text:
"""
    parse_kw(ps::ParseState)

Dispatch function for when the parser has reached a keyword.
"""
function parse_kw(ps::ParseState)
    k = kindof(ps.t)
    if ps.closer.precedence == 20 && ps.lt.kind === Tokens.EX_OR && k !== Tokens.END
        return EXPR(:IDENTIFIER, ps)
    end
    if k === Tokens.IF
        return @default ps @closer ps :block parse_if(ps)
    elseif k === Tokens.LET
        return @default ps @closer ps :block parse_blockexpr(ps, :let)
    elseif k === Tokens.TRY
        return @default ps @closer ps :block parse_try(ps)
    elseif k === Tokens.FUNCTION
        return @default ps @closer ps :block parse_blockexpr(ps, :function)
    elseif k === Tokens.MACRO
        return @default ps @closer ps :block parse_blockexpr(ps, :macro)
    elseif k === Tokens.BEGIN
        @static if VERSION < v"1.4"
            return @default ps @closer ps :block parse_blockexpr(ps, :begin)
        else
            if ps.closer.inref
                ret = EXPR(ps)
            else
                return @default ps @closer ps :block parse_blockexpr(ps, :begin)
            end
        end
    elseif k === Tokens.QUOTE
        return @default ps @closer ps :block parse_blockexpr(ps, :quote)
    elseif k === Tokens.FOR
        return @default ps @closer ps :block parse_blockexpr(ps, :for)
    elseif k === Tokens.WHILE
        return @default ps @closer ps :block parse_blockexpr(ps, :while)
    elseif k === Tokens.BREAK
        return EXPR(ps)
    elseif k === Tokens.CONTINUE
        return EXPR(ps)
    elseif k === Tokens.IMPORT
        return parse_imports(ps)
    elseif k === Tokens.IMPORTALL
        # Old keyword..
        return EXPR(:IDENTIFIER, ps)
    elseif k === Tokens.USING
        return parse_imports(ps)
    elseif k === Tokens.EXPORT
        return parse_export(ps)
    elseif k === Tokens.MODULE
        return @default ps @closer ps :block parse_blockexpr(ps, :module)
    elseif k === Tokens.BAREMODULE
        return @default ps @closer ps :block parse_blockexpr(ps, :baremodule)
    elseif k === Tokens.CONST
        return @default ps parse_const(ps)
    elseif k === Tokens.GLOBAL
        return @default ps parse_local_global(ps, false)
    elseif k === Tokens.LOCAL
        return @default ps parse_local_global(ps)
    elseif k === Tokens.RETURN
        return @default ps parse_return(ps)
    elseif k === Tokens.END
        if ps.closer.square
            ret = EXPR(ps)
        else
            ret = mErrorToken(ps, EXPR(:IDENTIFIER, ps), UnexpectedToken)
        end
        return ret
    elseif k === Tokens.ELSE || k === Tokens.ELSEIF || k === Tokens.CATCH || k === Tokens.FINALLY
        return mErrorToken(ps, EXPR(:IDENTIFIER, ps), UnexpectedToken)
    elseif k === Tokens.ABSTRACT
        return @default ps parse_abstract(ps)
    elseif k === Tokens.PRIMITIVE
        return @default ps parse_primitive(ps)
    elseif k === Tokens.TYPE
        return EXPR(:IDENTIFIER, ps)
    elseif k === Tokens.STRUCT
        return @default ps @closer ps :block parse_blockexpr(ps, :struct)
    elseif k === Tokens.MUTABLE
        return @default ps @closer ps :block parse_mutable(ps)
    elseif k === Tokens.OUTER
        return EXPR(:IDENTIFIER, ps)
    else
        return mErrorToken(ps, Unknown)
    end
end

function parse_const(ps::ParseState)
    kw = EXPR(ps)
    arg = parse_expression(ps)
    if !(isassignment(unwrapbracket(arg)) || (headof(arg) === :global && length(arg.args) > 0 && isassignment(unwrapbracket(arg.args[1]))))
        arg = mErrorToken(ps, arg, ExpectedAssignment)
    end
    ret = EXPR(:const, EXPR[arg], EXPR[kw])
    return ret
end

function parse_local_global(ps::ParseState, islocal = true)
    kw = EXPR(ps)
    if ps.nt.kind === Tokens.CONST
        arg1 = parse_const(next(ps))
        EXPR(:const, EXPR[EXPR(islocal ? :local : :global, EXPR[arg1.args[1]], nothing)], EXPR[kw, arg1.trivia[1]])
    else
        args, trivia = EXPR[], EXPR[kw]
        @closer ps :comma while !closer(ps)
            push!(args, parse_expression(ps))
            if iscomma(ps.nt)
                accept_comma(ps, trivia)
            else
                break
            end
        end
        EXPR(islocal ? :local : :global, args, trivia)
    end
end

function parse_return(ps::ParseState)
    kw = EXPR(ps)
    # Note to self: Nothing could be treated as implicit and added
    # during conversion to Expr.
    arg = closer(ps) ? EXPR(:NOTHING, 0, 0, "") : @precedence ps AssignmentOp - 1 parse_expression(ps)

    return EXPR(:return, EXPR[arg], EXPR[kw])
end

function parse_abstract(ps::ParseState)
    if kindof(ps.nt) === Tokens.TYPE
        kw1 = EXPR(ps)
        kw2 = EXPR(next(ps))
        sig = @closer ps :block parse_expression(ps)
        ret = EXPR(:abstract, EXPR[sig], EXPR[kw1, kw2, accept_end(ps)])
    else
        ret = EXPR(:IDENTIFIER, ps)
    end
    return ret
end

function parse_primitive(ps::ParseState)
    if kindof(ps.nt) === Tokens.TYPE
        kw1 = EXPR(ps)
        kw2 = EXPR(next(ps))
        sig = @closer ps :ws @closer ps :wsop parse_expression(ps)
        arg = @closer ps :block parse_expression(ps)
        ret = EXPR(:primitive, EXPR[sig, arg], EXPR[kw1, kw2, accept_end(ps)])
    else
        ret = EXPR(:IDENTIFIER, ps)
    end
    return ret
end

function parse_mutable(ps::ParseState)
    if kindof(ps.nt) === Tokens.STRUCT
        kw = EXPR(ps)
        next(ps)
        ret = parse_blockexpr(ps, :mutable)
        pushfirst!(ret.trivia, setparent!(kw, ret))
        update_span!(ret)
    else
        ret = EXPR(:IDENTIFIER, ps)
    end
    return ret
end

function parse_imports(ps::ParseState)
    kw = EXPR(ps)
    allow_as = is_import(kw)
    kwt = allow_as ? :import : :using

    arg = parse_dot_mod(ps, false, allow_as)
    if !iscomma(ps.nt) && !iscolon(ps.nt)
        ret = EXPR(kwt, EXPR[arg], EXPR[kw])
    elseif iscolon(ps.nt)
        ret = EXPR(kwt, EXPR[EXPR(EXPR(:OPERATOR, next(ps)), EXPR[arg])], EXPR[kw])

        arg = parse_dot_mod(ps, true, allow_as)
        push!(ret.args[1], arg)
        prevpos = position(ps)
        while iscomma(ps.nt)
            pushtotrivia!(ret.args[1], accept_comma(ps))
            arg = parse_dot_mod(ps, true, allow_as)
            push!(ret.args[1], arg)
            prevpos = loop_check(ps, prevpos)
        end
        update_span!(ret)
    else
        ret = EXPR(kwt, EXPR[arg], EXPR[kw])
        prevpos = position(ps)
        while iscomma(ps.nt)
            pushtotrivia!(ret, accept_comma(ps))
            arg = parse_dot_mod(ps, false, allow_as)
            push!(ret, arg)
            prevpos = loop_check(ps, prevpos)
        end
    end

    return ret
end

function parse_export(ps::ParseState)
    args = EXPR[]
    trivia = EXPR[EXPR(ps)]
    push!(args, parse_importexport_item(ps))

    prevpos = position(ps)
    while iscomma(ps.nt)
        push!(trivia, EXPR(next(ps)))
        arg = parse_importexport_item(ps)
        push!(args, arg)
        prevpos = loop_check(ps, prevpos)
    end

    return EXPR(:export, args, trivia)
end

"""
    parse_blockexpr_sig(ps::ParseState, head)

Utility function to parse the signature of a block statement (i.e. any statement preceding
the main body of the block). Returns `nothing` in some cases (e.g. `begin end`)
"""
function parse_blockexpr_sig(ps::ParseState, head)
    if head === :struct || head == :mutable || head === :while
        return @closer ps :ws parse_expression(ps)
    elseif head === :for
        iters, trivia = EXPR[], EXPR[]
        parse_iterators(ps, iters, trivia)
        if length(iters) == 1
            return first(iters)
        else
            return EXPR(:block, iters, trivia)
        end
    elseif head === :function || head === :macro
        sig = @closer ps :inwhere @closer ps :ws parse_expression(ps)
        if convertsigtotuple(sig)
            sig = EXPR(:tuple, sig.args, sig.trivia)
        end
        prevpos = position(ps)
        while kindof(ps.nt) === Tokens.WHERE && kindof(ps.ws) != Tokens.NEWLINE_WS
            sig = @closer ps :inwhere @closer ps :ws parse_operator_where(ps, sig, INSTANCE(next(ps)), false)
            prevpos = loop_check(ps, prevpos)
        end
        return sig
    elseif head === :let
        if isendoflinews(ps.ws)
            return EXPR(:block, EXPR[], nothing)
        else
            arg = @closer ps :comma @closer ps :ws  parse_expression(ps)
            if iscomma(ps.nt) || !(is_wrapped_assignment(arg) || isidentifier(arg))
                arg = EXPR(:block, EXPR[arg])
                prevpos = position(ps)
                while iscomma(ps.nt)
                    pushtotrivia!(arg, accept_comma(ps))
                    nextarg = @closer ps :comma @closer ps :ws parse_expression(ps)
                    push!(arg, nextarg)
                    prevpos = loop_check(ps, prevpos)
                end
            end
            return arg
        end
    elseif head === :do
        args, trivia = EXPR[], EXPR[]
        prevpos = position(ps)
        @closer ps :comma @closer ps :block while !closer(ps)
            push!(args, @closer ps :ws a = parse_expression(ps))
            if kindof(ps.nt) === Tokens.COMMA
                push!(trivia, accept_comma(ps))
            elseif @closer ps :ws closer(ps)
                break
            end
            prevpos = loop_check(ps, prevpos)
        end
        return EXPR(:tuple, args, trivia)
    elseif head === :module || head === :baremodule
        return isidentifier(ps.nt) ? EXPR(:IDENTIFIER, next(ps)) :
            @precedence ps 15 @closer ps :ws parse_expression(ps)
    end
    return nothing
end

function parse_do(ps::ParseState, pre::EXPR)
    args, trivia = EXPR[pre], EXPR[EXPR(next(ps))]
    args1, trivia1 = EXPR[], EXPR[]
    @closer ps :comma @closer ps :block while !closer(ps)
        push!(args1, @closer ps :ws a = parse_expression(ps))
        if kindof(ps.nt) === Tokens.COMMA
            push!(trivia1, accept_comma(ps))
        elseif @closer ps :ws closer(ps)
            break
        else
            # we've errored, let's add a dummy comma
            push!(trivia1, EXPR(:COMMA, 0, 0))
        end
    end
    blockargs = parse_block(ps, EXPR[], (Tokens.END,))
    push!(args, (EXPR(EXPR(:OPERATOR, 0, 0, "->"), EXPR[EXPR(:tuple, args1, trivia1), EXPR(:block, blockargs, nothing)])))
    push!(trivia, accept_end(ps))
    return EXPR(:do, args, trivia)
end

"""
    parse_blockexpr(ps::ParseState, head)

General function for parsing block expressions comprised of a series of statements
terminated by an `end`.
"""
function parse_blockexpr(ps::ParseState, head)
    kw = EXPR(ps)
    sig = parse_blockexpr_sig(ps, head)
    blockargs = parse_block(ps, EXPR[], (Tokens.END,), docable(head))
    if head === :begin
        EXPR(:block, blockargs, EXPR[kw, accept_end(ps)])
    elseif sig === nothing
        EXPR(head, EXPR[EXPR(:block, blockargs, nothing)], EXPR[kw, accept_end(ps)])
    elseif (head === :function || head === :macro) && is_either_id_op_interp(sig)
        if isempty(blockargs)
            EXPR(head, EXPR[sig], EXPR[kw, accept_end(ps)])
        else
            sig = mErrorToken(ps, sig, SignatureOfFunctionDefIsNotACall)
            EXPR(head, EXPR[sig, EXPR(:block, blockargs, nothing)], EXPR[kw, accept_end(ps)])
        end
    elseif head === :mutable
        EXPR(:struct, EXPR[EXPR(:TRUE, 0, 0), sig, EXPR(:block, blockargs, nothing)], EXPR[kw, accept_end(ps)])
    elseif head === :module
        EXPR(head, EXPR[EXPR(:TRUE, 0, 0), sig, EXPR(:block, blockargs, nothing)], EXPR[kw, accept_end(ps)])
    elseif head === :baremodule
        EXPR(:module, EXPR[EXPR(:FALSE, 0, 0), sig, EXPR(:block, blockargs, nothing)], EXPR[kw, accept_end(ps)])
    elseif head === :struct
        EXPR(head, EXPR[EXPR(:FALSE, 0, 0), sig, EXPR(:block, blockargs, nothing)], EXPR[kw, accept_end(ps)])
    else
        EXPR(head, EXPR[sig, EXPR(:block, blockargs, nothing)], EXPR[kw, accept_end(ps)])
    end
end

