text:
### A Pluto.jl notebook ###
# v0.17.5

using Markdown
using InteractiveUtils

# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).
macro bind(def, element)
    quote
        local iv = try Base.loaded_modules[Base.PkgId(Base.UUID("6e696c72-6542-2067-7265-42206c756150"), "AbstractPlutoDingetjes")].Bonds.initial_value catch; b -> missing; end
        local el = $(esc(element))
        global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : iv(el)
        el
    end
end

# ╔═╡ f9e8c9c0-86f6-4f27-938c-307f4fae7d52
using Plots

# ╔═╡ 0af2c481-e7c4-4ce5-a0f1-a940caeefed5
using PlutoUI

# ╔═╡ 2b72f685-7705-45f9-afa9-b38f8308e887
using OffsetArrays

# ╔═╡ 60d767b2-9d44-4fed-8564-6ec7d66d0a21
using ReinforcementLearning

# ╔═╡ 954cd93f-2c30-4826-944c-293deeba452e
using KernelDensity

# ╔═╡ 3ac7c63e-7423-11ec-1ae1-8f50247003da
md"""
# Notes on [Distributional Reinforcement Learning](https://www.distributional-rl.org/)
"""

# ╔═╡ ed570ba8-efcf-45ef-8db6-5a2ca58e8698
md"""
## Chapter 1

In Chapter 1, the [Kuhn Poker](https://en.wikipedia.org/wiki/Kuhn_poker) is introduced to explain the distribution of rewards after playing ``T`` rounds. We can calculate the probability when ``T = 1`` based on the given policy.

| First player holding a ... | Jack | Queen | King|
|:--------------------------:|:----:|:-----:|:----|
|Probability of raising      | 1/3  | 0     | 1   |
|Probability of calling      | 0    | 2/3   | 1   |

[![](https://juntian.me/reading/Notes_on_Distributional_Reinforcement_Learning/img/kuhn_poker.svg)](https://juntian.me/reading/Notes_on_Distributional_Reinforcement_Learning/img/kuhn_poker.svg)

(You can click to view the image.)
"""

# ╔═╡ e5b88be0-c50a-468e-bb64-6faa3832b600
md"""
Then we can get the initial reward distribution by some simple calculations:

$$\begin{alignedat}{4}

& p(r=-2) && = && \left( \frac{1}{6} * \frac{1}{3} * \frac{1}{2} \right) && + \\
& &&        && \left( \frac{1}{6} * \frac{1}{3} * \frac{1}{2} \right) && +  \\
& &&        && \left( \frac{1}{6} * 1 * \frac{1}{2} * \frac{2}{3} \right) \\
& && = && \frac{4}{36} \\

& p(r=-1) && = && \left( \frac{1}{6} * \frac{2}{3} * \frac{1}{2} \right) && + \\
& &&        && \left( \frac{1}{6} * \frac{2}{3} * \frac{1}{2} \right) &&  +  \\
& &&        && \left( \frac{1}{6} * \frac{2}{3} * \frac{1}{2} \right) &&  + \\
& &&        && \left( \frac{1}{6} * \frac{2}{3} * \frac{1}{2} \right) && + \\
& &&        && \left( \frac{1}{6} * 1 * \frac{1}{2} * \frac{1}{3} \right)&&  + \\
& &&        && \left( \frac{1}{6} * 1 * \frac{1}{2} \right) && + \\
& &&        && \left( \frac{1}{6} * 1 * \frac{1}{2} * \frac{1}{3} \right) \\
& && = && \frac{13}{36} \\

& p(r=+1) && = && \left( \frac{1}{6} * \frac{1}{3} * \frac{1}{2} \right) && + \\
& &&        &&  \left( \frac{1}{6} * \frac{1}{3} * \frac{1}{2} \right) && + \\
& &&        &&  \left( \frac{1}{6} * 1 * \frac{1}{2} \right) && + \\
& &&        &&  \left( \frac{1}{6} * 1 * \frac{1}{2} \right) && + \\
& &&        &&  \left( \frac{1}{6} * 1 * \frac{1}{2} \right)  \\
& && = && \frac{11}{36} \\

& p(r=+2) && = && \left( \frac{1}{6} * 1 * \frac{1}{2} * \frac{2}{3} \right)  && +\\
& &&        &&  \left( \frac{1}{6} * 1 * \frac{1}{2} \right) && + \\
& &&        &&  \left( \frac{1}{6} * 1 * \frac{1}{2} \right) \\
& && = && \frac{8}{36} \\

\end{alignedat}$$
"""

# ╔═╡ 21cbc023-2ea2-48a3-b16c-285aba0737f8
INIT_WININGS = OffsetArray([zeros(8)..., 4/36, 13/36, 0, 11/36, 8/36, zeros(8)...], -10:10)

# ╔═╡ 85773172-ee70-45b8-96b2-51ea2a5b94bd
bar(-10:10, collect(INIT_WININGS), label="T=1")

# ╔═╡ acc2b47b-7857-40ac-b004-7e8ae416e9ba
fill!(similar(INIT_WININGS), 0.)

# ╔═╡ 8d13fbca-b1a3-4b3c-ae66-9da08ae0272b
firstindex(INIT_WININGS)

# ╔═╡ 5168b209-6cc1-49c1-afd1-784c4ce8ff77
function winings_after_step(T, init=INIT_WININGS)
	pre = copy(init)
	cur = fill!(similar(init), 0.)
	for _ in 1:T
		for (i, w) in zip(axes(pre,1), pre)
			if i == firstindex(pre) || i == lastindex(pre)
				cur[i] += pre[i]
			else
				for (j, v) in zip(axes(init,1), init)
					k = i + j
					cur[min(lastindex(init), max(firstindex(init), k))] += w * v
				end
			end
		end
		pre, cur = cur, pre
		fill!(cur, 0.)
	end
	pre
end

# ╔═╡ af2d315f-7ad5-4035-9408-e410d57cafbe
@bind T Slider(0:200, default=15)

# ╔═╡ 4a74562c-f16a-4b35-b4c1-d1f2a5636694
bar(-10:10, collect(winings_after_step(T)), label="T=$T")

# ╔═╡ af23ad49-346b-4e36-8784-87f05e3bc60b
md"""
# Chapter 2
"""

# ╔═╡ bf24b6c7-c606-44b3-aa59-55539dc88fef
md"""
### Figure 2.2
We'll use APIs in [ReinforcementLearning.jl](https://github.com/JuliaReinforcementLearning/ReinforcementLearning.jl) to define the Cliff World environment. Part of the code are borrowed from [Reinforcement Learning: An Introduction/Chapter06 Temporal-Difference Learning](https://juliareinforcementlearning.org/ReinforcementLearningAnIntroduction.jl/notebooks/Chapter06_Cliff_Walking.html)
"""

# ╔═╡ 0b27269d-0a9f-4a20-9b65-1f2ee092d536
begin
	NX = 4
	NY = 12
	Start = CartesianIndex(4, 1)
	Goal = CartesianIndex(4, 12)
	LRUD = [
		CartesianIndex(0, -1),  # left
		CartesianIndex(0, 1),   # right
		CartesianIndex(-1, 0),  # up
		CartesianIndex(1, 0),   # down
	]
	LinearInds = LinearIndices((NX, NY))
end

# ╔═╡ 2cacc2d3-89ed-47c1-b727-2c2a597ecc10
function iscliff(p::CartesianIndex{2})
    x, y = Tuple(p)
    x == 4 && y > 1 && y < NY
end

# ╔═╡ 8049593f-5b5a-4640-8699-e443a00a008b
heatmap((!iscliff).(CartesianIndices((NX, NY))); yflip = true)

# ╔═╡ 401ca430-dc57-4580-8b04-f7125f311e5f
begin
	Base.@kwdef mutable struct CliffWalkingEnv <: AbstractEnv
		position::CartesianIndex{2} = Start
	end
	function (env::CliffWalkingEnv)(a::Int)
		if rand() <= 0.25
			a_unintended = rand(1:4)
			while a == a_unintended
				a_unintended = rand(1:4)
			end
			a = a_unintended
		end
		x, y = Tuple(env.position + LRUD[a])
		env.position = CartesianIndex(min(max(x, 1), NX), min(max(y, 1), NY))
	end
end

# ╔═╡ 96858c59-fc12-4ab1-b37a-cb94ffdd05bc
RLBase.state(env::CliffWalkingEnv) = LinearInds[env.position]

# ╔═╡ 8347f825-f739-4f3a-89e2-0d85d051cf6e
RLBase.state_space(env::CliffWalkingEnv) = Base.OneTo(length(LinearInds))

# ╔═╡ 0fe2ab3c-e2a4-4e8a-bfe7-14d963eb1c7b
RLBase.action_space(env::CliffWalkingEnv) = Base.OneTo(length(LRUD))

# ╔═╡ e2b0991d-ef04-4de9-87f8-402ef5460afa
RLBase.reward(env::CliffWalkingEnv) = env.position == Goal ? 1.0 : (iscliff(env.position) ? -1.0 : 0.)

