text:
### A Pluto.jl notebook ###
# v0.17.0

using Markdown
using InteractiveUtils

# ╔═╡ 16eab80b-325b-43bd-8bda-6b9ed27513a8
begin
	using IndividualDisplacements, OceanStateEstimation, DataFrames, MeshArrays, NetCDF
	import CairoMakie as Mkie
end

# ╔═╡ 68c92218-40d3-11ec-0397-1747ac61c311
md"""# Three Dimensional Ocean Circulation

Advect particles with climatological mean flow in three dimensions starting from a selected depth level (e.g. `k=10` for 95 m) and region using a near-global ocean state estimate ([OCCA](https://doi.org/10.1175/2009JPO4043.1) which is here repeated for two years. For additional documentation e.g. see : [1](https://JuliaClimate.github.io/MeshArrays.jl/dev/), [2](https://JuliaClimate.github.io/IndividualDisplacements.jl/dev/), [3](https://docs.juliadiffeq.org/latest/solvers/ode_solve.html), [4](https://en.wikipedia.org/wiki/Displacement_(vector))

"""
#![Three dimensional simulation](https://user-images.githubusercontent.com/20276764/94491485-595ee900-01b6-11eb-95e6-c2cacb812f46.png)

# ╔═╡ 44346351-f249-4376-b002-8147755ed489
md"""## Initialization"""

# ╔═╡ 00464caa-fab2-4fd2-b39b-177e505d6d89
md"""## Compute Displacements"""

# ╔═╡ b66f8e31-6aae-429b-9c42-bb9ea2d01eb3
md"""## Helper Functions"""

# ╔═╡ a879b36d-8536-4b9e-a22d-b3d2161e589c
"""
    OCCA_FlowFields(;backward_in_time::Bool=false,nmax=Inf)

Define gridded variables and return result as NamedTuple
"""
function OCCA_FlowFields(;backward_in_time::Bool=false,nmax=Inf)

   γ=GridSpec("PeriodicChannel",MeshArrays.GRID_LL360)
   Γ=GridLoad(γ;option="full")
   n=length(Γ.RC)
   isfinite(nmax) ? n=min(n,Int(nmax)) : nothing

   g=Γ.XC.grid
   func=(u -> IndividualDisplacements.update_location_dpdo!(u,g))

   jj=[:hFacC, :hFacW, :hFacS, :DXG, :DYG, :RAC, :RAZ, :RAS]
   ii=findall([!in(i,jj) for i in keys(Γ)])
   Γ=(; zip(Symbol.(keys(Γ)[ii]), values(Γ)[ii])...)

   backward_in_time ? s=-1.0 : s=1.0
   s=Float32(s)

   function rd(filename, varname,n)
   fil = NetCDF.open(filename, varname)
   siz = size(fil)
   tmp = zeros(siz[1:2]...,n)
   [tmp .+= fil[:,:,1:n,t] for t=1:12]
   tmp ./= 12.0
   tmp[findall(tmp.<-1e22)] .= 0.0
   return tmp
   end

   fileIn=OCCAclim_path*"DDuvel.0406clim.nc"
   u=s*read(rd(fileIn,"u",n),MeshArray(γ,Float32,n))

   fileIn=OCCAclim_path*"DDvvel.0406clim.nc"
   v=s*read(rd(fileIn,"v",n),MeshArray(γ,Float32,n))

   fileIn=OCCAclim_path*"DDwvel.0406clim.nc"
   w=s*rd(fileIn,"w",n)
   w=-cat(w,zeros(360, 160),dims=3)
   w[:,:,1] .=0.0
   w=read(w,MeshArray(γ,Float32,n+1))

   fileIn=OCCAclim_path*"DDtheta.0406clim.nc"
   θ=read(rd(fileIn,"theta",n),MeshArray(γ,Float32,n))

#   fileIn=OCCAclim_path*"DDsalt.0406clim.nc"
#   𝑆=read(rd(fileIn,"salt",n),MeshArray(γ,Float64,n))

   for i in eachindex(u)
      u[i]=u[i]./Γ.DXC[1]
      v[i]=v[i]./Γ.DYC[1]
   end

   for i in eachindex(u)
      u[i]=circshift(u[i],[-180 0])
      v[i]=circshift(v[i],[-180 0])
      θ[i]=circshift(θ[i],[-180 0])
#      𝑆[i]=circshift(𝑆[i],[-180 0])
   end

   for i in eachindex(w)
      w[i]=w[i]./Γ.DRC[min(i[2]+1,n)]
      w[i]=circshift(w[i],[-180 0])
   end

   tmpx=circshift(Γ.XC[1],[-180 0])
   tmpx[1:180,:]=tmpx[1:180,:] .- 360.0
   Γ.XC[1]=tmpx

   tmpx=circshift(Γ.XG[1],[-180 0])
   tmpx[1:180,:]=tmpx[1:180,:] .- 360.0
   Γ.XG[1]=tmpx
   Γ.Depth[1]=circshift(Γ.Depth[1],[-180 0])

   t0=0.0; t1=86400*366*2.0;

   for k=1:n
    (tmpu,tmpv)=exchange(u[:,k],v[:,k],1)
    u[:,k]=tmpu
    v[:,k]=tmpv
   end
   for k=1:n+1
    tmpw=exchange(w[:,k],1)
    w[:,k]=tmpw
   end

   𝑃=FlowFields(u,u,v,v,w,w,[t0,t1],func)

   𝐷 = (θ0=θ, θ1=θ, XC=exchange(Γ.XC), YC=exchange(Γ.YC),
   RF=Γ.RF, RC=Γ.RC,ioSize=(360,160,n))

   return 𝑃,𝐷,Γ

end

# ╔═╡ 66c95828-227c-4db5-a6f1-3e3004a99785
begin
	OceanStateEstimation.get_occa_velocity_if_needed();
	𝑃,𝐷,Γ=OCCA_FlowFields(nmax=5)
	"done with flow fields"
end

# ╔═╡ 378b6547-898d-477a-a796-285a1f7e9b08
"""
    initial_positions(Γ; nf=10000, lon_rng=(-160.0,-159.0), lat_rng=(30.0,31.0))

Randomly assign initial positions in longitude,latitude ranges. Positions are
expressed in, normalized, grid point units (x,y in the 0,nx and 0,ny range).
To convert from longitude,latitude here we take advantage of the regularity
of the 1 degree grid being used -- for a more general alternative, see the
global ocean example.
"""
function initial_positions(Γ::NamedTuple, nf=10000, lon_rng=(-160.0,-159.0), lat_rng=(30.0,31.0))
   lon=lon_rng[1] .+(lon_rng[2]-lon_rng[1]).*rand(nf)
   lat=lat_rng[1] .+(lat_rng[2]-lat_rng[1]).*rand(nf)
   x=lon .+ (21. - Γ.XC[1][21,1])
   y=lat .+ (111. - Γ.YC[1][1,111])
   return x,y
end

