text:
### A Pluto.jl notebook ###
# v0.17.2

using Markdown
using InteractiveUtils

# ╔═╡ bddf36ac-5547-491c-94bb-763ce7df3cd9
using Pkg, DrWatson

# ╔═╡ 7bf4154b-0009-46e4-9d03-8e3b81a202f2
begin
	using CSV
	using Random
	using StatsBase
	using DataFrames
	using Turing
	using StatsPlots
	using StatsFuns
	using LaTeXStrings
	using StatisticalRethinking
	using StatisticalRethinking: link
	using StatisticalRethinkingPlots
	using ParetoSmooth
	using ParetoSmoothedImportanceSampling
	using Logging
end

# ╔═╡ 8720cc1f-58a0-4a70-9497-480456b26f85
begin
	default(labels=false)
	Logging.disable_logging(Logging.Warn);
end

# ╔═╡ fdbf8fe2-7e99-4da6-bd8d-6a21bb68bc71
md"## 8.1 Building an interaction."

# ╔═╡ 04affaff-2593-4b6c-8638-ab54fde6d2cc
md"### Code 8.1"

# ╔═╡ 73ee2643-330f-485f-94f5-be46fbd33d4f
begin
	rugged = CSV.read(sr_datadir("rugged.csv"), DataFrame)
	dd = rugged[completecases(rugged, :rgdppc_2000),:]
	dd[:,:log_gdp] = log.(dd.rgdppc_2000);
	dd[:,:log_gdp_std] = dd.log_gdp / mean(dd.log_gdp)
	dd[:,:rugged_std] = dd.rugged / maximum(dd.rugged)
end;

# ╔═╡ eb3fef03-acf9-4c41-9760-e0f492df55e2
md"### Code 8.2"

# ╔═╡ 3eeab20c-6d42-4b31-bd8f-bfa691cb4587
begin
	r̄ = mean(dd.rugged_std)

	@model function model_m8_1(rugged_std, log_gdp_std)
	    σ ~ Exponential()
	    a ~ Normal(1, 1)
	    b ~ Normal(0, 1)
	    μ = @. a + b * (rugged_std - r̄)
	    log_gdp_std ~ MvNormal(μ, σ)
	end
end

# ╔═╡ bc0a3a04-1b8c-4332-a6ba-3da2b8eaea05
md"### Code 8.3"

# ╔═╡ f74d5bf7-ae57-4678-be5f-25af1959173b
begin
	m8_1_p = sample(model_m8_1(dd.rugged_std, dd.log_gdp_std), Prior(), 1000)
	m8_1_p_df = DataFrame(m8_1_p);
end

# ╔═╡ 1bc733fa-0062-4521-8617-4e757a9fff24
begin
	rugged_seq = range(-0.1, 1.1; length=30)
	μ = link(m8_1_p_df, (r, x) -> r.a + r.b*(x - r̄), rugged_seq)
	μ = hcat(μ...)

	p = plot(
	    xlim=(0, 1),
	    ylim=(0.5, 1.5),
	    #title=L"a \sim \mathcal{N}(1,1), b \sim \mathcal{N}(0, 1)",
	    xlab="ruggedness", ylab="log GDP",
	)
	hline!(collect(extrema(dd.log_gdp_std)); c=:black, s=:dash)
	for μ₀ ∈ first(eachrow(μ), 50)
	    plot!(rugged_seq, μ₀; c=:black, alpha=0.3)
	end
	p
end

# ╔═╡ 4f444d5f-6058-4cb8-975f-17782d8b86c9
md"### Code 8.4"

# ╔═╡ cee469b4-83b1-4e29-aa75-8bb6dfc4d207
mean(abs.(m8_1_p_df.b) .> 0.6)

# ╔═╡ 3d677fac-ff09-45b1-9377-4d29cfa86a7f
md"### Code 8.5"

# ╔═╡ 8f904830-dc3e-4875-9af7-1e7285e7fc43
r̄_std = mean(dd.rugged_std)

# ╔═╡ ad981e3c-df35-43f0-9b19-e128a9f2a12d
@model function model_m8_1a(rugged_std, log_gdp_std)
    σ ~ Exponential()
    a ~ Normal(1, 0.1)
    b ~ Normal(0, 0.3)
    μ = @. a + b * (rugged_std - r̄_std)
    log_gdp_std ~ MvNormal(μ, σ)
end

# ╔═╡ 527fd7d7-7375-4dee-9405-cee0363ca92e
begin
	m8_1 = sample(model_m8_1a(dd.rugged_std, dd.log_gdp_std), NUTS(), 1000)
	m8_1_df = DataFrame(m8_1)
end

# ╔═╡ d11c957d-bc78-4011-9dc8-5335a9d3f4f7
md"### Code 8.6"

# ╔═╡ 16574ab6-a53e-4f9d-bbbe-23714ee39abd
PRECIS(m8_1_df)

# ╔═╡ 40799cc2-cdaa-4a86-a678-1b50b3dff6cc
md"### Code 8.7"

# ╔═╡ ca390706-13d7-4aa0-952e-8f06b4bc370e
dd[:,:cid] = @. ifelse(dd.cont_africa == 1, 1, 2);

# ╔═╡ 1d902b61-2387-4664-a39e-7ff73440dd43
md"### Code 8.8"

# ╔═╡ 62e35bf2-cefd-413d-a2ea-60e7193c94b6
@model function model_m8_2(rugged_std, cid,  log_gdp_std)
    σ ~ Exponential()
    a ~ MvNormal([1, 1], 0.1)
    b ~ Normal(0, 0.3)
    μ = @. a[cid] + b * (rugged_std - r̄)
    log_gdp_std ~ MvNormal(μ, σ)
end

# ╔═╡ 43e22387-2afd-4ed1-80c2-608f82c224f6
begin
	m8_2 = sample(model_m8_2(dd.rugged_std, dd.cid,  dd.log_gdp_std), NUTS(), 1000)
	m8_2_df = DataFrame(m8_2);
end# -

# ╔═╡ f5cb4db3-99d4-4498-ba6d-5b36064b5328
md"### Code 8.9"

# ╔═╡ 5f24eb52-4bf3-47b9-ab55-f92aaeca0d45
let
	# Compute log likelihoods for both models
	fun = (r, (x,y)) -> normlogpdf(r.a + r.b * (x - r̄), r.σ, y)
	global m8_1_ll = link(m8_1_df, fun, zip(dd.rugged_std, dd.log_gdp_std))
	m8_1_ll = hcat(m8_1_ll...)
end

# ╔═╡ 612456bd-2bee-4f1c-beed-d3f697c80509
let
	# need DF with a as a vector of both a[1] and a[2]
	global df = DataFrame(m8_2_df)
	df[!,:a] = collect.(zip(m8_2_df.:"a[1]", m8_2_df.:"a[2]"))

	fun = (r, (x,c,y)) -> normlogpdf(r.a[c] + r.b * (x - r̄), r.σ, y)
	global m8_2_ll = link(df, fun, zip(dd.rugged_std, dd.cid, dd.log_gdp_std))
	m8_2_ll = hcat(m8_2_ll...);

	compare([m8_1_ll, m8_2_ll], :waic, mnames=["m8.1", "m8.2"])
end

# ╔═╡ 8ca7b83a-ffab-42cc-bca1-13cb60a45b97
md"### Code 8.10"

# ╔═╡ fd2ed8f7-c276-4df6-8d08-9d4ea02effd7
PRECIS(m8_2_df)

# ╔═╡ e396ec23-f67f-4f1b-b6e1-3968120463a9
md"### Code 8.11"

# ╔═╡ 38d6aeb9-5b1f-456b-9de6-7484ba94f9c8
PI(map(r -> r[1] - r[2], df.a))

# ╔═╡ 4850e051-166a-4b4a-b7bd-4181578338dc
md"### Code 8.12"

# ╔═╡ 356ef2ab-ee33-42ba-802e-84e1d7425a61
begin
	rugged_seq_2 = range(-0.1, 1.1, length=30)
	africa     = link(df, (r, x) -> r.a[1] + r.b*(x-r̄), rugged_seq_2)
	africa     = hcat(africa...)'
	not_africa = link(df, (r, x) -> r.a[2] + r.b*(x-r̄), rugged_seq_2)
	not_africa = hcat(not_africa...)'

