text:
using KernelDensity, Formatting, PyPlot, Seaborn, LinearAlgebra
# using Formatting
#collection of commonly used plots
function plot_ss_hists(shots_0, shots_1)
  #single-shot readout histograms
  hist_0 = kde(shots_0[:])
  hist_1 = kde(shots_1[:])
  fidelity, threshold = get_fidelity(shots_0, shots_1)
  println("Treshold = ", threshold)
  w=4
  figure(figsize=(w,w/1.4))
  plot(hist_0.x, hist_0.density/sum(hist_0.density),label=L"$|0\rangle$")
  plot(hist_1.x, hist_1.density/sum(hist_1.density),label=L"$|1\rangle$")
  ylabel("Fraction of counts")
  xlabel("Homodyne voltage (a.u.)")
  lgd = legend()
  annotate(sprint(show, "Fid. = $(fidelity)"),
  xy=[0.1;0.7],
  xycoords="axes fraction",
  xytext=[0,10],
  textcoords="offset points",
  fontsize=10.0,
  ha="left",
  va="center")
  return hist_0, hist_1
end

function plot1D(data, group = "main"; quad = :real, label_y = "V (a.u.)", cals = false, cal0::String = "0", cal1::String = "1", fit_name = "", save_fig = "png", doplot=true, fig = nothing)
  if fig == nothing && doplot == true
      fig = figure(figsize=(3,3))
  end
  xpoints = data[2][group]["axes"]
  xpoints_values = collect(values(xpoints))[1]
  if cals
    data_values = cal_data(data, qubit=group, quad = quad, cal0=cal0, cal1=cal1)[1]
    label_y = L"\langle Z\rangle"
  else
    data_values = dropdims(eval(quad).(data[1][group]),dims=1)
    label_y = string(quad, "(Voltage)")
  end
  xpoints_values = xpoints_values[1:length(data_values)]
  if doplot
      plot(xpoints_values, data_values, marker=".")
  end
  if ~isempty(fit_name)
    fit_function = eval(Meta.parse(string("fit_", fit_name)))
    fit_result = nothing
    try
        fit_result = (fit_function)(xpoints_values, data_values)
        println("Fitting to model: ", fit_result.model_str)
        ax = gca()
        if doplot
            plot(xpoints_values, fit_result.fit_curve(xpoints_values),label="fit", color=ax.lines[end].get_color(), linewidth=1)
        end
    catch
        println("Fit failed")
        return (xpoints_values, data_values, nothing)
    end
  end
  label_x = collect(keys(xpoints))[1]
  units = data[2][group]["units"][label_x]
  if units != nothing
    label_x = string(label_x, " (", units, ")" )
  end
  if doplot
      xlabel(label_x)
      ylabel(label_y)
      title(get_partial_filename(data[2][group]["filename"]))
      ~isempty(save_fig) && savefig(string(splitext(data[2][group]["filename"])[1],'-',group,'.', save_fig), bbox_inches = "tight")
  end
  if isempty(fit_name)
    return (xpoints_values, data_values)
  else
    return (xpoints_values, data_values, fit_result)
  end
end

function plot2D(data, group = "main"; quad = :real, transpose = false, normalize = false, vmin = NaN, vmax = NaN, cmap = "terrain", show_plot = true, save_fig = "png")
  data_values = data[1][group]
  axes = data[2][group]["axes"]
  xpoints_values = collect(values(axes))[1]
  ypoints_values = collect(values(axes))[2]
  data_quad = eval(quad).(data_values)
  if normalize == 1
   data_quad = (data_quad'./data_quad[1,:])'
  elseif normalize == 2
   data_quad./=data_quad[:,1]
  end
  label_x = collect(keys(axes))[1]
  occursin("_metadata", label_x) && (label_x = split(label_x, "_metadata")[1])
  units_x = data[2][group]["units"][label_x]
  if units_x != nothing
    label_x = string(label_x, " (", units_x, ")" )
  end
  label_y = collect(keys(axes))[2]
  units_y= data[2][group]["units"][label_y]
  occursin("_metadata", label_y) && (label_y = split(label_y, "_metadata")[1])
  if units_y != nothing
    label_y = string(label_y, " (", units_y, ")" )
  end
  if isnan(vmin)
    vmin = minimum(data_quad)
  end
  if isnan(vmax)
    vmax = maximum(data_quad)
  end
  if show_plot
    fig = figure("pyplot_surfaceplot",figsize=(4,4))
    ax = gca()
    ax[:ticklabel_format](useOffset=false)
    if transpose
      pcolormesh(ypoints_values, xpoints_values, data_quad', cmap = cmap, vmin = vmin, vmax = vmax)
      ylabel(label_x)
      xlabel(label_y)
    else
      pcolormesh(xpoints_values, ypoints_values, data_quad, cmap = cmap, vmin = vmin, vmax = vmax)
      xlabel(label_x)
      ylabel(label_y)
    end
    colorbar()
    if !haskey(data[2][group],"filename")
        data[2][group]["filename"] = "None"
    end
    title(get_partial_filename(data[2][group]["filename"]))
    ~isempty(save_fig) && savefig(string(splitext(data[2][group]["filename"])[1],'-',group,'.', save_fig), bbox_inches = "tight")
  end
  return xpoints_values, ypoints_values, data_quad
end

function reshape2D(data, group = "main"; quad = :real, normalize = false)
  return plot2D(data, group; quad = quad, normalize = normalize, show_plot = false)
end

"""
  plot_multi

Plot multiple 1D traces on top of each other.
group: data group name
Optional arguments
-------------------------
quad: quadrature: real, imag, or abs
offset: vertical offset between curves
cals: normalize to 0/1 using metadata
show_legend: show legend in plot
fit_name: name of fit function as in fit_###
fit_param_name: fit parameter of interest (output)
save_fig: format of the figure to be saved (empty string to disable)
"""