"""
    parse_if(ps, nested=false)

Parse an `if` block.
"""
function parse_if(ps::ParseState, nested = false)
    args = EXPR[]
    trivia = EXPR[EXPR(ps)]

    push!(args, isendoflinews(ps.ws) ? mErrorToken(ps, MissingConditional) : @closer ps :ws parse_expression(ps))
    push!(args, EXPR(:block, parse_block(ps, EXPR[], (Tokens.END, Tokens.ELSE, Tokens.ELSEIF)), nothing))

    elseblockargs = EXPR[]
    if kindof(ps.nt) === Tokens.ELSEIF
        push!(args, parse_if(next(ps), true))
    end
    elsekw = kindof(ps.nt) === Tokens.ELSE
    if kindof(ps.nt) === Tokens.ELSE
        push!(trivia, EXPR(next(ps)))
        parse_block(ps, elseblockargs)
    end

    # Construction
    if !(isempty(elseblockargs) && !elsekw)
        push!(args, EXPR(:block, elseblockargs, nothing))
    end
    !nested && push!(trivia, accept_end(ps))

    return EXPR(nested ? :elseif : :if, args, trivia)
end

function parse_try(ps::ParseState)
    kw = EXPR(ps)
    args = EXPR[]
    trivia = EXPR[kw]
    tryblockargs = parse_block(ps, EXPR[], (Tokens.END, Tokens.CATCH, Tokens.FINALLY))
    push!(args, EXPR(:block, tryblockargs, nothing))

    #  catch block
    if kindof(ps.nt) === Tokens.CATCH
        push!(trivia, EXPR(next(ps)))
        # catch closing early
        if kindof(ps.nt) === Tokens.FINALLY || kindof(ps.nt) === Tokens.END
            caught = EXPR(:FALSE, 0, 0, "")
            catchblock = EXPR(:block, EXPR[])
        else
            if isendoflinews(ps.ws)
                caught = EXPR(:FALSE, 0, 0, "")
            else
                caught = @closer ps :ws parse_expression(ps)
            end

            catchblockargs = parse_block(ps, EXPR[], (Tokens.END, Tokens.FINALLY))
            if !(is_either_id_op_interp(caught) || headof(caught) === :FALSE)
                pushfirst!(catchblockargs, caught)
                caught = EXPR(:FALSE, 0, 0, "")
            end
            catchblock = EXPR(:block, catchblockargs, nothing)
        end
    else
        push!(trivia, EXPR(:CATCH, 0, 0))
        caught = EXPR(:FALSE, 0, 0, "")
        catchblock = EXPR(:block, EXPR[], nothing)
    end
    push!(args, caught)
    push!(args, catchblock)

    # finally block
    if kindof(ps.nt) === Tokens.FINALLY
        if isempty(catchblock.args)
            args[3] = EXPR(:FALSE, 0, 0, "")
        end
        push!(trivia, EXPR(next(ps)))
        finallyblockargs = parse_block(ps)
        push!(args, EXPR(:block, finallyblockargs))
    end

    push!(trivia, accept_end(ps))
    return EXPR(:try, args, trivia)
end


text:
using NonconvexIpopt
function convert_common_kwargs(opt::NonconvexIpopt.IpoptAlg, opt_kwargs;
    callback=nothing,
    maxiters=nothing,
    maxtime=nothing,
    abstol=nothing,
    reltol=nothing)

    conv_opt_kwargs = (; opt_kwargs...)

    if !isnothing(callback)
        @warn "common callback argument is currently not used by $(opt)"
    end

    if !isnothing(maxiters)
        conv_opt_kwargs = (; conv_opt_kwargs..., max_iter=maxiters)
    end

    if !isnothing(maxtime)
        conv_opt_kwargs = (; conv_opt_kwargs..., max_cpu_time = maxtime)
    end

    if !isnothing(abstol)
        @warn "common abstol argument is currently not used by $(opt)"
    end

    if !isnothing(reltol)
        conv_opt_kwargs = (; conv_opt_kwargs..., tol =reltol)
    end

    return conv_opt_kwargs
end

function __create_options(opt::NonconvexIpopt.IpoptAlg;
    opt_kwargs=nothing)

    options = !isnothing(opt_kwargs) ? NonconvexIpopt.IpoptOptions(;opt_kwargs...) : NonconvexIpopt.IpoptOptions()

    return options
end

function _create_options(opt::NonconvexIpopt.IpoptAlg;
    opt_kwargs=nothing,
    sub_options=nothing,
    convergence_criteria=nothing)

    options = (; options = __create_options(opt, opt_kwargs=opt_kwargs))

    return options
end

check_optimizer_backend(opt::NonconvexIpopt.IpoptAlg) = false

ext: jl


text:
# Prints out a list of outliers for best-fit model:
using JLD2
using PyPlot
using Printf

include("../src/NbodyGradient/src/ttv.jl")

#@load "output_files/T1_run_hmc_student_ecc_lgndof_V1exp2nuinv_nstep2000_eps0.1_nleap20_201.jld2"
@load "T1_timing_posterior.jld2"
@load "T1_likelihood_profile_student_planetc_3.0sig.jld2"

# Print deviations of each transit:
chi_planet = zeros(Float64,n-1)
nobs_planet = zeros(Int64,n-1)
dev = zeros(Float64,ntrans)
elements = elements_grid[1,:,:,jgrid[1]]
dq0 = ttv_elements!(n,t0,h,tmax,elements,tt1,count1,0.0,0,0,rstar)
println("| Transit number | Planet | Model time | Observed time | Obs - mod | (Obs-mod)/sig | (Obs-mod)^2/sig^2 |")
println("| ---| --- | --- | --- | ---- | --- | --- |")
for j=1:ntrans
  dev[j] = (tobs_tot[j]-tt1[iplanet[j],indx[j]])/sobs_tot[j]
  chi_planet[iplanet[j]-1] += dev[j]^2
  nobs_planet[iplanet[j]-1] += 1
  if abs(dev[j]) > 3
    println("|",@sprintf("%4i",j),"| ",@sprintf("%2i",iplanet[j]-1),"| ",@sprintf("%4i",indx[j]),"| ",
        @sprintf("%12.5f",tt1[iplanet[j],indx[j]]),"| ",@sprintf("%12.5f",tobs_tot[j]),"| ",
        @sprintf("%8.5f",tobs_tot[j]-tt1[iplanet[j],indx[j]]),"| ",
        @sprintf("%8.5f",sobs_tot[j]),"| ",@sprintf("%5.1f",dev[j]),"| ",@sprintf("%5.1f",dev[j]^2),"|")
  end
end


text:
### A Pluto.jl notebook ###
# v0.14.7

using Markdown
using InteractiveUtils

# ╔═╡ b9dae3f2-5db7-4465-9c1e-f95df29fd48c
function ingredients(path::String)
	# this is from the Julia source code (evalfile in base/loading.jl)
	# but with the modification that it returns the module instead of the last object
	name = Symbol(basename(path))
	m = Module(name)
	Core.eval(m,
        Expr(:toplevel,
             :(eval(x) = $(Expr(:core, :eval))($name, x)),
             :(include(x) = $(Expr(:top, :include))($name, x)),
             :(include(mapexpr::Function, x) = $(Expr(:top, :include))(mapexpr, $name, x)),
             :(include($path))))
	m
end

# ╔═╡ db715d40-c599-11eb-0b28-f5c7b076c6ab
SVM = ingredients("./script.jl").SVM

# ╔═╡ 27defb3d-f5fb-4334-a441-50f7a347819d
import Plots

# ╔═╡ fc31cf17-5e20-4130-b02c-9c2bbf9d3b72
import Random

# ╔═╡ cc3033c8-4c74-4d28-ad2c-344246bbc827
JuTools = ingredients("../tools.jl").JuTools

# ╔═╡ 3c0bdafc-b98d-47c8-8b9f-69a7b94c5d85
begin
	X_data1, Y_data1 = JuTools.data_generate_cluster_2d(pos1=(20.0, 20.0), pos2=(50.0, 50.0), radius1=5.0, radius2=5.0, random_scale=8.0, data_size=1000)
	Y_data1 .= Y_data1 .* 2.0 .- 1.0;
end

# ╔═╡ b4f64a60-b8a7-4487-8897-6bd4efa37c76
size(X_data1)

# ╔═╡ 71ed19eb-602f-4b21-a874-3c0cd6ef19d7
size(Y_data1)

# ╔═╡ de69ceeb-c9fb-4038-bf3d-2c08430e973c
begin
	plot_X1 = [X_data1[i, 1] for i in 1:(size(X_data1)[1]) if Y_data1[i] == -1.0]
	plot_Y1 = [X_data1[i, 2] for i in 1:(size(X_data1)[1]) if Y_data1[i] == -1.0]
	plot_X2 = [X_data1[i, 1] for i in 1:(size(X_data1)[1]) if Y_data1[i] == 1.0]
	plot_Y2 = [X_data1[i, 2] for i in 1:(size(X_data1)[1]) if Y_data1[i] == 1.0]
end

# ╔═╡ e18d49b7-3ef2-4d5e-ae80-83af5836277f
let
	Plots.gr()
	p = Plots.scatter(plot_X1, plot_Y1, leg=false, c="red", background_color=Plots.RGB(0.2, 0.2, 0.2))
	Plots.scatter!(p, plot_X2, plot_Y2, leg=false, c="blue")
	Plots.xlims!(p, (0.0, 70.0))
	Plots.ylims!(p, (0.0, 70.0))
	p
end

# ╔═╡ 43aff583-73af-482a-82be-f3823fd40799
md"""
### Linear SVM
"""

# ╔═╡ 6e7a9a46-e0da-442d-8f45-6b5089f6d953
let
	weights = SVM.train_linear(X_data1, Y_data1, 10.0, learning_rate=0.05, max_iter=10, random_weights=false)
	JuTools.compute_accuracy(SVM.predict(X_data1, weights), Y_data1)
end

# ╔═╡ 394fb389-532f-41a7-a387-22d86e1c8e3e
let
	weights = SVM.train_linear(X_data1, Y_data1, 10.0, learning_rate=0.05, max_iter=100, random_weights=false)
	JuTools.compute_accuracy(SVM.predict(X_data1, weights), Y_data1)
end

# ╔═╡ 7ab6dbb1-194b-4816-8ad6-52abe6a7a80f
let
	weights = SVM.train_linear(X_data1, Y_data1, 10.0, learning_rate=0.05, max_iter=1000, random_weights=false)
	JuTools.compute_accuracy(SVM.predict(X_data1, weights), Y_data1)
end

# ╔═╡ 3bdb1ccd-18fe-4c91-ab1d-2e149de06097
let
	weights = SVM.train_linear(X_data1, Y_data1, 10.0, learning_rate=0.05, max_iter=1000, random_weights=false)
	X_test_1 = 0:0.1:70
	X_test_2 = 0:0.1:70
	f_test1(X1, X2) = begin
   		SVM.predict([X1, X2], weights)[1]
	end
	Plots.gr()
	Plots.contour(X_test_1, X_test_2, f_test1, leg=false, fill=true, c=Plots.cgrad(:matter, rev=true, categorical=false), linewidth=0, background_color=Plots.RGB(0.2, 0.2, 0.2))
	Plots.scatter!(plot_X1, plot_Y1, leg=false, c="red")
	Plots.scatter!(plot_X2, plot_Y2, leg=false, c="blue")
	Plots.xlims!((0.0, 70.0))
	Plots.ylims!((0.0, 70.0))
end

# ╔═╡ fcc3d16f-1545-40ca-b87d-df959fb25ed4
let
	weights = SVM.train_linear(X_data1, Y_data1, 10.0, learning_rate=0.05, max_iter=1000, random_weights=false)
	X_test_1 = 0:0.1:70
	X_test_2 = 0:0.1:70
	f_test2(X1, X2) = begin
   		abs(SVM.predict_proba([X1, X2], weights)[1])
	end
	Plots.gr()
	Plots.contour(X_test_1, X_test_2, f_test2, leg=false, fill=true, c=Plots.cgrad(:matter, rev=false, categorical=false), linewidth=0, background_color=Plots.RGB(0.2, 0.2, 0.2))
	Plots.scatter!(plot_X1, plot_Y1, leg=false, c="red")
	Plots.scatter!(plot_X2, plot_Y2, leg=false, c="blue")
	plot_X3 = collect(0:70)
	plot_Y3 = -(weights.b .+ (weights.w[1] .* plot_X3)) ./ weights.w[2]
	Plots.plot!(plot_X3, plot_Y3, c="purple", linewidth=5, alpha=0.5)
	Plots.xlims!((0.0, 70.0))
	Plots.ylims!((0.0, 70.0))
end

# ╔═╡ 869aacb1-74bd-4b8c-99f9-416732c4e783
begin
	# try with different data
	X_data2, Y_data2 = JuTools.data_generate_cluster_2d(pos1=(30.0, 80.0), pos2=(80.0, 30.0),
	    radius1=5.0, radius2=10.0, random_scale=8.0, data_size=1000)
	Y_data2 .= Y_data2 .* 2.0 .- 1.0
end

# ╔═╡ 76d280c5-3838-409c-b016-b4311ba6f071
let
	weights = SVM.train_linear(X_data2, Y_data2, 10.0, learning_rate=0.05, max_iter=1000, random_weights=false)
	JuTools.compute_accuracy(SVM.predict(X_data2, weights), Y_data2)
end