# ╔═╡ f018cfeb-af07-44af-b022-2c2f9180ce34
RLBase.reset!(env::CliffWalkingEnv) = env.position = Start

# ╔═╡ 469496c5-6549-4616-814f-8ce80cb3bde6
RLBase.is_terminated(env::CliffWalkingEnv) = env.position == Goal || iscliff(env.position)

# ╔═╡ da8c59d6-4458-40d5-9640-77291daab666
safe_policy = TabularPolicy(
	Dict(
		i => if i > NX * (NY-1)
			4 # down
		elseif i % NX == 1
			2 # right
		else
			3
		end

		for i in LinearInds
	),
	4
)

# ╔═╡ b7580a82-3e5c-449f-80f2-be69712db36c
map(i -> safe_policy(i), LinearInds)

# ╔═╡ 476b82d9-f5f6-4a96-9351-e909f6408731
quick_policy = TabularPolicy(
	Dict(
		i => if i == NX
			3 # up
		elseif i % NX == 3 && i <= NX * (NY -1)
			2 # right
		else
			4
		end

		for i in LinearInds
	),
	4
)

# ╔═╡ e4eb00c0-0bb0-44b8-99f5-95b940c82d8f
map(i -> quick_policy(i), LinearInds)

# ╔═╡ e99571ee-1543-450b-b5a1-88a89a45d884
quick_policy_hook = ComposedHook(
	TotalRewardPerEpisode(),
	StepsPerEpisode()
)

# ╔═╡ df7371bc-25c9-46c5-868e-d29f9ea5128a
run(quick_policy, CliffWalkingEnv(), StopAfterEpisode(100_000), quick_policy_hook)

# ╔═╡ 6dc05c0e-61cd-482d-b6e2-1318a66a12a7
safe_policy_hook = ComposedHook(
	TotalRewardPerEpisode(),
	StepsPerEpisode()
)

# ╔═╡ 3017eb45-e96c-4713-bb14-c6e49e1cf32c
run(safe_policy, CliffWalkingEnv(), StopAfterEpisode(100_000), safe_policy_hook)

# ╔═╡ a03ffe02-b0bb-4d82-83c3-4bb4fc27142c
begin
	quick_policy_kde = kde(0.99 .^ (quick_policy_hook[2].steps[1:end-1] .- 1) .* quick_policy_hook[1].rewards, bandwidth=0.02)
	safe_policy_kde = kde(0.99 .^ (safe_policy_hook[2].steps[1:end-1] .- 1) .* safe_policy_hook[1].rewards, bandwidth=0.02)
	plot(safe_policy_kde.x, safe_policy_kde.density, label="safe policy", legend=:topleft)
	plot!(quick_policy_kde.x, quick_policy_kde.density, label="quick policy")
end

# ╔═╡ 00000000-0000-0000-0000-000000000001
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text:
using Statistics
using LaTeXStrings
using Measures
using Plots
using Plots.PlotMeasures
gr()

dropmean(A; dims=:) = dropdims(mean(A; dims=dims); dims=dims)
dropstd(A; dims=:) = dropdims(std(A; dims=dims); dims=dims)

# @userplot Ucurve
# @recipe function f(u::Ucurve)
#     x,y = u.args
#     # println(u.args)
#     legend := false

#     my = dropmean(y,dims=2)
#     sd = dropstd(y,dims=2)
#     ymax = max(y...)
#     multiplier = 10/(round(ymax,sigdigits=1))
#     print(multiplier)
#     my = my*multiplier
#     sd = sd*multiplier
#     ymax = ymax*multiplier
#     to_power_of = -Int(log10(multiplier))
#     power_label = raw"$ \times 10^"*"{$(to_power_of)}"*raw"$"
#     println(min(x...))
#     println(1.2*ymax)
#     @series begin
#         ribbon --> sd
#         # yformatter --> :scientific
#         ylims-->(0,ymax)
#         xticks --> range(min(x...),max(x...), length=3)
#         # yguide --> power_label
#         annotations := (1.1*min(x...),0.9*ymax,power_label, :black)
#         # xlims -->(0, max(x...))
#         x,my
#     end
# end

# upd = [OptimalPlasticityRatios.fixedNormFluctuatingNoisyGD(qual,rat*dis,dis) for qual in cQuality, dis in disturbance_sizes, rat in sysfluct_ratios, rep in num_reps]

function extract_endmean(rec::OptimalPlasticityRatios.recorder; length_to_average=100)
    return mean(rec.value[end-100:end])
end

function set_ticksize(min_,max_, step; sigdigits=2)
    labs = [string(Int(round(i, sigdigits=sigdigits))) for i in min_:step:max_ ]
    return (collect(min_:step:max_), labs)
end

function make_single_plot(x_axis, ds::Array{Float64,2})
    my = dropmean(ds, dims=2)
    sd = dropstd(ds, dims=2)
    ymax = max(ds...)
    # println(ymax)
    to_power_of = ceil(-log10(ymax))
    multiplier = 10^(to_power_of)
    my = my*multiplier
    sd = sd*multiplier
    ymax = ymax*multiplier

    to_power_of = -Int(log10(multiplier))

    if ymax < 2
        my *= 10 ; sd *= 10; multiplier *= 10; ymax *=10; to_power_of -= 1
    end

    st = 1
    nt = length(0:st:ymax)
    while (nt >6 || nt < 3)
        nt > 6 ? st*=2 : st /= 2
        nt = length(0:st:ymax)
    end

    power_label = raw"$ \times 10^"*"{$(to_power_of)}"*raw"$"
    p = plot(x_axis, my , ribbon=sd
            , ylims = (0,ymax)
            , xlims = (0, x_axis[end])
            , xticks = range(0, x_axis[end], length=5)
            , yticks = set_ticksize(0,ymax,st, sigdigits=1)
            , legend = false
            # , annotations = (2.5 *x_axis[1],ymax, power_label, :black)
            # ; margin = 5mm
            # , title = power_label
            # , titleloc = :left
            # , titlefont = font(8)
            , margin = 10pt
            )

    annotate!(p, 0.1, ymax, text(power_label, :black, :left, 10))

end

function make_all_plots(x_axis, recStore)

dd = extract_endmean.(recStore; length_to_average=100) # get final loss values from the recorders, averaged over some amount of time
dat(i,j) = dd[i,j,:,:]  #  cQuality, disturbance_sizes,  sys_fluct_ratios, num_reps
all_plots = [make_single_plot(x_axis, dat(i,j)) for i in 1:size(dd)[1], j in 1:size(dd)[2]]