# ╔═╡ b754f4f6-b513-4eda-b689-8e0529223417
custom🔴 = DataFrame(ID=Int[], fid=Int[], x=Float64[], y=Float64[],
   k=Float64[], z=Float64[], iso=Float64[], t=Float64[],
   lon=Float64[], lat=Float64[], year=Float64[], col=Symbol[])

# ╔═╡ fd541c93-1b58-47a3-8c48-2d635fe0bda6
"""
    isosurface(θ,T,z)

```
isosurface(𝐼.𝑃.θ0,15,Γ.RC)
```
"""
function isosurface(θ,T,z)
    d=NaN*similar(θ[:,1])
    nr=size(θ,2)
    for j=1:size(d,1)
        for k=1:nr-1
            i=findall(isnan.(d[j]).&(θ[j,k].>T).&(θ[j,k+1].<=T))
            a=(θ[j,k][i] .- T)./(θ[j,k][i] .- θ[j,k+1][i])
            d[j][i]=(1 .- a).*Γ.RC[k] + a.*Γ.RC[k+1]
            i=findall(isnan.(d[j]).&(θ[j,k].<=T).&(θ[j,k+1].>T))
            a=(θ[j,k+1][i] .- T)./(θ[j,k+1][i] .- θ[j,k][i])
            d[j][i]=(1 .- a).*Γ.RC[k+1] + a.*Γ.RC[k]
        end
    end
    return d
end

# ╔═╡ c5eeb1f0-2c6f-4cd5-8a66-538da09c282d
function custom🔧(sol,𝑃::𝐹_MeshArray3D;id=missing,𝑇=missing)
   df=postprocess_MeshArray(sol,𝑃,id=id,𝑇=𝑇)
   add_lonlat!(df,𝐷.XC,𝐷.YC)

   #add year (convenience time axis for plotting)
   df.year=df.t ./86400/365

   #add depth (i.e. the 3rd, vertical, coordinate)
   k=[sol[1,i,j][3] for i in 1:size(sol,2), j in 1:size(sol,3)]
   nz=length(𝐷.RC)
   df.k=min.(max.(k[:],Ref(0.0)),Ref(nz)) #level
   k=Int.(floor.(df.k)); w=(df.k-k);
   df.z=𝐷.RF[1 .+ k].*(1 .- w)+𝐷.RF[2 .+ k].*w #depth

   #add one isotherm depth
   θ=0.5*(𝐷.θ0+𝐷.θ1)
   d=isosurface(θ,15,𝐷.RC)
   d[findall(isnan.(d))].=0.
   df.iso=interp_to_xy(df,exchange(d));

   #add color = f(iso-z)
   c=fill(:gold,length(df.iso))
   c[findall(df.iso.<df.z)].=:violet
   df.col=c

   #to plot e.g. Pacific Ocean transports, shift longitude convention?
   df.lon[findall(df.lon .< 0.0 )] = df.lon[findall(df.lon .< 0.0 )] .+360.0
   return df
end

# ╔═╡ f199f321-976a-4ccd-a003-140211aa67fe
begin
	nf=100; lo=(-160.0,-150.0); la=(30.0,40.0); kk=2.5;
	df=DataFrame(:z => fill(kk,nf),:f => fill(1,nf))
	(df.x,df.y)=initial_positions(Γ, nf, lo, la)

	𝐼=Individuals(𝑃,df.x,df.y,df.z,df.f,(🔴=custom🔴,🔧=custom🔧))
end

# ╔═╡ 938fdaa8-357d-477e-8fa2-e6da53806242
begin
	𝑇=(0.0,10*86400.0)
	∫!(𝐼,𝑇)
	🔴_by_t = groupby(𝐼.🔴, :t)
end

# ╔═╡ dafc7de0-d2c4-42cd-8cd9-cc152cadb33e
begin
	Mkie.set_theme!(Mkie.theme_light())
	fig=Mkie.Figure(resolution = (900, 600))
	a = Mkie.Axis(fig[1, 1],xlabel="longitude",ylabel="latitude")
	Mkie.scatter!(a,🔴_by_t[1].lon,🔴_by_t[1].lat,color=:green2)
	Mkie.scatter!(a,🔴_by_t[end].lon,🔴_by_t[end].lat,color=:red)
	fig
end

# ╔═╡ 7f3b1f13-abfa-4fc0-8169-b69f1c3519f4
"""
    plot(𝐼::Individuals)

Plot the initial and final positions as scatter plot in x,y plane.
"""
function plot(𝐼::Individuals)
    🔴_by_t = groupby(𝐼.🔴, :t)
    if (sum(names(🔴_by_t).=="lon")==0)
        fig=scatter(🔴_by_t[1].x,🔴_by_t[1].y,c=:red,label="t0",marker = (:circle, stroke(0)))
        scatter!(🔴_by_t[end].x,🔴_by_t[end].y,c=:blue,label="t1",marker = (:circle, stroke(0)))
    else
        fig=scatter(🔴_by_t[1].lon,🔴_by_t[1].lat,c=:red,label="t0",marker = (:circle, stroke(0)))
        scatter!(🔴_by_t[end].lon,🔴_by_t[end].lat,c=:blue,label="t1",marker = (:circle, stroke(0)))
    end
    return fig
end

# ╔═╡ 00000000-0000-0000-0000-000000000001
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[[XMLDict]]
deps = ["EzXML", "IterTools", "OrderedCollections"]
git-tree-sha1 = "d9a3faf078210e477b291c79117676fca54da9dd"
uuid = "228000da-037f-5747-90a9-8195ccbf91a5"
version = "0.4.1"

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

[[Zarr]]
deps = ["AWS", "Blosc", "CodecZlib", "DataStructures", "Dates", "DiskArrays", "HTTP", "JSON", "OffsetArrays", "Pkg"]
git-tree-sha1 = "18ac3fd29790edeee42bfed5020b12ae61a029d0"
uuid = "0a941bbe-ad1d-11e8-39d9-ab76183a1d99"
version = "0.6.3"

[[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"

[[isoband_jll]]
deps = ["Libdl", "Pkg"]
git-tree-sha1 = "a1ac99674715995a536bbce674b068ec1b7d893d"
uuid = "9a68df92-36a6-505f-a73e-abb412b6bfb4"
version = "0.2.2+0"

[[libass_jll]]
deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
git-tree-sha1 = "acc685bcf777b2202a904cdcb49ad34c2fa1880c"
uuid = "0ac62f75-1d6f-5e53-bd7c-93b484bb37c0"
version = "0.14.0+4"

