text:
### A Pluto.jl notebook ###
# v0.18.0

using Markdown
using InteractiveUtils

# ╔═╡ 18d9e080-ac17-11ec-1845-897491a08319
begin
	import CairoMakie
	using Flux, FastAI, DataFramesMeta, RData
end

# ╔═╡ 41234cee-7c8d-4ce9-a1fa-8bdaf1de2f74
using RCall, MLJ

# ╔═╡ 841da28c-b48e-4bb4-8c36-9fd09427d7ce
using Dates, Colors, StatsPlots

# ╔═╡ aac22bd4-1435-4c75-a59f-0d4e93f42c56
using Interact, PlutoUI

# ╔═╡ f4e60282-37af-4ee7-a2ec-836e263812da
CairoMakie.activate!(type = "png")

# ╔═╡ 0825feb0-2dea-4649-88c2-5cb17802da5a
import Plots

# ╔═╡ ea0d4501-5007-4fe9-9a16-d37edc64bbd3
TablesofContent

# ╔═╡ f20d1c83-0c79-4db0-ab09-71f1da9597d3
df = rcopy(R"""readRDS("N:/ATHE/HELG/Office/Technik/03_Fachbereiche/Prozessentwicklung/02_Datentransparenz/datenlesen/data_ZF/dfFokus.rds")""")

# ╔═╡ f2cb1772-0f21-4cd8-978a-da4df2c430ba
dat = @chain df begin
	@rsubset :Bauteil == "HP09120"
	#@rsubset :ANFA == "0"
	@rsubset !ismissing(:BA)
	dropmissing([:M1, :M2, :TZ, :SchussNr, :CATEGORY])
end

# ╔═╡ 3d5b125e-a80a-4729-92c0-f54b6c1c8ba4
dat[!, :CATEGORY_reduced ] .= ifelse.(dat.CATEGORY .== "Gut", "iO", "NiO")

# ╔═╡ fbefd95f-b0ab-4da1-acb9-89634ed8d3c0
y, X_indentifier, X_cat, X_cont, rest = unpack(dat, ==(:CATEGORY), in([:FormNr, :Bauteil, :MACHINED_PART]), in(Symbol.(["DGM", "BA", "MACHINING_PLANT_LINE"])), in(Symbol.(["DR", "M1", "M2", "H3", "TZ", "V1", "V2"])))

# ╔═╡ b8beb299-e3f3-4488-b4bb-7a24c6c49919
begin
	param = "DR"
	plots = []
	for (ix,form) in enumerate(unique(dat.FormNr))
		dat2 = @rsubset(dat, :FormNr == form)
		param_val = dat2[!,param]

		p1 = Plots.scatter(
			dat2.Datum, param_val, group = dat2.DGM,
			msw = 0.1, title = form,
			legendtitle = "DGM"
			#color = convert(Array{String}, replace(dat2.ANFA,"0" => "steelblue", "1" => "red", missing => "orange"))
		)

		p2 = Plots.scatter(
			dat2.Datum, param_val, group = dat2.ANFA,
			msw = 0.1, title = form,
			color = convert(Array{String}, replace(dat2.ANFA,"0" => "steelblue", "1" => "red", missing => "orange")),
			legendtitle = "ANFA"
		)

		p3 = Plots.scatter(
			dat2.Datum, param_val, group = dat2.CATEGORY_reduced,
			msw = 0.1, title = form,
			color = convert(Array{String}, replace(dat2.CATEGORY_reduced,"iO" => "steelblue", "NiO" => "orange")),
			legendtitle = "CATEGORY",
				ms = 1
		)
		push!(
			plots, Plots.plot(p1,p2, p3, layout = (1,3), link = :all)
		)
	end
	p = Plots.plot(plots..., layout = (length(unique(dat.FormNr)),1), legend = :outertopright, link = :all, plot_title = "HP09120",plot_xlab = "Datum", ylab = "$param", size = [2000,2000])