# with_spaces = [all_plots[1:3], plot()

end
# points = Point2f0[[0.0, 0.0], [1., 0.0], [1., 1.], [0.0, 1.]]
# spoly!(axes[1,2],points, alpha=0.1, color = :skyblue2, :fxaa = true)

ext: jl


text:
#####
##### Upwind-biased 3rd-order advection scheme
#####

struct UpwindBiasedThirdOrder <: AbstractUpwindBiasedAdvectionScheme end

const U3 = UpwindBiasedThirdOrder

@inline boundary_buffer(::U3) = 1

@inline symmetric_interpolate_xᶠᵃᵃ(i, j, k, grid, ::U3, c) = ℑxᶠᵃᵃ(i, j, k, grid, c)
@inline symmetric_interpolate_yᵃᶠᵃ(i, j, k, grid, ::U3, c) = ℑyᵃᶠᵃ(i, j, k, grid, c)
@inline symmetric_interpolate_zᵃᵃᶠ(i, j, k, grid, ::U3, c) = ℑzᵃᵃᶠ(i, j, k, grid, c)

@inline symmetric_interpolate_xᶜᵃᵃ(i, j, k, grid, ::U3, u) = ℑxᶜᵃᵃ(i, j, k, grid, u)
@inline symmetric_interpolate_yᵃᶜᵃ(i, j, k, grid, ::U3, v) = ℑyᵃᶜᵃ(i, j, k, grid, v)
@inline symmetric_interpolate_zᵃᵃᶜ(i, j, k, grid, ::U3, w) = ℑzᵃᵃᶜ(i, j, k, grid, w)

@inline left_biased_interpolate_xᶠᵃᵃ(i, j, k, grid, ::U3, c) = @inbounds (2 * c[i, j, k] + 5 * c[i-1, j, k] - c[i-2, j, k]) / 6
@inline left_biased_interpolate_yᵃᶠᵃ(i, j, k, grid, ::U3, c) = @inbounds (2 * c[i, j, k] + 5 * c[i, j-1, k] - c[i, j-2, k]) / 6
@inline left_biased_interpolate_zᵃᵃᶠ(i, j, k, grid, ::U3, c) = @inbounds (2 * c[i, j, k] + 5 * c[i, j, k-1] - c[i, j, k-2]) / 6

@inline left_biased_interpolate_xᶜᵃᵃ(i, j, k, grid, scheme::U3, u) = left_biased_interpolate_xᶠᵃᵃ(i+1, j, k, grid, scheme, u)
@inline left_biased_interpolate_yᵃᶜᵃ(i, j, k, grid, scheme::U3, v) = left_biased_interpolate_yᵃᶠᵃ(i, j+1, k, grid, scheme, v)
@inline left_biased_interpolate_zᵃᵃᶜ(i, j, k, grid, scheme::U3, w) = left_biased_interpolate_zᵃᵃᶠ(i, j, k+1, grid, scheme, w)

@inline right_biased_interpolate_xᶠᵃᵃ(i, j, k, grid, ::U3, c) = @inbounds (- c[i+1, j, k] + 5 * c[i, j, k] + 2 * c[i-1, j, k]) / 6
@inline right_biased_interpolate_yᵃᶠᵃ(i, j, k, grid, ::U3, c) = @inbounds (- c[i, j+1, k] + 5 * c[i, j, k] + 2 * c[i, j-1, k]) / 6
@inline right_biased_interpolate_zᵃᵃᶠ(i, j, k, grid, ::U3, c) = @inbounds (- c[i, j, k+1] + 5 * c[i, j, k] + 2 * c[i, j, k-1]) / 6

@inline right_biased_interpolate_xᶜᵃᵃ(i, j, k, grid, scheme::U3, u) = right_biased_interpolate_xᶠᵃᵃ(i+1, j, k, grid, scheme, u)
@inline right_biased_interpolate_yᵃᶜᵃ(i, j, k, grid, scheme::U3, v) = right_biased_interpolate_yᵃᶠᵃ(i, j+1, k, grid, scheme, v)
@inline right_biased_interpolate_zᵃᵃᶜ(i, j, k, grid, scheme::U3, w) = right_biased_interpolate_zᵃᵃᶠ(i, j, k+1, grid, scheme, w)


text:
module FindLocalIndexTests

using Gridap
using Gridap.CellValuesGallery

c = [[2,1,3,4,8],[1,8,4,3,4,30],[3,5,2]]
cv = CellValueFromArray(c)

g = [1,3,2,1]
cn = CellValueFromArray(g)

r = [2,3,4,4]

li = find_local_index(cn,cv)
test_index_cell_value(li,r)

li = get_local_item(cv,2)
r = [1,8,5]
test_index_cell_value(li,r)

end # module


text:
using Painter
using Test

@testset "Painter.jl" begin
    # Write your tests here.
end


text:
function returnFileLength(file_name::String)
    data1 = read(file_name, String)
    splitdata = split(data1, "\n")
    return length(splitdata) - 2
end

function createGlobalLocalMapIndex(global_local_map_index, global_local_direct_index, folder_name::String)
    index_flag = 1
    # println("Reading multiple files")
    for iter in 1:length(workers())
        if iter - 1 < 10
            filename = folder_name * "/" * "partGrid000" * string(iter-1)
        elseif iter - 1 < 100
            filename = folder_name * "/" * "partGrid00" * string(iter-1)
        elseif iter - 1 < 1000
            filename = folder_name * "/" * "partGrid0" * string(iter-1)
        else
            filename = folder_name * "/" * "partGrid" * string(iter-1)
        end
        # println(filename)
        data = read(filename, String)
        splitdata = @view split(data, "\n")[1:end-1]
        itmdata = split(splitdata[1])
        local_point_count = parse(Int,itmdata[3])
        for (idx, itm) in enumerate(splitdata)
            if idx == 1
                continue
            elseif idx <= local_point_count + 1
                itmdata = split(itm)
                global_local_direct_index[parse(Int,itmdata[1])] = index_flag
                global_local_map_index[(parse(Float64,itmdata[2]), parse(Float64, itmdata[3]))] = index_flag
                index_flag += 1
            end
        end
    end
end

function readGhostFile(folder_name::String, ghost_holder, global_local_map_index, dist_globaldata)
    # println(ghost_folder_name)
    for iter in 1:length(workers())
        if iter - 1 < 10
            filename = folder_name * "/" * "partGrid000" * string(iter-1)
        elseif iter - 1 < 100
            filename = folder_name * "/" * "partGrid00" * string(iter-1)
        elseif iter - 1 < 1000
            filename = folder_name * "/" * "partGrid0" * string(iter-1)
        else
            filename = folder_name * "/" * "partGrid" * string(iter-1)
        end
        # println(filename)
        idx = 1
        local_point_count = 0
        ghost_point_count = 0
        ghost_holder[iter] = Dict{Int64,Point}()
        for splitdata in eachline(filename)
            if idx == local_point_count + ghost_point_count + 2
               break
            end

            if idx == 1
                itmdata = split(splitdata)
                local_point_count = parse(Int,itmdata[3])
                ghost_point_count = parse(Int,itmdata[4])
                # print(itmdata)
            end

            # for (idx, itm) in enumerate(splitdata)
            if idx >= local_point_count + 2
                itmdata = split(splitdata)
                ghost_holder[iter][idx-1] = dist_globaldata[global_local_map_index[(parse(Float64,itmdata[2]), parse(Float64, itmdata[3]))]]
                # if idx == local_point_count + 2
                    # println(idx, " ", splitdata, " ", ghost_holder[iter][idx-1])
                # end
            end
            # end
            idx+=1
        end
    end
end