[[libfdk_aac_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "7a5780a0d9c6864184b3a2eeeb833a0c871f00ab"
uuid = "f638f0a6-7fb0-5443-88ba-1cc74229b280"
version = "0.1.6+4"

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

[[libsodium_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "848ab3d00fe39d6fbc2a8641048f8f272af1c51e"
uuid = "a9144af2-ca23-56d9-984f-0d03f7b5ccf8"
version = "1.0.20+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 = "d713c1ce4deac133e3334ee12f4adff07f81778f"
uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a"
version = "2020.7.14+2"

[[x265_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "487da2f8f2f0c8ee0e83f39d13037d6bbf0a45ab"
uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76"
version = "3.0.0+3"
"""

# ╔═╡ Cell order:
# ╟─68c92218-40d3-11ec-0397-1747ac61c311
# ╠═16eab80b-325b-43bd-8bda-6b9ed27513a8
# ╟─44346351-f249-4376-b002-8147755ed489
# ╟─66c95828-227c-4db5-a6f1-3e3004a99785
# ╟─f199f321-976a-4ccd-a003-140211aa67fe
# ╟─00464caa-fab2-4fd2-b39b-177e505d6d89
# ╟─938fdaa8-357d-477e-8fa2-e6da53806242
# ╟─dafc7de0-d2c4-42cd-8cd9-cc152cadb33e
# ╟─b66f8e31-6aae-429b-9c42-bb9ea2d01eb3
# ╟─a879b36d-8536-4b9e-a22d-b3d2161e589c
# ╟─378b6547-898d-477a-a796-285a1f7e9b08
# ╟─b754f4f6-b513-4eda-b689-8e0529223417
# ╟─c5eeb1f0-2c6f-4cd5-8a66-538da09c282d
# ╟─fd541c93-1b58-47a3-8c48-2d635fe0bda6
# ╠═7f3b1f13-abfa-4fc0-8169-b69f1c3519f4
# ╟─00000000-0000-0000-0000-000000000001
# ╟─00000000-0000-0000-0000-000000000002


text:
using Test

@testset "POMDPIDMModel.jl" begin
            include("tests.jl")
end

ext: jl


text:
using CSV, Statistics

rfile(name) = CSV.read(name, header=false)[:,1]
data = rfile.(["../data/machine1.csv",
		"../data/machine2.csv",
		"../data/machine3.csv"])
println("Sample means for each treatment: ",round.(mean.(data),digits=2))
println("Overall sample mean: ",round(mean(vcat(data...)),digits=2))