	μₐ = mean.(eachrow(africa))
	μₙ = mean.(eachrow(not_africa))
	PIₐ = PI.(eachrow(africa))
	PIₐ = vcat(PIₐ'...)
	PIₙ = PI.(eachrow(not_africa))
	PIₙ = vcat(PIₙ'...);
end

# ╔═╡ 02a727d7-5095-4728-abd4-413100d56acd
let
	p = plot(xlab="ruggedness (std)", ylab="log GDP")
	scatter!(dd.rugged_std[dd.cid.==1], dd.log_gdp_std[dd.cid.==1], c=:blue)
	scatter!(dd.rugged_std[dd.cid.==2], dd.log_gdp_std[dd.cid.==2], c=:white)

	plot!(rugged_seq, [μₐ, μₐ], c=:black, fillrange=PIₐ, fillalpha=0.4)
	plot!(rugged_seq, [μₙ, μₙ], c=:black, fillrange=PIₙ, fillalpha=0.2)
	annotate!([
	    (1, 0.87, ("Africa", 9)),
	    (1, 1.05, ("Not Africa", 9))
	])
end

# ╔═╡ 083b4a77-da6e-43e7-a7f0-f45cb2b56ff8
md"### Code 8.13"

# ╔═╡ 3571a823-63f5-4124-ab8f-d7b060b82d6a
@model function model_m8_3(rugged_std, cid,  log_gdp_std)
    σ ~ Exponential()
    a ~ MvNormal([1, 1], 0.1)
    b ~ MvNormal([0, 0], 0.3)
    μ = @. a[cid] + b[cid] * (rugged_std - r̄)
    log_gdp_std ~ MvNormal(μ, σ)
end

# ╔═╡ f2a34af7-2706-4d58-a3c9-88ca3539538e
begin
	m8_3 = sample(model_m8_3(dd.rugged_std, dd.cid,  dd.log_gdp_std), NUTS(), 1000)
	m8_3_df = DataFrame(m8_3);
end

# ╔═╡ e77013bc-1386-4f07-bf64-2571fdbc5212
md"### Code 8.14"

# ╔═╡ 87311d6b-db5f-41d3-a2ec-1e76aa13dd8d
PRECIS(m8_3_df)

# ╔═╡ 1462e546-c794-45aa-8ac2-097b5a0fc168
md"### Code 8.15"

# ╔═╡ 3a55d73d-2cf8-4411-a075-81157a544973
let
	global df3 = DataFrame(m8_3_df)
	df3[!,:a] = collect.(zip(m8_3_df.:"a[1]", m8_3_df.:"a[2]"))
	df3[!,:b] = collect.(zip(m8_3_df.:"b[1]", m8_3_df.:"b[2]"))

	fun = (r, (x,c,y)) -> normlogpdf(r.a[c] + r.b[c] * (x - r̄), r.σ, y)
	global m8_3_ll = link(df3, fun, zip(dd.rugged_std, dd.cid, dd.log_gdp_std))
	m8_3_ll = hcat(m8_3_ll...);

	compare([m8_1_ll, m8_2_ll, m8_3_ll], :psis, mnames=["m8.1", "m8.2", "m8.3"])
end

# ╔═╡ 80eca5f8-762b-4b73-a246-5883a04d72e5
md"### Code 8.16"

# ╔═╡ 9b1a7350-4311-48b2-91ce-8f4f87e34aef
let
	t = m8_3_ll'
	m8_3_t = collect(reshape(t, size(t)..., 1))
	PSIS_m8_3 = psis_loo(m8_3_t)
	scatter(PSIS_m8_3.pointwise(:pareto_k))
end

# ╔═╡ b384f828-985d-4f6e-9040-2af4bc888ec8
md"### Code 8.17"

# ╔═╡ 8d2cc03b-4e3f-409d-8bdb-e99ed0365046
let
	# build data
	africa     = link(df3, (r, x) -> r.a[1] + r.b[1]*(x-r̄), rugged_seq)
	africa     = hcat(africa...)'
	not_africa = link(df3, (r, x) -> r.a[2] + r.b[2]*(x-r̄), rugged_seq)
	not_africa = hcat(not_africa...)'

	μₐ = mean.(eachrow(africa))
	μₙ = mean.(eachrow(not_africa))
	PIₐ = PI.(eachrow(africa))
	PIₐ = vcat(PIₐ'...)
	PIₙ = PI.(eachrow(not_africa))
	PIₙ = vcat(PIₙ'...);

	# plot Africa, cid=1
	p1 = plot(xlab="ruggedness (std)", ylab="log GDP", title="African nations")
	scatter!(dd.rugged_std[dd.cid.==1], dd.log_gdp_std[dd.cid.==1], c=:blue)
	plot!(rugged_seq, [μₐ, μₐ], c=:blue, fillrange=PIₐ, fillalpha=0.2)

	# plot non Africa, cid=2
	p2 = plot(xlab="ruggedness (std)", ylab="log GDP", title="Non-African nations")
	scatter!(dd.rugged_std[dd.cid.==2], dd.log_gdp_std[dd.cid.==2], c=:white)
	plot!(rugged_seq, [μₙ, μₙ], c=:black, fillrange=PIₙ, fillalpha=0.2)

	plot(p1, p2, size=(800, 400))
end

# ╔═╡ e8cbf4c4-885e-4e2f-b058-4b41a3ce6857
md"## 8.2 Symmetry of interations"

# ╔═╡ b0a849d8-315a-44ea-ac6a-769bf47de6ff
md"### Code 8.18"

# ╔═╡ 3383537c-7af4-4de9-b9eb-9389a02d4aaf
let
	rugged_seq = range(-0.2, 1.2, length=30)
	μA = link(df3, (r, x) -> r.a[1] + r.b[1]*(x-r̄), rugged_seq)
	μA = vcat(μA'...)
	μN = link(df3, (r, x) -> r.a[2] + r.b[2]*(x-r̄), rugged_seq)
	μN = vcat(μN'...)
	delta = μA .- μN;

	# +
	μ = mean.(eachrow(delta))
	PI_v = PI.(eachrow(delta))
	PI_v = vcat(PI_v'...)

	plot(xlab="ruggedness", ylab="expected difference log GDP",)
	plot!(rugged_seq, [μ, μ], c=:blue, fillrange=PI_v, fillalpha=0.2)
	hline!([0.0], s=:dash, c=:black)
	annotate!([
	    (0.0, 0.03, ("Africa higher GPD", 10)),
	    (0.0, -0.03, ("Africa lower GPD", 10)),
	])
end

# ╔═╡ 7d5e4157-7abe-4fb1-893a-51a520a934d0
md"## 8.3 Continuous interaction"

# ╔═╡ 48573194-9cf9-4c9d-b883-62547e4debbc
md"### Code 8.19"

# ╔═╡ 9025cb4c-80cc-463f-b588-913e59c35560
begin
	tulips = CSV.read(sr_datadir("tulips.csv"), DataFrame)
	describe(tulips)
end

# ╔═╡ b2dbe2bd-19ae-4a8f-a408-0353cccd3b84
md"## Code 8.20"

# ╔═╡ 9c905e24-fa5d-4169-bf73-684f5e0a5b8c
begin
	tulips.blooms_std = tulips.blooms / maximum(tulips.blooms)
	tulips.water_cent = tulips.water .- mean(tulips.water)
	tulips.shade_cent = tulips.shade .- mean(tulips.shade);
end;

# ╔═╡ 2f086d7b-658d-42e8-a112-d22807a66586
md"### Code 8.21"

# ╔═╡ 69e42d8d-062a-4061-896b-2781a4aab5b0
let
	Random.seed!(1)
	a = rand(Normal(0.5, 1), 10^4)
	sum(@. (a < 0) | (a > 1))/length(a)
end

# ╔═╡ 219bc61b-a458-488c-91cb-0b3708c74a48
md"### Code 8.22"

# ╔═╡ 4443c25d-7f6a-4d01-b672-1c9606ab28f8
let
	Random.seed!(1)
	a = rand(Normal(0.5, 0.25), 10^4)
	sum(@. (a < 0) | (a > 1))/length(a)
end

# ╔═╡ 738c7fb8-8f9c-4fce-ad6a-b58fd7085be6
md"### Code 8.23"

# ╔═╡ 01b45f6a-adbe-4791-88d5-fa0623bb107c
# +
@model function m8_4(water_cent, shade_cent, blooms_std)
    a ~ Normal(0.5, 0.25)
    bw ~ Normal(0, 0.25)
    bs ~ Normal(0, 0.25)
    μ = @. a + bw*water_cent + bs*shade_cent
    σ ~ Exponential(1)
    blooms_std ~ MvNormal(μ, σ)
end