	#savefig(p, "../results/HP09120_analysis_$param.png")
end

# ╔═╡ 2d50d232-40f2-47a8-8126-579fe33771e9
Plots.gr()

# ╔═╡ 7bbb6ebc-238e-4819-98d8-defd566ebc07
md"""
# FastAI Modeling
"""

# ╔═╡ 54cf6e38-2557-4070-8685-b0ff959dc927
data = TableDataset(df)

# ╔═╡ c27b9326-18db-4659-aa39-b57dc4d9ae6c
begin
	inputblock = TableRow(cat, cont, catdict)
	targetblock = Label(unique(data.table[:, target]))
end

# ╔═╡ 00000000-0000-0000-0000-000000000001
PLUTO_PROJECT_TOML_CONTENTS = """
[deps]
CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0"
Colors = "5ae59095-9a9b-59fe-a467-6f913c188581"
DataFramesMeta = "1313f7d8-7da2-5740-9ea0-a2ca25f37964"
Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
FastAI = "5d0beca9-ade8-49ae-ad0b-a3cf890e669f"
Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
Interact = "c601a237-2ae4-5e1e-952c-7a85b0c7eef1"
MLJ = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7"
Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
PlutoUI = "7f904dfe-b85e-4ff6-b463-dae2292396a8"
RCall = "6f49c342-dc21-5d91-9882-a32aef131414"
RData = "df47a6cb-8c03-5eed-afd8-b6050d6c41da"
StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd"

[compat]
CairoMakie = "~0.7.5"
Colors = "~0.12.8"
DataFramesMeta = "~0.10.0"
FastAI = "~0.4.0"
Flux = "~0.12.9"
Interact = "~0.10.4"
MLJ = "~0.17.3"
Plots = "~1.27.2"
PlutoUI = "~0.7.37"
RCall = "~0.13.13"
RData = "~0.8.3"
StatsPlots = "~0.14.33"
"""

# ╔═╡ 00000000-0000-0000-0000-000000000002
PLUTO_MANIFEST_TOML_CONTENTS = """
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[[Isoband]]
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[[StatsFuns]]
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[[StatsModels]]
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[[TensorBoardLogger]]
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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"

[[ZipFile]]
deps = ["Libdl", "Printf", "Zlib_jll"]
git-tree-sha1 = "3593e69e469d2111389a9bd06bac1f3d730ac6de"
uuid = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea"
version = "0.9.4"

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

[[Zstd_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "e45044cd873ded54b6a5bac0eb5c971392cf1927"
uuid = "3161d3a3-bdf6-5164-811a-617609db77b4"
version = "1.5.2+0"

[[Zygote]]
deps = ["AbstractFFTs", "ChainRules", "ChainRulesCore", "DiffRules", "Distributed", "FillArrays", "ForwardDiff", "IRTools", "InteractiveUtils", "LinearAlgebra", "MacroTools", "NaNMath", "Random", "Requires", "SparseArrays", "SpecialFunctions", "Statistics", "ZygoteRules"]
git-tree-sha1 = "52adc0a505b6421a8668f13dcdb0c4cb498bd72c"
uuid = "e88e6eb3-aa80-5325-afca-941959d7151f"
version = "0.6.37"

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

[[isoband_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "51b5eeb3f98367157a7a12a1fb0aa5328946c03c"
uuid = "9a68df92-36a6-505f-a73e-abb412b6bfb4"
version = "0.2.3+0"

[[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 = "b910cb81ef3fe6e78bf6acee440bda86fd6ae00c"
uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a"
version = "1.3.7+1"

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

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

[[protoc_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "cd74748e54f76134ddf000a6e3022901485c2d26"
uuid = "c7845625-083e-5bbe-8504-b32d602b7110"
version = "3.19.4+0"