max_stars_repo_name: Yoshinobu-Ishizaki/StatsWithJuliaBook

max_issues_repo_name: Yoshinobu-Ishizaki/StatsWithJuliaBook

max_forks_repo_name: Yoshinobu-Ishizaki/StatsWithJuliaBook


text:
const CAPI_FUNC_SIGS = Dict{Symbol, Pair{Tuple, Type}}(
    # INITIALIZE
    :Py_Initialize => () => Cvoid,
    :Py_InitializeEx => (Cint,) => Cvoid,
    :Py_Finalize => () => Cvoid,
    :Py_FinalizeEx => () => Cint, # 3.6+
    :Py_AtExit => (Ptr{Cvoid},) => Cint,
    :Py_IsInitialized => () => Cint,
    :Py_SetPythonHome => (Ptr{Cwchar_t},) => Cvoid,
    :Py_SetProgramName => (Ptr{Cwchar_t},) => Cvoid,
    :Py_GetVersion => () => Ptr{Cchar},
    # REFCOUNT
    :Py_IncRef => (PyPtr,) => Cvoid,
    :Py_DecRef => (PyPtr,) => Cvoid,
    # EVAL
    :PyEval_EvalCode => (PyPtr, PyPtr, PyPtr) => PyPtr,
    :Py_CompileString => (Ptr{Cchar}, Ptr{Cchar}, Cint) => PyPtr,
    :PyEval_GetBuiltins => () => PyPtr,
    :PyRun_InteractiveOne => (Ptr{Cvoid}, Ptr{Cchar}) => Cint,
    # GIL & THREADS
    :PyEval_SaveThread => () => Ptr{Cvoid},
    :PyEval_RestoreThread => (Ptr{Cvoid},) => Cvoid,
    :PyGILState_Ensure => () => PyGILState_STATE,
    :PyGILState_Release => (PyGILState_STATE,) => Cvoid,
    # IMPORT
    :PyImport_ImportModule => (Ptr{Cchar},) => PyPtr,
    :PyImport_Import => (PyPtr,) => PyPtr,
    :PyImport_ImportModuleLevelObject => (PyPtr, PyPtr, PyPtr, PyPtr, Cint) => PyPtr,
    :PyImport_GetModuleDict => () => PyPtr, # borrowed
    # MODULE
    :PyModule_GetDict => (PyPtr,) => PyPtr, # borrowed
    # ERRORS
    :PyErr_Print => () => Cvoid,
    :PyErr_Occurred => () => PyPtr, # borrowed
    :PyErr_ExceptionMatches => (PyPtr,) => Cint,
    :PyErr_GivenExceptionMatches => (PyPtr, PyPtr) => Cint,
    :PyErr_Clear => () => Cvoid,
    :PyErr_SetNone => (PyPtr,) => Cvoid,
    :PyErr_SetString => (PyPtr, Ptr{Cchar}) => Cvoid,
    :PyErr_SetObject => (PyPtr, PyPtr) => Cvoid,
    :PyErr_Fetch => (Ptr{PyPtr}, Ptr{PyPtr}, Ptr{PyPtr}) => Cvoid,
    :PyErr_NormalizeException => (Ptr{PyPtr}, Ptr{PyPtr}, Ptr{PyPtr}) => Cvoid,
    :PyErr_Restore => (PyPtr, PyPtr, PyPtr) => Cvoid, # steals
    # OBJECT
    :_PyObject_New => (PyPtr,) => PyPtr,
    :PyObject_ClearWeakRefs => (PyPtr,) => Cvoid,
    :PyObject_HasAttrString => (PyPtr, Ptr{Cchar}) => Cint,
    :PyObject_HasAttr => (PyPtr, PyPtr) => Cint,
    :PyObject_GetAttrString => (PyPtr, Ptr{Cchar}) => PyPtr,
    :PyObject_GetAttr => (PyPtr, PyPtr) => PyPtr,
    :PyObject_GenericGetAttr => (PyPtr, PyPtr) => PyPtr,
    :PyObject_SetAttrString => (PyPtr, Ptr{Cchar}, PyPtr) => Cint,
    :PyObject_SetAttr => (PyPtr, PyPtr, PyPtr) => Cint,
    :PyObject_GenericSetAttr => (PyPtr, PyPtr, PyPtr) => PyPtr,
    :PyObject_RichCompare => (PyPtr, PyPtr, Cint) => PyPtr,
    :PyObject_RichCompareBool => (PyPtr, PyPtr, Cint) => Cint,
    :PyObject_Repr => (PyPtr,) => PyPtr,
    :PyObject_ASCII => (PyPtr,) => PyPtr,
    :PyObject_Str => (PyPtr,) => PyPtr,
    :PyObject_Bytes => (PyPtr,) => PyPtr,
    :PyObject_IsSubclass => (PyPtr, PyPtr) => Cint,
    :PyObject_IsInstance => (PyPtr, PyPtr) => Cint,
    :PyObject_Hash => (PyPtr,) => Py_hash_t,
    :PyObject_IsTrue => (PyPtr,) => Cint,
    :PyObject_Not => (PyPtr,) => Cint,
    :PyObject_Length => (PyPtr,) => Py_ssize_t,
    :PyObject_GetItem => (PyPtr, PyPtr) => PyPtr,
    :PyObject_SetItem => (PyPtr, PyPtr, PyPtr) => Cint,
    :PyObject_DelItem => (PyPtr, PyPtr) => Cint,
    :PyObject_Dir => (PyPtr,) => PyPtr,
    :PyObject_GetIter => (PyPtr,) => PyPtr,
    :PyObject_Call => (PyPtr, PyPtr, PyPtr) => PyPtr,
    :PyObject_CallObject => (PyPtr, PyPtr) => PyPtr,
    # TYPE
    :PyType_IsSubtype => (PyPtr, PyPtr) => Cint,
    :PyType_Ready => (PyPtr,) => Cint,
    :PyType_GenericNew => (PyPtr, PyPtr, PyPtr) => PyPtr,
    # MAPPING
    :PyMapping_HasKeyString => (PyPtr, Ptr{Cchar}) => Cint,
    :PyMapping_SetItemString => (PyPtr, Ptr{Cchar}, PyPtr) => Cint,
    :PyMapping_GetItemString => (PyPtr, Ptr{Cchar}) => PyPtr,
    # SEQUENCE
    :PySequence_Length => (PyPtr,) => Py_ssize_t,
    :PySequence_GetItem => (PyPtr, Py_ssize_t) => PyPtr,
    :PySequence_SetItem => (PyPtr, Py_ssize_t, PyPtr) => Cint,
    :PySequence_Contains => (PyPtr, PyPtr) => Cint,
    # NUMBER
    :PyNumber_Add => (PyPtr, PyPtr) => PyPtr,
    :PyNumber_Subtract => (PyPtr, PyPtr) => PyPtr,
    :PyNumber_Multiply => (PyPtr, PyPtr) => PyPtr,
    :PyNumber_MatrixMultiply => (PyPtr, PyPtr) => PyPtr,
    :PyNumber_FloorDivide => (PyPtr, PyPtr) => PyPtr,
    :PyNumber_TrueDivide => (PyPtr, PyPtr) => PyPtr,
    :PyNumber_Remainder => (PyPtr, PyPtr) => PyPtr,
    :PyNumber_Divmod => (PyPtr, PyPtr) => PyPtr,
    :PyNumber_Power => (PyPtr, PyPtr, PyPtr) => PyPtr,
    :PyNumber_Negative => (PyPtr,) => PyPtr,
    :PyNumber_Positive => (PyPtr,) => PyPtr,
    :PyNumber_Absolute => (PyPtr,) => PyPtr,
    :PyNumber_Invert => (PyPtr,) => PyPtr,
    :PyNumber_Lshift => (PyPtr, PyPtr) => PyPtr,
    :PyNumber_Rshift => (PyPtr, PyPtr) => PyPtr,
    :PyNumber_And => (PyPtr, PyPtr) => PyPtr,
    :PyNumber_Xor => (PyPtr, PyPtr) => PyPtr,
    :PyNumber_Or => (PyPtr, PyPtr) => PyPtr,
    :PyNumber_InPlaceAdd => (PyPtr, PyPtr) => PyPtr,
    :PyNumber_InPlaceSubtract => (PyPtr, PyPtr) => PyPtr,
    :PyNumber_InPlaceMultiply => (PyPtr, PyPtr) => PyPtr,
    :PyNumber_InPlaceMatrixMultiply => (PyPtr, PyPtr) => PyPtr,
    :PyNumber_InPlaceFloorDivide => (PyPtr, PyPtr) => PyPtr,
    :PyNumber_InPlaceTrueDivide => (PyPtr, PyPtr) => PyPtr,
    :PyNumber_InPlaceRemainder => (PyPtr, PyPtr) => PyPtr,
    :PyNumber_InPlacePower => (PyPtr, PyPtr, PyPtr) => PyPtr,
    :PyNumber_InPlaceLshift => (PyPtr, PyPtr) => PyPtr,
    :PyNumber_InPlaceRshift => (PyPtr, PyPtr) => PyPtr,
    :PyNumber_InPlaceAnd => (PyPtr, PyPtr) => PyPtr,
    :PyNumber_InPlaceXor => (PyPtr, PyPtr) => PyPtr,
    :PyNumber_InPlaceOr => (PyPtr, PyPtr) => PyPtr,
    :PyNumber_Long => (PyPtr,) => PyPtr,
    :PyNumber_Float => (PyPtr,) => PyPtr,
    :PyNumber_Index => (PyPtr,) => PyPtr,
    # ITERATION
    :PyIter_Next => (PyPtr,) => PyPtr,
    # INT
    :PyLong_FromLongLong => (Clonglong,) => PyPtr,
    :PyLong_FromUnsignedLongLong => (Culonglong,) => PyPtr,
    :PyLong_FromString => (Ptr{Cchar}, Ptr{Ptr{Cchar}}, Cint) => PyPtr,
    :PyLong_AsLongLong => (PyPtr,) => Clonglong,
    :PyLong_AsUnsignedLongLong => (PyPtr,) => Culonglong,
    # FLOAT
    :PyFloat_FromDouble => (Cdouble,) => PyPtr,
    :PyFloat_AsDouble => (PyPtr,) => Cdouble,
    # COMPLEX
    :PyComplex_FromDoubles => (Cdouble, Cdouble) => PyPtr,
    :PyComplex_RealAsDouble => (PyPtr,) => Cdouble,
    :PyComplex_ImagAsDouble => (PyPtr,) => Cdouble,
    :PyComplex_AsCComplex => (PyPtr,) => Py_complex,
    # STR
    :PyUnicode_DecodeUTF8 => (Ptr{Cchar}, Py_ssize_t, Ptr{Cchar}) => PyPtr,
    :PyUnicode_AsUTF8String => (PyPtr,) => PyPtr,
    :PyUnicode_InternInPlace => (Ptr{PyPtr},) => Cvoid,
    # BYTES
    :PyBytes_FromStringAndSize => (Ptr{Cchar}, Py_ssize_t) => PyPtr,
    :PyBytes_AsStringAndSize => (PyPtr, Ptr{Ptr{Cchar}}, Ptr{Py_ssize_t}) => Cint,
    # TUPLE
    :PyTuple_New => (Py_ssize_t,) => PyPtr,
    :PyTuple_Size => (PyPtr,) => Py_ssize_t,
    :PyTuple_GetItem => (PyPtr, Py_ssize_t) => PyPtr, # borrowed
    :PyTuple_SetItem => (PyPtr, Py_ssize_t, PyPtr) => Cint, # steals
    # LIST
    :PyList_New => (Py_ssize_t,) => PyPtr,
    :PyList_Append => (PyPtr, PyPtr) => Cint,
    :PyList_AsTuple => (PyPtr,) => PyPtr,
    :PyList_SetItem => (PyPtr, Py_ssize_t, PyPtr) => Cint, # steals
    # DICT
    :PyDict_New => () => PyPtr,
    :PyDict_GetItem => (PyPtr, PyPtr) => PyPtr, # borrowed
    :PyDict_GetItemString => (PyPtr, Ptr{Cchar}) => PyPtr, # borrowed
    :PyDict_SetItem => (PyPtr, PyPtr, PyPtr) => Cint,
    :PyDict_SetItemString => (PyPtr, Ptr{Cchar}, PyPtr) => Cint,
    :PyDict_DelItemString => (PyPtr, Ptr{Cchar}) => Cint,
    # SET
    :PySet_New => (PyPtr,) => PyPtr,
    :PyFrozenSet_New => (PyPtr,) => PyPtr,
    :PySet_Add => (PyPtr, PyPtr) => Cint,
    # SLICE
    :PySlice_New => (PyPtr, PyPtr, PyPtr) => PyPtr,
    # METHOD
    :PyInstanceMethod_New => (PyPtr,) => PyPtr,
)