# ╔═╡ 9c94a40f-2dd0-4222-b7fd-4cb83fda0080
let
	weights = SVM.train_linear(X_data2, Y_data2, 10.0, learning_rate=0.05, max_iter=1000, random_weights=false)
	X_test_1 = 0:120
	X_test_2 = 0:120
	f_test1(X1, X2) = begin
   		SVM.predict([X1, X2], weights)[1]
	end
	plot_X1 = [X_data2[i, 1] for i in 1:(size(X_data2)[1]) if Y_data2[i] == -1.0]
	plot_Y1 = [X_data2[i, 2] for i in 1:(size(X_data2)[1]) if Y_data2[i] == -1.0]
	plot_X2 = [X_data2[i, 1] for i in 1:(size(X_data2)[1]) if Y_data2[i] == 1.0]
	plot_Y2 = [X_data2[i, 2] for i in 1:(size(X_data2)[1]) if Y_data2[i] == 1.0]
	Plots.gr()
	Plots.contour(X_test_1, X_test_2, f_test1, leg=false, fill=true, c=Plots.cgrad(:matter, rev=true, categorical=false), linewidth=0, background_color=Plots.RGB(0.2, 0.2, 0.2))
	Plots.scatter!(plot_X1, plot_Y1, leg=false, c="red")
	Plots.scatter!(plot_X2, plot_Y2, leg=false, c="blue")
	Plots.xlims!((0.0, 120.0))
	Plots.ylims!((0.0, 120.0))
end

# ╔═╡ e1788d22-2d0d-4bfd-962e-500d5ba67eab
let
	weights = SVM.train_linear(X_data2, Y_data2, 10.0, learning_rate=0.05, max_iter=1000, random_weights=false)
	X_test_1 = 0:120
	X_test_2 = 0:120
	f_test2(X1, X2) = begin
   		abs(SVM.predict_proba([X1, X2], weights)[1])
	end
	plot_X1 = [X_data2[i, 1] for i in 1:(size(X_data2)[1]) if Y_data2[i] == -1.0]
	plot_Y1 = [X_data2[i, 2] for i in 1:(size(X_data2)[1]) if Y_data2[i] == -1.0]
	plot_X2 = [X_data2[i, 1] for i in 1:(size(X_data2)[1]) if Y_data2[i] == 1.0]
	plot_Y2 = [X_data2[i, 2] for i in 1:(size(X_data2)[1]) if Y_data2[i] == 1.0]
	Plots.gr()
	Plots.contour(X_test_1, X_test_2, f_test2, leg=false, fill=true, c=Plots.cgrad(:matter, rev=false, categorical=false), linewidth=0, background_color=Plots.RGB(0.2, 0.2, 0.2))
	Plots.scatter!(plot_X1, plot_Y1, leg=false, c="red")
	Plots.scatter!(plot_X2, plot_Y2, leg=false, c="blue")
	plot_X3 = collect(0:120)
	plot_Y3 = -(weights.b .+ (weights.w[1] .* plot_X3)) ./ weights.w[2]
	Plots.plot!(plot_X3, plot_Y3, c="purple", linewidth=5, alpha=0.5)
	Plots.xlims!((0.0, 120.0))
	Plots.ylims!((0.0, 120.0))
end

# ╔═╡ 5e6de600-114c-495f-80a6-494565971fe2
begin
	weights_linear = SVM.train(X_data2, Y_data2, 1.0, kernel="linear", gamma="scale", coef=0.0, degree=2.0, verbose=false)
	string("Linear Kernel Accuracy: ", JuTools.compute_accuracy(SVM.predict(X_data2, X_data2, Y_data2, weights_linear), Y_data2))
end

# ╔═╡ b53fea06-c0bf-4cab-99fc-394a2f6ca0bd
begin
	weights_rbf = SVM.train(X_data2, Y_data2, 1.0, kernel="rbf", gamma="scale", coef=0.0, degree=2.0, verbose=false)
	string("RBF Kernel Accuracy: ", JuTools.compute_accuracy(SVM.predict(X_data2, X_data2, Y_data2, weights_rbf), Y_data2))
end

# ╔═╡ 82f44cd6-c1a4-4ad6-9d6c-484e4909aedc
begin
	weights_poly = SVM.train(X_data2, Y_data2, 1.0, kernel="polynomial", gamma="scale", coef=0.0, degree=2.0, verbose=false)
	string("Polynomial Kernel (degree=2) Accuracy: ", JuTools.compute_accuracy(SVM.predict(X_data2, X_data2, Y_data2, weights_poly), Y_data2))
end

# ╔═╡ af2ec3ff-3468-4954-b5b7-b35ee45f7833
begin
	weights_poly_3 = SVM.train(X_data2, Y_data2, 1.0, kernel="polynomial", gamma="scale", coef=0.0, degree=3.0, verbose=false)
	string("Polynomial Kernel (degree=3) Accuracy: ", JuTools.compute_accuracy(SVM.predict(X_data2, X_data2, Y_data2, weights_poly_3), Y_data2))
end

# ╔═╡ d0bd3eb0-b6ea-47a9-959e-e385e033cd49
begin
	weights_sigmoid = SVM.train(X_data2, Y_data2, 10.0, kernel="sigmoid", gamma="scale", coef=0.0, degree=2.0, verbose=false)
	string("Sigmoid Kernel Accuracy: ", JuTools.compute_accuracy(SVM.predict(X_data2, X_data2, Y_data2, weights_sigmoid), Y_data2))
end

# ╔═╡ 1390261d-f5e1-4b55-9865-78450a944885
let
	# plot the data
	X_test_1 = 0:120
	X_test_2 = 0:120
	plot_X1 = [X_data2[i, 1] for i in 1:(size(X_data2)[1]) if Y_data2[i] == -1.0]
	plot_Y1 = [X_data2[i, 2] for i in 1:(size(X_data2)[1]) if Y_data2[i] == -1.0]
	plot_X2 = [X_data2[i, 1] for i in 1:(size(X_data2)[1]) if Y_data2[i] == 1.0]
	plot_Y2 = [X_data2[i, 2] for i in 1:(size(X_data2)[1]) if Y_data2[i] == 1.0]

	X_test_linear = hcat(vec(repeat(reshape(X_test_1, :, 1), 1, 121)'), vec(repeat(X_test_2, 121, 1)))
	X_test_linear = abs.(reshape(SVM.predict_proba(X_test_linear, X_data2, Y_data2, weights_linear), 121, :))

	Plots.gr()
	plot_linear = Plots.contour(X_test_1, X_test_2, X_test_linear, fill=true, c=Plots.cgrad(:matter, rev=false, categorical=false), linewidth=0, background_color=Plots.RGB(0.2, 0.2, 0.2))
	Plots.scatter!(plot_X1, plot_Y1, leg=false, c="red")
	Plots.scatter!(plot_X2, plot_Y2, leg=false, c="blue")
	Plots.xlims!((0.0, 120.0))
	Plots.ylims!((0.0, 120.0))
	Plots.title!("SVM (Linear Kernel)")
end

# ╔═╡ dfde4be0-4d84-42b7-96eb-9cfa75976947
let
	# plot the data
	X_test_1 = 0:120
	X_test_2 = 0:120
	plot_X1 = [X_data2[i, 1] for i in 1:(size(X_data2)[1]) if Y_data2[i] == -1.0]
	plot_Y1 = [X_data2[i, 2] for i in 1:(size(X_data2)[1]) if Y_data2[i] == -1.0]
	plot_X2 = [X_data2[i, 1] for i in 1:(size(X_data2)[1]) if Y_data2[i] == 1.0]
	plot_Y2 = [X_data2[i, 2] for i in 1:(size(X_data2)[1]) if Y_data2[i] == 1.0]

	X_test_rbf = hcat(vec(repeat(reshape(X_test_1, :, 1), 1, 121)'), vec(repeat(X_test_2, 121, 1)))
X_test_rbf = abs.(reshape(SVM.predict_proba(X_test_rbf, X_data2, Y_data2, weights_rbf), 121, :))

	Plots.gr()
	plot_linear = Plots.contour(X_test_1, X_test_2, X_test_rbf, fill=true, c=Plots.cgrad(:matter, rev=false, categorical=false), linewidth=0, background_color=Plots.RGB(0.2, 0.2, 0.2))
	Plots.scatter!(plot_X1, plot_Y1, leg=false, c="red")
	Plots.scatter!(plot_X2, plot_Y2, leg=false, c="blue")
	Plots.xlims!((0.0, 120.0))
	Plots.ylims!((0.0, 120.0))
	Plots.title!("SVM (RBF Kernel)")
end

# ╔═╡ d2a3a80f-5096-440c-9cdd-bd6aa80d81f5
let
	# plot the data
	X_test_1 = 0:120
	X_test_2 = 0:120
	plot_X1 = [X_data2[i, 1] for i in 1:(size(X_data2)[1]) if Y_data2[i] == -1.0]
	plot_Y1 = [X_data2[i, 2] for i in 1:(size(X_data2)[1]) if Y_data2[i] == -1.0]
	plot_X2 = [X_data2[i, 1] for i in 1:(size(X_data2)[1]) if Y_data2[i] == 1.0]
	plot_Y2 = [X_data2[i, 2] for i in 1:(size(X_data2)[1]) if Y_data2[i] == 1.0]

	X_test_sigmoid = hcat(vec(repeat(reshape(X_test_1, :, 1), 1, 121)'), vec(repeat(X_test_2, 121, 1)))
X_test_sigmoid = abs.(reshape(SVM.predict_proba(X_test_sigmoid, X_data2, Y_data2, weights_sigmoid), 121, :))

	Plots.gr()
	plot_linear = Plots.contour(X_test_1, X_test_2, X_test_sigmoid, fill=true, c=Plots.cgrad(:matter, rev=false, categorical=false), linewidth=0, background_color=Plots.RGB(0.2, 0.2, 0.2))
	Plots.scatter!(plot_X1, plot_Y1, leg=false, c="red")
	Plots.scatter!(plot_X2, plot_Y2, leg=false, c="blue")
	Plots.xlims!((0.0, 120.0))
	Plots.ylims!((0.0, 120.0))
	Plots.title!("SVM (Sigmoid Kernel)")
end

# ╔═╡ f45c2f60-45e9-48f4-b81d-ea7111111f5c
let
	# plot the data
	X_test_1 = 0:120
	X_test_2 = 0:120
	plot_X1 = [X_data2[i, 1] for i in 1:(size(X_data2)[1]) if Y_data2[i] == -1.0]
	plot_Y1 = [X_data2[i, 2] for i in 1:(size(X_data2)[1]) if Y_data2[i] == -1.0]
	plot_X2 = [X_data2[i, 1] for i in 1:(size(X_data2)[1]) if Y_data2[i] == 1.0]
	plot_Y2 = [X_data2[i, 2] for i in 1:(size(X_data2)[1]) if Y_data2[i] == 1.0]

	X_test_poly = hcat(vec(repeat(reshape(X_test_1, :, 1), 1, 121)'), vec(repeat(X_test_2, 121, 1)))
X_test_poly = abs.(reshape(SVM.predict_proba(X_test_poly, X_data2, Y_data2, weights_poly), 121, :))

	Plots.gr()
	plot_linear = Plots.contour(X_test_1, X_test_2, X_test_poly, fill=true, c=Plots.cgrad(:matter, rev=false, categorical=false), linewidth=0, background_color=Plots.RGB(0.2, 0.2, 0.2))
	Plots.scatter!(plot_X1, plot_Y1, leg=false, c="red")
	Plots.scatter!(plot_X2, plot_Y2, leg=false, c="blue")
	Plots.xlims!((0.0, 120.0))
	Plots.ylims!((0.0, 120.0))
	Plots.title!("SVM (Polynomial Kernel, degree=2)")
end

# ╔═╡ ad5bb232-ed6b-416c-8472-a15a25c65b5b
let
	# plot the data
	X_test_1 = 0:120
	X_test_2 = 0:120
	plot_X1 = [X_data2[i, 1] for i in 1:(size(X_data2)[1]) if Y_data2[i] == -1.0]
	plot_Y1 = [X_data2[i, 2] for i in 1:(size(X_data2)[1]) if Y_data2[i] == -1.0]
	plot_X2 = [X_data2[i, 1] for i in 1:(size(X_data2)[1]) if Y_data2[i] == 1.0]
	plot_Y2 = [X_data2[i, 2] for i in 1:(size(X_data2)[1]) if Y_data2[i] == 1.0]

	X_test_poly = hcat(vec(repeat(reshape(X_test_1, :, 1), 1, 121)'), vec(repeat(X_test_2, 121, 1)))
X_test_poly = abs.(reshape(SVM.predict_proba(X_test_poly, X_data2, Y_data2, weights_poly_3), 121, :))

	Plots.gr()
	plot_linear = Plots.contour(X_test_1, X_test_2, X_test_poly, fill=true, c=Plots.cgrad(:matter, rev=false, categorical=false), linewidth=0, background_color=Plots.RGB(0.2, 0.2, 0.2))
	Plots.scatter!(plot_X1, plot_Y1, leg=false, c="red")
	Plots.scatter!(plot_X2, plot_Y2, leg=false, c="blue")
	Plots.xlims!((0.0, 120.0))
	Plots.ylims!((0.0, 120.0))
	Plots.title!("SVM (Polynomial Kernel, degree=3)")
end

# ╔═╡ Cell order:
# ╟─b9dae3f2-5db7-4465-9c1e-f95df29fd48c
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# ╠═f45c2f60-45e9-48f4-b81d-ea7111111f5c
# ╠═ad5bb232-ed6b-416c-8472-a15a25c65b5b

max_stars_repo_name: teamclouday/JuliaLearn

max_issues_repo_name: teamclouday/JuliaLearn

max_forks_repo_name: teamclouday/JuliaLearn


text:
# This file was generated by the Julia Swagger Code Generator
# Do not modify this file directly. Modify the swagger specification instead.

@doc raw"""APIVersions lists the versions that are available, to allow clients to discover the API at /api, which is the root path of the legacy v1 API.