# ╔═╡ 715b797a-88c6-46b9-87b9-a59d5765cd99
begin
	m8_4_c = sample(m8_4(tulips.water_cent, tulips.shade_cent, tulips.blooms_std),
		NUTS(), 1000)
	m8_4_df = DataFrame(m8_4_c)
	PRECIS(m8_4_df)
end

# ╔═╡ 1f7c87b9-a93c-442e-b5b2-8501a289200c
md"### Code 8.24"

# ╔═╡ 6a5a4d0f-5932-4862-87b0-1fafed29318e
@model function m8_5(water_cent, shade_cent, blooms_std)
    a ~ Normal(0.5, 0.25)
    bw ~ Normal(0, 0.25)
    bs ~ Normal(0, 0.25)
    bws ~ Normal(0, 0.25)
    μ = @. a + bw*water_cent + bs*shade_cent + bws*water_cent*shade_cent
    σ ~ Exponential(1)
    blooms_std ~ MvNormal(μ, σ)
end

# ╔═╡ a369c511-017d-406f-9178-b4cced1b97e9
begin
	m8_5_c = sample(m8_5(tulips.water_cent, tulips.shade_cent, tulips.blooms_std),
		NUTS(), 1000)
	m8_5_df = DataFrame(m8_5_c)
	PRECIS(m8_5_df)
end

# ╔═╡ 04be14ef-fe26-447b-823e-243d9cba8466
md"### Code 8.25"

# ╔═╡ f822f689-a92c-4535-ae88-ef604e31753f
let
	plts = []

	for shade ∈ -1:1
	    idx = findall(==(shade), tulips.shade_cent)
	    p = plot(xlims=(-1.2,1.2), ylims=(-.2,1.2), xlab="water", ylab="blooms",
	             title="shade=$shade", titlefontsize=12)
	    scatter!(tulips.water_cent[idx], tulips.blooms_std[idx])
	    water_seq = -1:1
	    mu = link(m8_4_df, (r, water) -> r.a + r.bw * water + r.bs * shade,
			water_seq)
	    mu = hcat(mu...);
	    for μ ∈ first(eachrow(mu), 20)
	        plot!(water_seq, μ, c=:black, alpha=0.2)
	    end
	    push!(plts, p)
	end
	plot(plts..., layout=(1, 3), size=(800, 400), plot_title="m8.4 post",
		plot_titlefontsize=14)
end

# ╔═╡ 745a0c84-4243-4efa-9871-7d2dcd096553
let
	plts = []

	for shade ∈ -1:1
	    idx = findall(==(shade), tulips.shade_cent)
	    p = plot(xlims=(-1.2,1.2), ylims=(-.2,1.2), xlab="water", ylab="blooms",
	             title="shade=$shade", titlefontsize=12)
	    scatter!(tulips.water_cent[idx], tulips.blooms_std[idx])
	    water_seq = -1:1
	    mu = link(m8_5_df, (r, water) -> r.a + r.bw*water + r.bs*shade +
			r.bws*water*shade, water_seq)
	    mu = hcat(mu...);
	    for μ ∈ first(eachrow(mu), 20)
	        plot!(water_seq, μ, c=:black, alpha=0.2)
	    end
	    push!(plts, p)
	end
	plot(plts..., layout=(1, 3), size=(800, 400), plot_title="m8.5 post",
		plot_titlefontsize=14)
end

# ╔═╡ e0bc4345-c125-42ca-a088-b70a7c2f762c
md"### Code 8.26"

# ╔═╡ 0c58026c-ec86-4b16-9f00-398bed8d627a
begin
	Random.seed!(7)
	m8_5p_c = sample(m8_5(tulips.water_cent, tulips.shade_cent, tulips.blooms_std),
		Prior(), 1000)
	m8_5p_df = DataFrame(m8_5p_c);
end

# ╔═╡ 20a3b233-a892-4518-8912-a49dc8ee2126
let
	plts = []

	for shade ∈ -1:1
	    p = plot(xlims=(-1, 1), ylims=(-0.5, 1.5), xlab="water", ylab="blooms",
	             title="shade=$shade", titlefontsize=12)
	    water_seq = -1:1
	    mu = link(m8_5p_df, (r, water) -> r.a + r.bw*water + r.bs*shade +
			r.bws*water*shade, water_seq)
	    mu = hcat(mu...);
	    for μ ∈ first(eachrow(mu), 20)
	        plot!(water_seq, μ, c=:black, alpha=0.2)
	    end
	    hline!([0.0, 1.0], s=:dash, c=:black)
	    push!(plts, p)
	end
	plot(plts..., layout=(1, 3), size=(800, 400), plot_title="m8.5 prior",
		plot_titlefontsize=14)
end

# ╔═╡ 00000000-0000-0000-0000-000000000001
PLUTO_PROJECT_TOML_CONTENTS = """
[deps]
CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
DrWatson = "634d3b9d-ee7a-5ddf-bec9-22491ea816e1"
LaTeXStrings = "b964fa9f-0449-5b57-a5c2-d3ea65f4040f"
Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
ParetoSmooth = "a68b5a21-f429-434e-8bfa-46b447300aac"
ParetoSmoothedImportanceSampling = "98f080ec-61e2-11eb-1c7b-31ea1097256f"
Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
StatisticalRethinking = "2d09df54-9d0f-5258-8220-54c2a3d4fbee"
StatisticalRethinkingPlots = "e1a513d0-d9d9-49ff-a6dd-9d2e9db473da"
StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
StatsFuns = "4c63d2b9-4356-54db-8cca-17b64c39e42c"
StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd"
Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"

[compat]
CSV = "~0.9.11"
DataFrames = "~1.2.2"
DrWatson = "~2.7.5"
LaTeXStrings = "~1.3.0"
ParetoSmooth = "~0.7.0"
ParetoSmoothedImportanceSampling = "~1.1.0"
StatisticalRethinking = "~4.4.3"
StatisticalRethinkingPlots = "~1.0.0"
StatsBase = "~0.33.13"
StatsFuns = "~0.9.14"
StatsPlots = "~0.14.29"
Turing = "~0.19.0"
"""

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

[[ANSIColoredPrinters]]
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[[AbstractFFTs]]
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[[AbstractMCMC]]
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[[AbstractPPL]]
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[[AbstractTrees]]
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[[Adapt]]
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[[AdvancedHMC]]
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[[AdvancedMH]]
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[[AdvancedPS]]
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[[AdvancedVI]]
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[[ArgCheck]]
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[[ArgTools]]
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[[Arpack]]
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[[Arpack_jll]]
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[[ArrayInterface]]
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[[Artifacts]]
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[[AxisAlgorithms]]
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[[AxisArrays]]
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[[AxisKeys]]
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[[BangBang]]
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[[Base64]]
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[[Baselet]]
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[[Bijectors]]
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[[BitTwiddlingConvenienceFunctions]]
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[[Bzip2_jll]]
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[[CPUSummary]]
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[[CSV]]
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git-tree-sha1 = "49f14b6c56a2da47608fe30aed711b5882264d7a"
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[[Cairo_jll]]
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git-tree-sha1 = "f2202b55d816427cd385a9a4f3ffb226bee80f99"
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[[ChangesOfVariables]]
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[[Clustering]]
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[[CodecZlib]]
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[[ColorSchemes]]
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[[ColorTypes]]
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[[Colors]]
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[[CommonSolve]]
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[[CommonSubexpressions]]
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[[CompilerSupportLibraries_jll]]
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git-tree-sha1 = "3c76dde64d03699e074ac02eb2e8ba8254d428da"
uuid = "90137ffa-7385-5640-81b9-e52037218182"
version = "1.2.13"

[[StatisticalRethinking]]
deps = ["AxisKeys", "CSV", "DataFrames", "Dates", "Distributions", "DocStringExtensions", "Documenter", "Formatting", "KernelDensity", "LinearAlgebra", "MCMCChains", "MonteCarloMeasurements", "NamedArrays", "NamedTupleTools", "Optim", "OrderedCollections", "Parameters", "ParetoSmooth", "ParetoSmoothedImportanceSampling", "PrettyTables", "Random", "Reexport", "Requires", "Statistics", "StatsBase", "StatsFuns", "StructuralCausalModels", "Tables", "Test", "Unicode"]
git-tree-sha1 = "89dd34c569b411ee10c81855844893639c73fef5"
uuid = "2d09df54-9d0f-5258-8220-54c2a3d4fbee"
version = "4.4.3"