function plot_multi(data, group = "main"; quad = :real, offset = 0.0, cals = false, show_legend = true,
  cal0::String = "0", cal1::String = "1", fit_name = "", fit_param_name = "T", save_fig = "png")
  data_values = data[1][group]
  axes = data[2][group]["axes"]
  xpoints_values = collect(values(axes))[1]
  ypoints_values = collect(values(axes))[2]
  Tvec = zeros(length(ypoints_values))
  dTvec = zeros(length(ypoints_values))
  if isempty(fit_name)
    fig = figure(figsize= (3.5,3))
  else
    fig = figure(figsize = (8,3))
    subplot(1,2,1)
    fit_function = eval(Meta.parse(string("fit_", fit_name)))
  end
  if cals
    data_values = cal_data(data[1], qubit=group, quad=quad, cal0=cal0, cal1=cal1)
  else
    data_values = eval(quad).(data[1][group])
    # reshape to array of array
    data_values = reshape(data_values, length(xpoints_values), length(ypoints_values))
    data_values = [data_values[:,k] for k in 1:size(data_values,2)]
  end
  ax = gca()
  for k in 1:length(data_values)
    xpts = xpoints_values[1:length(data_values[k])]
    plot(xpts, data_values[k] .+ offset*k, label=string(round(ypoints_values[k],digits=3)), linewidth = convert(Int,isempty(fit_name)), marker = "o", markersize = 4)
    if ~isempty(fit_name)
      try
          fit_result = (fit_function)(xpts, data_values[k])
          k==1 && println("Fitting to model: ", fit_result.model_str)
          plot(xpts, fit_result.fit_curve(xpts),label="fit", color=ax[:lines][end][:get_color](), linewidth=1)
          Tvec[k] = fit_result.fit_params[fit_param_name]
          dTvec[k] = fit_result.errors[fit_param_name]
      catch
          Tvec[k] = NaN
      end
    end
  end
  label_x = collect(keys(axes))[1]
  units = data[2][group]["units"][label_x]
  if units != nothing
    label_x = string(label_x, " (", units, ")" )
  end
  xlabel(label_x)
  if cals
    ylabel(L"\langle Z\rangle")
  else
    ylabel(string(quad, "(Voltage)"))
  end
  label_y = collect(keys(axes))[2]
  units = data[2][group]["units"][label_y]
  if show_legend
    label_y = collect(keys(axes))[2]
      units = data[2][group]["units"][label_y]
    if units != nothing
      label_y = string(label_y, " (", units, ")" )
  end
    legend(title=label_y, bbox_to_anchor=[1.05,1],loc=2,borderaxespad=0)
  end
  if ~isempty(fit_name)
    plr = subplot(1,2,2)
    errorbar(ypoints_values, Tvec, dTvec, marker = "o", markersize=4)
    ylabel(fit_param_name) #TODO: unit? Proper parameter name, e.g., T1?
    xlabel(y_label)
    plr[:axis](ymin = 0)
    subplots_adjust(wspace=0.3)
  end
  title(get_partial_filename(data[2][group]["filename"]))
  ~isempty(save_fig) && savefig(string(splitext(data[2][group]["filename"])[1],'-',group,'.', save_fig), bbox_inches = "tight")
  return xpoints_values[1:length(data_values[1])], data_values, (Tvec, dTvec)
end

function plot2D_matlab(data, quad = :real; normalize=false)
  fig = figure("pyplot_surfaceplot",figsize=(5,3))
  ax = gca()
  ax[:ticklabel_format](useOffset=false)
  data_quad = eval(quad).(data["data"])
  if normalize
    data_quad./=data_quad[:,1]
  end
  xpoints = repmat(data["xpoints"],1,length(data["ypoints"]))
  ypoints = repmat(data["ypoints"]',length(data["xpoints"]),1)
  pcolormesh(xpoints, ypoints, data_quad',cmap = "terrain")
  colorbar()
  xlabel(data["xlabel"])
  ylabel(data["ylabel"])
  xlim([minimum(xpoints),maximum(xpoints)])
  ylim([minimum(ypoints),maximum(ypoints)])
end

function pauli_set_plot(rho; rho_ideal=[], fig_width=10, fig_height=3.5, bar_width=0.6)
    pauli_vec, pauli_ops = rho2pauli(rho)
    figure(figsize=(fig_width,fig_height))
    ind = 1:length(pauli_vec)
    if ~isempty(rho_ideal)
        pauli_vec_ideal, _  = rho2pauli(rho_ideal)
        bar(ind, pauli_vec_ideal, bar_width, color="green", label=L"$\rho_{ideal}$")
    end
    bar(ind, pauli_vec[:], bar_width, label=L"$\rho_{actual}$")
    xticks(ind, map(string,pauli_ops[:]))
    ylim([-1.05,1.05])
    xlabel("Pauli operator")
    ylabel("Expectation value")
    title("State tomography")
    if ~isempty(rho_ideal)
        legend()
    end
end

function annotate_plot(message, vals...; coords = [0.75, 0.9], fontsize = 10.0)
  annotate(format(message, vals...),
    xy= coords,
    xycoords="axes fraction",
    xytext=[10,15],
    textcoords="offset points",
    fontsize=fontsize,
ha="left",
va="center")
end

function get_partial_filename(filename, num_dirs = 3)
  cur_path = ""
  for n=1:num_dirs+1
    filename, filename_dir = splitdir(filename)
    cur_path = n==1 ? filename_dir : joinpath(filename_dir, cur_path)
  end
  return cur_path
end

function load_T1_series(datapath::AbstractString, numstart::Int, numend::Int, group, subdir=Dates.format(Dates.today(),"yymmdd");quad=:real,delta=1)
    """
      load_T1_series

    Plot multiple exponentially decaying 1D traces on top of each other from different files.
    numstart/numend: file number start/end
    group: data group name
    Optional arguments
    -------------------------
    subdir = date
    """
    T1vec = fill(NaN, numend-numstart+1)
    y0vec = fill(NaN, numend-numstart+1)
    data_temp = load_data(datapath, numstart, subdir)
    datavec = zeros(length(data_temp[1][group])-4, numend-numstart+1)
    fig = figure(figsize=(3,3))
    for (k,num) in enumerate(numstart:delta:numend)
        data = load_data(datapath, num, subdir);
        _,data_values,fit_result = Qlab.plot1D(data, group, cals=true, fit_name="t1", doplot=true, fig=fig, quad=quad)
        if fit_result != nothing
            T1vec[k] = fit_result.fit_params["T"]
            y0vec[k] = fit_result.fit_params["b"]
        end
        datavec[:,k] = data_values
    end
    return datavec, T1vec, y0vec
end

"""
    _blob(x::Float64, y::Float64, w::Float64, w_max::Float64, area::Float64; cmap=nothing)

Draws a square-shaped blob with the given area (< 1) at
the given (x,y) coordinates.

# Arguments
- `x, y::Float64`:  Grid location for the center of the blob
- `w::Float64`   :  Weight of the square realtive to the largest square, w_max.
                    This is used to build the color map.
- `w_max::Float64`: Weight of the largest square.
- `area::Float64`: total area of the square
"""
function _blob(x::Float64,
              y::Float64,
              w::Float64,
              w_max::Float64,
              area::Float64; cmap=nothing)

    hs = sqrt(area) / 2
    xcorners = [x - hs, x + hs, x + hs, x - hs]
    ycorners = [y - hs, y - hs, y + hs, y + hs]

    plt.fill(xcorners, ycorners,
             color=cmap(round(Int64,((w + w_max) * 256 / (2 * w_max)))))
end

"""
    hinton(W::AbstractMatrix;
           xlabels::Array{String}="",
           ylabels::Array{String}="",
           title::String=nothing,
           ax=nothing,
           cmap::ColorMap=nothing,
           label_top::Bool=true)

Create a Hinton diagram from matrix data W.