    IoK8sApimachineryPkgApisMetaV1APIVersions(;
        apiVersion=nothing,
        kind=nothing,
        serverAddressByClientCIDRs=nothing,
        versions=nothing,
    )

    - apiVersion::String : APIVersion defines the versioned schema of this representation of an object. Servers should convert recognized schemas to the latest internal value, and may reject unrecognized values. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#resources
    - kind::String : Kind is a string value representing the REST resource this object represents. Servers may infer this from the endpoint the client submits requests to. Cannot be updated. In CamelCase. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#types-kinds
    - serverAddressByClientCIDRs::Vector{IoK8sApimachineryPkgApisMetaV1ServerAddressByClientCIDR} : a map of client CIDR to server address that is serving this group. This is to help clients reach servers in the most network-efficient way possible. Clients can use the appropriate server address as per the CIDR that they match. In case of multiple matches, clients should use the longest matching CIDR. The server returns only those CIDRs that it thinks that the client can match. For example: the master will return an internal IP CIDR only, if the client reaches the server using an internal IP. Server looks at X-Forwarded-For header or X-Real-Ip header or request.RemoteAddr (in that order) to get the client IP.
    - versions::Vector{String} : versions are the api versions that are available.
"""
mutable struct IoK8sApimachineryPkgApisMetaV1APIVersions <: SwaggerModel
    apiVersion::Any # spec type: Union{ Nothing, String } # spec name: apiVersion
    kind::Any # spec type: Union{ Nothing, String } # spec name: kind
    serverAddressByClientCIDRs::Any # spec type: Union{ Nothing, Vector{IoK8sApimachineryPkgApisMetaV1ServerAddressByClientCIDR} } # spec name: serverAddressByClientCIDRs
    versions::Any # spec type: Union{ Nothing, Vector{String} } # spec name: versions

    function IoK8sApimachineryPkgApisMetaV1APIVersions(;apiVersion=nothing, kind=nothing, serverAddressByClientCIDRs=nothing, versions=nothing)
        o = new()
        validate_property(IoK8sApimachineryPkgApisMetaV1APIVersions, Symbol("apiVersion"), apiVersion)
        setfield!(o, Symbol("apiVersion"), apiVersion)
        validate_property(IoK8sApimachineryPkgApisMetaV1APIVersions, Symbol("kind"), kind)
        setfield!(o, Symbol("kind"), kind)
        validate_property(IoK8sApimachineryPkgApisMetaV1APIVersions, Symbol("serverAddressByClientCIDRs"), serverAddressByClientCIDRs)
        setfield!(o, Symbol("serverAddressByClientCIDRs"), serverAddressByClientCIDRs)
        validate_property(IoK8sApimachineryPkgApisMetaV1APIVersions, Symbol("versions"), versions)
        setfield!(o, Symbol("versions"), versions)
        o
    end
end # type IoK8sApimachineryPkgApisMetaV1APIVersions

const _property_map_IoK8sApimachineryPkgApisMetaV1APIVersions = Dict{Symbol,Symbol}(Symbol("apiVersion")=>Symbol("apiVersion"), Symbol("kind")=>Symbol("kind"), Symbol("serverAddressByClientCIDRs")=>Symbol("serverAddressByClientCIDRs"), Symbol("versions")=>Symbol("versions"))
const _property_types_IoK8sApimachineryPkgApisMetaV1APIVersions = Dict{Symbol,String}(Symbol("apiVersion")=>"String", Symbol("kind")=>"String", Symbol("serverAddressByClientCIDRs")=>"Vector{IoK8sApimachineryPkgApisMetaV1ServerAddressByClientCIDR}", Symbol("versions")=>"Vector{String}")
Base.propertynames(::Type{ IoK8sApimachineryPkgApisMetaV1APIVersions }) = collect(keys(_property_map_IoK8sApimachineryPkgApisMetaV1APIVersions))
Swagger.property_type(::Type{ IoK8sApimachineryPkgApisMetaV1APIVersions }, name::Symbol) = Union{Nothing,eval(Base.Meta.parse(_property_types_IoK8sApimachineryPkgApisMetaV1APIVersions[name]))}
Swagger.field_name(::Type{ IoK8sApimachineryPkgApisMetaV1APIVersions }, property_name::Symbol) =  _property_map_IoK8sApimachineryPkgApisMetaV1APIVersions[property_name]

function check_required(o::IoK8sApimachineryPkgApisMetaV1APIVersions)
    (getproperty(o, Symbol("serverAddressByClientCIDRs")) === nothing) && (return false)
    (getproperty(o, Symbol("versions")) === nothing) && (return false)
    true
end

function validate_property(::Type{ IoK8sApimachineryPkgApisMetaV1APIVersions }, name::Symbol, val)
end


text:
module Paramed

# package code goes here

using DataFrames, NamedArrays, GLM

export paramed, rmse

function vcov_stata(vcov::Array{Float64,2})
	vcov2 = vcov
	n = size(vcov,1)
	for i = 2:n
		for j = 2:n
			vcov2[i-1,j-1] = vcov[i,j]
		end
	end
	vcov2[n,n] = vcov[1,1]
	for i = 1:n-1
		vcov2[i,n] = vcov[i,1]
		vcov2[n,i] = vcov[1,i]
	end
	return vcov2
end

function rmse(obj)
	return sqrt(sum((model_response(obj) .- predict(obj)).^2)/nobs(obj))
end

function paramed(yvar::Symbol,avar::Symbol, mvar::Symbol, a0::Int, a1::Int, m::Int, df::DataFrame; interaction::Bool = true,controlvars = [], logfile::IOStream = nothing)

	# convert them to Float64
	yvarf = Symbol(string(yvar,"f"))
	df[yvarf] = Vector{Float64,Missing}(df[yvar])

	mvarf = Symbol(string(mvar,"f"))
	df[mvarf] = Vector{Float64,Missing}(df[mvar])

	# process control variables
	if length(controlvars) > 0
		# convars = controlvars[1]
		# for i = 2:length(controlvars)
		# 	convars = :($convars + $(controlvars[i]))
		# end
		# fm2 = $mvar ~ $avar + $convars
		fm2 = Stella.formula($mvarf,vcat($avar,controlvars))
	else
		# fm2 = $mvar ~ $avar
		fm2 = @eval(@formula($mvarf  ~ $avar))
	end

	# process interaction terms
	if interaction == true
		ivar = Symbol(string("_",avar,"X",mvar))
		df[ivar] = df[avar] .* df[mvarf]

		fm1 = Stella.formula($yvarf,vcat($avar,$mvarf,$ivar,controlvars))

		# if length(controlvars) > 0
		# 	# fm1 = $yvar ~ $avar + $mvar + $ivar + $convars
		# 	fm1 = Stella.formula($yvar,vcat($avar,$mvar,$ivar,controlvars))
		# else
		# 	fm1 = Stella.formula($yvar,vcat($avar,$mvar,$ivar))
		# end
	else
		fm1 = Stella.formula($yvarf,vcat($avar,$mvarf,controlvars))
		# if length(controlvars) > 0
		# 	fm1 = $yvar ~ $avar + $mvar + $convars
		# else
		# 	fm1 = $yvar ~ $avar + $mvar
		# end
	end

	if isa(df[yvar],CategoricalArray) && isa(df[mvar],CategoricalArray)
		return paramed_logit_logit(df,fm1,fm2,a0,a1,m,interaction, logfile)
	elseif isa(df[yvar],CategoricalArray) && isa(df[mvar],CategoricalArray) == false
		return paramed_logit_linear(df,fm1,fm2,a0,a1,m,interaction, logfile)
	end
end

function paramed_logit_logit(df::DataFrame,fm1::Formula,fm2::Formula,a0,a1,m,interaction,logfile)

	# print(fm1)
	logt1 = glm(fm1, df, Binomial())
	print(logfile,"\n\n",coeftable(logt1),"\n\n")

	logt2 = glm(fm2, df, Binomial())
	print(logfile,"\n\n",coeftable(logt2),"\n\n")

	# covariate mean values
	cmean = transpose(mean(logt2.mm.m,1)[3:end])

    # save variance-covariance matrices and coefficients
    vcov1 = vcov_stata(vcov(logt1))
	theta = coef(logt1)
	theta1 = theta[2] # estimate for avar
	theta2 = theta[3] # estimate for mvar
	theta3 = interaction ? theta[4] : 0. # estimate for the interaction between avar and mvar

	vcov2 = vcov_stata(vcov(logt2))
	beta = coef(logt2)
	beta0 = beta[1] # estimate for the intercept
	beta1 = beta[2] # estimate for avar
	beta2 = transpose(beta[3:end]) # estimates for all covariates in the mvar logistic model

	# model 2: estimates * mean
	cmeansum = sum(beta2*cmean')

	# zero1, zero2 - empty matrices initialized to zeros
	zero1 = zeros(size(vcov1,1),size(vcov2,1))
	zero2 = zeros(size(vcov2,1),size(vcov1,1))

	# sigma
	sigma = vcat(hcat(vcov2, zero2),hcat(zero1, vcov1))

	# Marginal CDE
	z1 = zeros(1,size(beta,1)-2)
	z = hcat(0,z1,0,1,0)
	marggammacde = hcat(z,m,z1,0)

	# Marginal NDE
	A=exp(theta2 + theta3*a1 + beta0 + beta1*a0 + cmeansum)
	B = (1+exp(theta2 + theta3*a1 + beta0 + beta1*a0 + cmeansum))
	D = exp(theta2 + theta3*a0 + beta0 + beta1*a0 + cmeansum)
	E = (1 + exp(theta2 + theta3*a0 + beta0 + beta1*a0 + cmeansum))

	d1nde = A/B - D/E
	d2nde = a0*d1nde
	d3nde = d1nde .* cmean
	d4nde = 0
	d5nde = (a1 - a0)
	d6nde = d1nde
	d7nde = a1*A/B - a0*D/E
	d8nde = z1
	marggammapnde = hcat(d2nde, d3nde, d1nde, d5nde,d6nde,d7nde,d8nde, d4nde)

	# Marginal TNDE
	A = exp(theta2 + beta0 + beta1*a1 + cmeansum)
	B = (1 + exp(theta2 + beta0 + beta1*a1 + cmeansum))
	D = exp(theta2 + beta0 + beta1*a1 + cmeansum)
	E = (1 + exp(theta2 + beta0 + beta1*a1 + cmeansum))
	s = A/B - D/E
	x = a1 * s
	w = s .* cmean
	t = a1 - a0
	h=a1*A/B-a0*D/E
	marggammatnde = hcat(x, w, s,  t, s, h, z1, 0)

	# Marginal TNIE
	A = exp(theta2 + beta0 + beta1*a1 + cmeansum)
	B = (1 + exp(theta2 + beta0 + beta1*a1 + cmeansum))
	D = exp(theta2 + beta0 + beta1*a0 + cmeansum)
	E = (1 + exp(theta2 + beta0 + beta1*a0 + cmeansum))
	F = exp(beta0 + beta1*a0 + cmeansum)
	G = (1 + exp(beta0 + beta1*a0 + cmeansum))
	H = exp(beta0 + beta1*a1 + cmeansum)
	I = (1 + exp(beta0 + beta1*a1 + cmeansum))
	d1nie = F/G - H/I + A/B - D/E
	d2nie = a0*F/G - a1*H/I + a1*A/B - a1*D/E
	d3nie = cmean.*d1nie
	d4nie = 0
	d5nie = 0
	d6nie = A/B - D/E
	d7nie = a1*(A/B - D/E)
	d8nie = z1
	marggammatnie = hcat(d2nie, d3nie, d1nie,  d5nie, d6nie, d7nie, d8nie, d4nie)

	# Marginal PNIE
	A = exp(theta2 + beta0 + beta1*a1 + cmeansum)
	B = (1+exp(theta2 + beta0 + beta1*a1 + cmeansum))
	D = exp(theta2 + beta0 + beta1*a0 + cmeansum)
	E = (1 + exp(theta2 + beta0 + beta1*a0 + cmeansum))
	F = exp(beta0 + beta1*a0 + cmeansum)
	G = (1 + exp(beta0 + beta1*a0 + cmeansum))
	H = exp(beta0 + beta1*a1 + cmeansum)
	I = (1 + exp(beta0 + beta1*a1 + cmeansum))
	s = F/G - H/I + A/B - D/E
	x = a0*F/G - a1*H/I + a1*A/B - a1*D/E
	w = cmean.*s
	l = A/B - D/E
	k = a0*(A/B - D/E)
	marggammapnie = hcat(x, w, s,  0, l, k, z1, 0)

	# marg se cde
	intse6=sqrt(marggammacde*sigma*marggammacde')[1,1]

	# marg se pnde
	intse7=sqrt(marggammapnde*sigma*marggammapnde')[1,1]

	# marg se pnie
	intse8=sqrt(marggammapnie*sigma*marggammapnie')[1,1]

	# marg se tnde
	intse9=sqrt(marggammatnde*sigma*marggammatnde')[1,1]

	# marg se tnie
	intse10=sqrt(marggammatnie*sigma*marggammatnie')[1,1]

	d1 = d1nie + d1nde
	d2 = d2nie + d2nde
	d3 = d3nie + d3nde
	d4 = d4nie + d4nde
	d5 = d5nie + d5nde
	d6 = d6nie + d6nde
	d7 = d7nie + d7nde
	tegammamarg=hcat(d2,d3,d1, d5,d6,d7,z1,d4)
	tesemarg=sqrt(tegammamarg*sigma*tegammamarg')[1,1]