[[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:
# ╠═18d9e080-ac17-11ec-1845-897491a08319
# ╠═41234cee-7c8d-4ce9-a1fa-8bdaf1de2f74
# ╠═f4e60282-37af-4ee7-a2ec-836e263812da
# ╠═0825feb0-2dea-4649-88c2-5cb17802da5a
# ╠═841da28c-b48e-4bb4-8c36-9fd09427d7ce
# ╠═aac22bd4-1435-4c75-a59f-0d4e93f42c56
# ╠═ea0d4501-5007-4fe9-9a16-d37edc64bbd3
# ╠═f20d1c83-0c79-4db0-ab09-71f1da9597d3
# ╠═f2cb1772-0f21-4cd8-978a-da4df2c430ba
# ╠═3d5b125e-a80a-4729-92c0-f54b6c1c8ba4
# ╠═fbefd95f-b0ab-4da1-acb9-89634ed8d3c0
# ╠═b8beb299-e3f3-4488-b4bb-7a24c6c49919
# ╠═2d50d232-40f2-47a8-8126-579fe33771e9
# ╟─7bbb6ebc-238e-4819-98d8-defd566ebc07
# ╠═54cf6e38-2557-4070-8685-b0ff959dc927
# ╠═c27b9326-18db-4659-aa39-b57dc4d9ae6c
# ╟─00000000-0000-0000-0000-000000000001
# ╟─00000000-0000-0000-0000-000000000002


text:
struct NullInfoState <: CFR.AbstractInfoState end

# remove game from AbstractCFRSolver type parameters?
struct TabularStrategy{K,G} <: CFR.AbstractCFRSolver{K,G,NullInfoState}
    game::G
    d::Dict{K,Vector{Float64}}
end

# function TabularStrategy(game::Game{H,K}, dict::Dict{K,<:AbstractVector}) where {H,K}
#     return TabularStrategy{K, typeof(game)}(dict)
# end

CFR.strategy(sol::TabularStrategy{K}, I::K) where K = sol.d[I]

const α_opt = 0.10

const OptimalKuhnStrategy = TabularStrategy(CFR.Kuhn(), Dict(
    (1,1,SA[-1,-1,-1]) => [1. - α_opt, α_opt],
    (1,2,SA[-1,-1,-1]) => [1., 0.],
    (1,3,SA[-1,-1,-1]) => [1-3α_opt,3α_opt],

    (2,1,SA[0,-1,-1]) => [2/3,1/3],
    (2,2,SA[0,-1,-1]) => [1., 0.],
    (2,3,SA[0,-1,-1]) => [0., 1.],

    (2,1,SA[1,-1,-1]) => [1., 0.],
    (2,2,SA[1,-1,-1]) => [2/3,1/3],
    (2,3,SA[1,-1,-1]) => [0., 1.],

    (1,1,SA[0,1,-1]) => [1., 0.],
    (1,2,SA[0,1,-1]) => [2/3-α_opt,1/3+α_opt],
    (1,3,SA[0,1,-1]) => [0., 1.]
))

const SuboptimalKuhnStrategy = TabularStrategy(CFR.Kuhn(), Dict(
    (1,1,SA[-1,-1,-1]) => normalize!(rand(2),1),
    (1,2,SA[-1,-1,-1]) => normalize!(rand(2),1),
    (1,3,SA[-1,-1,-1]) => normalize!(rand(2),1),

    (2,1,SA[0,-1,-1]) => normalize!(rand(2),1),
    (2,2,SA[0,-1,-1]) => normalize!(rand(2),1),
    (2,3,SA[0,-1,-1]) => normalize!(rand(2),1),

    (2,1,SA[1,-1,-1]) => normalize!(rand(2),1),
    (2,2,SA[1,-1,-1]) => normalize!(rand(2),1),
    (2,3,SA[1,-1,-1]) => normalize!(rand(2),1),