const CAPI_EXCEPTIONS = Set([
    :PyExc_BaseException,
    :PyExc_Exception,
    :PyExc_StopIteration,
    :PyExc_GeneratorExit,
    :PyExc_ArithmeticError,
    :PyExc_LookupError,
    :PyExc_AssertionError,
    :PyExc_AttributeError,
    :PyExc_BufferError,
    :PyExc_EOFError,
    :PyExc_FloatingPointError,
    :PyExc_OSError,
    :PyExc_ImportError,
    :PyExc_IndexError,
    :PyExc_KeyError,
    :PyExc_KeyboardInterrupt,
    :PyExc_MemoryError,
    :PyExc_NameError,
    :PyExc_OverflowError,
    :PyExc_RuntimeError,
    :PyExc_RecursionError,
    :PyExc_NotImplementedError,
    :PyExc_SyntaxError,
    :PyExc_IndentationError,
    :PyExc_TabError,
    :PyExc_ReferenceError,
    :PyExc_SystemError,
    :PyExc_SystemExit,
    :PyExc_TypeError,
    :PyExc_UnboundLocalError,
    :PyExc_UnicodeError,
    :PyExc_UnicodeEncodeError,
    :PyExc_UnicodeDecodeError,
    :PyExc_UnicodeTranslateError,
    :PyExc_ValueError,
    :PyExc_ZeroDivisionError,
    :PyExc_BlockingIOError,
    :PyExc_BrokenPipeError,
    :PyExc_ChildProcessError,
    :PyExc_ConnectionError,
    :PyExc_ConnectionAbortedError,
    :PyExc_ConnectionRefusedError,
    :PyExc_FileExistsError,
    :PyExc_FileNotFoundError,
    :PyExc_InterruptedError,
    :PyExc_IsADirectoryError,
    :PyExc_NotADirectoryError,
    :PyExc_PermissionError,
    :PyExc_ProcessLookupError,
    :PyExc_TimeoutError,
    :PyExc_EnvironmentError,
    :PyExc_IOError,
    # :PyExc_WindowsError, # only on Windows
    :PyExc_Warning,
    :PyExc_UserWarning,
    :PyExc_DeprecationWarning,
    :PyExc_PendingDeprecationWarning,
    :PyExc_SyntaxWarning,
    :PyExc_RuntimeWarning,
    :PyExc_FutureWarning,
    :PyExc_ImportWarning,
    :PyExc_UnicodeWarning,
    :PyExc_BytesWarning,
    :PyExc_ResourceWarning,
])

const CAPI_FUNCS = Set(keys(CAPI_FUNC_SIGS))

const CAPI_OBJECTS = Set([
    # TYPES
    :PyBool_Type,
    :PyBytes_Type,
    :PyComplex_Type,
    :PyDict_Type,
    :PyFloat_Type,
    :PyLong_Type,
    :PyList_Type,
    :PyBaseObject_Type,
    :PySet_Type,
    :PyFrozenSet_Type,
    :PySlice_Type,
    :PyUnicode_Type,
    :PyTuple_Type,
    :PyType_Type,
    # OTHERS
    :_Py_TrueStruct,
    :_Py_FalseStruct,
    :_Py_NotImplementedStruct,
    :_Py_NoneStruct,
    :_Py_EllipsisObject,
])

@eval @kwdef mutable struct CAPIPointers
    $([:($name :: Ptr{Cvoid} = C_NULL) for name in CAPI_FUNCS]...)
    $([:($name :: PyPtr = C_NULL) for name in CAPI_EXCEPTIONS]...)
    $([:($name :: PyPtr = C_NULL) for name in CAPI_OBJECTS]...)
    PyJuliaBase_Type :: PyPtr = C_NULL
    PyExc_JuliaError :: PyPtr = C_NULL
end

const POINTERS = CAPIPointers()

@eval init_pointers(p::CAPIPointers=POINTERS, lib::Ptr=CTX.lib_ptr) = begin
    $([
        if name == :Py_FinalizeEx
            :(p.$name = dlsym_e(lib, $(QuoteNode(name))))
        else
            :(p.$name = dlsym(lib, $(QuoteNode(name))))
        end
        for name in CAPI_FUNCS
    ]...)
    $([:(p.$name = Base.unsafe_load(Ptr{PyPtr}(dlsym(lib, $(QuoteNode(name)))))) for name in CAPI_EXCEPTIONS]...)
    $([:(p.$name = dlsym(lib, $(QuoteNode(name)))) for name in CAPI_OBJECTS]...)
end

for (name, (argtypes, rettype)) in CAPI_FUNC_SIGS
    args = [Symbol("x", i) for (i,_) in enumerate(argtypes)]
    @eval $name($(args...)) = ccall(POINTERS.$name, $rettype, ($(argtypes...),), $(args...))
end


text:
module _Diff

import ForwardDiff
using DiffResults: DiffResult
import DiffResults

@inline function value_and_derivative(f, Y::Type, x::Real)
    diffresult = ForwardDiff.derivative!(DiffResult(zero(Y), zero(Y)), f, x)
    return DiffResults.value(diffresult), DiffResults.derivative(diffresult)
end

const derivative = ForwardDiff.derivative

@inline function value_and_derivative(f, x::Real)
    return f(x), derivative(f, x)
end

export derivative, value_and_derivative

end


text:
using Pkg
Pkg.activate(".")