	# Marginal CDE (Controlled Direct Effect)
	int6=exp((theta1+theta3*m)*(a1-a0))

	# Marginal NDE (Natural Direct Effect)
	int7=exp(theta1*(a1-a0))*(1+exp(theta2+theta3*a1+beta0+beta1*a0+cmeansum))/(1+exp(theta2+theta3*a0+beta0+beta1*a0+cmeansum))

	# Marginal NIE
	int8=((1+exp(beta0+beta1*a0+cmeansum))*(1+exp(theta2+theta3*a0+beta0+beta1*a1+cmeansum))) / ((1+exp(beta0+beta1*a1+cmeansum))*(1+exp(theta2+theta3*a0+beta0+beta1*a0+cmeansum)))

	# Marginal TNDE
	int9=exp(theta1*(a1-a0))*(1+exp(theta2+theta3*a1+beta0+beta1*a1+cmeansum))/(1+exp(theta2+theta3*a0+beta0+beta1*a1+cmeansum))

	# Marginal TNIE (Natural Indirect Effect)
	# println(theta1," ",theta2," ",theta3," ",beta0," ",beta1," ",cmeansum)
	int10=((1+exp(beta0+beta1*a0+cmeansum))*(1+exp(theta2+theta3*a1+beta0+beta1*a1+cmeansum))) / ((1+exp(beta0+beta1*a1+cmeansum))*(1+exp(theta2+theta3*a1+beta0+beta1*a0+cmeansum)))

	# Marginal Total Effects
	temarg = int7*int10
	logtemarg = log(int7*int10)

	# Proportion Mediated
	pm = int7*(int10 - 1) / (int7*int10-1)

	estse = [intse6,intse7,intse8,intse9,intse10]
	est = [int6,int7,int8,int9,int10]
	lnest = map(x->log(x),est)
	cilb  = map(x -> lnest[x] - 1.96*estse[x],1:5)
	ecilb = map(x -> exp(x), cilb)
	ciub  = map(x -> lnest[x] + 1.96*estse[x],1:5)
	eciub = map(x -> exp(x), ciub)

	# P-value and confidence intervals
	ptwosidetemarg = 2*ccdf(Normal(),logtemarg/tesemarg)
	citelmarg=exp(logtemarg-1.96*tesemarg)
	citeumarg=exp(logtemarg+1.96*tesemarg)

	ptwoside = map(x -> 2*ccdf(Normal(),lnest[x]/estse[x]),1:5)

	#
	value1 = hcat(est[1], est[2],est[5]) # int6 , int7, int10)
	se1 = hcat(estse[1], estse[2],estse[5]) #intse6, intse7, intse10)
	pvalue1 = hcat(ptwoside[1],ptwoside[2],ptwoside[5]) # 6 , ptwoside7, ptwoside10)
	cil1 = hcat(ecilb[1], ecilb[2], ecilb[5]) #ci6l,ci7l,ci10l)
	ciu1 = hcat(eciub[1], eciub[2], eciub[5]) #ci6u,ci7u,ci10u)
	x1 = hcat(value1', se1', pvalue1', cil1', ciu1')
	value2 = hcat(temarg , pm)
	se2 = hcat(tesemarg ,0)
	pvalue2 = hcat(ptwosidetemarg , 0)
	cil2 = hcat(citelmarg,0)
	ciu2 = hcat(citeumarg,0)
	x2 = hcat(value2',se2',pvalue2',cil2',ciu2')
	x = vcat(x1, x2)

	rownames = ["cde" => 1, "nde" => 2, "nie" => 3, "mte" => 4, "proportion mediated" => 5]
	colnames = ["Estimate" => 1, "SE" => 2, "P-Value" => 3, "95% CI LB" => 4, "95% CI UB" => 5]
    na = NamedArray(x, (rownames,colnames), ("Effects","Statistics") )

	# output logs
	print(logfile,"\n\n",na,"\n\n")

	return na
end

function paramed_logit_linear(df::DataFrame,fm1::Formula,fm2::Formula,a0,a1,m,interaction,logfile)

	# print(fm1)
	logt1 = glm(fm1, df, Binomial())
	print(logfile,"\n\n",coeftable(logt1),"\n\n")

	linear2 = glm(fm2, df, Normal())
	print(logfile,"\n\n",coeftable(linear2),"\n\n")

	# covariate mean values
	cmean = transpose(mean(linear2.mm.m,1)[3:end])

    # save variance-covariance matrices and coefficients
    vcov1 = vcov_stata(vcov(logt1))
	theta = coef(logt1)
	theta1 = theta[2] # estimate for avar
	theta2 = theta[3] # estimate for mvar
	theta3 = interaction ? theta[4] : 0. # estimate for the interaction between avar and mvar

	vcov2 = vcov_stata(vcov(linear2))
	beta = coef(linear2)
	s2 = rmse(linear2)
	beta0 = beta[1] # estimate for the intercept
	beta1 = beta[2] # estimate for avar
	beta2 = transpose(beta[3:end]) # estimates for all covariates in the mvar linear model

	# model 2: estimates * mean
	cmeansum = sum(theta3*beta2*cmean') # sum(beta2*cmean')

	# zero1, zero2 - empty matrices initialized to zeros
	zero1 = zeros(size(vcov1,1),size(vcov2,1))
	zero2 = zeros(size(vcov2,1),size(vcov1,1))
	z2 = zeros(size(vcov1,1),1)
	z3 = zeros(size(vcov2,1),1)

	# sigma
	sigma = vcat(hcat(vcov2, zero2, z3),hcat(zero1, vcov1, z2),hcat(zeros(1,size(vcov1,1)+size(vcov2,1)),s2))

	z1 = zeros(1,size(beta,1)-2)
	z = hcat(0,z1,0,1,0)

	# Marginal CDE
	marggammacde = hcat(z,m,z1,0,0)

	# Marginal NDE
	x = theta3*a0
	w = theta3 .* cmean'
	h = beta0 + beta1 * a0 + beta2*cmean' + theta2 * s2 + theta3 * s2 * (a1+a0)
	ts = s2*theta3
	f = theta3*theta2+0.5*(theta3^2)*(a1+a0)
	marggammapnde = hcat(x, w', theta3,  1, ts, h, z1, 0,  f)

	# Marginal TNDE
	x = theta3*a1
	w = theta3*cmean'
	h = beta0+beta1*a1+beta2*cmean'+theta2*s2+theta3*s2*(a1+a0)
	ts = s2*theta3
	f = theta3*theta2+0.5*theta3^2*(a1+a0)
	marggammatnde= hcat(x, w', theta3, 1, ts, h, z1, 0, f)

	# Marginal TNIE
	x=theta2+theta3*a1
	w=beta1*a1
	marggammatnie = hcat(x, z1, 0, 0, beta1, w, z1 , 0, 0)

	# Marginal PNIE
	x=theta2+theta3*a0
	w=beta1*a0
	marggammapnie = hcat(x, z1, 0, 0, beta1, w , z1, 0, 0)

	# marg se cde
	intse6=sqrt(marggammacde*sigma*marggammacde')[1,1]

	# marg se pnde
	intse7=sqrt(marggammapnde*sigma*marggammapnde')[1,1]

	# marg se pnie
	intse8=sqrt(marggammapnie*sigma*marggammapnie')[1,1]

	# marg se tnde
	intse9=sqrt(marggammatnde*sigma*marggammatnde')[1,1]

	# marg se tnie
	intse10=sqrt(marggammatnie*sigma*marggammatnie')[1,1]

	d2pnde=theta3*a0
	d3pnde=theta3 .* cmean
	d7pnde=beta0+beta1*a0+beta2*cmean'+theta2*s2+theta3*s2*(a1+a0)
	d6pnde=s2*theta3
	d9pnde=theta3*theta2+0.5*(theta3^2)*(a1+a0)
	d2tnie=theta2+theta3*a1
	d7tnie=beta1*a1
	d2=d2pnde+d2tnie
	d3=d3pnde
	d6=d6pnde+beta1
	d7=d7pnde+d7tnie
	d9=d9pnde
	tegammamarg=hcat(d2, d3, theta3, 1, d6, d7, z1, 0, d9)
	tesemarg=sqrt(tegammamarg*sigma*tegammamarg')[1,1]
	tsq=(theta3^2)
	rm=s2
	asq=(a1^2)
	a1sq=(a0^2)

	# Marginal CDE (Controlled Direct Effect)
	int6=exp((theta1 + theta3*m)*(a1 - a0))

	# Marginal NDE (Natural Direct Effect)
	int7 = exp((theta1 + theta3*beta0 + theta3*beta1*a0 + cmeansum + theta3*theta2*rm)*(a1-a0)+0.5*tsq*rm*(asq-a1sq))

	# Marginal NIE
	int8=exp((theta2*beta1 + theta3*beta1*a0)*(a1-a0))

	# Marginal TNDE
	int9=exp((theta1 + theta3*beta0 + theta3*beta1*a1 + cmeansum + theta3*theta2*rm)*(a1 - a0) + 0.5*tsq*rm*(asq - a1sq))

	# Marginal TNIE (Natural Indirect Effect)
	# println(theta1," ",theta2," ",theta3," ",beta0," ",beta1," ",cmeansum)
	int10=exp((theta2*beta1 + theta3*beta1*a1)*(a1 - a0))

	# Marginal Total Effects
	logtemarg = (theta1 + theta3*beta0 + theta3*beta1*a0 + cmeansum + theta2*beta1 + theta3*beta1*a1+theta3*(rm)*theta2)*(a1 - a0) + 0.5*(theta3^2)*(rm)*(a1^2 - a0^2)
	temarg = exp(logtemarg)

	# Proportion Mediated
	pm = int7*(int10 - 1) / (int7*int10-1)

	estse = [intse6,intse7,intse8,intse9,intse10]
	est = [int6,int7,int8,int9,int10]
	lnest = map(x->log(x),est)
	cilb  = map(x -> lnest[x] - 1.96*estse[x],1:5)
	ecilb = map(x -> exp(x), cilb)
	ciub  = map(x -> lnest[x] + 1.96*estse[x],1:5)
	eciub = map(x -> exp(x), ciub)

	# P-value and confidence intervals
	ptwosidetemarg = 2*ccdf(Normal(),logtemarg/tesemarg)
	citelmarg=exp(logtemarg-1.96*tesemarg)
	citeumarg=exp(logtemarg+1.96*tesemarg)

	ptwoside = map(x -> 2*ccdf(Normal(),lnest[x]/estse[x]),1:5)

	#
	value1 = hcat(est[1], est[2],est[5]) # int6 , int7, int10)
	se1 = hcat(estse[1], estse[2],estse[5]) #intse6, intse7, intse10)
	pvalue1 = hcat(ptwoside[1],ptwoside[2],ptwoside[5]) # 6 , ptwoside7, ptwoside10)
	cil1 = hcat(ecilb[1], ecilb[2], ecilb[5]) #ci6l,ci7l,ci10l)
	ciu1 = hcat(eciub[1], eciub[2], eciub[5]) #ci6u,ci7u,ci10u)
	x1 = hcat(value1', se1', pvalue1', cil1', ciu1')
	value2 = hcat(temarg , pm)
	se2 = hcat(tesemarg ,0)
	pvalue2 = hcat(ptwosidetemarg , 0)
	cil2 = hcat(citelmarg,0)
	ciu2 = hcat(citeumarg,0)
	x2 = hcat(value2',se2',pvalue2',cil2',ciu2')
	x = vcat(x1, x2)

	rownames = ["cde" => 1, "nde" => 2, "nie" => 3, "mte" => 4, "proportion mediated" => 5]
	colnames = ["Estimate" => 1, "SE" => 2, "P-Value" => 3, "95% CI LB" => 4, "95% CI UB" => 5]
    na = NamedArray(x, (rownames,colnames), ("Effects","Statistics") )
	print(logfile,"\n\n",na,"\n\n")

	return na
end

end # module

max_stars_repo_name: mwsohn/Paramed


text:
using Gasdynamics1D

@testset "Gases" begin

  @test typeof(Air) <: PerfectGas
  @test typeof(H2) <: PerfectGas
  @test typeof(O2) <: PerfectGas

  @test value(GasConstant(Air)) == 287u"J/kg/K"
  @test value(SpecificHeatRatio(Air)) == 1.4

  @test isapprox(ustrip(GasConstant(Air),u"ft*lbf/(slug*Ra)"),1716.25,atol=1e-2)

end

@testset "Quantities" begin

  p = Pressure(50)
  @test ustrip(p) == 50
  @test unit(p) == u"Pa"

  T = Temperature(20u"°F")
  @test ustrip(T) ≈ 15989//60
  @test unit(T) == u"K"
  @test ustrip(Temperature(20u"°F"),u"°C") ≈ -20//3

  s1 = Entropy(32)
  s2 = Entropy(45)
  @test ustrip(s1-s2) == -13
  @test unit(s1-s2) == u"J/kg/K"

  h1 = Enthalpy(45)
  a = SoundSpeed(10)
  h2 = Enthalpy(h1 + a^2)
  @test ustrip(h2) == 145
  @test unit(h2) == u"J/kg"

  p = Pressure(80u"kPa")
  ρ = Density(1.2)
  T = Temperature(p/ρ/GasConstant(287))
  @test T == Temperature(p,ρ)

end

@testset "Isentropic relations" begin

  p = Pressure(10u"psi")
  p0 = StagnationPressure(14.7u"psi")
  M = MachNumber(PressureRatio(p/p0),Isentropic,gas=He)
  @test M ≈ 0.7069936247021056 atol=1e-10
end


text:
using Test
using QuantumControlBase
using QuantumControlBase.Shapes: blackman