[[StatisticalRethinkingPlots]]
deps = ["Distributions", "DocStringExtensions", "KernelDensity", "LaTeXStrings", "Parameters", "Plots", "Reexport", "Requires", "StatisticalRethinking", "StatsPlots"]
git-tree-sha1 = "20135362e5daedd2f651f72ed7ea9e1363f9b81f"
uuid = "e1a513d0-d9d9-49ff-a6dd-9d2e9db473da"
version = "1.0.0"

[[StatisticalTraits]]
deps = ["ScientificTypesBase"]
git-tree-sha1 = "730732cae4d3135e2f2182bd47f8d8b795ea4439"
uuid = "64bff920-2084-43da-a3e6-9bb72801c0c9"
version = "2.1.0"

[[Statistics]]
deps = ["LinearAlgebra", "SparseArrays"]
uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"

[[StatsAPI]]
git-tree-sha1 = "0f2aa8e32d511f758a2ce49208181f7733a0936a"
uuid = "82ae8749-77ed-4fe6-ae5f-f523153014b0"
version = "1.1.0"

[[StatsBase]]
deps = ["DataAPI", "DataStructures", "LinearAlgebra", "LogExpFunctions", "Missings", "Printf", "Random", "SortingAlgorithms", "SparseArrays", "Statistics", "StatsAPI"]
git-tree-sha1 = "2bb0cb32026a66037360606510fca5984ccc6b75"
uuid = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
version = "0.33.13"

[[StatsFuns]]
deps = ["ChainRulesCore", "InverseFunctions", "IrrationalConstants", "LogExpFunctions", "Reexport", "Rmath", "SpecialFunctions"]
git-tree-sha1 = "bedb3e17cc1d94ce0e6e66d3afa47157978ba404"
uuid = "4c63d2b9-4356-54db-8cca-17b64c39e42c"
version = "0.9.14"

[[StatsPlots]]
deps = ["Clustering", "DataStructures", "DataValues", "Distributions", "Interpolations", "KernelDensity", "LinearAlgebra", "MultivariateStats", "Observables", "Plots", "RecipesBase", "RecipesPipeline", "Reexport", "StatsBase", "TableOperations", "Tables", "Widgets"]
git-tree-sha1 = "d6956cefe3766a8eb5caae9226118bb0ac61c8ac"
uuid = "f3b207a7-027a-5e70-b257-86293d7955fd"
version = "0.14.29"

[[StructArrays]]
deps = ["Adapt", "DataAPI", "StaticArrays", "Tables"]
git-tree-sha1 = "2ce41e0d042c60ecd131e9fb7154a3bfadbf50d3"
uuid = "09ab397b-f2b6-538f-b94a-2f83cf4a842a"
version = "0.6.3"

[[StructuralCausalModels]]
deps = ["CSV", "Combinatorics", "DataFrames", "DataStructures", "Distributions", "DocStringExtensions", "LinearAlgebra", "NamedArrays", "Reexport", "Statistics"]
git-tree-sha1 = "2ff1d30658057a17f3fca1ef501d8911ae66566e"
uuid = "a41e6734-49ce-4065-8b83-aff084c01dfd"
version = "1.1.0"

[[SuiteSparse]]
deps = ["Libdl", "LinearAlgebra", "Serialization", "SparseArrays"]
uuid = "4607b0f0-06f3-5cda-b6b1-a6196a1729e9"

[[TOML]]
deps = ["Dates"]
uuid = "fa267f1f-6049-4f14-aa54-33bafae1ed76"

[[TableOperations]]
deps = ["SentinelArrays", "Tables", "Test"]
git-tree-sha1 = "e383c87cf2a1dc41fa30c093b2a19877c83e1bc1"
uuid = "ab02a1b2-a7df-11e8-156e-fb1833f50b87"
version = "1.2.0"

[[TableTraits]]
deps = ["IteratorInterfaceExtensions"]
git-tree-sha1 = "c06b2f539df1c6efa794486abfb6ed2022561a39"
uuid = "3783bdb8-4a98-5b6b-af9a-565f29a5fe9c"
version = "1.0.1"

[[Tables]]
deps = ["DataAPI", "DataValueInterfaces", "IteratorInterfaceExtensions", "LinearAlgebra", "TableTraits", "Test"]
git-tree-sha1 = "fed34d0e71b91734bf0a7e10eb1bb05296ddbcd0"
uuid = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
version = "1.6.0"

[[Tar]]
deps = ["ArgTools", "SHA"]
uuid = "a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"

[[TerminalLoggers]]
deps = ["LeftChildRightSiblingTrees", "Logging", "Markdown", "Printf", "ProgressLogging", "UUIDs"]
git-tree-sha1 = "62846a48a6cd70e63aa29944b8c4ef704360d72f"
uuid = "5d786b92-1e48-4d6f-9151-6b4477ca9bed"
version = "0.1.5"

[[Test]]
deps = ["InteractiveUtils", "Logging", "Random", "Serialization"]
uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

[[Tracker]]
deps = ["Adapt", "DiffRules", "ForwardDiff", "LinearAlgebra", "MacroTools", "NNlib", "NaNMath", "Printf", "Random", "Requires", "SpecialFunctions", "Statistics"]
git-tree-sha1 = "bf4adf36062afc921f251af4db58f06235504eff"
uuid = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c"
version = "0.2.16"

[[TranscodingStreams]]
deps = ["Random", "Test"]
git-tree-sha1 = "216b95ea110b5972db65aa90f88d8d89dcb8851c"
uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa"
version = "0.9.6"

[[Transducers]]
deps = ["Adapt", "ArgCheck", "BangBang", "Baselet", "CompositionsBase", "DefineSingletons", "Distributed", "InitialValues", "Logging", "Markdown", "MicroCollections", "Requires", "Setfield", "SplittablesBase", "Tables"]
git-tree-sha1 = "bccb153150744d476a6a8d4facf5299325d5a442"
uuid = "28d57a85-8fef-5791-bfe6-a80928e7c999"
version = "0.4.67"

[[TreeViews]]
deps = ["Test"]
git-tree-sha1 = "8d0d7a3fe2f30d6a7f833a5f19f7c7a5b396eae6"
uuid = "a2a6695c-b41b-5b7d-aed9-dbfdeacea5d7"
version = "0.3.0"

[[Tullio]]
deps = ["ChainRulesCore", "DiffRules", "LinearAlgebra", "Requires"]
git-tree-sha1 = "0288b7a395fc412952baf756fac94e4f28bfec65"
uuid = "bc48ee85-29a4-5162-ae0b-a64e1601d4bc"
version = "0.3.2"

[[Turing]]
deps = ["AbstractMCMC", "AdvancedHMC", "AdvancedMH", "AdvancedPS", "AdvancedVI", "BangBang", "Bijectors", "DataStructures", "Distributions", "DistributionsAD", "DocStringExtensions", "DynamicPPL", "EllipticalSliceSampling", "ForwardDiff", "Libtask", "LinearAlgebra", "MCMCChains", "NamedArrays", "Printf", "Random", "Reexport", "Requires", "SciMLBase", "SpecialFunctions", "Statistics", "StatsBase", "StatsFuns", "Tracker", "ZygoteRules"]
git-tree-sha1 = "c40eac01a46b8efc8af9481982a618d6e54b506d"
uuid = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
version = "0.19.0"

[[URIs]]
git-tree-sha1 = "97bbe755a53fe859669cd907f2d96aee8d2c1355"
uuid = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4"
version = "1.3.0"

[[UUIDs]]
deps = ["Random", "SHA"]
uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"

[[UnPack]]
git-tree-sha1 = "387c1f73762231e86e0c9c5443ce3b4a0a9a0c2b"
uuid = "3a884ed6-31ef-47d7-9d2a-63182c4928ed"
version = "1.0.2"

[[Unicode]]
uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"

[[UnicodeFun]]
deps = ["REPL"]
git-tree-sha1 = "53915e50200959667e78a92a418594b428dffddf"
uuid = "1cfade01-22cf-5700-b092-accc4b62d6e1"
version = "0.4.1"

[[VectorizationBase]]
deps = ["ArrayInterface", "CPUSummary", "HostCPUFeatures", "Hwloc", "IfElse", "LayoutPointers", "Libdl", "LinearAlgebra", "SIMDTypes", "Static"]
git-tree-sha1 = "17e5847bb36730d90801170ecd0ce4041a3dde86"
uuid = "3d5dd08c-fd9d-11e8-17fa-ed2836048c2f"
version = "0.21.22"

[[Wayland_jll]]
deps = ["Artifacts", "Expat_jll", "JLLWrappers", "Libdl", "Libffi_jll", "Pkg", "XML2_jll"]
git-tree-sha1 = "3e61f0b86f90dacb0bc0e73a0c5a83f6a8636e23"
uuid = "a2964d1f-97da-50d4-b82a-358c7fce9d89"
version = "1.19.0+0"