using Distributed

if ARGS[1] == "--help" || ARGS[1] == "-h" || ARGS[1] == "help"
    println("\n\tFormat: julia tsp_generator.jl dirname n number [threads]

n is the number of cities, number is number of instances. dirname must not
exists. n should be small. Note (n-1)^2 variables are present for QUBO.
threads is the number of threads used, defaults to 1

Generated QUBO and HOBO assume that first cite is visted at time 1.

Following modules are needed: BinaryOptimization, NPZ, LinearAlgebra, Distributed")
    exit()
end

dir_out = ARGS[1]

n = parse(Int, ARGS[2])
@assert n >= 1

repeating = parse(Int, ARGS[3])
@assert repeating >= 1

if isdir(dir_out) || isfile(dir_out)
    error("$dir_out exists, please change or remove it")
    exit(1)
else
    mkdir(dir_out)
end

threads_no = length(ARGS) >= 4 ? parse(Int, ARGS[4]) : 0
addprocs(threads_no)
@everywhere using Pkg;
@everywhere Pkg.activate(".")

@everywhere using LinearAlgebra
@everywhere using BinaryOptimization
@everywhere using NPZ
@everywhere include("tsp_encodings.jl")

@everywhere function simplified_tsp_qubo(w::Matrix{<:Real})
    qubo = tsp_qubo(w, A=2*maximum(w))
    n, _ = indexcapacities(vars(qubo))
    set_one!(qubo, [1,n])
    for i=1:(n-1)
        set_zero!(qubo, [1,i])
        set_zero!(qubo, [i+1,n])
    end
    qubo
end

@everywhere function simplified_tsp_qubo_xy(w::Matrix{<:Real})
    qubo = tsp_qubo(w, Variables("TSP", collect(size(w)), ["timestep", "city"]), 1., 0., 2 * maximum(w))
    n, _ = indexcapacities(vars(qubo))
    set_one!(qubo, [1,n])
    for i=1:(n-1)
        set_zero!(qubo, [1,i])
        set_zero!(qubo, [i+1,n])
    end
    qubo
end

@everywhere function _save_dict_ising(d::Dict, filename::String)
    result = zeros(length(d), 5)

    for (i, (k, v)) = enumerate(collect(d))
        if length(k) >= 1
            result[i, 1] = k[1][1]
            result[i, 2] = k[1][2]
            if length(k) == 2
                result[i, 3] = k[2][1]
                result[i, 4] = k[2][2]
            end
        end
        result[i, 5] = v
    end
    npzwrite(filename, result)
    result
end

@everywhere function save_dict_ising(hobo::HOBO, filename::String)
    save_dict_ising(Ising(hobo), filename)
end

@everywhere function save_dict_ising(ising::Ising, filename::String)
    _save_dict_ising(ising.values, filename)
end

println("arguments are fine. Generating data started ($threads_no threads, $repeating samples)")
@sync @distributed for m = 1:repeating
    println("> $m ($repeating)")
    w = rand(1:10, (n,n))
    qubo_xy = simplified_tsp_qubo_xy(w)
    save_dict_ising(qubo_xy, "$dir_out/tsp_dict_xy_$m.npz")

    qubo = bom_to_hamiltonian(simplified_tsp_qubo(w))
    npzwrite("$dir_out/qubo_$m.npz", qubo)
end

rmprocs()

max_stars_repo_name: iitis/ec-qaoa-code

max_issues_repo_name: iitis/ec-qaoa-code

max_forks_repo_name: iitis/ec-qaoa-code


text:
# This file is a part of JuliaFEM.
# License is MIT: see https://github.com/JuliaFEM/FEMBeam.jl/blob/master/LICENSE

## Rotation matrix test

using FEMBeam
using FEMBeam: get_rotation_matrix
using FEMBase.Test

# If tangent vector ``t`` is parallel to the first beam section axis ``n_1``,
# local basis is not uniquely defined and error is thrown.

X1 = [0.0, 0.0, 0.0]
X2 = [1.0, 0.0, 0.0]
n1 = [1.0, 0.0, 0.0]
@test_throws Exception get_rotation_matrix(X1, X2, n1)


text:
# Test 0: Solve 1D Burgers Equation
# u(x,t)_t+(0.5*u²(x,t))_{x}=0
# u(0,x) = f0(x)

@testset "1D Scalar Algorithms" begin

using ConservationLawsDiffEq
using LinearAlgebra
using OrdinaryDiffEq

include("burgers.jl")
CL = ConservationLawsDiffEq

CFL = 0.5
Tend = 1.0
ul = 1.0
ur = 0.0
x0 = 0.0

prob1 = RiemannProblem(Burgers(), ul, ur, x0, 0.0)
sol_ana  = get_solution(prob1)

# First define the problem data (Jacobian is optional but useful)
Jf(u) = u           #Jacobian
f(u) = u^2/2        #Flux function

f0(x) = (x < x0) ? ul : ur

# Now discretizate the domain
mesh = Uniform1DFVMesh(50, [-3.0, 3.0])

function get_problem(alg::CL.AbstractFVAlgorithm, use_threads, mesh, Tend,CFL)
  # Now get a explicit semidiscretization (discrete in space) du_h(t)/dt = f_h(u_h(t))
  f_h = getSemiDiscretization(f,alg,mesh,[Dirichlet(;side=:Both)]; Df = Jf, use_threads = use_threads,numvars = 1)

  #Compute discrete initial data
  u0 = getInitialState(mesh,f0,use_threads = true)

  #Setup ODE problem for a time interval = [0.0,1.0]
  ode_prob = ODEProblem(f_h,u0,(0.0,Tend))

  #Setup callback in order to fix CFL constant value
  cb = getCFLCallback(f_h, CFL)