    (1,1,SA[0,1,-1]) => normalize!(rand(2),1),
    (1,2,SA[0,1,-1]) => normalize!(rand(2),1),
    (1,3,SA[0,1,-1]) => normalize!(rand(2),1),
))

const OptimalRPSStrategy = TabularStrategy(CFR.IIEMatrixGame(), Dict(
    0 => fill(1/3,3),
    1 => fill(1/3,3)
))

const SuboptimalRPSStrategy = TabularStrategy(CFR.IIEMatrixGame(), Dict(
    0 => Float64[1,0,0],
    1 => Float64[1,0,0]
))

const SlightlySuboptimalRPSStrategy = TabularStrategy(CFR.IIEMatrixGame(), Dict(
    0 => Float64[0.35,0.30,0.35],
    1 => Float64[0.30,0.35,0.35]
))

@testset "Exploitability" begin
    @testset "RPS" begin
        atol = 1e-3

        RPS = CFR.IIEMatrixGame()
        e_sol1 = ExploitabilitySolver(RPS, 1)
        e_opt1 = exploitability(e_sol1, OptimalRPSStrategy)

        e_sol2 = ExploitabilitySolver(RPS, 2)
        e_opt2 = exploitability(e_sol2, OptimalRPSStrategy)

        @test ≈(e_opt1, 0.0, atol=atol)
        @test ≈(e_opt2, 0.0, atol=atol)

        e_sol1 = ExploitabilitySolver(RPS, 1)
        e_ssopt1 = exploitability(e_sol1, SlightlySuboptimalRPSStrategy)

        e_sol2 = ExploitabilitySolver(RPS, 2)
        e_ssopt2 = exploitability(e_sol2, SlightlySuboptimalRPSStrategy)

        @test e_ssopt1 > 0.0
        @test e_ssopt2 > 0.0

        e_sol1 = ExploitabilitySolver(RPS, 1)
        e_sopt1 = exploitability(e_sol1, SuboptimalRPSStrategy)

        e_sol2 = ExploitabilitySolver(RPS, 2)
        e_sopt2 = exploitability(e_sol2, SuboptimalRPSStrategy)

        @test e_sopt1 > 0.0
        @test e_sopt2 > 0.0

        @test e_sopt1 > e_ssopt1 > e_opt1
        @test e_sopt2 > e_ssopt2 > e_opt2
    end

    @testset "Kuhn" begin
        atol = 1e-3

        kuhn = CFR.Kuhn()
        e_sol1 = ExploitabilitySolver(kuhn, 1)
        e_opt1 = exploitability(e_sol1, OptimalKuhnStrategy)

        e_sol2 = ExploitabilitySolver(kuhn, 2)
        e_opt2 = exploitability(e_sol2, OptimalKuhnStrategy)

        @test ≈(e_opt1, 0.0, atol=atol)
        @test ≈(e_opt2, 0.0, atol=atol)

        e_sol1 = ExploitabilitySolver(kuhn, 1)
        e_sopt1 = exploitability(e_sol1, SuboptimalKuhnStrategy)

        e_sol2 = ExploitabilitySolver(kuhn, 2)
        e_sopt2 = exploitability(e_sol2, SuboptimalKuhnStrategy)

        @test e_sopt1 > 0.0
        @test e_sopt2 > 0.0
    end
end

ext: jl

max_stars_repo_name: WhiffleFish/HelloCFR

max_issues_repo_name: WhiffleFish/HelloCFR

max_forks_repo_name: WhiffleFish/HelloCFR


text:
# ------------------------------------------------------------------------------------------
# # Quick start
# (c) Deniz Yuret, 2019
#
# This notebook is for the impatient reader who wants to get a flavor of Julia/Knet possibly
# to compare it with other deep learning frameworks. In 15 lines of code and 30 seconds of
# GPU time we define, train, and evaluate the LeNet convolutional neural network model from
# scratch without any predefined layers.
# ------------------------------------------------------------------------------------------

using Knet

# Define convolutional layer:
struct Conv; w; b; f; end
(c::Conv)(x) = c.f.(pool(conv4(c.w, x) .+ c.b))
Conv(w1,w2,cx,cy,f=relu) = Conv(param(w1,w2,cx,cy), param0(1,1,cy,1), f);