[[Wayland_protocols_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "66d72dc6fcc86352f01676e8f0f698562e60510f"
uuid = "2381bf8a-dfd0-557d-9999-79630e7b1b91"
version = "1.23.0+0"

[[WeakRefStrings]]
deps = ["DataAPI", "InlineStrings", "Parsers"]
git-tree-sha1 = "c69f9da3ff2f4f02e811c3323c22e5dfcb584cfa"
uuid = "ea10d353-3f73-51f8-a26c-33c1cb351aa5"
version = "1.4.1"

[[Widgets]]
deps = ["Colors", "Dates", "Observables", "OrderedCollections"]
git-tree-sha1 = "80661f59d28714632132c73779f8becc19a113f2"
uuid = "cc8bc4a8-27d6-5769-a93b-9d913e69aa62"
version = "0.6.4"

[[WoodburyMatrices]]
deps = ["LinearAlgebra", "SparseArrays"]
git-tree-sha1 = "de67fa59e33ad156a590055375a30b23c40299d3"
uuid = "efce3f68-66dc-5838-9240-27a6d6f5f9b6"
version = "0.5.5"

[[XML2_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Libiconv_jll", "Pkg", "Zlib_jll"]
git-tree-sha1 = "1acf5bdf07aa0907e0a37d3718bb88d4b687b74a"
uuid = "02c8fc9c-b97f-50b9-bbe4-9be30ff0a78a"
version = "2.9.12+0"

[[XSLT_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Libgcrypt_jll", "Libgpg_error_jll", "Libiconv_jll", "Pkg", "XML2_jll", "Zlib_jll"]
git-tree-sha1 = "91844873c4085240b95e795f692c4cec4d805f8a"
uuid = "aed1982a-8fda-507f-9586-7b0439959a61"
version = "1.1.34+0"

[[Xorg_libX11_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxcb_jll", "Xorg_xtrans_jll"]
git-tree-sha1 = "5be649d550f3f4b95308bf0183b82e2582876527"
uuid = "4f6342f7-b3d2-589e-9d20-edeb45f2b2bc"
version = "1.6.9+4"

[[Xorg_libXau_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4e490d5c960c314f33885790ed410ff3a94ce67e"
uuid = "0c0b7dd1-d40b-584c-a123-a41640f87eec"
version = "1.0.9+4"

[[Xorg_libXcursor_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXfixes_jll", "Xorg_libXrender_jll"]
git-tree-sha1 = "12e0eb3bc634fa2080c1c37fccf56f7c22989afd"
uuid = "935fb764-8cf2-53bf-bb30-45bb1f8bf724"
version = "1.2.0+4"

[[Xorg_libXdmcp_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4fe47bd2247248125c428978740e18a681372dd4"
uuid = "a3789734-cfe1-5b06-b2d0-1dd0d9d62d05"
version = "1.1.3+4"

[[Xorg_libXext_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "b7c0aa8c376b31e4852b360222848637f481f8c3"
uuid = "1082639a-0dae-5f34-9b06-72781eeb8cb3"
version = "1.3.4+4"

[[Xorg_libXfixes_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "0e0dc7431e7a0587559f9294aeec269471c991a4"
uuid = "d091e8ba-531a-589c-9de9-94069b037ed8"
version = "5.0.3+4"

[[Xorg_libXi_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll", "Xorg_libXfixes_jll"]
git-tree-sha1 = "89b52bc2160aadc84d707093930ef0bffa641246"
uuid = "a51aa0fd-4e3c-5386-b890-e753decda492"
version = "1.7.10+4"

[[Xorg_libXinerama_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll"]
git-tree-sha1 = "26be8b1c342929259317d8b9f7b53bf2bb73b123"
uuid = "d1454406-59df-5ea1-beac-c340f2130bc3"
version = "1.1.4+4"

[[Xorg_libXrandr_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll", "Xorg_libXrender_jll"]
git-tree-sha1 = "34cea83cb726fb58f325887bf0612c6b3fb17631"
uuid = "ec84b674-ba8e-5d96-8ba1-2a689ba10484"
version = "1.5.2+4"

[[Xorg_libXrender_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "19560f30fd49f4d4efbe7002a1037f8c43d43b96"
uuid = "ea2f1a96-1ddc-540d-b46f-429655e07cfa"
version = "0.9.10+4"

[[Xorg_libpthread_stubs_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "6783737e45d3c59a4a4c4091f5f88cdcf0908cbb"
uuid = "14d82f49-176c-5ed1-bb49-ad3f5cbd8c74"
version = "0.1.0+3"

[[Xorg_libxcb_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "XSLT_jll", "Xorg_libXau_jll", "Xorg_libXdmcp_jll", "Xorg_libpthread_stubs_jll"]
git-tree-sha1 = "daf17f441228e7a3833846cd048892861cff16d6"
uuid = "c7cfdc94-dc32-55de-ac96-5a1b8d977c5b"
version = "1.13.0+3"

[[Xorg_libxkbfile_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "926af861744212db0eb001d9e40b5d16292080b2"
uuid = "cc61e674-0454-545c-8b26-ed2c68acab7a"
version = "1.1.0+4"

[[Xorg_xcb_util_image_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "0fab0a40349ba1cba2c1da699243396ff8e94b97"
uuid = "12413925-8142-5f55-bb0e-6d7ca50bb09b"
version = "0.4.0+1"

[[Xorg_xcb_util_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxcb_jll"]
git-tree-sha1 = "e7fd7b2881fa2eaa72717420894d3938177862d1"
uuid = "2def613f-5ad1-5310-b15b-b15d46f528f5"
version = "0.4.0+1"

[[Xorg_xcb_util_keysyms_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "d1151e2c45a544f32441a567d1690e701ec89b00"
uuid = "975044d2-76e6-5fbe-bf08-97ce7c6574c7"
version = "0.4.0+1"

[[Xorg_xcb_util_renderutil_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "dfd7a8f38d4613b6a575253b3174dd991ca6183e"
uuid = "0d47668e-0667-5a69-a72c-f761630bfb7e"
version = "0.3.9+1"

[[Xorg_xcb_util_wm_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "e78d10aab01a4a154142c5006ed44fd9e8e31b67"
uuid = "c22f9ab0-d5fe-5066-847c-f4bb1cd4e361"
version = "0.4.1+1"

[[Xorg_xkbcomp_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxkbfile_jll"]
git-tree-sha1 = "4bcbf660f6c2e714f87e960a171b119d06ee163b"
uuid = "35661453-b289-5fab-8a00-3d9160c6a3a4"
version = "1.4.2+4"

[[Xorg_xkeyboard_config_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xkbcomp_jll"]
git-tree-sha1 = "5c8424f8a67c3f2209646d4425f3d415fee5931d"
uuid = "33bec58e-1273-512f-9401-5d533626f822"
version = "2.27.0+4"

[[Xorg_xtrans_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "79c31e7844f6ecf779705fbc12146eb190b7d845"
uuid = "c5fb5394-a638-5e4d-96e5-b29de1b5cf10"
version = "1.4.0+3"

[[Zlib_jll]]
deps = ["Libdl"]
uuid = "83775a58-1f1d-513f-b197-d71354ab007a"

[[Zstd_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "cc4bf3fdde8b7e3e9fa0351bdeedba1cf3b7f6e6"
uuid = "3161d3a3-bdf6-5164-811a-617609db77b4"
version = "1.5.0+0"

[[ZygoteRules]]
deps = ["MacroTools"]
git-tree-sha1 = "8c1a8e4dfacb1fd631745552c8db35d0deb09ea0"
uuid = "700de1a5-db45-46bc-99cf-38207098b444"
version = "0.2.2"

[[libass_jll]]
deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "HarfBuzz_jll", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
git-tree-sha1 = "5982a94fcba20f02f42ace44b9894ee2b140fe47"
uuid = "0ac62f75-1d6f-5e53-bd7c-93b484bb37c0"
version = "0.15.1+0"

[[libfdk_aac_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "daacc84a041563f965be61859a36e17c4e4fcd55"
uuid = "f638f0a6-7fb0-5443-88ba-1cc74229b280"
version = "2.0.2+0"

[[libpng_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
git-tree-sha1 = "94d180a6d2b5e55e447e2d27a29ed04fe79eb30c"
uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f"
version = "1.6.38+0"