function readDistribuedFile(folder_name::String, defprimal, p, global_local_map_index)
    # println("Reading multiple files")
    # println(folder_name)
    iter = p - 1
    if iter - 1 < 10
        filename = folder_name * "/" * "partGrid000" * string(iter-1)
    elseif iter - 1 < 100
        filename = folder_name * "/" * "partGrid00" * string(iter-1)
    elseif iter - 1 < 1000
        filename = folder_name * "/" * "partGrid0" * string(iter-1)
    else
        filename = folder_name * "/" * "partGrid" * string(iter-1)
    end
    println(filename)
    data = read(filename, String)
    splitdata = @view split(data, "\n")[1:end-1]
    itmdata = split(splitdata[1])
    local_point_count = parse(Int,itmdata[3])
    ghost_point_count = parse(Int,itmdata[4])
    local_points_holder = Array{Point,1}(undef, local_point_count)
    for (idx, itm) in enumerate(splitdata)
        if idx == 1
            continue
        elseif idx <= local_point_count + 1
            itmdata = split(itm)
            globalID = global_local_map_index[(parse(Float64,itmdata[2]), parse(Float64, itmdata[3]))]
            local_points_holder[idx-1] = Point(parse(Int,itmdata[1]),
                parse(Float64, itmdata[2]),
                parse(Float64, itmdata[3]),
                parse(Int, itmdata[4]),
                parse(Int, itmdata[5]),
                parse(Int8,itmdata[6]),
                parse(Int8,itmdata[7]),
                parse(Float64,itmdata[8]),
                parse(Int8,itmdata[9]),
                parse.(Int, itmdata[10:end]),
                0.0,
                0.0,
                copy(defprimal),
                zeros(Float64, 4),
                zeros(Float64, 4),
                zeros(Float64, 4), zeros(Float64, 4), zeros(Float64, 4), zeros(Float64, 4), 0.0, 0, 0, 0, 0, Array{Int32,1}(undef, 0), Array{Int32,1}(undef, 0),
                Array{Int32,1}(undef, 0), Array{Int32,1}(undef, 0), 0.0, zeros(Float64, 4), zeros(Float64, 4), zeros(Float64, 4),
                globalID)
        end
    end
    return local_points_holder
end

function readDistribuedFileQuadtree(folder_name::String, defprimal, p, global_local_map_index)
    # println(folder_name)
    iter = p - 1
    if iter - 1 < 10
        filename = folder_name * "/" * "partGrid000" * string(iter-1)
    elseif iter - 1 < 100
        filename = folder_name * "/" * "partGrid00" * string(iter-1)
    elseif iter - 1 < 1000
        filename = folder_name * "/" * "partGrid0" * string(iter-1)
    else
        filename = folder_name * "/" * "partGrid" * string(iter-1)
    end
    # println(filename)
    data = read(filename, String)
    splitdata = @view split(data, "\n")[1:end-1]
    itmdata = split(splitdata[1])
    local_point_count = parse(Int,itmdata[3])
    ghost_point_count = parse(Int,itmdata[4])
    local_points_holder = Array{Point,1}(undef, local_point_count)
    for (idx, itm) in enumerate(splitdata)
        if idx == 1
            continue
        elseif idx <= local_point_count + 1
            itmdata = split(itm)
            globalID = global_local_map_index[(parse(Float64,itmdata[2]), parse(Float64, itmdata[3]))]
            local_points_holder[idx-1] = Point(parse(Int,itmdata[1]),
                parse(Float64, itmdata[2]),
                parse(Float64, itmdata[3]),
                parse(Int, itmdata[4]),
                parse(Int, itmdata[5]),
                parse(Int8,itmdata[6]),
                parse(Int8,itmdata[7]),
                parse(Float64,itmdata[11]),
                parse(Int8,itmdata[12]),
                parse.(Int, itmdata[13:end]),
                parse(Float64, itmdata[7]),
                parse(Float64, itmdata[8]),
                copy(defprimal),
                SVector{4}([zero(Float64) for iter in 1:4]),
                zeros(Float64, 4),
                SVector{4}([zero(Float64) for iter in 1:4]),
                SVector{4}([zero(Float64) for iter in 1:4]),
                SVector{4}([zero(Float64) for iter in 1:4]),
                SVector{4}([zero(Float64) for iter in 1:4]),
                0.0, 0, 0, 0, 0, Array{Int32,1}(undef, 0), Array{Int32,1}(undef, 0),
                Array{Int32,1}(undef, 0), Array{Int32,1}(undef, 0), 0.0, zeros(Float64, 4), zeros(Float64, 4), zeros(Float64, 4),
                globalID)
        end
    end
    return local_points_holder
end

function readDistribuedFileQ(folder_name::String, defprimal, p, global_local_map_index)

    # println(folder_name)
    iter = p - 1
    if iter - 1 < 10
        filename = folder_name * "/" * "partGrid000" * string(iter-1)
    elseif iter - 1 < 100
        filename = folder_name * "/" * "partGrid00" * string(iter-1)
    elseif iter - 1 < 1000
        filename = folder_name * "/" * "partGrid0" * string(iter-1)
    else
        filename = folder_name * "/" * "partGrid" * string(iter-1)
    end
    # println(filename)
    idx = 1
    local_point_count = 0
    ghost_point_count = 0
    local_points_holder = []

    for splitdata in eachline(filename)
        if idx == 1
            itmdata = split(splitdata)
            local_point_count = parse(Int,itmdata[3])
            ghost_point_count = parse(Int,itmdata[4])
            local_points_holder = Array{TempQ,1}(undef, local_point_count)
        elseif idx <= local_point_count + 1
            local_points_holder[idx-1] = TempQ(SVector{4}([zero(Float64) for iter in 1:4]))
        elseif idx > local_point_count + 1
            break
        end

        idx+=1
    end

    return local_points_holder
end

function readDistribuedFileQPack(folder_name::String, defprimal, p, global_local_map_index)

    # println(folder_name)
    iter = p - 1
    if iter - 1 < 10
        filename = folder_name * "/" * "partGrid000" * string(iter-1)
    elseif iter - 1 < 100
        filename = folder_name * "/" * "partGrid00" * string(iter-1)
    elseif iter - 1 < 1000
        filename = folder_name * "/" * "partGrid0" * string(iter-1)
    else
        filename = folder_name * "/" * "partGrid" * string(iter-1)
    end
    idx = 1
    local_point_count = 0
    ghost_point_count = 0
    local_points_holder = []

    for splitdata in eachline(filename)
        if idx == 1
            itmdata = split(splitdata)
            local_point_count = parse(Int,itmdata[3])
            ghost_point_count = parse(Int,itmdata[4])
            local_points_holder = Array{TempQPack,1}(undef, local_point_count)
        elseif idx <= local_point_count + 1
            local_points_holder[idx-1] = TempQPack(SVector{4}([zero(Float64) for iter in 1:4]),
                                            SVector{4}([zero(Float64) for iter in 1:4]),
                                            SVector{4}([zero(Float64) for iter in 1:4]),
                                            SVector{4}([zero(Float64) for iter in 1:4]),
                                            SVector{4}([zero(Float64) for iter in 1:4]))
        elseif idx > local_point_count + 1
            break
        end

        idx+=1
    end
    return local_points_holder
end

function returnKeys(loc_ghost_holder)
    keysIter = keys(loc_ghost_holder[1])
    keysLength = length(keysIter)
    keyHolder = zeros(Int64, keysLength)
    idx = 1
    for item in keysIter
        keyHolder[idx] = item
        idx += 1
    end
    return keyHolder
end

max_stars_repo_name: TestSubjector/HybridMeshfreeSolver

max_issues_repo_name: TestSubjector/HybridMeshfreeSolver

max_forks_repo_name: TestSubjector/HybridMeshfreeSolver


text:
# PowerModels exports everything except internal symbols, which are defined as
# those whose name starts with an underscore. If you don't want all of these
# symbols in your environment, then use `import PowerModels` instead of
# `using PowerModels`.