# Define dense layer:
struct Dense; w; b; f; end
(d::Dense)(x) = d.f.(d.w * mat(x) .+ d.b)
Dense(i::Int,o::Int,f=relu) = Dense(param(o,i), param0(o), f);

# Define a chain of layers:
struct Chain; layers; Chain(args...)=new(args); end
(c::Chain)(x) = (for l in c.layers; x = l(x); end; x)
(c::Chain)(x,y) = nll(c(x),y)

# Load MNIST data
include(Knet.dir("data","mnist.jl"))
dtrn, dtst = mnistdata();

# Train and test LeNet (about 30 secs on a gpu to reach 99% accuracy)
LeNet = Chain(Conv(5,5,1,20), Conv(5,5,20,50), Dense(800,500), Dense(500,10,identity))
progress!(adam(LeNet, repeat(dtrn,10)))
accuracy(LeNet, dtst)

max_stars_repo_name: grenkoca/JuliaTutorials

max_issues_repo_name: grenkoca/JuliaTutorials

max_forks_repo_name: grenkoca/JuliaTutorials


text:
#function testNHHeldSuarezSphere
using CGDycore

OrdPoly = 4
OrdPolyZ=1
nz = 10
nPanel = 4
NF = 6 * nPanel * nPanel

# Cache
cache=CGDycore.Cache(OrdPoly, OrdPolyZ, nz, NF)

# Physical parameters
Param=CGDycore.PhysParameters(cache);

# Grid
Param.nPanel=nPanel;
Param.H=30000;
Param.Grid=CGDycore.CubedGrid(Param.nPanel,CGDycore.OrientFaceSphere,Param);

Param.Grid.nz=nz;
Param.Grid.zP=zeros(nz,1);
Param.Grid.z=zeros(nz+1,1);
Param.Grid.dz=Param.H/nz;
Param.Grid.zP[1]=Param.Grid.dz/2;
for i=2:nz
  Param.Grid.zP[i]=Param.Grid.zP[i-1]+Param.Grid.dz;
end
for i=2:nz+1
  Param.Grid.z[i]=Param.Grid.z[i-1]+Param.Grid.dz;
end

# Discretization
(CG,Param)=CGDycore.Discretization(OrdPoly,OrdPolyZ,CGDycore.JacobiSphere3,Param);
LRef=11*1.e5;
dx=2*pi*Param.RadEarth/4/Param.nPanel/OrdPoly;
Param.HyperVisc=true;
Param.HyperDCurl=2.e17; #1.e14*(dx/LRef)^3.2;
Param.HyperDGrad=2.e17;
Param.HyperDDiv=2.e17; # Scalars
Param.Upwind = false

# Model
Param.ModelType="Curl";
Param.RefProfile = false
Param.Deep=false;
Param.HeightLimit=30000.0;
Param.T0E=310.0;
Param.T0P=240.0;
Param.B=2.0;
Param.K=3.0;
Param.LapseRate=0.005;
Param.U0=-0.5;
Param.PertR=1.0/6.0;
Param.Up=0.0; #1.0;
Param.PertExpR=0.1;
Param.PertLon=pi/9.0;
Param.PertLat=2.0*pi/9.0;
Param.PertZ=15000.0;

Param.StrideDamp=6000;
Param.Relax=1.e-4;
Param.Damping=false;
Param.Coriolis=true;
Param.CoriolisType="Sphere";
Param.Buoyancy=true;
Param.Source=true;
Param.Th0=300;
Param.uMax=10;
Param.vMax=0;
Param.NBr=1.e-2;
Param.DeltaT=1;
Param.ExpDist=5;
Param.T0=300;
Param.Equation="Compressible";
Param.TopoS="";
Param.lat0=0;
Param.lon0=pi/2;
Param.ProfVel="BaroWaveSphere";
Param.ProfRho="BaroWaveSphere";
Param.ProfTheta="BaroWaveSphere";
Param.NumV=5;
Param.RhoPos=1;
Param.uPos=2;
Param.vPos=3;
Param.wPos=4;
Param.ThPos=5;
Param.Thermo="";#"Energy"
#Held Suarez
Param.day=3600*24;
Param.k_a=1/(40 * Param.day);
Param.k_f=1/Param.day;
Param.k_s=1/(4*Param.day);
Param.DeltaT_y=60;
Param.DeltaTh_z=10;
Param.T_equator=315;
Param.T_min=200;
Param.sigma_b=7/10;
Param.z_D=20.0e3;