@testset "discretize/discretize_on_midpoints" begin
    tlist = collect(range(0, 10, length=20))

    t₀ = 0.0
    T = 10.0

    function control_func(t)
        return blackman(t, t₀, T)
    end

    control_vals1 = discretize(control_func, tlist; via_midpoints=true)
    pulse_vals1 = discretize_on_midpoints(control_func, tlist)

    control_vals2 = discretize(pulse_vals1, tlist)
    pulse_vals2 = discretize_on_midpoints(control_vals1, tlist)

    control_vals3 = discretize(control_func, tlist; via_midpoints=false)

    # "controls' are on tlist, "pulses" are on intervals
    @test length(control_vals1) == length(control_vals2) == length(tlist)
    @test length(pulse_vals1) == length(pulse_vals2) == length(tlist) - 1

    # discretizing and converting to/from intervals should be interchangeable
    @test maximum(abs.(control_vals1 .- control_vals2)) < 1e-14
    @test maximum(abs.(pulse_vals1 .- pulse_vals2)) < 1e-14

    # discretizing via midpoints or directly should give different results
    @test 1e-3 < maximum(abs.(control_vals1 .- control_vals3)) < 1e-1
    @test control_vals3[13] == control_func(tlist[13])
    @test control_vals1[13] ≠ control_func(tlist[13])
    @test 1e-3 < abs(control_vals1[13] - control_func(tlist[13])) < 1e-1

end


text:
# Per-thread Random Number Generator. Initialized later...
const TRNG = Xoroshiro128Plus[]

function __init__()
	# Instantiate 1 RNG (Random Number Generator) per thread, for performance.
	# This can't be done during precompilation since the number of threads isn't known then.
	resize!(TRNG, Threads.nthreads())
	for i in 1:Threads.nthreads()
		TRNG[i] = Xoroshiro128Plus(i)
	end
	nothing
end


text:
using JET
using BenchmarkTools
using Combinatorics
include("auxfun.jl")

function solution1(n=1000)
    s = 0
    for i in 1:n-1
        if mod(i, 3) == 0 || mod(i, 5) == 0
            s += i
        end
    end
    s
end

"""

"""
function solution2(n=4000000)
    a = 1
    b = 2
    s = 0
    while b <= n
        if iseven(b)
            s += b
        end
        a, b = b, a + b
    end
    s
end

function solution3(n=600851475143) # Pollard Rho因数分解
    k = 2
    while n != k
        if n % k == 0
            n = round(Int, n / k)
        else
            k += k == 2 ? 1 : 2
        end
    end
return k
end

function solution4(n=3)
    maxprod=1
    for i in 10^n - 1:-1:10^(n - 1)
        for j in i:-1:10^(n - 1)
            product = i * j
            str= "$product"
            if str == reverse(str) && product >maxprod
                maxprod=product
            end
        end
    end
    maxprod
end

function solution5(n=20)
    lcm(1:n)
end

solution6(n=100) = sum(1:n)^2 - sum((1:n).^2)
function solution7(n=10001)
    primes = zeros(Int, n)
    primes[1:2] .= [2,3]
    k = 3
    counter = 2
    while counter < n
        k += 2
        is_prime = true
        for i in primes[2:counter]
            if k % i == 0
                is_prime = false
                break
            end
        end
        if is_prime
            counter += 1
            primes[counter] = k
        end
    end
    k
end

function solution8(cont=13)
    str = """
    73167176531330624919225119674426574742355349194934
96983520312774506326239578318016984801869478851843
85861560789112949495459501737958331952853208805511
12540698747158523863050715693290963295227443043557
66896648950445244523161731856403098711121722383113
62229893423380308135336276614282806444486645238749
30358907296290491560440772390713810515859307960866
70172427121883998797908792274921901699720888093776
65727333001053367881220235421809751254540594752243
52584907711670556013604839586446706324415722155397
53697817977846174064955149290862569321978468622482
83972241375657056057490261407972968652414535100474
82166370484403199890008895243450658541227588666881
16427171479924442928230863465674813919123162824586
17866458359124566529476545682848912883142607690042
24219022671055626321111109370544217506941658960408
07198403850962455444362981230987879927244284909188
84580156166097919133875499200524063689912560717606
05886116467109405077541002256983155200055935729725
71636269561882670428252483600823257530420752963450
    """
    str = strip(replace(str, "\n" => ""))
    max_prod = 0
    for i in 1:length(str) - cont
        s = prod(parse(Int, num) for num in str[i:i + cont - 1])
        if s > max_prod
            max_prod = s
        end
    end
    max_prod
end

function solution9(n=1000)
    for i in 1:floor(Int,n/3)
        for j in i:floor(Int,(n-i)/2)
            if  i^2+j^2==(n-j-i)^2
                return i*j*(n-i-j)
            end
        end
    end
end

function solution10(n=2000000)
    return sum(sieve(n))
end

function solution11(n=4)
    mat=[
08 02 22 97 38 15 00 40 00 75 04 05 07 78 52 12 50 77 91 08
49 49 99 40 17 81 18 57 60 87 17 40 98 43 69 48 04 56 62 00
81 49 31 73 55 79 14 29 93 71 40 67 53 88 30 03 49 13 36 65
52 70 95 23 04 60 11 42 69 24 68 56 01 32 56 71 37 02 36 91
22 31 16 71 51 67 63 89 41 92 36 54 22 40 40 28 66 33 13 80
24 47 32 60 99 03 45 02 44 75 33 53 78 36 84 20 35 17 12 50
32 98 81 28 64 23 67 10 26 38 40 67 59 54 70 66 18 38 64 70
67 26 20 68 02 62 12 20 95 63 94 39 63 08 40 91 66 49 94 21
24 55 58 05 66 73 99 26 97 17 78 78 96 83 14 88 34 89 63 72
21 36 23 09 75 00 76 44 20 45 35 14 00 61 33 97 34 31 33 95
78 17 53 28 22 75 31 67 15 94 03 80 04 62 16 14 09 53 56 92
16 39 05 42 96 35 31 47 55 58 88 24 00 17 54 24 36 29 85 57
86 56 00 48 35 71 89 07 05 44 44 37 44 60 21 58 51 54 17 58
19 80 81 68 05 94 47 69 28 73 92 13 86 52 17 77 04 89 55 40
04 52 08 83 97 35 99 16 07 97 57 32 16 26 26 79 33 27 98 66
88 36 68 87 57 62 20 72 03 46 33 67 46 55 12 32 63 93 53 69
04 42 16 73 38 25 39 11 24 94 72 18 08 46 29 32 40 62 76 36
20 69 36 41 72 30 23 88 34 62 99 69 82 67 59 85 74 04 36 16
20 73 35 29 78 31 90 01 74 31 49 71 48 86 81 16 23 57 05 54
01 70 54 71 83 51 54 69 16 92 33 48 61 43 52 01 89 19 67 48]
    max_t2b=maximum(prod(mat[i:i+n-1,j])  for j in 1:size(mat,2) for i in 1:size(mat,1)-n+1)
    max_l2r=maximum(prod(mat[i,j:j+n-1]) for j in 1:size(mat,2)-n+1  for i in 1:size(mat,1))
    max_diag=maximum(prod(mat[i+k,j+k] for k in 0:n-1) for j in 1:size(mat,2)-n+1 for i in 1:size(mat,1)-n+1)
    max_subdiag=maximum(prod(mat[i+k,j-k] for k in 0:n-1) for j in n:size(mat,2) for i in 1:size(mat,1)-n+1)
    maximum([max_t2b,max_l2r,max_diag,max_subdiag])
end

function solution12(n=500)
    tri_num(n)=round(Int,n*(n+1)/2)
    i=2
    K=tri_num(i)
    N=num_of_factors(K)
    while N<n
        i+=1
        K=tri_num(i)
        N=num_of_factors(K)
    end
    K
end

function solution13(n=10)
    nums=[
        37107287533902102798797998220837590246510135740250
        46376937677490009712648124896970078050417018260538
        74324986199524741059474233309513058123726617309629
        91942213363574161572522430563301811072406154908250
        23067588207539346171171980310421047513778063246676
        89261670696623633820136378418383684178734361726757
        28112879812849979408065481931592621691275889832738
        44274228917432520321923589422876796487670272189318
        47451445736001306439091167216856844588711603153276
        70386486105843025439939619828917593665686757934951
        62176457141856560629502157223196586755079324193331
        64906352462741904929101432445813822663347944758178
        92575867718337217661963751590579239728245598838407
        58203565325359399008402633568948830189458628227828
        80181199384826282014278194139940567587151170094390
        35398664372827112653829987240784473053190104293586
        86515506006295864861532075273371959191420517255829
        71693888707715466499115593487603532921714970056938
        54370070576826684624621495650076471787294438377604
        53282654108756828443191190634694037855217779295145
        36123272525000296071075082563815656710885258350721
        45876576172410976447339110607218265236877223636045
        17423706905851860660448207621209813287860733969412
        81142660418086830619328460811191061556940512689692
        51934325451728388641918047049293215058642563049483
        62467221648435076201727918039944693004732956340691
        15732444386908125794514089057706229429197107928209
        55037687525678773091862540744969844508330393682126
        18336384825330154686196124348767681297534375946515
        80386287592878490201521685554828717201219257766954
        78182833757993103614740356856449095527097864797581
        16726320100436897842553539920931837441497806860984
        48403098129077791799088218795327364475675590848030
        87086987551392711854517078544161852424320693150332
        59959406895756536782107074926966537676326235447210
        69793950679652694742597709739166693763042633987085
        41052684708299085211399427365734116182760315001271
        65378607361501080857009149939512557028198746004375
        35829035317434717326932123578154982629742552737307
        94953759765105305946966067683156574377167401875275
        88902802571733229619176668713819931811048770190271
        25267680276078003013678680992525463401061632866526
        36270218540497705585629946580636237993140746255962
        24074486908231174977792365466257246923322810917141
        91430288197103288597806669760892938638285025333403
        34413065578016127815921815005561868836468420090470
        23053081172816430487623791969842487255036638784583
        11487696932154902810424020138335124462181441773470
        63783299490636259666498587618221225225512486764533
        67720186971698544312419572409913959008952310058822
        95548255300263520781532296796249481641953868218774
        76085327132285723110424803456124867697064507995236
        37774242535411291684276865538926205024910326572967
        23701913275725675285653248258265463092207058596522
        29798860272258331913126375147341994889534765745501
        18495701454879288984856827726077713721403798879715
        38298203783031473527721580348144513491373226651381
        34829543829199918180278916522431027392251122869539
        40957953066405232632538044100059654939159879593635
        29746152185502371307642255121183693803580388584903
        41698116222072977186158236678424689157993532961922
        62467957194401269043877107275048102390895523597457
        23189706772547915061505504953922979530901129967519
        86188088225875314529584099251203829009407770775672
        11306739708304724483816533873502340845647058077308
        82959174767140363198008187129011875491310547126581
        97623331044818386269515456334926366572897563400500
        42846280183517070527831839425882145521227251250327
        55121603546981200581762165212827652751691296897789
        32238195734329339946437501907836945765883352399886
        75506164965184775180738168837861091527357929701337
        62177842752192623401942399639168044983993173312731
        32924185707147349566916674687634660915035914677504
        99518671430235219628894890102423325116913619626622
        73267460800591547471830798392868535206946944540724
        76841822524674417161514036427982273348055556214818
        97142617910342598647204516893989422179826088076852
        87783646182799346313767754307809363333018982642090
        10848802521674670883215120185883543223812876952786
        71329612474782464538636993009049310363619763878039
        62184073572399794223406235393808339651327408011116
        66627891981488087797941876876144230030984490851411
        60661826293682836764744779239180335110989069790714
        85786944089552990653640447425576083659976645795096
        66024396409905389607120198219976047599490197230297
        64913982680032973156037120041377903785566085089252
        16730939319872750275468906903707539413042652315011
        94809377245048795150954100921645863754710598436791
        78639167021187492431995700641917969777599028300699
        15368713711936614952811305876380278410754449733078
        40789923115535562561142322423255033685442488917353
        44889911501440648020369068063960672322193204149535
        41503128880339536053299340368006977710650566631954
        81234880673210146739058568557934581403627822703280
        82616570773948327592232845941706525094512325230608
        22918802058777319719839450180888072429661980811197
        77158542502016545090413245809786882778948721859617
        72107838435069186155435662884062257473692284509516
        20849603980134001723930671666823555245252804609722
        53503534226472524250874054075591789781264330331690
        ]
    s=sum(nums)
    return parse(Int,"$s"[1:n])
end
function solution14(n=1000000)
    n>1 || return 1
    f(n)= n%2==0 ? round(Int,n/2) : 3n+1
    function l(n)
        n> 1 || return 1
        a=n
        l=1
        while f(a) >1
            a=f(a)
            l+=1
        end
        l+=1
    end
    lengths=l.(2:n)
    findfirst(lengths.== maximum(lengths))+1 # 从2开始
end

function solution15(n=20)
    dict=Dict(i=> true for i in 1:n)
    s=1
    for j in n+1:2n
        s*=j
        for i in 1:n
            if dict[i] && s%i==0
                s=round(Int,s/i)
                dict[i]=false
            end
        end
    end
    s
end

solution16(n=1000)=sum(digits((BigInt(2))^n))
function solution17(n=1000)
    number2word =Dict(
    0=>"",
    1=>"one",
    2=>"two",
    3=>"three",
    4=>"four",
    5=>"five",
    6=>"six",
    7=>"seven",
    8=>"eight",
    9=>"nine",
    10=>"ten",
    11=>"eleven",
    12=>"twelve",
    13=>"thirteen",
    14=>"fourteen",
    15=>"fifteen",
    16=>"sixteen",
    17=>"seventeen",
    18=>"eighteen",
    19=>"nineteen",
    20=>"twenty",
    30=>"thirty",
    40=>"forty",
    50=>"fifty",
    60=>"sixty",
    70=>"seventy",
    80=>"eighty",
    90=>"ninety",
    100=>"hundred",
    1000=>"one thousand",
    )
    function num2text(m)
        @assert m <=1000
        m==100 && return "one hundred"
        if m in keys(number2word)
            return number2word[m]
        elseif m <100
            d=m -m %10
            m=m-d
            return num2text(d)*" "* num2text(m)
        else
            h=m-m%100
            m=m-h
            h=div(h,100)