# Do not add PowerModels-defined symbols to this exclude list. Instead, rename
# them with an underscore.

const _EXCLUDE_SYMBOLS = [Symbol(@__MODULE__), :eval, :include]

for sym in names(@__MODULE__, all=true)
    sym_string = string(sym)
    if sym in _EXCLUDE_SYMBOLS || startswith(sym_string, "_") || startswith(sym_string, "@_")
        continue
    end
    if !(Base.isidentifier(sym) || (startswith(sym_string, "@") &&
         Base.isidentifier(sym_string[2:end])))
       continue
    end
    #println("$(sym)")
    @eval export $sym
end

# the follow items are also exported for user-friendlyness when calling
# `using PowerModels`

# so that users do not need to import JuMP to use a solver with PowerModels
import JuMP: with_optimizer
export with_optimizer

# so that users do not need to import JuMP to use a solver with PowerModels
# note does appear to be work with JuMP v0.20, but throws "could not import" warning
import JuMP: optimizer_with_attributes
export optimizer_with_attributes

import MathOptInterface: TerminationStatusCode
export TerminationStatusCode

import MathOptInterface: ResultStatusCode
export ResultStatusCode

for status_code_enum in [TerminationStatusCode, ResultStatusCode]
    for status_code in instances(status_code_enum)
        @eval import MathOptInterface: $(Symbol(status_code))
        @eval export $(Symbol(status_code))
    end
end

# PowerModels Exports
export ACPPowerModel, ACRPowerModel, DCPPowerModel, IVRPowerModel, NFAPowerModel, conductor_ids, ismulticonductor

# PowerModelsDistribution Exports
export run_mc_model

# from InfrastructureModels
export ids, ref, var, con, sol, nw_ids, nws, optimize_model!


text:
using Test
using JSON
using MianalyzerBackend
const HierachicalClusteringsController = MianalyzerBackend.HierachicalClusteringsController

include("../TestSupport.jl")

@testset "HierachicalClusteringsController" begin
  @testset ".call" begin
    request = Dict([
                    ("records", [[100.0, 100.0], [100.0, 200.0], [300.0, 300.0]]),
                    ("labels", ["first", "second", "third"]),
                    ("normalized", false)
                   ])

    expected_charts = Dict(
                           "records" => [["first", "second"], "third"],
                           "linkage" => "ward",
                           "dist_type" => "euclidean"
                          )

    actual = JSON.parse(IOBuffer(HierachicalClusteringsController.call(request)))

    @test actual["charts"][1] == expected_charts
  end
end


text:
using PyPlot
x=logspace(-2,2,1000)
clf()
plot(x,log.(2x),label=L"$f(x)=\log{(2x)}$")
loglog(x,asinh.(x),label=L"$f(x)=\sinh^{-1}{x}$")
loglog(x,x,label=L"$f(x)=x$")
legend(loc="upper left")
ylabel(L"$f(x)$")
xlabel(L"$x$")

max_stars_repo_name: tagordon/limbdark

max_issues_repo_name: tagordon/limbdark

max_forks_repo_name: tagordon/limbdark


text:
#################################
"""
    replaceunit!(fgmax::VisClaw.FGmax, unit::Symbol)
    replaceunit!(gauge::VisClaw.Gauge, unit::Symbol)
    replaceunit!(amrs::VisClaw.AMR, unit::Symbol)
    replaceunit!(track::VisClaw.Track, unit::Symbol)

Time unit converter
The second arg unit must be any one of :second, :minute, :hour, or :day.
"""
function replaceunit!(fgmax::VisClaw.FGmax, unit::Symbol)

    !haskey(timedict, unit) && error("Invalid specification of unit")
    !haskey(timedict, fgmax.unittime) && error("Invalid symbol in fgmax")

    ratio = timedict[fgmax.unittime]/timedict[unit]
    if abs(ratio - 1.0) < 1e-5; return fgmax; end

    # convert
    fgmax.unittime = unit

    fgmax.tD = ratio.*fgmax.tD
    fgmax.tarrival = ratio.*fgmax.tarrival
    !isempty(fgmax.tv) && (fgmax.tv = ratio.*fgmax.tv)
    if !isempty(fgmax.tM)
        fgmax.tM = ratio.*fgmax.tM
        fgmax.tMflux = ratio.*fgmax.tMflux
        fgmax.tDmin = ratio.*fgmax.tDmin
    end
    return fgmax
end
#################################

#################################
function replaceunit!(gauge::VisClaw.Gauge, unit::Symbol)
    !haskey(timedict, unit) && error("Invalid specification: unit")
    !haskey(timedict, gauge.unittime) && error("Invalid symbol in gauge")

    ratio = timedict[gauge.unittime]/timedict[unit]
    if abs(ratio - 1.0) < 1e-5; return gauge; end

    # convert
    gauge.unittime = unit
    gauge.time = ratio.*gauge.time
    # return value
    return gauge
end
#################################

#################################
function replaceunit!(amrs::VisClaw.AMR, unit::Symbol)
    !haskey(timedict, unit) && error("Invalid specification: unit")
    !haskey(timedict, amrs.unittime) && error("Invalid symbol in AMR")

    ratio = timedict[amrs.unittime]/timedict[unit]
    if abs(ratio - 1.0) < 1e-5; return amrs; end

    # convert
    amrs.unittime = unit
    amrs.timelap = ratio.*amrs.timelap
    # return value
    return amrs
end
#################################

#################################
function replaceunit!(track::VisClaw.Track, unit::Symbol)
    !haskey(timedict, unit) && error("Invalid specification: unit")
    !haskey(timedict, track.unittime) && error("Invalid symbol in AMR")

    ratio = timedict[track.unittime]/timedict[unit]
    if abs(ratio - 1.0) < 1e-5; return track; end

    # convert
    track.unittime = unit
    track.timelap = ratio.*track.timelap
    # return value
    return track
end
#################################


text:
using Test, MeshAdaptiveDirectSearch, Random

@testset "halton" begin
    import MeshAdaptiveDirectSearch: haltonnumber,
    normalized_halton_direction, scaledhouseholder
    # examples from Audet & Dennis 2006
    @test 2*haltonnumber(2, 1) - 1 == 0.
    @test haltonnumber(5, 6) ≈ 6/25
    @test haltonnumber(7, 7) ≈ 1/49
    h = MeshAdaptiveDirectSearch.HaltonIterator{4}()
    u = first(Iterators.drop(h, 9))
    @test u ≈ [5/16, 10/27, 2/25, 22/49]
    q = normalized_halton_direction(u, 3)
    @test q == [-1, -1, -2, 0]
    @test scaledhouseholder([-1, -2]) == [3 -4; -4 -3]
    # example from Abramson et al. 2009, p. 958
    u = first(Iterators.drop(MeshAdaptiveDirectSearch.HaltonIterator{4}(), 12))
    q = normalized_halton_direction(u, 6)
    @test scaledhouseholder(q) == [36 0 -18 -36;
                                   0 54 0 0;
                                   -18 0 36 -36;
                                   -36 0 -36 -18]
end

@testset "orthomads" begin
    import MeshAdaptiveDirectSearch: scaledhouseholder,
    normalized_halton_direction, LogMesh, ℓ, Δ, determine_t!, update!
    mesh = LogMesh()
    g = MeshAdaptiveDirectSearch.OrthoDirectionGenerator(4, reduction = NoReduction(), t0 = 7)
    # p. 958 from Abramson et al. 2009
    for (success, t, Δᵐ) in zip([1, 1, -1, -1, -1, -1, 1, -1, -1],
                                [7, 8, 9, 10, 7, 8, 9, 11, 9, 10],
                                [1, 1, 1, 1, 1, 1/4, 1/16, 1/4, 1/16, 1/64])
        l = ℓ(mesh)
        τ = determine_t!(g, l)
        @test t == τ
        @test Δ(mesh) == Δᵐ
        update!(mesh, success)
    end
end

@testset "readme" begin
    Random.seed!(12418)
    f(x) = (1 - exp(-sum(abs2, x))) * max(sum(abs2, x .- [30, 40]), sum(abs2, x .+ [30, 40]))
    noisyf(x) = f(x) + .1 * randn()

    res = minimize(LtMADS(2), f, [-2.1, 1.7], lowerbound = [-10, -10], upperbound = [10, 10])
    @test res.f < 1e-9
    res = minimize(LtMADS(2), f, [-2.1, 1.7], lowerbound = [-10, -10], upperbound = [10, 10], constraints = [x -> sum(x) < .5])
    @test res.f < 1e-9
    res = minimize(OrthoMADS(2), f, [-2.1, 1.7], lowerbound = [-10, -10], upperbound = [10, 10])
    @test res.f < 1e-9
    res = minimize(RobustLtMADS(2), noisyf, [-2.1, 1.7], lowerbound = [-10, -10], upperbound = [10, 10])
    @test res.f < 1e-9
    res = minimize(RobustOrthoMADS(2), noisyf, [-2.1, 1.7], lowerbound = [-10, -10], upperbound = [10, 10])
    @test res.f < 1e-9
end


text:
# module NAssets
using Distributed
# addprocs(7)