# Arguments
- `W::AbstractMatrix`: Matrix data to be plotted
- `xlabels, ylabels::Array{String}`: labels
- `title::String`: title
- `ax::Axes`: axis to label/plot onto
- `cmap::ColorMap`: color map for the Hinton diagram

# Returns
- `fig`: plot figure
- `ax` : plot axes
"""
function hinton(W::AbstractMatrix;
                xlabels::Array{String}="",
                ylabels::Array{String}="",
                title::String="",
                ax=nothing,
                cmap=nothing,
                label_top::Bool=true)

    if cmap == nothing
        cmap = ColorMap("RdBu")
    end

    if ax == nothing
        fig, ax = subplots(1, 1, figsize=(8, 6))
    else
        fig = nothing
    end

    if xlabels == "" || ylabels == ""
        ax.axis("off")
    else
        ax.tick_params(axis="x", labelsize=14)
        ax.tick_params(axis="y", labelsize=14)
        ax.xaxis.set_major_locator(plt.IndexLocator(1, 0.5))
        ax.yaxis.set_major_locator(plt.IndexLocator(1, 0.5))
    end

    ax.axis("equal")
    ax.set_frame_on(false)

    height, width = size(W)

    w_max = 1.25 * maximum(abs.(Diagonal(W)))
    if w_max <= 0.0
        w_max = 1.0
    end

    ax.fill([0, width, width, 0], [0, 0, height, height],color=cmap(128))

    for x=1:width
        for y=1:height
            _x = x
            _y = y
            if real(W[x, y]) > 0.0
                _blob(_x - 0.5, height - _y + 0.5, abs(W[x,
                      y]), w_max, min(1, abs(W[x, y]) / w_max), cmap=cmap)
            else
                _blob(_x - 0.5, height - _y + 0.5, -abs(W[
                      x, y]), w_max, min(1, abs(W[x, y]) / w_max), cmap=cmap)
            end
        end
    end

    # color axis

    norm = matplotlib.colors.Normalize(-maximum(abs.(W)), maximum(abs.(W)))
    cax, kw = matplotlib.colorbar.make_axes(ax, shrink=0.75, pad=.1)
    matplotlib.colorbar.ColorbarBase(cax, norm=norm, cmap=cmap)

    # x axis

    if xlabels != ""
        ax.set_xticklabels(xlabels)
        if label_top
            ax.xaxis.tick_top()
        end
    end

    # y axis
    if ylabels != ""
        ax.set_yticklabels((reverse(ylabels)))
    end

    return fig, ax
end


text:
# This file is part of IntegerTriangles.jl.
# Copyright Peter Luschny. License is MIT.

(@__DIR__) ∉ LOAD_PATH && push!(LOAD_PATH, (@__DIR__))

module TrianglesExplorer

using Nemo, OrderedCollections
using TrianglesBase, TrianglesExamples, TrianglesUtils

export Explore, Triangles, SaveToCSV, Inspect

"""
Explore integer triangles via their traits.
The traits and their OEIS references will be saved
as csv-files in the data directory.
"""
const ModuleTrianglesExplorer = ""

const WARNING_ON_NOTFOUND = false

const Kind = ["Std", "Rev", "Inv", "RevInv", "InvRev"]

const Triangles = LittleDict{String, Function}(
    "Aitken"        => AitkenTriangle,
    "Bessel1"       => Bessel1Triangle,
    "Binomial"      => BinomialTriangle,
    "Catalan"       => CatalanTriangle,
    "DArcais"       => DArcaisTriangle,
    "Delannoy"      => DelannoyTriangle,
    "Euler"         => EulerTriangle,
    "EulerS"        => EulerSecTriangle,
    "EulerT"        => EulerTanTriangle,
    "Eulerian"      => EulerianTriangle,
    "EulerianSO2"   => EulerianSO2Triangle,
    "FallingFact"   => FallingFactTriangle,
    "Fibonacci"     => FibonacciTriangle,
    "Fine"          => FineTriangle,
    "Fubini"        => FubiniTriangle,
    "Hermite"       => HermiteTriangle,
    "Laguerre"      => LaguerreTriangle,
    "Lah"           => LahTriangle,
    "Motzkin"       => MotzkinTriangle,
    "Narayana"      => NarayanaTriangle,
    "RisingFact"    => RisingFactTriangle,
    "SchroederB"    => SchröderBTriangle,
    "SchroederL"    => SchröderLTriangle,
    "StirlingCycle" => StirlingCycleTriangle,
    "StirlingSet"   => StirlingSetTriangle,
    "Rencontres"    => RencontresTriangle,
    "Trinomial"     => TrinomialTriangle,
    "TTree"         => TTreeTriangle,
    "Uni"           => UniTriangle,
    "Worpitzky"     => WorpitzkyTriangle
)

const TraitFunc = LittleDict{String, Function}(
    "Triangle"   => Flat,
    "Reverse"    => Reverse,
    "Inverse"    => Inverse,
    "RevInv"     => RevInv,
    "InvRev"     => InvRev,
    "DiagTri"    => DiagTri,
    "PolyTri"    => PolyTri,
    "Sum"        => sum,
    "EvenSum"    => EvenSum,
    "OddSum"     => OddSum,
    "AltSum"     => AltSum,
    "DiagSum"    => DiagSum,
    "Middle"     => Middle,
    "Central"    => Central,
    "LeftSide"   => LeftSide,
    "RightSide"  => RightSide,
    "PosHalf"    => PosHalf,
    "NegHalf"    => NegHalf,
    "PolyVal2"   => PolyVal2,
    "PolyVal3"   => PolyVal3,
    "PolyValn"   => PolyValn,
    "BinConv"    => BinomialTransform,
    "IBinConv"   => IBinomialTransform,
    "TransSqrs"  => TransSqrs,
    "TransNat0"  => TransNat0,
    "TransNat1"  => TransNat1
)

function Show(io, name, kind, trait, seq, savetofile=false)
    seq == [] && return []
    if typeof(seq) === ℤTri
        seq = Flat(seq)
    end
    anum = GetSeqnum(seq, WARNING_ON_NOTFOUND)
    if savetofile
        print(".")
        print(io, anum, ",", name, ",", kind, ",", trait, ",")
        Println(io, SeqToString(seq, 10))
    else
        print(anum, " ", name, " ", kind, " ", trait, " ")
        Println(IOContext(stdout), SeqToString(seq, 10))
    end
end

function TriangleVariant(Tri, dim, kind="Std")
    if ! (kind in Kind)
        @warn("No valid kind!")
        return []
    end