  #Estimate an initial dt
  dt = update_dt!(u0, f_h, CFL)
  return ode_prob, cb, dt
end

function test_Scheme(alg, maxerror, threaded_mode=true)
  println("Testing ",split("$alg","(")[1],": ")
  prob,cb,dt = get_problem(alg, false, mesh, Tend,CFL)
  @time sol = solve(prob,SSPRK22(); dt = dt, callback = cb, save_everystep = false)
  @test get_L1_error(sol_ana, fv_solution(sol,mesh)) < maxerror
  if threaded_mode
    println("in threaded mode: ")
    prob,cb,dt = get_problem(alg, true, mesh, Tend,CFL)
    @time sol1 = solve(prob,SSPRK22(); dt = dt, callback = cb, save_everystep = false)
    @test get_L1_error(sol_ana, fv_solution(sol1,mesh)) ≈ get_L1_error(sol_ana, fv_solution(sol,mesh))
  end
end

# Test Schemes relative error
test_Scheme(FVSKTScheme(), 0.048)
test_Scheme(LaxFriedrichsScheme(), 0.24)
test_Scheme(LocalLaxFriedrichsScheme(), 0.17, false)
test_Scheme(GlobalLaxFriedrichsScheme(), 0.24)
test_Scheme(LaxWendroffScheme(), 0.043)
test_Scheme(LaxWendroff2sScheme(), 0.088)
test_Scheme(FVCompWENOScheme(), 0.045)
test_Scheme(FVCompMWENOScheme(), 0.041)
test_Scheme(FVSpecMWENOScheme(), 0.028, false)
test_Scheme(FVCUScheme(), 0.039)
test_Scheme(FVDRCUScheme(), 0.039)
test_Scheme(FVDRCU5Scheme(), 0.053)

# Test dt and CFL callback
prob,cb,dt = get_problem(FVSKTScheme(), false, mesh, Tend,CFL)
@time sol = solve(prob,SSPRK22(); dt = dt, callback = cb, save_everystep = true)
@test sol.t == [0.0,0.05999999999999994,0.11999999999999988,0.17999999999999983,0.23999999999999977,
                0.2999999999999997,0.35999999999999965,0.4199999999999996,0.47999999999999954,
                0.5399999999999995,0.5999999999999994,0.6599999999999994,0.7199999999999993,
                0.7799999999999992,0.8399999999999992,0.8999999999999991,0.9599999999999991,1.0]
end


text:
module FrostHelperYellowBoosterModule

using ..Ahorn, Maple

@mapdef Entity "FrostHelper/YellowBooster" FrostHelperYellowBoosterPlacement(x::Integer, y::Integer, boostTime::Number=0.3, respawnTime::Number=1.0, particleColor::String="Yellow", flashTint::String="Red", directory="objects/FrostHelper/yellowBooster/", reappearSfx::String="event:/game/04_cliffside/greenbooster_reappear", enterSfx::String="event:/game/04_cliffside/greenbooster_enter", boostSfx::String="event:/game/04_cliffside/greenbooster_dash", releaseSfx::String="event:/game/04_cliffside/greenbooster_end", dashes::Integer=-1, preserveSpeed::Bool=false)

const colors = sort(collect(keys(Ahorn.XNAColors.colors)))

const placements = Ahorn.PlacementDict(
	"Yellow Booster (Frost Helper)" => Ahorn.EntityPlacement(
		FrostHelperYellowBoosterPlacement
	)
)

Ahorn.editingOptions(entity::FrostHelperYellowBoosterPlacement) = Dict{String, Any}(
    "particleColor" => colors,
	"flashTint" => colors
)

sprite = "objects/booster/booster00"

function Ahorn.selection(entity::FrostHelperYellowBoosterPlacement)
    x, y = Ahorn.position(entity)
    return Ahorn.getSpriteRectangle(sprite, x, y)
end

function Ahorn.render(ctx::Ahorn.Cairo.CairoContext, entity::FrostHelperYellowBoosterPlacement, room::Maple.Room)
	texture = strip(get(entity.data, "directory", "objects/FrostHelper/yellowBooster/"), '/')
	realTexture = "$texture/booster00.png"

	Ahorn.drawSprite(ctx, realTexture, 0, 0)
end

end

max_stars_repo_name: JaThePlayer/FrostHelper

max_issues_repo_name: JaThePlayer/FrostHelper

max_forks_repo_name: JaThePlayer/FrostHelper


text:
#__precompile__()

module EvoDuplexes

using BioSymbols
using BioSequences
using GenomicFeatures
using IntervalTrees
using SuffixArrays
using Gadfly
using Automa

using BufferedStreams
using Libz

using PyCall
using PyPlot

using ScikitLearn
using ScikitLearn.Utils: meshgrid

@sk_import ensemble: IsolationForest

@pyimport matplotlib.font_manager as fm
@pyimport scipy.stats as stats

import Automa
import Automa.RegExp: @re_str
import Compat: take!

importall BioSymbols
importall GenomicFeatures

include("../src/pairs.jl")
include("../src/energy.jl")
include("../src/rnaduplex.jl")
include("../src/intervals.jl")
include("../src/traverse.jl")
include("../src/mafreader.jl")
include("../src/gtrmodel.jl")
include("../src/newick.jl")
include("../src/suffix.jl")
include("../src/evoduplex.jl")
include("../src/io.jl")
include("../src/regions.jl")
include("../src/train.jl")

export RNADuplex,
       EvoDuplex,
       MAFSpecies,
       MAFRecord,
       MAFReader,
       reverse_complement!,
       readmaf!,
       read!,
       done,
       RNASuffixArray,
       RNADuplexArray,
       DuplexCollection,
       DuplexInterval,
       energy,
       npairs,
       traverse,
       collect,
       stitch,
       covariance,
       distance,
       score!,
       score,
       dinucleotide_entropy,
       extend_branches!,
       set_prob_mat!,
       parsenewick,
       GTR_SINGLE_Q,
       GTR_PAIRED_Q,
       isgzipped,
       randcode,
       brackets,
       loadbed,
       writebed,
       loadbedgraph,
       DistanceForest,
       train!,
       predict
end


text:
# ---
# title: 221. Maximal Square
# id: problem221
# author: Tian Jun
# date: 2020-10-31
# difficulty: Medium
# categories: Dynamic Programming
# link: <https://leetcode.com/problems/maximal-square/description/>
# hidden: true
# ---
#
# Given an `m x n` binary `matrix` filled with `0`'s and `1`'s, _find the
# largest square containing only_ `1`'s _and return its area_.
#
#
#
# **Example 1:**
#
# ![](https://assets.leetcode.com/uploads/2020/11/26/max1grid.jpg)
#
#
#
#     Input: matrix = [["1","0","1","0","0"],["1","0","1","1","1"],["1","1","1","1","1"],["1","0","0","1","0"]]
#     Output: 4
#
#
# **Example 2:**
#
# ![](https://assets.leetcode.com/uploads/2020/11/26/max2grid.jpg)
#
#
#
#     Input: matrix = [["0","1"],["1","0"]]
#     Output: 1
#
#
# **Example 3:**
#
#
#
#     Input: matrix = [["0"]]
#     Output: 0
#
#
#
#
# **Constraints:**
#
#   * `m == matrix.length`
#   * `n == matrix[i].length`
#   * `1 <= m, n <= 300`
#   * `matrix[i][j]` is `'0'` or `'1'`.
#
#
## @lc code=start
using LeetCode