[[libvorbis_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"]
git-tree-sha1 = "c45f4e40e7aafe9d086379e5578947ec8b95a8fb"
uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a"
version = "1.3.7+0"

[[nghttp2_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"

[[p7zip_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"

[[x264_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4fea590b89e6ec504593146bf8b988b2c00922b2"
uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a"
version = "2021.5.5+0"

[[x265_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9"
uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76"
version = "3.5.0+0"

[[xkbcommon_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"]
git-tree-sha1 = "ece2350174195bb31de1a63bea3a41ae1aa593b6"
uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd"
version = "0.9.1+5"
"""

# ╔═╡ Cell order:
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text:
#=
This file is auto-generated. Do not edit.
=#
"""
    mutable struct TGFixed <: TurbineGov
        efficiency::Float64
        P_ref::Float64
        ext::Dict{String, Any}
        states::Vector{Symbol}
        n_states::Int64
        internal::InfrastructureSystemsInternal
    end

Parameters of a fixed Turbine Governor that returns a fixed mechanical torque
 given by the product of P_ref*efficiency

# Arguments
- `efficiency::Float64`:  Efficiency factor that multiplies P_ref, validation range: (0, nothing)
- `P_ref::Float64`: Reference Power Set-point, validation range: (0, nothing)
- `ext::Dict{String, Any}`
- `states::Vector{Symbol}`
- `n_states::Int64`
- `internal::InfrastructureSystemsInternal`: power system internal reference, do not modify
"""
mutable struct TGFixed <: TurbineGov
    " Efficiency factor that multiplies P_ref"
    efficiency::Float64
    "Reference Power Set-point"
    P_ref::Float64
    ext::Dict{String, Any}
    states::Vector{Symbol}
    n_states::Int64
    "power system internal reference, do not modify"
    internal::InfrastructureSystemsInternal
end

function TGFixed(efficiency, P_ref=1.0, ext=Dict{String, Any}(), )
    TGFixed(efficiency, P_ref, ext, Vector{Symbol}(), 0, InfrastructureSystemsInternal(), )
end

function TGFixed(; efficiency, P_ref=1.0, ext=Dict{String, Any}(), )
    TGFixed(efficiency, P_ref, ext, )
end

# Constructor for demo purposes; non-functional.
function TGFixed(::Nothing)
    TGFixed(;
        efficiency=0,
        P_ref=0,
        ext=Dict{String, Any}(),
    )
end

"""Get TGFixed efficiency."""
get_efficiency(value::TGFixed) = value.efficiency
"""Get TGFixed P_ref."""
get_P_ref(value::TGFixed) = value.P_ref
"""Get TGFixed ext."""
get_ext(value::TGFixed) = value.ext
"""Get TGFixed states."""
get_states(value::TGFixed) = value.states
"""Get TGFixed n_states."""
get_n_states(value::TGFixed) = value.n_states
"""Get TGFixed internal."""
get_internal(value::TGFixed) = value.internal

"""Set TGFixed efficiency."""
set_efficiency!(value::TGFixed, val::Float64) = value.efficiency = val
"""Set TGFixed P_ref."""
set_P_ref!(value::TGFixed, val::Float64) = value.P_ref = val
"""Set TGFixed ext."""
set_ext!(value::TGFixed, val::Dict{String, Any}) = value.ext = val
"""Set TGFixed states."""
set_states!(value::TGFixed, val::Vector{Symbol}) = value.states = val
"""Set TGFixed n_states."""
set_n_states!(value::TGFixed, val::Int64) = value.n_states = val
"""Set TGFixed internal."""
set_internal!(value::TGFixed, val::InfrastructureSystemsInternal) = value.internal = val

ext: jl


text:
"""
	Functions used in SeisPGVPGA.jl

Modified from SeisMonitoring.jl(https://github.com/kura-okubo/SeisMonitoring.jl)
2021.1.3 Kurama Okubo
"""

"""
	get_requeststations(fi_stations::String, locchan::Dict, stationlist_fopath::String)

return dictionary of request stations.

# Arguments
- 'fi_stations::String': path to the station list downloaded from IRIS GMAP2 text format.
- 'locchan::Dict': Dictionary of channel and location.

e.g. locchan=Dict("BP" => [(*, "BP*")]) requests loc=* and chan=BP* for BP network

- 'stationlist_fopath::String': Output file name for backup.

"""
function get_requeststations(fi_stations::String, locchan::Dict, stationlist_fopath::String)

	# read input file
	lines = readlines(fi_stations)

	#---make stationlist---#
	# parse algorithm:
	# 1. search until encounting #DATACENTER
	# 2. append stations to the chunc

	requeststations = Dict()
	i = 1
	while i <= length(lines)
	    if occursin("#DATACENTER", lines[i])
	        #start appending this srcchunk
	        src = string(split(split(lines[i], "=")[2], ",")[1])
	        # #manipulate IRIS variation
	        # occursin("IRIS", src) && (src = "IRIS")
	        srcchunk = DataFrame(network=String[],
	                               station=String[],
	                               location=String[],
	                               channel=String[],
	                               latitude=String[],
	                               longitude=String[],
	                               elevation=String[],
	                               sitename=String[],
	                               starttime=DateTime[],
	                               endtime=DateTime[])

	        i += 1
	        while true
	            # append request stations
	            lc = split(lines[i],"|")
	            net=lc[1]
	            sta=lc[2]
	            lat=lc[3]
	            lon=lc[4]
	            ele=lc[5]
	            sitename=lc[6]
	            starttime=DateTime(lc[7])
	            endtime=DateTime(lc[8])

	            # choose request channel if locchan dict contains the network; otherwise request all (e.g. *, *)
	            if net ∈ keys(locchan)
	                reqs = locchan[net]
	                for req in reqs
	                    loc, chan = string.(req)

	                    dftemp = DataFrame(network=net, station=sta,location=loc,channel=chan,
	                    latitude=lat, longitude=lon,elevation=ele, sitename=sitename, starttime=starttime,endtime=endtime)
	                    append!(srcchunk,dftemp)
	                end

	            else
	                dftemp = DataFrame(network=net, station=sta,location="*",channel="*",
	                latitude=lat, longitude=lon,elevation=ele, sitename=sitename, starttime=starttime,endtime=endtime)
	                append!(srcchunk,dftemp)
	            end

	            i += 1
	            if (i > length(lines) || isempty(lines[i]))
	                # push srcchunk to master Dictionary
	                requeststations[src] = srcchunk

	                # output csv
	                CSV.write(stationlist_fopath[1:end-5]*"_$(src).csv", srcchunk)
	                # next src chunk or end of file
	                break
	            end
	        end

	    else
	        i += 1
	        continue
	    end
	end
	return requeststations
end

"""

get_starttimelist(st::DateTime, et::DateTime, unittime::Float64)
calculate start time list for parallel downloading

    st: start time
    et: end time
    unittime: unit time in Second

    this function returns
    stlist: list of start time

    e.g.
    st = DateTime(2019,1,1,0,0,0)
    et = DateTime(2019,1,1,12,0,0)
    unittime = 3600

    stlist = get_starttimelist(st, et, unittime)

"""
function get_starttimelist(st::DateTime, et::DateTime, unittime::Real)

    reftime = st
    stlist = []

    while reftime < et
        push!(stlist, string(reftime))
        reftime += Dates.Second(float(unittime))
    end

    return stlist
end

"""
	get_requeststr(df::DataFrame, numstationperrequest::Int))

return request str following web_chanspec of SeisIO.get_data.

- 'numstationperrequest::Int': number of stations per one request to avoid too many request statinos at one time.
"""
function get_requeststr(df::DataFrame, numstationperrequest::Int)

	reqstrs = Array{Array{String,1},1}(undef, 0) # array of request stations per HTTP request
	for inds = Iterators.partition(1:size(df)[1], numstationperrequest)
		reqstr  = String[]
		for i in inds
			rst = join([df.network[i], df.station[i], df.location[i], df.channel[i]], ".")
			push!(reqstr, rst)
		end
		push!(reqstrs, reqstr)
	end
	#
	# for i = 1:size(df)[1]
	# 	rst = join([df.network[i], df.station[i], df.location[i], df.channel[i]], ".")
	# end
	return reqstrs
end

"""
    check_and_get_data(ex::Expr, requeststr::String)

Download seismic data, removing instrumental response and saving into JLD2 file.