    M = Tri(dim)
    kind == "Std" && return M
    kind == "Rev" && return reverse.(M)
    kind == "InvRev" && (M = reverse.(M))
    invM = InverseTriangle(M)
    (kind == "Inv" || kind == "InvRev"
        || invM == []) && return invM
    return reverse.(invM)
end

const LEN = 19

# Prints full sequence
function Inspect(triangle, kind, trait, dim, oeissearch=false)
    T = TriangleVariant(Triangles[triangle], dim, kind)
    seq = TraitFunc[trait](T)
    seq == [] && return []
    if typeof(seq) === ℤTri
        Println.(seq)
        seq = Flat(seq)
    end
    anum = GetSeqnum(seq, oeissearch)
    println(anum, " ", triangle, " ", kind, " ", trait)
    println(SeqToString(seq))
end

"""
(SIGNATURES)
"""
function Explore(triangle, kind, trait, dim)
    T = TriangleVariant(Triangles[triangle], dim, kind)
    seq = TraitFunc[trait](T)
    Show(stdout, triangle, kind, trait, seq)
end

function Explore(T::ℤTri, trait::Function)
    seq = trait(T)
    if seq == [] || seq === nothing
        return []
    end
    typeof(seq) === ℤTri && (seq = Flat(seq))
    println("$trait ")
    anum = GetSeqnumUri(seq)
    anum === nothing && (anum = "nothing")
    seqstr = string(seq[1:min(10, end)])[max(0,11):max(0,end-1)]
    String["$trait ", anum, seqstr]
end

"""

"""
function Explore(triangle, kind, trait)
    dim = 19
    T = TriangleVariant(Triangles[triangle], dim, kind)
    seq = TraitFunc[trait](T)
    anum = GetSeqnum(seq)
    anum === nothing && (anum = "nothing")
    seqstr = string(seq[1:min(10, end)])[max(0,11):max(0,end-1)]
    String[anum, triangle, kind, trait, seqstr]
end

function Explore(trait::String, dim::Int)
    for (name, triangle) in Triangles
        for kind in Kind
            T = TriangleVariant(triangle, dim, kind)
            if T != []
                seq = TraitFunc[trait](T)
                Show(stdout, name, kind, trait, seq)
            end
        end
    end
end

# The BIG LIST goes to data/profile.txt.
function Explore(savetofile::Bool)
    @warn "This will take several minutes and produce the file 'BIGLIST.csv' in the data directory."

    path = datapath("BIGLIST.csv")
    rm(path; force=true)
    open(path, "w") do io
        println(io, "Anumber,Triangle,Type,Trait,Sequence")
        for (name, triangle) in Triangles
            #for kind in Kind
            kind = "Std"
                T = TriangleVariant(triangle, LEN, kind)
                if T != []
                    for (trait, f) in TraitFunc
                        Show(io, name, kind, trait, f(T), savetofile)
                    end
                end
            #end
        end
    end
end

function SaveToCSV(name)
    T = Triangles[name](LEN)
    path = datapath(name * ".csv")
    rm(path; force=true)
    open(path, "w") do io
        println(io, "Trait,ANumber,Sequence")
        for (trait, f) in TraitFunc
            seq = f(T)
            seq == [] && continue
            if typeof(seq) === ℤTri
                seq = Flat(seq)
            end
            anum = GetSeqnum(seq)
            anum === nothing && (anum = "nothing")
            println(io, trait, ",", anum, ",", SeqToString(seq, 10))
        end
    end
end

function SaveAllToCSV()
    @warn "This will take several minutes and produce the csv-files in the data directory."

    for (name, f) in Triangles
        println(name)
        SaveToCSV(name)
    end
end

# START-TEST-##############################################

function test()
    TLEN = 19
    Explore("SchroederB", "Inv", "AltSum",    TLEN)
end

function demo()
    TLEN = 19
    Explore("Binomial",   "Std", "PolyVal3",  TLEN)
    Explore("SchroederB", "Inv", "AltSum",    TLEN)
    Explore("SchroederL", "Inv", "AltSum",    TLEN)
    Explore("Motzkin",    "Rev", "Central",   TLEN)
    Explore("Laguerre",   "Std", "PosHalf",   TLEN)
    Explore("Laguerre",   "Std", "TransNat0", TLEN)
    Explore("Laguerre",   "Std", "TransNat1", TLEN)
    Explore("Lah",        "Std", "TransSqrs", TLEN)
    Inspect("Lah",        "Std", "TransSqrs", TLEN)
    println()
end

function perf()
    #Explore(true)
    #SaveToCSV("Laguerre")
    #SaveAllToCSV()
end

function main()
    test()
    demo()
    perf()
end

main()

end # module

ext: jl


text:
@testset verbose=true "Test import statements" begin
    using JavaCall
    using JavaCall.JImport

    @testset "Import Float" begin
        @jimport "java.lang.Float"
        @test @isdefined JFloat
        @test @isdefined JFloatJuliaImpl

        a = JFloat(Float32(2.2))
        b = JFloat(Float32(2.2))
        @test a == b
    end
end


text:
### A Pluto.jl notebook ###
# v0.19.2

using Markdown
using InteractiveUtils

# ╔═╡ 28329d08-9860-11ec-0a4d-a733da82b9cf
begin
	using PlutoUI
	using CSV
	using DataFrames
	using DataFramesMeta
	using StatsPlots
	using Statistics
	using Flux
end

# ╔═╡ 78e2c184-8085-46f6-a9a3-e317034761ae
begin
	using LinearAlgebra
	using ProgressMeter
end

# ╔═╡ 7e89944e-3be2-4db4-86c0-44bb1e8317e1
iris = CSV.File("./train.tsv") |> DataFrame

# ╔═╡ a8612317-fcf6-4271-a6bc-6007baf3aaff
LocalResource("./iris.JPG")

# ╔═╡ 08421c62-5c47-4c2d-91f0-935ba4cda0b6
rename!(iris, [:id, :SepalLength, :SepalWidth, :PetalLength, :PetalWidth, :Class])

# ╔═╡ 3589c515-8a20-471f-b1d1-ac197210d6d8
# 要約統計量
describe(iris)

# ╔═╡ ac0c53f6-eb54-4736-992f-5ad8abe7fcb8
# 各グループごとにそれぞれの長さの平均を見る
combine(groupby(iris, :Class), [:SepalLength, :SepalWidth, :PetalLength, :PetalWidth] .=> mean)

