf9-94ec4579048e
# ╠═e89f5564-75ed-40e4-a26f-b9e91dc65d8c
# ╟─40222bdc-5e2a-4ca5-8bcb-3adf27cabad5
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# ╟─dc675b70-a7d2-435d-afdc-e05cc32a061f
# ╠═a8467e8e-9555-4bda-a0bd-da1164c8b232
# ╠═8d64d55c-0068-4d12-9947-e6b5094835ff
# ╠═720d0ca2-5da3-4c28-ab55-20a778adc7e4
# ╠═1772d4e8-42b4-4932-8d0c-170aae556f2b
# ╟─e0a4514a-83fc-4c21-a555-d89407cd436d
# ╟─6a8503ed-e59a-4391-9f9a-88b761976e36
# ╠═b4faacd2-4df5-4b30-b928-aeb160049678
# ╠═b4f5a355-3625-4f9a-b01b-4355d49ece98
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# ╠═55487f5c-2542-4ecd-9091-92c618a9b3cd
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# ╟─cd85f891-6f1e-4cea-9307-bf4231f4a446
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# ╟─bf6e9034-3eae-4875-8f36-2cc7b31bf3b1
# ╠═2017c596-f6dd-4b4b-bb9c-bf05e9980432
# ╠═b9ac0fc0-076c-448b-ad2f-18da9ab8e9fa
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# ╠═0fb17118-ed57-4871-aded-4e71c4688f56
# ╠═97400496-6816-4214-9bbc-bc577cf23097
# ╠═d78c1a5e-3ecd-4fc2-b03a-5cb81a94b963
# ╟─0f8d8031-56c0-4973-b378-310745f5731b
# ╠═9b95b6dc-5a09-4ed3-aa67-789ed7ce6072
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# ╟─36b6cc07-fa03-4155-934c-16561bd0d741
# ╠═5a63d30b-e0a3-4270-93ba-f5e37b060f0f