@everywhere using Agents: vertices
@everywhere using MetaGraphs: add_vertex!, has_edge
@everywhere using Agents, AgentsPlots, Plots, LightGraphs, MetaGraphs, GraphPlot, GraphRecipes, NetworkLayout
@everywhere using Tables, DataFrames
@everywhere using CSV, JSON, Serialization, DelimitedFiles
@everywhere using BritishNationalGrid
@everywhere using ZipFile, Shapefile
@everywhere using Random
@everywhere using Match
@everywhere using LinearAlgebra
@everywhere using StatsBase
@everywhere using Distributions
@everywhere using StatsPlots
@everywhere using SparseArrays
@everywhere using Laplacians
@everywhere using DataStructures
@everywhere using RollingFunctions
@everywhere using BenchmarkTools
@everywhere using Statistics
@everywhere using Logging,LoggingExtras, LoggingFacilities
@everywhere using Dates,TimeZones

#Core structures
@everywhere include("core/core_structs.jl")
#graph-related
@everywhere include("core/graph_functions.jl")
#events
@everywhere include("eve/artificial_events.jl")
#various util functions
@everywhere include("utils/util_functions.jl")
#logging
@everywhere include("utils/logging_functions.jl")
#Plotting functions
@everywhere include("utils/plotting_functions.jl")
# time-to-event functions
@everywhere include("utils/tte_functions.jl")
#running functions
@everywhere include("utils/running_functions.jl")

#maintenance model
@everywhere include("phy/maintenance_model.jl")

@everywhere include("ntw/of_switch.jl")
@everywhere include("ntw/of_control.jl")
@everywhere include("ctl/agent_control.jl")

@everywhere include("phy/physical_model.jl")

@everywhere include("ntw/network_model.jl")
@everywhere include("phy/geo_model.jl")

#Main Functions
@everywhere include("model/netManFunctions.jl")

#Agents.jl function implementation for this model
@everywhere include("model/netManModel.jl")

#Basic queries
#include("model/queries_basic.jl")
#Multiple queries
@everywhere include("ctl/queries_multiple.jl")

export load_run_configs, single_run_with_logging

base_cfgs = load_base_cfgs("data/configs/configs.csv")
#obtain services that provide this coverage of the network
coverage = 0.95
configs = []
for bcfg in base_cfgs
    ntw_services = get_end_points(bcfg.seed,get_graph(bcfg.seed,bcfg.size,GraphModel(bcfg.ntw_topo_n);k=bcfg.k,adj_m_csv=bcfg.ntw_csv_adj_matrix),coverage)
    push!(configs,config(bcfg,ntw_services))

end

BenchmarkTools.DEFAULT_PARAMETERS.samples = 100

single_run_with_logging(configs[1])
#single_run_with_file_logging(configs[1])
#pmap(single_run_with_file_logging,configs)

# end # module


text:
module TestParseJuliaPrograms

using Test

using Catlab, Catlab.Theories, Catlab.WiringDiagrams
using Catlab.Programs
using Catlab.Programs.ParseJuliaPrograms: normalize_arguments

@present C(FreeBiproductCategory) begin
  W::Ob
  X::Ob
  Y::Ob
  Z::Ob
  f::Hom(X,Y)
  g::Hom(Y,Z)
  h::Hom(Z,W)
  l::Hom(otimes(Z,W),otimes(Y,X))
  m::Hom(otimes(X,Y),otimes(W,Z))
  n::Hom(otimes(W,Z),otimes(X,Y))
end

# Parsing
#########

W, X, Y, Z = (generator(C, name) for name in [:W, :X, :Y, :Z])
f, g, h, m, n = (generator(C, name) for name in [:f, :g, :h, :m, :n])

# Generator.

parsed = @program(C, (x::X) -> f(x))
@test parsed == to_wiring_diagram(f)
@test parsed isa WiringDiagram{BiproductCategory}

# Composition: one-dimensional.

parsed = @program(C, (x::X) -> g(f(x)))
@test parsed == to_wiring_diagram(compose(f,g))

parsed = @program C (x::X) begin
  y = f(x)
  z = g(y)
  return z
end
@test parsed == to_wiring_diagram(compose(f,g))

parsed = @program C (v::X) begin
  v = f(v)
  v = g(v)
  v = h(v)
  v
end
@test parsed == to_wiring_diagram(compose(f,g,h))

# Composition: multidimensional.

parsed = @program C (x::X, y::Y) begin
  w, z = m(x, y)
  x2, y2 = n(w, z)
  return (x2, y2)
end
@test parsed == to_wiring_diagram(compose(m,n))

parsed = @program(C, (x::X, y::Y) -> n(m(x,y)))
@test parsed == to_wiring_diagram(compose(m,n))

# Constants and co-constants.

I = munit(FreeBiproductCategory.Ob)
c = add_generator!(C, Hom(:c, I, X))
d = add_generator!(C, Hom(:d, X, I))

parsed = @program(C, () -> c())
@test parsed == to_wiring_diagram(c)

parsed = @program(C, (x::X) -> c(d(x)))
@test parsed == to_wiring_diagram(compose(d,c))

parsed = @program C (x::X) begin
  d(x)
  c()
end
@test parsed == to_wiring_diagram(compose(d,c))

# Diagonals: implicit syntax.

parsed = @program(C, (x::X) -> (f(x),f(x)))
@test parsed == to_wiring_diagram(compose(mcopy(X),otimes(f,f)))

parsed = @program(C, (x::X) -> nothing)
@test parsed == to_wiring_diagram(delete(X))

# Codiagonals: special syntax.

parsed = @program(C, (x1::X, x2::X) -> f([x1,x2]))
@test parsed == to_wiring_diagram(compose(mmerge(X),f))

parsed = @program(C, (x1::X, x2::X) -> [f(x1),f(x2)])
@test parsed == to_wiring_diagram(compose(otimes(f,f),mmerge(Y)))

parsed = @program(C, (x::X, y::Y) -> n([m(x,y), m(x,y)]))
@test parsed == to_wiring_diagram(compose(
  mcopy(otimes(X,Y)), otimes(m,m), mmerge(otimes(W,Z)), n))

parsed = @program(C, () -> f([]))
@test parsed == to_wiring_diagram(compose(create(X),f))

# Explicit syntax for special objects and morphisms.

parsed = @program(C, (x::X) -> id{X}(x))
@test parsed == to_wiring_diagram(id(X))

parsed = @program(C, (x::X) -> mcopy{X}(x))
@test parsed == to_wiring_diagram(mcopy(X))

parsed = @program(C, (x::X) -> delete{X}(x))
@test parsed == to_wiring_diagram(delete(X))

parsed = @program(C, (x1::X, x2::X) -> mmerge{X}(x1,x2))
@test parsed == to_wiring_diagram(mmerge(X))

parsed = @program(C, () -> create{X}())
@test parsed == to_wiring_diagram(create(X))

parsed = @program(C, (x::X, y::Y) -> mcopy{otimes{X,Y}}(x,y))
@test parsed == to_wiring_diagram(mcopy(otimes(X,Y)))

parsed = @program(C, (x::X, y::Y) -> delete{otimes{X,Y}}(x,y))
@test parsed == to_wiring_diagram(delete(otimes(X,Y)))

parsed = @program(C, (x::X) -> Δ{X}(x))
@test parsed == to_wiring_diagram(mcopy(X))

parsed = @program(C, (x1::X, x2::X) -> ∇{X}(x1,x2))
@test parsed == to_wiring_diagram(mmerge(X))

parsed = @program(C, (xy::otimes{X,Y}) -> m(xy))
@test parsed == to_wiring_diagram(m)

parsed = @program C (xy::otimes{X,Y}) begin
  x, y = xy
  (f(x), g(y))
end
@test parsed == to_wiring_diagram(otimes(f,g))

parsed = @program(C, (xy::otimes{X,Y}, wz::otimes{W,Z}) -> (m(xy), n(wz)))
@test parsed == to_wiring_diagram(otimes(m,n))