# ╔═╡ db754358-61ce-4233-83c9-72d541edba2d
ua = unique(iris[!, :Class])

# ╔═╡ 52cfe26c-f816-44c8-a05f-accb70479607
iris[!, :Class] = [findfirst(==(x), ua) - 1 for x in iris[!,:Class]]

# ╔═╡ 4e88a468-6852-4be6-89cf-807929f8b44d
iris

# ╔═╡ 49546d8f-177d-4330-bde3-3eaef617096b
setosa = @linq iris |> where(:Class .== 0)

# ╔═╡ 11b5f1ff-e3c7-4958-878c-4cc6bd82216a
versicolor = @linq iris |> where(:Class .== 1)

# ╔═╡ 9f452c48-90fc-47e9-bc0b-81af4236aae5
virginica = @linq iris |> where(:Class .== 2)

# ╔═╡ 9ffa10fb-b3b1-4d8c-b4fd-872ccc74b74d
let
	plot(xlim=(4.0, 7.5), ylim=(0.8, 5.8), framestyle=:box, legend=:topleft)
	plot!(xlabel="sepal length [cm]", ylabel="petal length [cm]")
	@df setosa scatter!(:SepalLength, :PetalLength; shape=:circle, c=:blue, label="setosa")
	@df versicolor scatter!(:SepalLength, :PetalLength; shape=:x, c=:red, label="versicolor")

end

# ╔═╡ 8f5656bc-0757-4f5c-b6fc-ea43a456a0e6
sort!(iris, [:Class])

# ╔═╡ de1a2edd-785c-49df-9ab1-902e2050f01a
features_raw = Matrix(iris[!, 2:5])

# ╔═╡ a0e28ba3-6bf1-44ff-aad6-105b70c25e42
labels_raw = iris[!, :Class]

# ╔═╡ 8d3599e2-d4e0-4dce-806b-efad129c0e3f
length(labels_raw)

# ╔═╡ 49909aef-843b-4d32-9909-8fc8fe84416f
begin
	X1 = features_raw[1:25, 1]
	Y1 = features_raw[1:25, 2]
	Z1 = features_raw[1:25, 3]
	W1 = features_raw[1:25, 4]

	X2 = features_raw[26:50, 1]
	Y2 = features_raw[26:50, 2]
	Z2 = features_raw[26:50, 3]
	W2 = features_raw[26:50, 4]

	X3 = features_raw[51:75, 1]
	Y3 = features_raw[51:75, 2]
	Z3 = features_raw[51:75, 3]
	W3 = features_raw[51:75, 4]
end

# ╔═╡ 201212f2-68b3-4e58-b01d-20679cf3a46a
let
	default( markersize = 4 )
	plot(xaxis=("sepal length (cm)"), yaxis=("sepal width (cm)"), zaxis=("petal length (cm)"))
	scatter!( X1, Y1, Z1, label = "Iris setosa")
	scatter!( X2, Y2, Z2, label = "Iris versicolor")
	scatter!( X3, Y3, Z3, label = "Iris virginica")
end

# ╔═╡ e298a8ca-d239-4162-a242-4990322eb558

# ╔═╡ bc719327-d7d7-4f9a-a96d-603fa3019ae5
# ラベルをもらって、どの分類化に書き換える関数
function LabelToNum(labels)
	if labels == 0
		return [ 1.0, 0, 0 ]
	elseif labels == 1
		return [ 0, 1.0, 0 ]
	else
		return [ 0, 0, 1.0 ]
	end
end

# ╔═╡ a1daf9f7-1406-407f-9ab9-7b15d9a1c04c
labels = LabelToNum.(labels_raw)

# ╔═╡ 10c86974-250c-4bbc-8098-8154bf4e6364
begin
	toCheckIDs = vcat( 21:25, 46:50, 71:75)
	features_raw_toCheck = features_raw[toCheckIDs, :]
	labels_toCheck = labels[toCheckIDs]
end

# ╔═╡ 76205f2c-990e-4927-b819-e3a4efb2d4ea
begin
	SampleIDs = setdiff( 1:75, toCheckIDs)
	features_raw_sample = features_raw[SampleIDs, :]
	labels_sample = labels[SampleIDs]
end

# ╔═╡ dedb846e-584b-4a58-8c45-a19c1cf161c4
begin
	Std = [ std( features_raw_sample[:,i] ) for i in 1:4]
	Mean = [ mean( features_raw_sample[:,i] ) for i in 1:4]
end

# ╔═╡ 7b9d1531-3fd8-438f-9c98-31721fbf050f
begin
	features_toCheck = (features_raw_toCheck' .- Mean) ./ Std
	features_sample  = (features_raw_sample'  .- Mean) ./ Std
end

# ╔═╡ fdbe39d0-ac58-4a28-8ae4-af650968a797
features_toCheck

# ╔═╡ 309fb568-f7ef-4fe9-b50d-c4cb04e86f65
function nn(M, input; n=20)

    Ci = view( M, :, 1:4)
    bi = view( M, :, 5 )

    C1 = view( M, :, 6:n+5)
    b1 = view( M, :, n+6)

    C2 = view( M, :, n+7:2n+6)
    b2 = view( M, :, 2n+7)

    C3 = view( M, :, 2n+8:3n+7)
    b3 = view( M, :, 3n+8)

    C4 = view( M, :, 3n+9:4n+8)
    b4 = view( M, :, 4n+9)

    C5 = view( M, :, 4n+10:5n+9)
    b5 = view( M, :, 5n+10)

    Co = ( view( M, :, 5n+11:5n+13) )'  # 転置
    bo = view( M, 1:3, 5n+14 )

    r1 = sigmoid.( Ci * input + bi)

    r2 = sigmoid.( C1 * r1 + b1 )
    r3 = sigmoid.( C2 * r2 + b2 )
    r4 = sigmoid.( C3 * r3 + b3 )
    r5 = sigmoid.( C4 * r4 + b4 )
    r6 = sigmoid.( C5 * r5 + b5 )

    output = softmax( Co * r6 + bo)

    return output
end

# ╔═╡ 219fc7e5-6c91-44a8-b989-de494ec713d5
loss( output, v_true ) = Flux.crossentropy( output, v_true )

# ╔═╡ 90bb8c35-8094-4019-a0b1-f80c5b05f653
begin
	n = 20
	H = 10.0 * (rand(n, 5n+14) .- 0.5)

	g(H, i) = loss( nn(H, features_sample[:, i]), labels_sample[i])

	itr = 500
	datasize = size(labels_sample)[1]