# Arguments
- `ex::Expr`        : expression of get data includin all request information

# Output
- `S::SeisData`     : downloaded SeisData
"""
function check_and_get_data(ex::Expr)

	# we try the same download request upto 3 times because it sometimes fails
	# due to network error.
	download_itr = 1

	S = SeisData()

	while download_itr <= 3
		S = try
			#remove comment out below if you want to print contents of get_data()
			# println(ex)
			eval(ex);
		catch
		end

		if !isnothing(S) && !isempty(S)
			break;
		else
			download_itr += 1
			#println("retry downloading $(download_itr).")
		end
	end

	if isnothing(S) || isempty(S)
		return 1
	end

	for j = 1:S.n
		!isnothing(S[j]) && !isempty(S[j]) && !isempty(S[j].t) ? S.misc[j]["dlerror"] = 0 : S.misc[j]["dlerror"] = 1
	end

	return S
end

"""
    norm2(A; dims)

Compute norm along dimension of array.
https://github.com/JuliaLang/julia/issues/34830
"""
function norm2(A; dims)
    B = sum(x -> x^2, A; dims=dims)
    B .= sqrt.(B)
end

"""
    df_rms(P_vel::DSP.Periodograms.Periodogram,  fmin::Real, fmax::Real)

compute rms displacement from velocity power spectrum density.
Modified from SeismoRMS (https://github.com/ThomasLecocq/SeismoRMS) written by Thomas Lecocq.
https://github.com/ThomasLecocq/SeismoRMS/blob/master/seismosocialdistancing.py l.256
"""
function df_rms(P_vel::DSP.Periodograms.Periodogram, fmin::Real, fmax::Real)

    ix = findall( x-> (fmin<x<fmax), P_vel.freq)
    spec = P_vel.power[ix]
    f = P_vel.freq[ix]
    w2f = (2.0 * pi .* f)
    # NOTE: welch_pgram() returns power spectrum density, not in dB. so we don't compute 10^(S/10)
    vamp = spec
    damp = vamp ./ (w2f.^2)
    d_rms = sqrt(trapz(f, damp))

    return d_rms
end

"""
    compute_pgvpga_drms(S::SeisData, bp_fmin::Real, bp_fmax::Real, drms_fmin::Real, drms_fmax::Real)

compute pgv, pga and rms displacement from 3 components SeisData.

# Arguments
- `S::SeisData`     : 3 components (i.e. S.n==3) SeisData.
- `bp_fmin::Real`   : minimum frequency for preprocessing.
- `bp_fmax::Real`   : maximum frequency for preprocessing.
- `drms_fmin::Real`   : minimum frequency for d_rms integration
- `drms_fmax::Real`   : maximum frequency for d_rms integration

# Output
- `pgv::Float64`     : Peak ground velocity magnitude (norm2(vx, vy, vz))
- `pga::Float64`     : Peak ground acceleration magnitude (norm2(ax, ay, az))
- `d_rms_mean::Float64`     : mean strength of noise displacement between drms_fmin and drms_fmax
"""
function compute_pgvpga_drms(S::SeisData, bp_fmin::Real, bp_fmax::Real, drms_fmin::Real, drms_fmax::Real)

    if S.n != 3
        @warn "$(S.id) does not have 3 components. skip."
        return (NaN, NaN, NaN)
    end

	#assert data length
	if !(length(S[1].x[:]) == length(S[2].x[:]) == length(S[3].x[:]))
		@warn "$(S.id) data length is different between components. skip."
		return (NaN, NaN, NaN)
	end

    #---Preprocessing---#
    detrend!(S)
    demean!(S)
    taper!(S, t_max=180, α=0.1)
    filtfilt!(S, fl=bp_fmin, fh=bp_fmax, rt="Bandpass", dm="Butterworth")

    #---Convert trace---#
    S_acc = convert_seis(S, units_out="m/s2", v=0);
    S_vel = convert_seis(S, units_out="m/s", v=0);

    #---Preprocessing again for velocity and acceleration---#
    detrend!(S_acc)
    demean!(S_acc)
    taper!(S_acc, t_max=180, α=0.1)
    filtfilt!(S_acc, fl=bp_fmin, fh=bp_fmax, rt="Bandpass", dm="Butterworth")

    detrend!(S_vel)
    demean!(S_vel)
    taper!(S_vel, t_max=180, α=0.1)
    filtfilt!(S_vel, fl=bp_fmin, fh=bp_fmax, rt="Bandpass", dm="Butterworth")

    #---Compute acceleration and velocity magnitude---#
    acc_arr=zeros(size(S_acc[1].x, 1), 3)
    vel_arr=zeros(size(S_vel[1].x, 1), 3)

    for i = 1:3
        acc_arr[:,i]=S_acc[i].x[:]
        vel_arr[:,i]=S_vel[i].x[:]
    end

    S_accmag = deepcopy(S_acc[1])
    S_accmag.id = S_accmag.id[1:end-3]*"MAG"
    S_accmag.x = norm2(acc_arr, dims=2)[:, 1]

    S_velmag = deepcopy(S_vel[1])
    S_velmag.id = S_velmag.id[1:end-3]*"MAG"
    S_velmag.x = norm2(vel_arr, dims=2)[:, 1];

    #---Pick up PGV and PGA---#
    pgv = maximum(S_velmag.x)
    pga = maximum(S_accmag.x)

    #---Compute d_rms---#

    d_rms_3comp=zeros(3)
    for i = 1:3
        N = length(S_vel[i].x)
        welch_n = div(N, 8)
        welch_noverlap = div(welch_n, 2)
        P_vel = welch_pgram(S_vel[i].x, welch_n, welch_noverlap, onesided=true, nfft=nextfastfft(welch_n), fs=S_acc[1].fs, window=DSP.Windows.hanning)
        P_vel.power[P_vel.power.<=1e-20] .= 1e-20 # avoid zero in power
        d_rms_3comp[i] = df_rms(P_vel, drms_fmin, drms_fmax)
    end

    # take mean rms of 3 components
    d_rms_mean = mean(d_rms_3comp)

    return (pgv, pga, d_rms_mean)

end


text:
using AlphaZero

depth = 5
# gspec = Examples.games["tictactoe"]
gspec = Examples.games["bonbon-rectangle"]
computer = MinMax.Player(depth=depth, amplify_rewards=true, τ=0.2)
interactive!(gspec, computer, Human())


text:
using FactoredValueMCTS
using Documenter

makedocs(;
    modules=[FactoredValueMCTS],
    authors="Stanford Intelligent Systems Laboratory",
    repo="https://github.com/JuliaPOMDP/FactoredValueMCTS.jl/blob/{commit}{path}#L{line}",
    sitename="FactoredValueMCTS.jl",
    format=Documenter.HTML(;
        prettyurls=get(ENV, "CI", "false") == "true",
        canonical="https://JuliaPOMDP.github.io/FactoredValueMCTS.jl",
        assets=String[],
    ),
    pages=[
        "Home" => "index.md",
    ],
)

deploydocs(;
    repo="github.com/JuliaPOMDP/FactoredValueMCTS.jl",
)


text:
# Weave readme
using Pkg
cd("c:/git/JLBoostMLJ")
Pkg.activate("c:/git/JLBoostMLJ/jlboost-test")

using Weave

weave("c:/git/JLBoostMLJ/README.jmd", out_path="c:/git/JLBoostMLJ", doctype="github")

if false
    tangle("c:/git/JLBoostMLJ/README.jmd")
end


text:
function chop_sections(set1, set2, n :: Integer, spacing = "cosine"; flip = false)
    if lowercase(spacing) == "uniform"
        space = uniform_spacing(0., 1., n + 1)
    elseif lowercase(spacing) == "sine"
        space = sine_spacing(0., 1., (n + 1) * ifelse(flip, -1, 1))
    else
        space = cosine_spacing(0.5, 1., n + 1)
    end

    @views [ weighted_vector.(set1, set2, μ) for μ ∈ space ][1:end-1]
end

chop_coordinates(coords, n, spacings = "cosine", flip = false) = @views [ reduce(vcat, chop_sections.(coords[1:end-1], coords[2:end], n, spacings; flip = flip)); [ coords[end] ] ]

chord_sections(lead, trail) = [ [ l'; t' ] for (l, t) ∈ zip(lead, trail) ]

chop_chords(coords, n) = @views [ [ weighted_vector(chord[1,:], chord[2,:], μ) for μ ∈ cosine_spacing(0.5, 1., n + 1) ] for chord ∈ coords ]

chop_spans(lead, trail, div, spacing = "cosine", flip = false) = chop_coordinates(lead, div, spacing, flip), chop_coordinates(trail, div, spacing, flip)

chop_wing(lead, trail, span_num, chord_num; span_spacing = "cosine", flip = false) = let (lead, trail) = chop_spans(lead, trail, span_num, span_spacing, flip); chop_chords(chord_sections(lead, trail), chord_num) end