# Helper function: normalization of arguments.

normalize(args...) = normalize_arguments(Tuple(args))

@test normalize(nothing) == ()
@test normalize(1) == ([1],)
@test normalize(1,nothing,2) == ([1],[2])
@test normalize(1,2,3) == ([1],[2],[3])
@test normalize((1,2,3)) == ([1],[2],[3])
@test normalize((1,2),3) == ([1],[2],[3])
@test normalize(((1,2),3)) == ([1],[2],[3])

@test normalize([]) == ([],)
@test normalize([1]) == ([1],)
@test normalize([1,2]) == ([1,2],)
@test normalize([1,2],[3,4]) == ([1,2],[3,4])
@test normalize([(1,2),(3,4)]) == ([1,3],[2,4])
@test normalize([[(1,2),(3,4)],(5,6)]) == ([1,3,5],[2,4,6])

# Roundtrip
###########

""" Convert morphism expression to Julia code and then back to wiring diagram.
"""
function roundtrip(f::HomExpr)::WiringDiagram
  expr = compile_expr(f, arg_types=first.(collect(dom(f))))
  parse_wiring_diagram(C, expr)
end

function test_roundtrip(f::HomExpr)
  @test roundtrip(f) == to_wiring_diagram(f)
end

f, g, h, l, m, n = (generator(C, name) for name in [:f, :g, :h, :l, :m, :n])

test_roundtrip(f)
test_roundtrip(id(X))
test_roundtrip(compose(f,g))
test_roundtrip(otimes(f,h))
test_roundtrip(compose(m,n))
test_roundtrip(compose(l, braid(Y,X), m))
test_roundtrip(compose(mcopy(X), otimes(f,f)))
test_roundtrip(compose(f, mcopy(Y), otimes(g,g)))

end


text:
using PyPlot, transD_GP, Random, LinearAlgebra

## model
ρbg        = 1000
zfixed     = [-1e5,   0,   (20:20:200)...]
rho        = [1e12,   ρbg, ρbg*ones(10)...]
ianom      = 5
rho[ianom] = 10.
δ          = 1e-4  # fd step size in log10

## Gauss Newton settings
use_fd          = false # in the one step for Gauss Newton
startfromcloser = false  # whether to start midway between anomaly and bg or plain bg in G-N step
λ²              = 5e-17 # model update damping

##  geometry and frequencies for VMD in frequency domain
nFreqsPerDecade     = 5
freqLowLimit        = 1e-3
freqHighLimit       = 1e5
freqs = 10 .^(log10(freqLowLimit):1/nFreqsPerDecade:log10(freqHighLimit))
rRx = 100.
zRx = -37.
zTx = -35.
nkᵣeval = 50
modelprimary = true

Fvmd = transD_GP.AEM_VMD_HMD.HFieldDHT(
                      calcjacobian = true,
                      nmax = length(zfixed),
                      zTx    = zTx,
                      rRx    = rRx,
                      freqs  = freqs,
                      zRx    = zRx,
                      nkᵣeval = nkᵣeval,
                      modelprimary = modelprimary)
transD_GP.AEM_VMD_HMD.getfieldFD!(Fvmd, zfixed, rho)

## plot
f, ax = plt.subplots(1,3, sharey=true, figsize=(8,6))
delz, delf = diff(zfixed[2:end]), diff(log10.(freqs))
flabel = [(log10.(freqs) .- delf[1]/2)..., log10.(freqs)[end]+delf[end]/2]
zlab = [zfixed[2:end]..., zfixed[end]+delz[end]]
ax[1].step(log10.(rho[2:end]), zfixed[2:end])
im2 = ax[2].pcolormesh(flabel, zlab, abs.(Fvmd.HFD_z_J[2:end,:]), shading="auto")
cb2 = colorbar(im2, ax=ax[2])
im3 = ax[3].pcolormesh(flabel, zlab, log10.(abs.(Fvmd.HFD_z_J[2:end,:])))
cb2 = colorbar(im3, ax=ax[3])
ax[1].set_xlabel("log10 Ω-m")
ax[1].invert_xaxis()
ax[2].invert_yaxis()
ax[2].set_xlabel("log10 Hz")
ax[3].set_xlabel("log10 Hz")
ax[1].set_ylabel("Depth m")
ax[2].set_title("∂f/∂(log10σ)")
ax[3].set_title("∂f/∂(log10σ)")

## approximate partials with central differences and compare
J = copy(Fvmd.HFD_z_J)

function getJ!(Fin, zin, ρ, Δ)
    # sensitivities computed in log10σ
    # same as in AEM_VMD_HMD code
    Jout = similar(Fin.HFD_z_J)
    modelfd = copy(ρ)
    jflag = Fin.calcjacobian
    Fin.calcjacobian = false
    for i = 2:length(zin)
        modelfd[:] = ρ
        modelfd[i] = 10 .^-(-log10(ρ[i]) .+ Δ/2)
        transD_GP.AEM_VMD_HMD.getfieldFD!(Fin, zin, modelfd)
        HFD_z1 = copy(Fin.HFD_z)
        modelfd[i] = 10 .^-(-log10(ρ[i]) .- Δ/2)
        transD_GP.AEM_VMD_HMD.getfieldFD!(Fin, zin, modelfd)
        Jout[i,:] = (HFD_z1 - Fin.HFD_z) / Δ
    end
    Fin.calcjacobian = jflag
    Jout
end
J_fd = getJ!(Fvmd, zfixed, rho, δ)

## comparison plots
f, ax = plt.subplots(1,3, sharey=true, figsize=(10,6))
ax[1].step(log10.(rho[2:end]), zfixed[2:end])
im2 = ax[2].pcolormesh(flabel, zlab, abs.(J[2:end,:]))
cb2 = colorbar(im2, ax=ax[2])
im3 = ax[3].pcolormesh(flabel, zlab, abs.(J_fd[2:end,:]))
cb2 = colorbar(im3, ax=ax[3])
ax[1].set_xlabel("log10 Ω-m")
ax[1].invert_xaxis()
ax[2].invert_yaxis()
ax[2].set_xlabel("log10 Hz")
ax[3].set_xlabel("log10 Hz")
ax[1].set_ylabel("Depth m")
ax[2].set_title("∂f/∂(log10σ) analytic")
ax[3].set_title("∂f/∂(log10σ) FD")

## let's try this in an optimisation framework to see if the gradient makes sense
log₁₀σ₀ = -3
mtrue = copy(rho)
m = copy(mtrue)
m[ianom] = startfromcloser ? ρbg+(rho[ianom]-ρbg)/2 : ρbg
transD_GP.AEM_VMD_HMD.getfieldFD!(Fvmd, zfixed, mtrue)
d = copy(Fvmd.HFD_z)
transD_GP.AEM_VMD_HMD.getfieldFD!(Fvmd, zfixed, m)
f = copy(Fvmd.HFD_z)
r = (f-d)
J = use_fd ? getJ!(Fvmd, zfixed, m, δ)[2:end,:] : Fvmd.HFD_z_J[2:end,:]
Δm = -real((J*J' + λ²*I)\(J*r + λ²*I*(-log10.(m[2:end]) .- log₁₀σ₀)))
m2 = vcat(mtrue[1], 10 .^-(-log10.(m[2:end]) + Δm))