	@showprogress for i in 1:(itr * datasize)
		global H
		num = rand(1:datasize)
		grad_f = Flux.gradient(M -> g(M, num), H)[1]
		grad_size = norm(grad_f)^2

		if grad_size < 0.01
			γ = 0.1
		else
			γ = g(H, num) / grad_size
		end

		H += -γ * grad_f
	end
end

# ╔═╡ 21a1013e-2ca2-4910-87af-7ed6b9138e3a
begin
	nn_sample(i) = nn( H, features_sample[:, i])
	nn_sample(1)
end

# ╔═╡ e3f61d75-d2ea-4702-8971-4f81b30430f6
labels_sample[1]

# ╔═╡ dd18e4ee-5b77-4302-9d81-7c384193b19c
nn_sample(30)

# ╔═╡ 1e51cf50-410f-4a50-8b70-8684af0fa497
function decision(x)
	v = similar(x)
	max_i = argmax(x)

	for i in 1:size(x)[1]
		if i == max_i
			v[i] = 1.0
		else
			v[i] = 0.0
		end
	end
	return v
end

# ╔═╡ 84bf9edf-fb54-44de-ad8a-eea9b93a4616
error_sample = labels_sample - decision.(nn_sample.(1:60))

# ╔═╡ 8a85bf9b-8314-48a8-842c-f3c53e155459
nn_toCheck(i) = nn( H, features_toCheck[:, i])

# ╔═╡ 3ae1d144-ce7b-4729-b5a4-d3748088ae41
features_toCheck[:, 1]

# ╔═╡ cab39868-cd2e-4641-85ee-4912c4aafe07
nn_toCheck(1)

# ╔═╡ 724392ba-6973-4db9-8c21-fa7ddd76ca66
test_iris = CSV.File("./test.tsv") |> DataFrame

# ╔═╡ e89b3ec1-cacf-4501-9a1e-bc2a7a29c76d
features_raw_toTest = Matrix(test_iris)[:,2:end]

# ╔═╡ 2308910f-7091-4779-97de-30d9f080eeb8
id_Test =  Matrix(test_iris)[:,1]

# ╔═╡ bfb95b52-d0b7-4a56-9642-ea96200ebeef
features_toTest = (features_raw_toTest' .- Mean) ./ Std

# ╔═╡ 02e78399-1ade-40fd-ad72-1dd7320c0e4d
nn_toTest(i) = nn( H, features_toTest[:,i])

# ╔═╡ 568105a2-a9fa-4250-99a5-37ffa81b7d4d
function get_answers()
	answers = []
	for i in 1:75
		ans = nn_toTest(i) |> argmax
		push!(answers, ans)
	end
	return answers
end

# ╔═╡ 90d6d48a-04cc-44a2-8b00-20188484d472
ans = get_answers()

# ╔═╡ 81018fb1-496c-4985-804a-f189d60f6e65
test_iris[:,1]

# ╔═╡ 08479be2-3e69-4f33-a04b-bdbd14620e14
out = hcat( test_iris[:,1], ans) |> x -> DataFrame(x, :auto)

# ╔═╡ 8d394251-e703-46e1-982c-8b427ada625f
begin
	out.x3 = similar(out.x2)
	for i in 1:75
		if out.x2[i] == 1
			out.x3[i] = "Iris-setosa"
		elseif out.x2[i] == 2
			out.x3[i] = "Iris-versicolor"
		else
			out.x3[i] = "Iris-virginica"
		end
	end
end

# ╔═╡ dc23ed0d-55b7-4ac2-9532-b38765aa0b6e
out

# ╔═╡ e2f6378c-869e-4307-8858-78ddbb063139
out2 = hcat( out.x1, out.x3) |> x -> DataFrame(x, :auto)

# ╔═╡ 0032fc94-b4b5-40fd-af95-11f678d4450d
CSV.write("./output.csv", out2)

# ╔═╡ 00000000-0000-0000-0000-000000000001
PLUTO_PROJECT_TOML_CONTENTS = """
[deps]
CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
DataFramesMeta = "1313f7d8-7da2-5740-9ea0-a2ca25f37964"
Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
PlutoUI = "7f904dfe-b85e-4ff6-b463-dae2292396a8"
ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd"

[compat]
CSV = "~0.10.2"
DataFrames = "~1.3.2"
DataFramesMeta = "~0.10.0"
Flux = "~0.12.9"
PlutoUI = "~0.7.35"
ProgressMeter = "~1.7.1"
StatsPlots = "~0.14.33"
"""

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

julia_version = "1.7.2"
manifest_format = "2.0"

[[deps.AbstractFFTs]]
deps = ["ChainRulesCore", "LinearAlgebra"]
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[[deps.AbstractPlutoDingetjes]]
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[[deps.AbstractTrees]]
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[[deps.Adapt]]
deps = ["LinearAlgebra"]
git-tree-sha1 = "af92965fb30777147966f58acb05da51c5616b5f"
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[[deps.ArgTools]]
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[[deps.Static]]
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[[deps.Statistics]]
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[[deps.StatsAPI]]
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[[deps.StatsBase]]
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[[deps.StatsFuns]]
deps = ["ChainRulesCore", "HypergeometricFunctions", "InverseFunctions", "IrrationalConstants", "LogExpFunctions", "Reexport", "Rmath", "SpecialFunctions"]
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[[deps.StatsPlots]]
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[[deps.TOML]]
deps = ["Dates"]
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[[deps.TableOperations]]
deps = ["SentinelArrays", "Tables", "Test"]
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[[deps.TableTraits]]
deps = ["IteratorInterfaceExtensions"]
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[[deps.Tables]]
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[[deps.Tar]]
deps = ["ArgTools", "SHA"]
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[[deps.Test]]
deps = ["InteractiveUtils", "Logging", "Random", "Serialization"]
uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

[[deps.TimerOutputs]]
deps = ["ExprTools", "Printf"]
git-tree-sha1 = "97e999be94a7147d0609d0b9fc9feca4bf24d76b"
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[[deps.TranscodingStreams]]
deps = ["Random", "Test"]
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uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa"
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[[deps.URIs]]
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[[deps.UUIDs]]
deps = ["Random", "SHA"]
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[[deps.Unicode]]
uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"

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

[[deps.Unzip]]
git-tree-sha1 = "34db80951901073501137bdbc3d5a8e7bbd06670"
uuid = "41fe7b60-77ed-43a1-b4f0-825fd5a5650d"
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[[deps.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"

[[deps.Wayland_protocols_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4528479aa01ee1b3b4cd0e6faef0e04cf16466da"
uuid = "2381bf8a-dfd0-557d-9999-79630e7b1b91"
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[[deps.WeakRefStrings]]
deps = ["DataAPI", "InlineStrings", "Parsers"]
git-tree-sha1 = "c69f9da3ff2f4f02e811c3323c22e5dfcb584cfa"
uuid = "ea10d353-3f73-51f8-a26c-33c1cb351aa5"
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[[deps.Widgets]]
deps = ["Colors", "Dates", "Observables", "OrderedCollections"]
git-tree-sha1 = "505c31f585405fc375d99d02588f6ceaba791241"
uuid = "cc8bc4a8-27d6-5769-a93b-9d913e69aa62"
version = "0.6.5"