# Output
Param.RadPrint=Param.H;
Param.Flat=true;
Param.vtkFileName="HeldSuarezNeu";
Param.vtk=0;
vtkGrid=CGDycore.vtkCGGrid(CG,CGDycore.TransSphere,CGDycore.Topo,Param);
Param.cNames = [
  "Rho",
  "u",
  "v",
  "w",
  "Th"
]
#SphericalGrid=

# Initial conditions
U=zeros(CG.NumG,nz,Param.NumV);
U[:,:,Param.RhoPos]=CGDycore.Project(CGDycore.fRho,CG,Param);
(U[:,:,Param.uPos],U[:,:,Param.vPos])=CGDycore.ProjectVec(CGDycore.fVel,CG,Param);
U[:,:,Param.ThPos]=CGDycore.Project(CGDycore.fTheta,CG,Param).*U[:,:,Param.RhoPos];

# Integration
CFL=0.125;
dtau=600;
time=[0];

IntMethod="Rosenbrock";
# IntMethod="RungeKutta";
if strcmp(IntMethod,"Rosenbrock")
  dtau=600;
else
  dtau=8;
end
Param.RK=CGDycore.RungeKuttaMethod("RK4");
Param.ROS=CGDycore.RosenbrockMethod("SSP-Knoth");
SimDays=100;
# SimDays=1;
PrintDay=10;
nIter=24*3600*SimDays/dtau;
PrintInt=24*3600*PrintDay/dtau;
# Print initial conditions
Param.vtk=CGDycore.vtkOutput(U,vtkGrid,CG,Param);
#
str = IntMethod
OP=CG.OrdPoly+1;
NF=Param.Grid.NumFaces;
nz=Param.Grid.nz;
Param.CacheF1=zeros(OP,OP,NF,nz+1);
Param.CacheF2=zeros(OP,OP,NF,nz+1);
Param.CacheF3=zeros(OP,OP,NF,nz+1);
Param.CacheF4=zeros(OP,OP,NF,nz+1);
Param.CacheF5=zeros(OP,OP,NF,nz+1);
Param.CacheF6=zeros(OP,OP,NF,nz+1);
Param.CacheC1 = view(Param.CacheF1,:,:,:,1:nz)
Param.CacheC2 = view(Param.CacheF2,:,:,:,1:nz)
Param.CacheC3 = view(Param.CacheF3,:,:,:,1:nz)
Param.CacheC4 = view(Param.CacheF4,:,:,:,1:nz)
Param.CacheC5 = view(Param.CacheF5,:,:,:,1:nz)
Param.CacheC6 = view(Param.CacheF6,:,:,:,1:nz)
Param.Cache1=zeros(CG.NumG,nz)
Param.Cache2=zeros(CG.NumG,nz)
Param.Cache3=zeros(CG.NumG,nz)
Param.Cache4=zeros(CG.NumG,nz)
Param.Pres=zeros(OP,OP,NF,nz)
Param.KE=zeros(OP,OP,NF,nz)
Param.FCG=zeros(OP,OP,NF,nz,size(U,3))
Param.k=zeros(size(U)..., Param.ROS.nStage);
Param.fV=zeros(size(U))
Param.Vn=zeros(size(U))
Param.RhoCG=zeros(OP,OP,NF,nz)
Param.v1CG=zeros(OP,OP,NF,nz)
Param.v2CG=zeros(OP,OP,NF,nz)
Param.wCG=zeros(OP,OP,NF,nz+1)
Param.wCCG=zeros(OP,OP,NF,nz+1)
Param.ThCG=zeros(OP,OP,NF,nz)
Param.J = CGDycore.JacStruct(CG.NumG,nz)
if str == "Rosenbrock"
    @time begin
      for i=1:nIter
        Δt = @elapsed begin
          CGDycore.RosenbrockSchur!(U,dtau,CGDycore.FcnNHCurlVec!,CGDycore.JacSchur,CG,Param);
          if mod(i,PrintInt)==0
            Param.vtk=CGDycore.vtkOutput(U,vtkGrid,CG,Param);
          end
        end
        time[1] += dtau;
        if mod(i,PrintInt)==0
          percent = i/nIter*100
          @info "Iteration: $i took $Δt, $percent% complete"
        end
      end
    end