## plot everything
figure()
step(log10.(m[2:end]), zfixed[2:end], linewidth=2, label="start model")
step(log10.(m2[2:end]), zfixed[2:end], label="1 step")
step(log10.(mtrue[2:end]), zfixed[2:end], linestyle="--", color="k", label="true model")
gca().invert_yaxis()
gca().invert_xaxis()
legend()
xlabel("log10 ρ")
ylabel("Depth m")


text:
function logit∂𝑙(y::AbstractFloat, ŷ::AbstractFloat)
    res = ŷ - y
    isnan(res) ? zero(res) : res
end

function logit∂²𝑙(ŷ::T) where {T<:AbstractFloat}
    res = max(ŷ * (one(T) - ŷ), eps(T))
    isnan(res) ? zero(T) : res
end

function logitraw(p::T) where {T<:AbstractFloat}
    -log(one(T) / p - one(T))
end

function sigmoid(x::T) where {T<:AbstractFloat}
    one(T) / (one(T) + exp(-x))
end

function logloss(y::AbstractFloat, ŷ::AbstractFloat)
    ϵ = eps(ŷ)
    if ŷ < ϵ
        -y * log(ϵ) - (one(y) - y)  * log(one(ϵ) - ϵ)
    elseif one(ŷ) - ŷ < ϵ
        -y * log(one(ϵ) - ϵ) - (one(y) - y)  * log(ϵ)
    else
        -y * log(ŷ) - (one(y) - y) * log(one(ŷ) - ŷ)
    end
end

function xgblogit(label::AbstractCovariate, factors::Vector{<:AbstractFactor};
                  selector::AbstractBoolVariate = BoolVariate("", BitArray{1}(0)),
                  η::Real = 0.3, λ::Real = 1.0, γ::Real = 0.0, maxdepth::Integer = 6, nrounds::Integer = 2,
                  minchildweight::Real = 1.0, caching::Bool = true, slicelength::Integer = 0, usefloat64::Bool = false,
                  singlethread::Bool = false)

    T = usefloat64 ? Float64 : Float32
    factors = caching ? map(cache, widenfactors(filter((f -> getname(f) != getname(label)), factors))) : filter((f -> getname(f) != getname(label)), factors)
    label = caching ? cache(label) : label
    slicelength = slicelength <= 0 ? length(label) : slicelength
    λ = T(λ)
    γ = T(γ)
    η = T(η)
    minchildweight = T(minchildweight)
    μ = T(0.5f0)
    f0 = Vector{T}(length(label))
    fill!(f0, T(logitraw(μ)))
    zerocov = ConstCovariate(zero(T), length(selector))
    fm, trees = fold((f0, Vector{XGTree}()), Seq(1:nrounds)) do x, m
        fm, trees = x
        ŷ = Covariate(sigmoid.(fm))
        ∂𝑙 = length(selector) == 0 ? Trans2Covariate(T, "∂𝑙", label, ŷ, logit∂𝑙) |> cache : IfElseCovariate(selector, Trans2Covariate(T, "∂𝑙", label, ŷ, logit∂𝑙), zerocov) |> cache
        ∂²𝑙 = length(selector) == 0 ? TransCovariate(T, "∂²𝑙", ŷ, logit∂²𝑙) |> cache : IfElseCovariate(selector, TransCovariate(T, "∂²𝑙", ŷ, logit∂²𝑙), zerocov) |> cache
        tree, predraw = growtree(factors, ∂𝑙, ∂²𝑙, maxdepth, λ, γ, minchildweight, slicelength, singlethread)
        fm .= muladd.(η, predraw, fm)
        push!(trees, tree)
        (fm, trees)
    end
    pred = sigmoid.(fm)
    XGModel{T}(trees, λ, γ, η, minchildweight, maxdepth, pred)
end

function cvxgblogit(label::AbstractCovariate, factors::Vector{<:AbstractFactor}, nfolds::Integer;
                    aucmetric::Bool = true, loglossmetric::Bool = true, trainmetric::Bool = false,
                    η::Real = 0.3, λ::Real = 1.0, γ::Real = 0.0, maxdepth::Integer = 6, nrounds::Integer = 2,
                    minchildweight::Real = 1.0, caching::Bool = true, slicelength::Integer = 0, usefloat64::Bool = false,
                    singlethread::Bool = false)

    cvfolds = getnfolds(nfolds, false, length(label))
    trainaucfold = Vector{Float64}(nfolds)
    trainloglossfold = Vector{Float64}(nfolds)
    testaucfold = Vector{Float64}(nfolds)
    testloglossfold = Vector{Float64}(nfolds)
    for i in 1:nfolds
        trainselector = cvfolds .!= UInt8(i)
        testselector = cvfolds .== UInt8(i)
        model = xgblogit(label, factors; selector = BoolVariate("", trainselector), η = η, λ = λ, γ = γ, maxdepth = maxdepth,
                         nrounds = nrounds, minchildweight = minchildweight,
                         caching = caching, slicelength = slicelength, usefloat64 = usefloat64, singlethread = singlethread)
        if aucmetric
            testaucfold[i] = getauc(model.pred, label; selector = testselector)
            if trainmetric
                trainaucfold[i] = getauc(model.pred, label; selector = trainselector)
            end
        end
        if loglossmetric
            testloglossfold[i] = getlogloss(model.pred, label; selector = testselector)
            if trainmetric
                trainloglossfold[i] = getlogloss(model.pred, label; selector = trainselector)
            end
        end
    end
    res = Dict{String, Float64}()
    if aucmetric
        if trainmetric
            res["train_auc_mean"] = mean(trainaucfold)
            res["train_auc_std"] = std(trainaucfold)
        end
        res["test_auc_mean"] = mean(testaucfold)
        res["test_auc_std"] = std(testaucfold)
    end
    if loglossmetric
        if trainmetric
            res["train_logloss_mean"] = mean(trainloglossfold)
            res["train_logloss_std"] = std(trainloglossfold)
        end
        res["test_logloss_mean"] = mean(testloglossfold)
        res["test_logloss_std"] = std(testloglossfold)
    end
    res
end

function predict(model::XGModel{T}, dataframe::AbstractDataFrame) where {T<:AbstractFloat}
    trees = model.trees
    μ = T(0.5f0)
    η = model.η
    f0 = Vector{T}(length(dataframe))
    fill!(f0, T(logitraw(μ)))
    for tree in trees
        predraw = predict(tree, dataframe)
        f0 .= muladd.(η, predraw, f0)
    end
    sigmoid.(f0)
end

function getauc(pred::Vector{T}, label::AbstractCovariate{S}; selector::BitArray{1} = BitArray{1}()) where {T <: AbstractFloat} where {S <: AbstractFloat}
    label = convert(Vector{S}, label)
    label = length(selector) == 0 ? label : label[selector]
    pred = length(selector) == 0 ? pred : pred[selector]
    perm = sortperm(pred; rev = true)
    len = length(label)
    sumlabel = sum(label)
    cumlabel = cumsum(label[perm])
    x = ones(S, len)
    cumcount = cumsum!(x, x)
    pred = pred[perm]
    diff = view(pred, 2:len) .- view(pred, 1:(len - 1))
    isstep = diff .!= zero(T)
    push!(isstep, true)
    cumlabel = cumlabel[isstep]
    cumcount = cumcount[isstep]
    tpr = (cumlabel ./ sumlabel)
    fpr = ((cumcount .- cumlabel) ./ (length(label) .- sumlabel))
    len = length(tpr)
    fpr1 = view(fpr, 1:(len-1))
    fpr2 = view(fpr, 2:len)
    tpr1 = view(tpr, 1:(len - 1))
    tpr2 = view(tpr, 2:len)
    area0 = fpr[1] * tpr[1]
    0.5 * (sum((tpr1 .+ tpr2) .* (fpr2 .- fpr1)) + area0)
end

function getlogloss(pred::Vector{T}, label::AbstractCovariate{S}; selector::BitArray{1} = BitArray{1}()) where {T <: AbstractFloat} where {S <: AbstractFloat}
    label = convert(Vector{S}, label)
    label::Vector{S} = length(selector) == 0 ? label : label[selector]
    pred::Vector{T} = length(selector) == 0 ? pred : pred[selector]
    mean(logloss.(label, pred))
end

max_stars_repo_name: vikashdwd/JuML

max_issues_repo_name: vikashdwd/JuML

max_forks_repo_name: vikashdwd/JuML