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

[[deps.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"

[[deps.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"

[[deps.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"

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

[[deps.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"

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

[[deps.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"

[[deps.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"

[[deps.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"

[[deps.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"

[[deps.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"

[[deps.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"

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

[[deps.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"

[[deps.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"

[[deps.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"

[[deps.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"

[[deps.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"

[[deps.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"

[[deps.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"

[[deps.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"

[[deps.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"

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

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

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

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

[[deps.Zygote]]
deps = ["AbstractFFTs", "ChainRules", "ChainRulesCore", "DiffRules", "Distributed", "FillArrays", "ForwardDiff", "IRTools", "InteractiveUtils", "LinearAlgebra", "MacroTools", "NaNMath", "Random", "Requires", "SparseArrays", "SpecialFunctions", "Statistics", "ZygoteRules"]
git-tree-sha1 = "93285d2877f1f1b09b2a2b029f90e9db10127022"
uuid = "e88e6eb3-aa80-5325-afca-941959d7151f"
version = "0.6.35"

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

[[deps.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"

[[deps.libblastrampoline_jll]]
deps = ["Artifacts", "Libdl", "OpenBLAS_jll"]
uuid = "8e850b90-86db-534c-a0d3-1478176c7d93"

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

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

[[deps.libvorbis_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"]
git-tree-sha1 = "b910cb81ef3fe6e78bf6acee440bda86fd6ae00c"
uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a"
version = "1.3.7+1"

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

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

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

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

[[deps.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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max_stars_repo_name: harakiso/pluto

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text:
r = 0.5
function T(t)
    return 1/log(t)
end
# target function
function h(x)
    return (cos(50x) + sin(20x))^2
end

N = 2500
x = ones(N)
y = ones(N)
for t = 1:(N-1)
    # step 1
    at = max(x[t]-r, 0)
    bt = min(x[t]+r, 1)
    u = rand() * (bt - at) + at
    # step 2
    rho = min(exp( (h(u) - h(x[t])) / T(t) ), 1)
    if rand() < rho
        x[t+1] = u
        y[t+1] = h(u)
    else
        x[t+1] = x[t]
        y[t+1] = y[t]
    end
end

max_stars_repo_name: szcf-weiya/MCMC

max_issues_repo_name: szcf-weiya/MCMC

max_forks_repo_name: szcf-weiya/MCMC


text:
using ApproxFun, Test, DualNumbers
    import ApproxFun: eigs

@testset "Extras" begin
    @testset "Dual numbers" begin
        @test dual(1.5,1) ∈  Segment(dual(1.0,1),dual(2.0))

        f=Fun(exp,Segment(dual(1.0,1),dual(2.0)),20)
        @test Fun(h->Fun(exp,Segment(1.0+h,2.0)).coefficients[1],0..1)'(0.) ≈ DualNumbers.epsilon(f.coefficients[1])

        ud=let d= Segment(dual(0.0,1.0),1.0)
            B = ldirichlet(d)
            D = Derivative(d)
            a = Fun(exp,d)
            u = [B;D+dual(1.0,4.0)*a] \ [dual(1.0,2.0),0.0]
            u(0.5)
        end

        u0=let d= Segment(0.0,1.0)
            B = ldirichlet(d)
            D = Derivative(d)
            a = Fun(exp,d)
            u = [B;D+a] \ [1.0,0.0]
            u(0.5)
        end
        h=0.00001
        uh=let d= Segment(h,1.0)
            B = ldirichlet(d)
            D = Derivative(d)
            a = Fun(exp,d)
            u = [B;D+(1+4h)*a] \ [1.0+2h,0.0]
            u(0.5)
        end

        @test absdual(ud - dual(u0,(uh-u0)/h)) ≤ h

        let d=Segment(0.0,1.0)
            B = ldirichlet(d)
            D = Derivative(d)
            a = Fun(exp,d)
            u = [B;D+a] \ [dual(1.0,2.0),0.0]
            ur = [B;D+a] \ [1.0,0.0]
            ud = [B;D+a] \ [2.0,0.0]
            @test absdual(u(0.5)  - dual(ur(0.5),ud(0.5))) < 10eps()
        end
    end

    @testset "Eig test #336" begin
        d = 0..π
        A=Derivative(d)^2
        λ=eigvals(Dirichlet(d),A,100)
        @test sort(λ)[end-5:end] ≈ -(-6:-1).^2
        λ=eigvals(Dirichlet(), A, 100)
        @test sort(λ)[end-5:end] ≈ -(-6:-1).^2

        F = x->x^8
        d = Interval(0.0,1.0)
        f = Fun(F,d)
        ginf = Fun(x->exp(-x),d)
        gp = ginf'
        Af = Fun(x->x+f(x),d)
        transport_ = Fun(x-> x - 1,d)
        damping = Fun(x-> 1 - f(x),d)
        A = transport_*Derivative(d) + damping
        P = -DefiniteIntegral(Chebyshev(d))[LowRankFun((x,y)->gp(x)*(y+f(y)),d^2)];
        λ,V = eigs(A,100)
        @test norm(sort(real(filter(x->isreal(x),λ)))[1:5]-(0:4)) ≤ 100000eps()

        λ,V = eigs(A+P,100)
        @test sort(real(filter(x->isreal(x),λ)))[5] ≈ 3.93759261234502 atol=1E-3
    end

    @testset "findmin/max" begin
        f = Fun(x -> exp(0.25x) + sin(x) + 0.5cos(10x), -4..4)
        @test [findmax(f)...] ≈ [3.0531164509549584, 1.886754631165656]
        @test [findmin(f)...] ≈ [-0.825047261209411, -1.5741041425422948]
    end

    @testset "Newton iteration" begin
        x=Fun(Chebyshev(0..1))

        # Test two coupled systems of nonlinear ODEs with nonlinear BCs
        u1 = 0.5*one(x)
        u2 = 0.5*one(x)

        N_dep = (u1,u2) -> [
                u1(0)*u1(0)*u1(0) - 0.125;
                u2(0) - 1;
                u1' - u1*u1;
                u2' + u1*u1*u2;
            ]
        u1,u2 = newton(N_dep, [u1,u2])

        u1_exact = -1 / (x - 2)
        u2_exact = exp(1 / (x - 2) + 1/2)
        @test norm(u2 - u2_exact) < 1e-15
        @test norm(u1 - u1_exact) < 1e-15

        # Test two independent systems of nonlinear ODEs with nonlinear BCs
        u1 = 0.5*one(x)
        u2 = 0.5*one(x)

        N_ind = (u1,u2) -> [
                u1(0)*u1(0)*u1(0) - 0.125;
                u2(0)*u2(0)*u2(0) + 0.125;
                u1' - u1*u1;
                u2' - u2*u2;
            ]

        # note takes a few more iterations to converge to accuracy
        u1,u2 = newton(N_ind, [u1,u2], maxiterations=25)

        u1_exact = -1 / (x - 2)
        u2_exact = -1 / (x + 2)
        @test norm(u2 - u2_exact) < 1e-15
        @test norm(u1 - u1_exact) < 1e-15

        # Compare to original newton for one equation and verify all solutions correct
        N_single = u -> [u(0)*u(0)*u(0) - 0.125;
                        u' - u*u]

        u = 0.5*one(x)
        u = newton(N_single, [u])
        @test norm(u - u1) < 1e-15

    end
end


text:
using STREAMBenchmark
using Test, Statistics

Base.Threads.nthreads() > 1 || (@warn "Running test suite with only a single thread!")