            return num2text(h)*" hundred"*(m!=0 ? " and " *num2text(m) : "")
        end
    end
    lettercount=0
    for i in 1:n
        lettercount+=length(replace(num2text(i)," "=>""))
    end
    lettercount
end

function solution18()
    nums = [
        [75],
        [95 64],
        [17 47 82],
        [18 35 87 10],
        [20 04 82 47 65],
        [19 01 23 75 03 34],
        [88 02 77 73 07 63 67],
        [99 65 04 28 06 16 70 92],
        [41 41 26 56 83 40 80 70 33],
        [41 48 72 33 47 32 37 16 94 29],
        [53 71 44 65 25 43 91 52 97 51 14],
        [70 11 33 28 77 73 17 78 39 68 17 57],
        [91 71 52 38 17 14 91 43 58 50 27 29 48],
        [63 66 04 68 89 53 67 30 73 16 69 87 40 31],
        [04 62 98 27 23 09 70 98 73 93 38 53 60 04 23]]
    rows=length(nums)
    rows> 1 || return maximum(nums[1])
    cols=maximum(length.(nums))
    dist=zeros(Int,(rows,cols))
    dist[1,1]=nums[1][1]
    for i in 2: rows
        dist[i,1]=nums[i][1]+dist[i-1][1]
    end
    for i in 2:rows
        for j in 2:length(nums[i])
            dist[i,j]=nums[i][j]+max(dist[i-1,j],dist[i-1,j-1])
        end
    end
    maximum(dist[rows,:])
end

function solution19()
    Y=1901
    M=1
    D=1
    W=1
    cnt=0
    leap_year(Y)= (Y%4==0 && Y%100 !=0) || Y%400==0
    function days_in_month(M,Y)
        dict=Dict{Int,Int}(1=>31, 2=> 28,3=>31,4=>30,5=>31,6=>30,7=>31,8=>31,9=>30,10=>31,11=>30,12=>31)
        if leap_year(Y)
            dict[2]+=1
        end
        dict[M]
    end
    function date_add_one(Y,M,D)
        days=days_in_month(M,Y)
        if D==days
            D=1
            if M<12
                M+=1
            else
                Y+=1
                M=1
            end
        else
            D+=1
        end
        Y,M,D
    end
    weekday_add_one(W)= W<7 ? W+1 : 1
    while Y<=2000 && M<=12 && D<=31
        if D==1 && W==7
            cnt+=1
        end
        Y,M,D=date_add_one(Y,M,D)
        W=weekday_add_one(W)
    end
    cnt
end

solution20(n=100)=sum(digits(prod(1:BigInt(n))))

function solution21(n=10000)
    d(n)::Int=sum(x for x in 1:n-1 if n%x==0) # n>=2
    s=0
    for i in 2:n
        t=d(i)
        if t >1 && t <=n && d(t)==i && t!=i # 一个数不能构成亲和数
            s+=i
        end
    end
    s
end

function solution22()
    char2num(s)=Int(lowercase(s))-Int('a')+1
    name_score(name,idx)=sum(char2num(c) for c in name)*idx
    names = sort(split(replace(read("names.txt",String),"\""=>""),","))
    sum(name_score(names[i],i) for i in 1:length(names))
end

function solution23()
    abundants=(1:28123)[is_abundant.(1:28123)]
    function is_sum_of_two_abundant(n)
        n>=24 || return false
        for i in abundants
            if   i <n && (n-i) in abundants
                return true
            end
        end
        return false
    end
    sum(x for x in 1:28123 if !is_sum_of_two_abundant(x))
end

function solution24(n=1000000)
    nums=collect(0:9)
    P=permutations(nums)
    i=0
    for p in P
        i+=1
        if i ==n
            return sum(collect(p) .* [10^(10-i) for i in 1:10])
        end
    end
end

function solution25(dig=1000)
    a = BigInt(1)
    b = BigInt(1)
    n = 2
    digits_n=length(digits(b))
    while digits_n <dig
        a,b=b,a+b
        n+=1
        digits_n=length(digits(b))
    end
    return n
end

function  solution26(n=1000)
    lens=cyclelen.(1:1000)
    findmax(lens)[2]
end
solution26()

function solution27(ub=1000)
    maxcnt=0
    a0=0
    b0=0
    for a in -(ub-1):(ub-1)
        for b in -(ub-1):(ub-1)
            mycnt = primecounter(n-> n*n+a*n+b)
            if mycnt >= maxcnt
                a0=a
                b0=b
                maxcnt=mycnt
            end
        end
    end
    return a0*b0
end
# @enter solution27()

solution27()

function solution28(w::Integer=1001)
    @assert isodd(w)
    a(n)=n*n # anti-clockwise
    b(n)=a(n)-n+1
    c(n)=b(n)-n+1
    d(n)=c(n)-n+1
    s=0
    for i in 1:div(w,2)+1
        s+=a(2i-1)+b(2i-1)+c(2i-1)+d(2i-1)
    end
    s-=3 # 1 has been added for 4 times
end

solution28()

function solution29(n=100)
    list=zeros(BigInt,99*99)
    i=1
    for a in 2:100
        for b in 2:100
            list[i]=BigInt(a)^b
            i+=1
        end
    end
    length(unique(list))
end

solution29()

function solution30(k=5)
    d=length(digits(9^k))+1
    sp_nums=Int[]
    for i in 2:10^d
        if i== sum(x->x^k,digits(i))
            push!(sp_nums,i)
        end
    end
    sum(sp_nums)
end

solution30()

function solution31(n::Int=200)
    currency=Int[1,2,5,10,20,50,100,200]
    cnt=0
    for c200 in 0:div(n,200)
        for c100 in 0:div(n,100)
            for c50 in 0:div(n,50)
                for c20 in 0:div(n,20)
                    for c10 in 0:div(n,10)
                        for c5 in 0:div(n,5)
                            for c2 in 0:div(n,2)
                                for c1 in 0:n
                                    t=c1+2c2+5c5+10c10+20c20+50c50+100c100+200c200
                                    if t==n
                                        cnt+=1
                                    elseif t>n
                                        break
                                    end
                                end
                            end
                        end
                    end
                end
            end
        end
    end
    cnt
end

function solution32()
    # This function is fairly slow!
    # 1000 x 1000= 1000,000,  10 digits!
    p=Set(0) # Initialization
    for a in 1:10000
        for b in a:10000
            c=a*b
            digs=vcat(digits(a),digits(b),digits(c))
            if length(digs)==9 && sort(digs)==collect(1:9)
                push!(p,c)
            end
        end
    end
    sum(p)
end

function solution33()
    # 49/98=1/2
    # 30/50=3/5
    # 2*2*5*5=100
    100
end

function solution34()
    factof1to9=collect(0:9)
    factof1to9[1]=1
    for i in 2:10
        factof1to9[i]=factof1to9[i-1]*(i-1)
    end
    s=0
    ub=sum(factof1to9)
    for k in 10:ub
        if k == sum(factof1to9[digits(k).+1])
            s+=k
        end
    end
    s
end

function solution35(n=1000_000)
    cnt=0
    for i in 2:n
        if isprime(i)
            all_prime=true
            for num in cycles(i)
                if !isprime(num)
                    all_prime=false
                    break
                end
            end
            if all_prime
                cnt+=1
            end
        end
    end
    cnt
end

function solution36(n=1000_000)
    s=0
    for i in 1:n
        if !iseven(i)
            tenary=string(i)
            binary=string(i,base=2)
            if tenary==reverse(tenary) && binary ==reverse(binary)
                s+=i
            end
        end
    end
    s
end

function solution37()
    s=0
    cnt=0
    i=11
    while true
        if istruncatableprime(i)
            cnt+=1
            s+=i
        end
        i+=2
        if cnt ==11
            return s
        end
    end
end


text:
using PEGParser

@grammar csv begin
  start = list(record, crlf){ children }
  record = list(field, comma){ AbstractString[children...] }
  field = escaped_field | unescaped_field
  escaped_field = (dquote + escaped_field_value + dquote){ _2 }
  escaped_field_value = r"[ ,\n\r!#$%&'()*+\-./0-~]+|\"\""{ _0 }
  unescaped_field = r"[ !#$%&'()*+\-./0-~]+"{ _0 }
  crlf = r"[\n\r]+"
  dquote = '"'
  dqoute2 = "\"\""
  comma = ','
end

data = """
1,2,3
4,5,6
this,is,a,"test and

only a test"
"""

(ast, pos, error) = parse(csv, data)
println("ast = $ast")


text:
module LabelledArrays

using LinearAlgebra, StaticArrays, ArrayInterface

include("slarray.jl")
include("larray.jl")

# Common
@generated function __getindex(x::Union{LArray,SLArray},::Val{s}) where s
    syms = symnames(x)
    idx = syms isa NamedTuple ? syms[s] : findfirst(y->y==s,syms)
    if idx === nothing
        :(error("type $(typeof(x)) has no field $(s)"))
    elseif idx isa Tuple
        :(Base.@_propagate_inbounds_meta; view(getfield(x,:__x), $idx...))
    else
        :(Base.@_propagate_inbounds_meta; @views getfield(x,:__x)[$idx])
    end
end

using MacroTools

struct PrintWrapper{T,N,F,X<:AbstractArray{T,N}} <: AbstractArray{T,N}
    f::F
    x::X
end

import Base: eltype, length, ndims, size, axes, eachindex, stride, strides
MacroTools.@forward PrintWrapper.x eltype, length, ndims, size, axes, eachindex, stride, strides
Base.getindex(A::PrintWrapper, idxs...) = A.f(A.x, A.x[idxs...], idxs)

function lazypair(A, x, idxs)
    syms = symnames(typeof(A))
    II = LinearIndices(A)
    key = eltype(syms) <: Symbol ? syms[II[idxs...]] : findfirst(syms) do sym
        ii = idxs isa Tuple ? II[idxs...] : II[idxs]
        sym isa Tuple ? ii in II[sym...] : ii in II[sym]
    end
    key => x
end

Base.show(io::IO, ::MIME"text/plain", x::Union{LArray,SLArray}) = show(io, x)
function Base.show(io::IO, x::Union{LArray,SLArray})
    syms = symnames(typeof(x))
    n = length(syms)
    pwrapper = PrintWrapper(lazypair, x)
    if io isa IOContext && get(io, :limit, false) && displaysize(io) isa Tuple{Integer, Integer}
        io = IOContext(io, :limit => true, :displaysize => cld.(2 .*displaysize(io), 3))
    end
    println(io, summary(x), ':')
    Base.print_array(io, pwrapper)
end

Base.NamedTuple(x::Union{LArray,SLArray}) = NamedTuple{symnames(typeof(x))}(x.__x)
@inline Base.reshape(a::SLArray, s::Size) = StaticArrays.similar_type(a, s)(Tuple(a))

export SLArray, LArray, SLVector, LVector, @SLVector, @LArray, @LVector, @SLArray

export @SLSliced, @LSliced

export symbols, dimSymbols, rowSymbols, colSymbols

end # module


text:
#
# This file is a part of MolecularGraph.jl
# Licensed under the MIT License http://opensource.org/licenses/MIT
#

export
    Atom, QueryAtom,
    Bond, QueryBond

abstract type Atom <: AbstractNode end
abstract type QueryAtom <: AbstractNode end

abstract type Bond <: UndirectedEdge end
abstract type QueryBond <: UndirectedEdge end


text:
######################################################################
# Window built on GLFW library
# -----
# Licensed under MIT License
using KirUtil
using GenerateProperties
using GLFW

export WindowEvent, FramebufferSizeEvent, WindowCloseEvent, WindowContentScaleEvent, WindowFocusEvent, WindowIconifyEvent, WindowMaximizeEvent, WindowPosEvent, WindowRefreshEvent, WindowSizeEvent
"""Enum of various window-related events. These are a relatively low-level abstraction used with the
`register_window_callback` method. However, a more Julian solution exists. Refer to the documentation's *Event System*
section for more details."""
@enum WindowEvent FramebufferSizeEvent WindowCloseEvent WindowContentScaleEvent WindowFocusEvent WindowIconifyEvent WindowMaximizeEvent WindowPosEvent WindowRefreshEvent WindowSizeEvent

export ClientAPI, OpenGLAPI, OpenGLESAPI, NoAPI
"""Enum of the various possible client APIs which GLFW can use to create an OpenGL (ES) context."""
@enum ClientAPI OpenGLAPI = GLFW.OPENGL_API OpenGLESAPI = GLFW.OPENGL_ES_API NoAPI = GLFW.NO_API

export ContextCreationAPI, NativeContextAPI, EGLContextAPI
"""Enum of the various OpenGL (ES) context creation APIs which GLFW may use."""
@enum ContextCreationAPI NativeContextAPI = GLFW.NATIVE_CONTEXT_API EGLContextAPI = GLFW.EGL_CONTEXT_API

export OpenGLProfile, CoreProfile, CompatProfile, AnyProfile
"""Enum of possible OpenGL profiles."""
@enum OpenGLProfile CoreProfile = GLFW.OPENGL_CORE_PROFILE CompatProfile = GLFW.OPENGL_COMPAT_PROFILE AnyProfile = GLFW.OPENGL_ANY_PROFILE

export ContextReleaseBehavior, AnyReleaseBehavior, FlushReleaseBehavior, NoReleaseBehavior
"""Enum of context release behaviors GLFW may employ upon destroying an OpenGL (ES) context."""
@enum ContextReleaseBehavior AnyReleaseBehavior = GLFW.ANY_RELEASE_BEHAVIOR FlushReleaseBehavior = GLFW.RELEASE_BEHAVIOR_FLUSH NoReleaseBehavior = GLFW.RELEASE_BEHAVIOR_NONE

export ContextRobustness, LoseContextOnReset, NoResetNotification, NoRobustness
"""Context robustness solutions GLFW may employ when an OpenGL (ES) context fails."""
@enum ContextRobustness LoseContextOnReset = GLFW.LOSE_CONTEXT_ON_RESET NoResetNotification = GLFW.NO_RESET_NOTIFICATION NoRobustness = GLFW.NO_ROBUSTNESS

export Window, window
mutable struct Window{L} <: GLFWWrapper{GLFW.Window}
    handle::GLFW.Window
    function Window{L}(handle) where L
        inst = new(handle)
        register_default_window_callbacks(inst)
        register_default_input_callbacks(inst)
        inst
    end
end
function window(label::Symbol, title::AbstractString, width::Integer, height::Integer, monitor::Optional{Monitor} = nothing, share::Optional{Window} = nothing)
    Window{label}(GLFW.CreateWindow(width, height, title, convert(GLFW.Monitor, monitor), convert(GLFW.Window, share)))
end