"""
    make_panels(xyzs)

Convert an array of coordinates corresponding to a wing, ordered from root to tip and leading-edge to trailing-edge, into panels.
"""
make_panels(xyzs) = @views Panel3D.(xyzs[1:end-1,1:end-1], xyzs[2:end,1:end-1], xyzs[2:end,2:end], xyzs[1:end-1,2:end])

# WTF was I thinking?
# spanlist = vectarray.(coords)
# spanlist = zip(coords, coords[2:end,:])
# adjacent_sections = zip(spanlist, spanlist[2:end])
# @views hcat(( Panel3D.(root[1:end-1], root[2:end], tip[2:end], tip[1:end-1]) for (root, tip) ∈ adjacent_sections )...)

max_forks_repo_name: GodotMisogi/AeroMDAO


text:
using ThinModules
using Test

@testset "ThinModules.jl" begin
end


text:
module SeawaterPolynomials

"""
    AbstractSeawaterPolynomial

Abstract type for Boussinesq equations of state expressed as polynomial functions
of conservative temperature, absolute salinity, and geopotential depth.
"""
abstract type AbstractSeawaterPolynomial end

"""
    ρ(Θ, Sᴬ, Z, equation_of_state)

Returns the total density of a seawater parcel with conservative tempertuare `Θ`, absolute
salinity `Sᴬ`, at the geopotential height `Z`, using the Boussinesq `equation_of_state`.

The geopotential height is defined such that ``Z(x, y) = 0`` at sea level and *decreases*
downwards to negative values, towards the bottom of the ocean.

This function aliases `total_density`.
"""
@inline ρ(Θ, Sᴬ, Z, eos) = eos.reference_density + ρ′(Θ, Sᴬ, Z, eos)

"""
    ρ′(Θ, Sᴬ, Z, equation_of_state)

Returns the total density of a seawater parcel with conservative tempertuare `Θ`, absolute
salinity `Sᴬ`, at the geopotential height `Z`, using the Boussinesq `equation_of_state`.

The geopotential height is defined such that ``Z(x, y) = 0`` at sea level and *decreases*
downwards to negative values, towards the bottom of the ocean.

The function aliases `density_anomaly`.
"""
function ρ′ end

"""
    thermal_expansion(Θ, Sᴬ, Z, equation_of_state)

Returns the thermal expansion coefficient,

```math
α = - ∂ρ / ∂Θ ,
```

for a seawater parcel with conservative tempertuare `Θ`, absolute salinity `Sᴬ`, at the
geopotential height `Z`, and using the Boussinesq `equation_of_state`. The thermal expansion
coefficient measures how much seawater density changes when conservative temperature is changed.
'Thermal expansion' is so named because, due to sign convention, positive values reflect decreasing
seawater density with increasing conservative temperature, and thus an 'expansion' of oceanic
fluid parcels. In many, but not all conditions in Earth's ocean (at temperatures greater than
4ᵒC in freshwater), the thermal expansion coefficient is positive.

The geopotential height is defined such that ``Z(x, y) = 0`` at sea level and *decreases*
downwards to negative values, towards the bottom of the ocean.
"""
function thermal_expansion end

"""
    haline_contraction(Θ, Sᴬ, Z, equation_of_state)

Returns the haline contraction coefficient,

```math
β = ∂ρ / ∂Sᴬ ,
```

for a seawater parcel with absolute salinity `Sᴬ` and at fixed conservative tempertuare `Θ`
and geopotential height `Z`, using the Boussinesq `equation_of_state`. The haline contraction
coefficient measures how much seawater density changes when absolute salinity is changed.
'Haline contraction' is so named because, due to sign convention, positive values reflect increasing
seawater density with increasing absolute salinity, and thus a slight `contraction' of oceanic
fluid parcels.

The geopotential height is defined such that ``Z(x, y) = 0`` at sea level and *decreases*
downwards to negative values, towards the bottom of the ocean.
"""
function haline_contraction end

const total_density = ρ
const density_anomaly = ρ′

struct BoussinesqEquationOfState{P, FT}
    seawater_polynomial :: P
      reference_density :: FT
end

@inline reference_density(eos) = eos.reference_density

include("SecondOrderSeawaterPolynomials.jl")
include("TEOS10.jl")

using .SecondOrderSeawaterPolynomials
using .TEOS10

end # module


text:
using ArrayViews

#import Base.LinAlg.LAPACK
import Base.LinAlg.BLAS.BlasFloat
import Base.LinAlg.BLAS.BlasInt
import Base.lufact!
#using Base.LinAlg.LAPACK.gttrs!
using Base.LU

function lufact!{T<:BlasFloat}(A::TridiagonalP{T}, pivot::Union{Type{Val{false}}, Type{Val{true}}} = Val{true})
    n = length(A.d)
    cn = A.du[n]
    b1 = A.dl[1]

    ipiv = zeros(BlasInt, n-1)
    dl1, d, du1, du2, ipiv = Lapack.gttrf!(view(A.dl, 2:n-1), view(A.d, 1:n-1), view(A.du, 1:n-2), A.du2, ipiv)
    LU{T,TridiagonalP{T}}(A, ipiv, 0)
end

import Base.factorize
factorize(A::TridiagonalP) = lufact(A)

import Base.A_ldiv_B!
function A_ldiv_B!{T<:BlasFloat}(A::LU{T,TridiagonalP{T}}, B::AbstractVecOrMat)
    n = size(A, 1)
    n1 = n-1
    qn1 = view(B, 1:n1, :)
    nrhs = size(B,2)

    D = view(A.factors.d, 1:n1)
    Dl = view(A.factors.dl, 2:n1)
    Du = view(A.factors.du, 1:n-2)
    Lapack.gttrs!('N', Dl, D, Du, A.factors.du2, A.ipiv, qn1)

    x2 = A.factors.x2

    b1  = A.factors.dl[1]
    bn = A.factors.dl[n]
    cn1 = A.factors.du[n1]
    cn = A.factors.du[n]
    an = A.factors.d[n]

    for i = 1:nrhs
        fill!(x2, 0)
        x2[1] = -b1
        x2[n1] = -cn1
        Lapack.gttrs!('N', Dl, D, Du, A.factors.du2, A.ipiv, x2)
        xn = (B[n,i] - cn*qn1[1,i] - bn*qn1[n1,i]) / (an + cn*x2[1] + bn*x2[n1])
        B[n,i] = xn
        for k = 1:n1
            B[k,i] = B[k,i] + xn*x2[k]
        end
    end
    B
end

using Base.LinAlg.Cholesky
import Base.cholfact!
immutable Lixo{T,S<:AbstractMatrix} <: Factorization{T}
    factors::S
    uplo::Char
end

function cholfact!{T<:BlasFloat}(A::SymTridiagonalP{T})

    n = length(A.d)
    cn = A.du[n]

    Lapack.pttrf!(view(A.d, 1:n-1), view(A.du, 1:n-2))
    Cholesky{T,SymTridiagonalP}(A, 'U')
end

import Base.cholfact
cholfact{T<:BlasFloat}(A::SymTridiagonalP{T}) = cholfact!(copy(A))

factorize(A::SymTridiagonalP) = cholfact(A)

function A_ldiv_B!{T<:BlasFloat}(A::Cholesky{T,SymTridiagonalP{T}}, B::AbstractVecOrMat{T})
    n = size(A, 1)
    n1 = n-1
    qn1 = view(B, 1:n1, :)
    nrhs = size(B,2)

    D = view(A.factors.d, 1:n1)
    Du = view(A.factors.du, 1:n-2)
    Lapack.pttrs!(D, Du, qn1)

    x2 = A.factors.x2

    b1  = A.factors.du[n]
    bn = A.factors.du[n-1]
    cn1 = bn
    cn = b1
    an = A.factors.d[n]
    for i = 1:nrhs
        fill!(x2, 0)
        x2[1] = -b1
        x2[n1] = -cn1
        Lapack.pttrs!(D, Du, x2)
        println("CHEGOU")
        xn = (B[n,i] - cn*qn1[1,i] - bn*qn1[n1,i]) / (an + cn*x2[1] + bn*x2[n1])
        B[n,i] = xn
        for k = 1:n1
            B[k,i] = B[k,i] + xn*x2[k]
        end
    end
    B
end

import Base.\

function \{T<:BlasFloat}(A::Cholesky{T,SymTridiagonalP{T}}, B::StridedMatrix{T})
    A_ldiv_B!(A, copy(B))
end