## add your code here:
## @lc code=end


text:
const DOC_FINITE =
    "`AbstractArray{<:Finite}` (multiclass classification)"
const DOC_FINITE_BINARY =
    "`AbstractArray{<:Finite{2}}` (binary classification)"
const DOC_ORDERED_FACTOR =
    "`AbstractArray{<:OrderedFactor}` (classification of ordered target)"
const DOC_ORDERED_FACTOR_BINARY =
    "`AbstractArray{<:OrderedFactor{2}}` "*
    "(binary classification where choice of \"true\" effects the measure)"
const DOC_CONTINUOUS = "`AbstractArray{Continuous}` (regression)"

## TRAITS

is_measure_type(::Any) = false

const MEASURE_TRAITS =
    [:name, :instances, :human_name, :target_scitype, :supports_weights,
     :prediction_type, :orientation,
     :reports_each_observation, :aggregation, :is_feature_dependent, :docstring,
     :distribution_type, :supports_class_weights]

# already defined in model_traits.jl:
# name              - fallback for non-MLJType is string(coretype(M))
#                                                      where M is arg
# target_scitype    - fallback value = Unknown
# supports_weights  - fallback value = false
# prediction_type   - fallback value = :unknown (also: :deterministic,
#                                           :probabilistic, :interval)
# docstring         - fallback value is value of `name` trait.

# specfic to measures:
orientation(::Type) = :loss  # other options are :score, :other
reports_each_observation(::Type) = false
aggregation(::Type) = Mean()  # other option is Sum() or callable object
is_feature_dependent(::Type) = false
human_name(M::Type) = snakecase(name(M), delim=' ')
instances(::Type) = String[]

# specific to `Finite` measures:
supports_class_weights(::Type) = false

# specific to probabilistic measures:
distribution_type(::Type) = missing

# extend to instances:
for trait in [:orientation, :reports_each_observation, :aggregation,
              :is_feature_dependent, :supports_class_weights,
              :distribution_type]
    eval(:($trait(m) = $trait(typeof(m))))
end

## FOR BUILT-IN MEASURES (subtyping Measure)

abstract type Measure <: MLJType end
is_measure_type(::Type{<:Measure}) = true
is_measure(m) = is_measure_type(typeof(m))

# docstring fall-back:
_decorate(s::AbstractString) = "`$s`"
_decorate(v::Vector{<:AbstractString}) = join(_decorate.(v), ", ")
function MMI.docstring(M::Type{<:Measure})
    list = _decorate(instances(M))
    ret = "`$(name(M))` - $(human_name(M)) type"
    isempty(list) || (ret *= " with instances $list")
    ret *= ". "
    return ret
end

# display:
show_as_constructed(::Type{<:Measure}) = true

# info (see also src/init.jl):
function MLJScientificTypes.info(M, ::Val{:measure_type})
    values = Tuple(@eval($trait($M)) for trait in MEASURE_TRAITS)
    return NamedTuple{Tuple(MEASURE_TRAITS)}(values)
end
MLJScientificTypes.info(m, ::Val{:measure}) = info(typeof(m))

## AGGREGATION

abstract type AggregationMode end

struct Sum <: AggregationMode end
(::Sum)(v) = sum(v)
(::Sum)(v::LittleDict) = sum(values(v))

struct Mean <: AggregationMode end
(::Mean)(v) = mean(v)
(::Mean)(v::LittleDict) = mean(values(v))

# for rms and it's cousins:
struct RootMeanSquare <: AggregationMode end
(::RootMeanSquare)(v) = sqrt(mean(v.^2))

aggregate(v, measure) = aggregation(measure)(v)

# aggregation is no-op on scalars:
const MeasureValue = Union{Real,Tuple{<:Real,<:Real}} # number or interval
aggregate(x::MeasureValue, measure) = x

## DISPATCH FOR EVALUATION

# yhat - predictions (point or probabilisitic)
# X - features
# y - target observations
# w - per-observation weights

function value(measure, yhat, X, y, w)
    vfdep     = Val(is_feature_dependent(measure))
    vsweights = Val(supports_weights(measure))
    return value(measure, yhat, X, y, w, vfdep, vsweights)
end

## DEFAULT EVALUATION INTERFACE

#  is feature independent, weights not supported:
value(m, yhat, X, y, w, ::Val{false}, ::Val{false}) = m(yhat, y)

#  is feature dependent:, weights not supported:
value(m, yhat, X, y, w, ::Val{true}, ::Val{false}) = m(yhat, X, y)

#  is feature independent, weights supported:
value(m, yhat, X, y, w,         ::Val{false}, ::Val{true}) = m(yhat, y, w)
value(m, yhat, X, y, ::Nothing, ::Val{false}, ::Val{true}) = m(yhat, y)

#  is feature dependent, weights supported:
value(m, yhat, X, y, w,         ::Val{true}, ::Val{true}) = m(yhat, X, y, w)
value(m, yhat, X, y, ::Nothing, ::Val{true}, ::Val{true}) = m(yhat, X, y)

## helper

function check_pools(ŷ, y)
    levels(y) == levels(ŷ[1]) ||
        error("Conflicting categorical pools found "*
              "in observations and predictions. ")
    return nothing
end

## INCLUDE SPECIFIC MEASURES AND TOOLS

include("meta_utilities.jl")
include("continuous.jl")
include("confusion_matrix.jl")
include("finite.jl")
include("loss_functions_interface.jl")

## DEFAULT MEASURES
default_measure(T, S) = nothing

# Deterministic + Continuous / Count ==> RMS
default_measure(::Type{<:Deterministic},
                ::Type{<:Union{Vec{<:Continuous}, Vec{<:Count}}}) = rms

# Deterministic + Finite ==> Misclassification rate
default_measure(::Type{<:Deterministic},
                ::Type{<:Vec{<:Finite}}) = misclassification_rate

# default_measure(::Type{Probabilistic},
#                 ::Type{<:Union{Vec{<:Continuous},
#                                Vec{<:Count}}}) = ???

# Probabilistic + Finite ==> Cross entropy
default_measure(::Type{<:Probabilistic},
                ::Type{<:Vec{<:Finite}}) = cross_entropy

# Fallbacks
default_measure(M::Type{<:Supervised}) = default_measure(M, target_scitype(M))
default_measure(::M) where M <: Supervised = default_measure(M)