# Use virtual properties to reduce namespace clutter
@generate_properties Window begin
    # Getters & Setters
    @get auto_iconify = GLFW.GetWindowAttrib(self.handle, GLFW.AUTO_ICONIFY)
    @set auto_iconify = GLFW.SetWindowAttrib(self.handle, GLFW.AUTO_ICONIFY, value)

    @get decorated = GLFW.GetWindowAttrib(self.handle, GLFW.DECORATED)
    @set decorated = GLFW.SetWindowAttrib(self.handle, GLFW.DECORATED, value)

    @get floating = GLFW.GetWindowAttrib(self.handle, GLFW.FLOATING)
    @set floating = GLFW.SetWindowAttrib(self.handle, GLFW.FLOATING, value)

    @get focus_on_show = GLFW.GetWindowAttrib(self.handle, GLFW.FOCUS_ON_SHOW)
    @set focus_on_show = GLFW.SetWindowAttrib(self.handle, GLFW.FOCUS_ON_SHOW, value)

    @get opacity = GLFW.GetWindowOpacity(self.handle)
    @set opacity = GLFW.SetWindowOpacity(self.handle, value)

    @get position = (pos = GLFW.GetWindowPos(self.handle); pos.x × pos.y)
    @set position = GLFW.SetWindowPos(self.handle, value[1], value[2])

    @get resizable = GLFW.GetWindowAttrib(self.handle, GLFW.RESIZABLE)
    @set resizable = GLFW.SetWindowAttrib(self.handle, GLFW.RESIZABLE, value)

    @get shouldclose = GLFW.WindowShouldClose(self.handle)
    @set shouldclose = GLFW.SetWindowShouldClose(self.handle, value)

    @get visible = GLFW.GetWindowAttrib(self.handle, GLFW.VISIBLE)
    @set visible = if value; GLFW.ShowWindow(self.handle) else; GLFW.HideWindow(self.handle) end

    # Setters Only
    @set aspect_ratio = set_aspect_ratio(self, value)
    @set icon = GLFW.SetWindowIcon(self.handle, value) # can be a single 2D matrix of pixels or an array of matrices of pixels.
    @set title = GLFW.SetWindowTitle(self.handle, value)

    # Getters Only
    @get content_scale    = (scales = GLFW.GetWindowContentScale(self.handle); scales.xscale × scales.yscale)
    @get focused          = GLFW.GetWindowAttrib(self.handle, GLFW.FOCUSED)
    @get framebuffer_size = (size = GLFW.GetFramebufferSize(self.handle); size.width × size.height)
    @get hovered          = GLFW.GetWindowAttrib(self.handle, GLFW.HOVERED)
    @get iconified        = GLFW.GetWindowAttrib(self.handle, GLFW.ICONIFIED)
    @get maximized        = GLFW.GetWindowAttrib(self.handle, GLFW.MAXIMIZED)
    @get transparent      = GLFW.GetWindowAttrib(self.handle, GLFW.TRANSPARENT_FRAMEBUFFER)

    # Context-related Properties
    @get client_api                 = ClientAPI(GLFW.GetWindowAttrib(self.handle, GLFW.CLIENT_API))
    @get context_creation_api       = ContextCreationAPI(GLFW.GetWindowAttrib(self.handle, GLFW.CONTEXT_CREATION_API))
    @get context_version            = VersionNumber(GLFW.GetWindowAttrib(self.handle, GLFW.CONTEXT_VERSION_MAJOR), GLFW.GetWindowAttrib(self.handle, GLFW.CONTEXT_VERSION_MINOR), GLFW.GetWindowAttrib(self.handle, GLFW.CONTEXT_REVISION))
    @get context_forward_compatible = GLFW.GetWindowAttrib(self.handle, GLFW.OPENGL_FORWARD_COMPAT) != 0
    @get context_debug              = GLFW.GetWindowAttrib(self.handle, GLFW.OPENGL_DEBUG_CONTEXT) != 0
    @get context_profile            = OpenGLProfile(GLFW.GetWindowAttrib(self.handle, GLFW.OPENGL_PROFILE))
    @get context_release_behavior   = ContextReleaseBehavior(GLFW.GetWindowAttrib(self.handle, GLFW.CONTEXT_RELEASE_BEHAVIOR))
    @get context_generates_errors   = GLFW.GetWindowAttrib(self.handle, GLFW.CONTEXT_NO_ERROR) == 0
    @get context_robustness         = ContextRobustness(GLFW.GetWindowAttrib(self.handle, GLFW.CONTEXT_ROBUSTNESS))
end

function Base.close(wnd::Window)
    GLFW.DestroyWindow(wnd.handle)
    wnd.handle = nothing
end

KirUtil.use(wnd::Window) = (GLFW.MakeContextCurrent(wnd.handle); nothing)

monitor(wnd::Window) = monitor(GLFW.GetWindowMonitor(wnd.handle))
monitor(wnd::Window, monitor::Monitor, refresh_rate::Integer) = GLFW.SetWindowMonitor(wnd.handle, monitor.handle, 0, 0, size(wnd)..., refresh_rate)
monitor(wnd::Window, ::Nothing; xpos::Integer = 0, ypos::Integer = 0) = GLFW.SetWindowMonitor(wnd.handle, nothing, xpos, ypos, size(wnd)..., GLFW.DONT_CARE)

set_aspect_ratio(wnd::Window, ::Nothing) = GLFW.SetWindowAspectRatio(wnd.handle, GLFW.DONT_CARE, GLFW.DONT_CARE)
set_aspect_ratio(wnd::Window, aspect_ratio::Rational) = GLFW.SetWindowAspectRatio(wnd.handle, numerator(aspect_ratio), denominator(aspect_ratio))
set_aspect_ratio(wnd::Window, values) = GLFW.SetWindowAspectRatio(wnd.handle, values[1], values[2])

export limitsize
function limitsize(wnd::Window; min_width::Integer = -1, min_height::Integer = -1, max_width::Integer = -1, max_height::Integer = -1)
    real_limits(lim) = if lim < 1; GLFW.DONT_CARE else; lim end
    real_min_width, real_min_height, real_max_width, real_max_height = real_limits.((min_width, min_height, max_width, max_height))
    GLFW.SetWindowSizeLimits(wnd.handle, real_min_width, real_min_height, real_max_width, real_max_height)
    wnd
end

export maximize, restore
maximize(wnd::Window) = GLFW.MaximizeWindow(wnd.handle)
restore(wnd::Window)  = GLFW.RestoreWindow(wnd.handle)

export swapbuffers
swapbuffers(wnd::Window) = GLFW.SwapBuffers(wnd.handle)

export request_attention
request_attention(wnd::Window) = GLFW.RequestWindowAttention(wnd.handle)

macro make_register_window_callback(event, registrar)
    esc(:(register_window_callback(handler, wnd::Window, ::Ident{$event}) = GLFW.$registrar(wnd.handle, handler)))
end

"""Registers the default callbacks for the window. These callbacks forward events to concrete, semantic signatures in a more Julian approach."""
function register_default_window_callbacks(wnd::Window)
    register_window_callback(wnd, FramebufferSizeEvent)    do _::GLFW.Window, width::Integer, height::Integer; on_framebuffer_resize(wnd, width, height) end
    register_window_callback(wnd, WindowCloseEvent)        do _::GLFW.Window; on_window_close(wnd) end
    register_window_callback(wnd, WindowContentScaleEvent) do _::GLFW.Window, scalex::AbstractFloat, scaley::AbstractFloat; on_window_content_scale(wnd, scalex, scaley) end
    register_window_callback(wnd, WindowFocusEvent)        do _::GLFW.Window, focused::Integer; if focused != 0; on_window_focus(wnd) else; on_window_defocus(wnd) end; end
    register_window_callback(wnd, WindowIconifyEvent)      do _::GLFW.Window, iconified::Integer; if iconified != 0; on_window_iconify(wnd) else; on_window_restore(wnd) end; end
    register_window_callback(wnd, WindowMaximizeEvent)     do _::GLFW.Window, maximized::Integer; if maximized != 0; on_window_maximize(wnd) else; on_window_restore(wnd) end; end
    register_window_callback(wnd, WindowPosEvent)          do _::GLFW.Window, posx::Integer, posy::Integer; on_window_move(wnd, posx, posy) end
    register_window_callback(wnd, WindowRefreshEvent)      do _::GLFW.Window; on_window_refresh(wnd) end
    register_window_callback(wnd, WindowSizeEvent)         do _::GLFW.Window, sizex::Integer, sizey::Integer; on_window_resize(wnd, sizex, sizey) end
end
register_window_callback(handler, wnd::Window, event::WindowEvent) = register_window_callback(handler, wnd, Ident{event}())
@make_register_window_callback FramebufferSizeEvent    SetFramebufferSizeCallback
@make_register_window_callback WindowCloseEvent        SetWindowCloseCallback
@make_register_window_callback WindowContentScaleEvent SetWindowContentScaleCallback
@make_register_window_callback WindowFocusEvent        SetWindowFocusCallback
@make_register_window_callback WindowIconifyEvent      SetWindowIconifyCallback
@make_register_window_callback WindowMaximizeEvent     SetWindowMaximizeCallback
@make_register_window_callback WindowPosEvent          SetWindowPosCallback
@make_register_window_callback WindowRefreshEvent      SetWindowRefreshCallback
@make_register_window_callback WindowSizeEvent         SetWindowSizeCallback

# Window Events
"""Called when the window's framebuffer has been resized."""
function on_framebuffer_resize(::Window, width::Integer, height::Integer) end
"""Called when the window is *requested* to close."""
function on_window_close(::Window) end
"""Called when the window's content scale is adjusted."""
function on_window_content_scale(::Window, scalex::AbstractFloat, scaley::AbstractFloat) end
"""Called when the window loses focus."""
function on_window_defocus(::Window) end
"""Called when the window gains focus."""
function on_window_focus(::Window) end
"""Called when the window is iconified (minimized to taskbar)."""
function on_window_iconify(::Window) end
"""Called when the window is maximized."""
function on_window_maximize(::Window) end
"""Called when the window is moved."""
function on_window_move(::Window, posx::Integer, posy::Integer) end
"""Called when the window should refresh."""
function on_window_refresh(::Window) end
"""Called when the window is resized."""
function on_window_resize(::Window, sizex::Integer, sizey::Integer) end
"""Called when the window is restored, either from being iconified or maximized."""
function on_window_restore(::Window) end

export @windowhint
"""`@windowhint hint value` is a more semantic & Julian, albeit comparatively low-level interface which sets the window
creation hint. It is equivalent to `GLFW.WindowHint(...)`. However, most hints are can be adjusted post creation through
`Window`'s virtual properties. Some, especially framebuffer context hints, can only be configured prior to creation
through this macro.

`hint` may be either the desired GLFW hint constant (e.g. `RED_BITS`) or a camel-cased version (e.g. `RedBits` or `redBits`).

`value` may be the appropriate value (Integer/Bool) which is passed to the hint directly.

If `value` is `nothing`, `GLFW.DONT_CARE` is passed instead.

If `value` is an `Enum`, it's `Integer(value)` is passed in. This allows setting the hint to a value such as `OpenGLAPI`
(a `ClientAPI` enum value) which is assigned the appropriate GLFW constant.

Example
-------
```julia
@windowhint TransparentFramebuffer true
@windowhint ClientAPI OpenGLAPI"""
macro windowhint(hint::Symbol, value)
    glfw_hint = Expr(:., :GLFW, QuoteNode(Symbol(decamelcase(string(hint)))))
    Expr(:call, :windowhint, glfw_hint, esc(value))
end
windowhint(hint::UInt32, ::Union{Nothing, Missing}) = windowhint(hint, GLFW.DONT_CARE)
windowhint(hint::UInt32, value::Enum) = windowhint(hint, Integer(value))
windowhint(hint::UInt32, value) = GLFW.WindowHint(hint, value)

decamelcase(sym::AbstractString) = Symbol(uppercase(replace(sym, r"([a-z])([A-Z])" => s"\1_\2")))

Base.convert(::Type{GLFW.Window}, wnd::Window) = wnd.handle
Base.convert(::Type{GLFW.Window}, ::Nothing) = GLFW.Window(C_NULL)