# check if we are a GitHub runner
const is_github_runner = haskey(ENV, "GITHUB_ACTIONS")

function with_avxt(f)
    @eval STREAMBenchmark.avxt() = true
    f()
    @eval STREAMBenchmark.avxt() = false
end

VECTOR_LENGTH = 0
if is_github_runner
    VECTOR_LENGTH = 100_000
end
if haskey(ENV, "STREAM_VECTOR_LENGTH")
    VECTOR_LENGTH = parse(Int, ENV["STREAM_VECTOR_LENGTH"])
end
if !iszero(VECTOR_LENGTH)
    @info("Using vector length: ", VECTOR_LENGTH)
end

@testset "STREAMBenchmark.jl" begin
    @testset "Benchmarks" begin
        @test STREAMBenchmark.default_vector_length() >= STREAMBenchmark.last_cachesize() / sizeof(Float64)

        iszero(VECTOR_LENGTH) || (STREAMBenchmark.default_vector_length() = VECTOR_LENGTH)
        @show STREAMBenchmark.default_vector_length()

        # memory_bandwidth
        @test keys(memory_bandwidth()) == (:median, :minimum, :maximum)
        @test 10 < memory_bandwidth().median < 1_000_000
        @test 10 < memory_bandwidth(nthreads=1).median < 1_000_000
        with_avxt() do
            @test 10 < memory_bandwidth().median < 1_000_000
        end
        @test !isnothing(memory_bandwidth(nthreads=max(Threads.nthreads()-1,1)))

        # TODO: add verbose=true test
        membw = memory_bandwidth().median
        membw_nowrtalloc = memory_bandwidth(write_allocate=false).median
        @test (membw > membw_nowrtalloc) || (abs(membw - membw_nowrtalloc) < 0.3 * membw)

        # benchmark
        let nt = benchmark()
            @test keys(nt) == (:single, :multi)
            @test keys(nt.single) == (:median, :minimum, :maximum)
            @test keys(nt.multi) == (:median, :minimum, :maximum)
        end
        # vector_length_dependence
        let d = STREAMBenchmark.vector_length_dependence(nthreads=1)
            @test typeof(d) == Dict{Int64, Float64}
            @test length(d) == 4
            @test maximum(abs.(diff(collect(values(d))))) / median(values(d)) < 0.2
        end
        let d = STREAMBenchmark.vector_length_dependence(n=2)
            @test length(d) == 2
        end

        # scaling benchmark
        membws = scaling_benchmark()
        @test typeof(membws) == Vector{Float64}
        @test length(membws) == Threads.nthreads()
    end

    @testset "Kernels" begin
        A = [1.0,2.0,3.0,4.0,5.0]
        B = [0.8450044149444245, 0.2991196515689396, 0.5449487174110352, 0.06376462113406589, 0.817610835138292]
        C = [42.0,42.0,42.0,42.0,42.0]
        s = 13

        nthreads = min(2, Threads.nthreads())
        thread_indices = STREAMBenchmark._threadidcs(length(A), nthreads)

        STREAMBenchmark.copy(C, A)
        @test C == A
        STREAMBenchmark.copy_allthreads(C, A)
        @test C == A
        STREAMBenchmark.copy_nthreads(C, A; nthreads, thread_indices)
        @test C == A
        STREAMBenchmark.scale(B,C,s)
        @test B ≈ s .* C
        STREAMBenchmark.scale_allthreads(B,C,s)
        @test B ≈ s .* C
        STREAMBenchmark.scale_nthreads(B,C,s; nthreads, thread_indices)
        @test B ≈ s .* C
        STREAMBenchmark.add(C,A,B)
        @test C ≈ A .+ B
        STREAMBenchmark.add_allthreads(C,A,B)
        @test C ≈ A .+ B
        STREAMBenchmark.add_nthreads(C,A,B; nthreads, thread_indices)
        @test C ≈ A .+ B
        STREAMBenchmark.triad(A,B,C,s)
        @test A ≈ B .+ s .* C
        STREAMBenchmark.triad_allthreads(A,B,C,s)
        @test A ≈ B .+ s .* C
        STREAMBenchmark.triad_nthreads(A,B,C,s; nthreads, thread_indices)
        @test A ≈ B .+ s .* C

        # @avxt threading
        with_avxt() do
           STREAMBenchmark.copy_allthreads(C, A)
           @test C == A
           STREAMBenchmark.scale_allthreads(B,C,s)
           @test B ≈ s .* C
           STREAMBenchmark.add_allthreads(C,A,B)
           @test C ≈ A .+ B
           STREAMBenchmark.triad_allthreads(A,B,C,s)
           @test A ≈ B .+ s .* C
        end
    end

    @testset "Original C STREAM Benchmark" begin
        mktempdir() do tmpdir
            cd(tmpdir) do
                STREAMBenchmark.download_original_STREAM()
                @test isdir("stream")
                isdir("stream") && cd("stream") do
                    @test isfile("stream.c")
                    @test isfile("mysecond.c")
                end

                try
                    STREAMBenchmark.compile_original_STREAM()
                    @test isfile("stream/stream")
                catch e
                    @warn "Compilation of original C STREAM benchmark failed."
                    println(e)
                end
                @test_throws ErrorException STREAMBenchmark.compile_original_STREAM(compiler=:carsten)
            end
        end

        mktempdir() do tmpdir
            cd(tmpdir) do
                @test_logs (:warn,"Couldn't find source code \"stream/stream.c\". Have you run STREAMBenchmark.download_original_STREAM()?") STREAMBenchmark.compile_original_STREAM()
                @test_logs (:warn,"Couldn't find executable \"stream/stream\". Have you run STREAMBenchmark.compile_original_STREAM()?") STREAMBenchmark.execute_original_STREAM()
            end
        end
    end
end