elseif str == "RungeKutta"
    for i=1:nIter
      @info "Iteration: $i"

      U .= CGDycore.RungeKuttaExplicit(U,dtau,CGDycore.FcnNHCurlVec,CG,Param);

      time[1] += dtau;
      if mod(i,PrintInt)==0
        Param.vtk=CGDycore.vtkOutput(U,vtkGrid,CG,Param);
      end
    end
else
  error("Bad str")
end
Param.vtk=CGDycore.vtkOutput(U,vtkGrid,CG,Param)

@info "Success!"


text:
println("£")
println("\302\243"); # works if your terminal is utf-8

max_stars_repo_name: ethansaxenian/RosettaDecode

max_issues_repo_name: ethansaxenian/RosettaDecode

max_forks_repo_name: ethansaxenian/RosettaDecode


text:
@testset "RSwM3" begin

using DiffEqNoiseProcess

W = WienerProcess(0.0,0.0,0.0,rswm=RSWM(adaptivealg=:RSwM3))

dt = 0.2
calculate_step!(W,dt,nothing,nothing)
W.curW,W.dW,W.curZ,W.dZ
isempty(W.S₂)

reject_step!(W,0.1,nothing,nothing)
W.curW,W.dW,W.curZ,W.dZ
DiffEqNoiseProcess.first(W.S₁)
isempty(W.S₂)

accept_step!(W,0.2,nothing,nothing)
W.curW,W.dW,W.curZ,W.dZ
isempty(W.S₂)

reject_step!(W,0.1,nothing,nothing)
W.curW,W.dW,W.curZ,W.dZ
DiffEqNoiseProcess.first(W.S₁)

accept_step!(W,0.2,nothing,nothing)
W.curW,W.dW,W.curZ,W.dZ

for i in 1:100
  reject_step!(W,0.1,nothing,nothing)
  accept_step!(W,0.1,nothing,nothing)
end

dt = dt/100
for t in dt:dt:W.t[end]-dt
  W(t)
end

W = WienerProcess(0.0,0.0,0.0,rswm=RSWM(adaptivealg=:RSwM3))

dt = 0.2
calculate_step!(W,dt,nothing,nothing)
W.curW,W.dW,W.curZ,W.dZ
isempty(W.S₂)

for i in 1:100
  reject_step!(W,0.01,nothing,nothing)
  accept_step!(W,0.2,nothing,nothing)
  accept_step!(W,0.1,nothing,nothing)
end

dt = dt/100
for t in dt:dt:W.t[end]-dt
  W(t)
end

end


text:
import Base: ==

export Token, token_type

"""
A string of code is turned into an array of Tokens by the lexer. Each
symbol or word in code is represented as a Token.
"""
struct Token
    kind::TokenType
    lexeme::String
    function Token(kind::TokenType, s::AbstractString)
        # Don't want to store the quotes on quoted strings.
        if length(s) > 1
            new(kind, strip(s, '"'))
        else
            new(kind, s)
        end
    end
end

function Token(kind::TokenType)
    ch = Char(kind)
    if ch in "{}(),=;"
        Token(kind, string(ch))
    else
       Token(kind, "")
    end
end

==(t1::Token, t2::Token) = t1.kind == t2.kind && t1.lexeme == t2.lexeme

token_type(token::Token) = token.kind