p5.jl

### A Pluto.jl notebook ###
# v0.19.18

using Markdown
using InteractiveUtils

# ╔═╡ 3b07c362-a1be-11ed-220f-bb7a984af75d
using DataFrames

# ╔═╡ 30a30649-1b0a-42ba-9e57-c58c9487c24e
using CSV

# ╔═╡ 826c34a2-d07e-4e00-9206-794e422f5d64
using ColorSchemes

# ╔═╡ b8dbef1c-1497-4f2b-b144-8a8b36406979
using Gadfly

# ╔═╡ a5367edd-7efa-43d3-8bcd-89ab5030801d
using YAML

# ╔═╡ 19b28374-44d1-4554-9bf4-bc452dac1261
using Dates

# ╔═╡ f34e8779-809d-4b00-b8fa-7acab4c08322
using StatsBase

# ╔═╡ 252aff18-14eb-41c8-a47f-900df098af2c
using MLJ

# ╔═╡ 8f159c5f-0994-4f25-bfde-e577bcaa8fe6
using Latexify

# ╔═╡ 68606de6-932b-48b8-8ea5-b33ee7bf347e
using Compose

# ╔═╡ dcda4940-6c1f-45f2-84c5-05ac616e1cc4
using Plots

# ╔═╡ df01f430-3d39-428e-a74b-24c67c8dbb07
using Random

# ╔═╡ 3c798c19-8fa9-49fd-bbb8-eb4b22681ce1
import Cairo, Fontconfig

# ╔═╡ db313831-96d4-4cbc-b6f5-1f49f0cff221
seasons = Dict(
	1 => "Winter",
	2 => "Winter",
	3 => "Spring",
	4 => "Spring",
	5 => "Spring",
	6 => "Summer",
	7 => "Summer",
	8 => "Summer",
	9 => "Autumn",
	10 => "Autumn",
	11 => "Autumn",
	12 => "Winter"
)

# ╔═╡ 7299bc57-f7c3-41d5-8a28-49e30799cf93


# ╔═╡ 0acd8872-98bc-4f2b-960d-d8d449d16407


# ╔═╡ 9462e8e8-5f45-48d2-978c-ff03695cc115
common_terms = [
	"Property Id",
	"date",
	"month",
	"prediction",
	"recorded",
	"model",
	"zone",
	"zipcode",
	"weather_station_distance",
	"area",
	"heightroof"
]

# ╔═╡ d53007de-27f5-4cbb-9012-cacb281d612b
errfunc(deltay) = abs(deltay)

# ╔═╡ 90d09570-3eab-4999-bb7b-4b74ccae2f6f


# ╔═╡ e4b11e6f-bc70-4470-b407-92eb192489ca


# ╔═╡ 73d18d6d-b236-4fd4-9981-e70f8c1c37b7
nmbe(ŷ, y, p=1) = 100 * sum(y.-ŷ) / (( length(y)-p ) * mean(y) )

# ╔═╡ 31ea0f25-69d5-4da8-826b-a95e80772375
cvrmse(ŷ, y, p=1) = 100 * (sum((y.-ŷ).^2) / (length(y)-p))^0.5 / mean(y)

# ╔═╡ 51fee1ff-78fc-4d5c-950c-5f6158d068b9
cvstd(ŷ, y, p=1) = (100 * mean(y)) / ((sum( (y .- mean(y)).^2 )/(length(y)-1))^0.5)

# ╔═╡ 7d671ae3-4061-4f8a-a04b-6caed84a2bd9
md"""
#### Overall Benefits
Individual Building UBEM metrics
"""

# ╔═╡ 50c292c1-dcbc-47bb-a545-1ca12a0784fe
# modelname = "793244" # s1
# modelname = "689269" # s2
modelname = "746694" # s3

# ╔═╡ 258dc61d-e20e-4ec3-a8c7-feca49515225
modelname

# ╔═╡ 750c5adc-9f84-4a4a-9f8a-b9d347b00a2b
begin
sources_file = joinpath(pwd(), "sources.yml")
sources = YAML.load_file(sources_file)
data_destination = sources["output-destination"]
data_path = joinpath(data_destination, "data", "nyc")
	
input_dir = joinpath(joinpath(data_path, "p3_o"), modelname)
output_dir = joinpath(joinpath(data_path, "p5_o"), modelname)
mkpath(output_dir)

dataout_dir = joinpath(input_dir, "data_out")
end;

# ╔═╡ 6f9bb8eb-977c-499a-9ee7-d34b58ecc03e
epw = CSV.read(joinpath(data_path, "p1_o", "epw.csv"), DataFrame)

# ╔═╡ 5b27b6a5-140b-41b6-b0d5-208b83c5ff8c
sources

# ╔═╡ 4ec7e98e-c183-4c51-ac6b-4c79495eac67
begin
electricₜ = CSV.read(joinpath(input_dir, "training_electric.csv"), DataFrame);
gasₜ = CSV.read(joinpath(input_dir, "training_gas.csv"), DataFrame);
end;

# ╔═╡ 113966ec-2c5f-4778-866e-5d0e8f5185f5
describe(electricₜ, :detailed)[:,["variable","q25","median","q75"]]

# ╔═╡ 37a25f49-0283-4b63-82df-f627cd9b1f92
skewness(electricₜ.electricity_mwh)

# ╔═╡ 56d7a517-3e32-4ae9-ac9a-11ac6f62f6a8
describe(gasₜ, :detailed)[:,["variable","q25","median","q75"]]

# ╔═╡ a83ced13-9959-4c9c-8cba-2e136115f7a7
skewness(gasₜ.naturalgas_mwh)

# ╔═╡ b5852716-bec1-4b41-bb36-1588751ca824
begin
# electric
teₐ′₀ = CSV.read(joinpath(dataout_dir, "tea0.csv"), DataFrame)
teₐ′₂ = CSV.read(joinpath(dataout_dir, "tea2.csv"), DataFrame)
teₐ′₃ = CSV.read(joinpath(dataout_dir, "tea3.csv"), DataFrame)
teₐ′₄ = CSV.read(joinpath(dataout_dir, "tea4.csv"), DataFrame)
teₐ′₅ = CSV.read(joinpath(dataout_dir, "tea5.csv"), DataFrame)
teₐ′₆ = CSV.read(joinpath(dataout_dir, "tea6.csv"), DataFrame)
teₐ′₇ = CSV.read(joinpath(dataout_dir, "tea7.csv"), DataFrame)
teₐ′₈ = CSV.read(joinpath(dataout_dir, "tea8.csv"), DataFrame)
teₐ′₉ = CSV.read(joinpath(dataout_dir, "tea9.csv"), DataFrame)
teₐ′ₑ = CSV.read(joinpath(dataout_dir, "teae.csv"), DataFrame)

# # natural gas
teᵧ′₀ = CSV.read(joinpath(dataout_dir, "teg0.csv"), DataFrame)
teᵧ′₂ = CSV.read(joinpath(dataout_dir, "teg2.csv"), DataFrame)
teᵧ′₃ = CSV.read(joinpath(dataout_dir, "teg3.csv"), DataFrame)
teᵧ′₄ = CSV.read(joinpath(dataout_dir, "teg4.csv"), DataFrame)
teᵧ′₅ = CSV.read(joinpath(dataout_dir, "teg5.csv"), DataFrame)
teᵧ′₆ = CSV.read(joinpath(dataout_dir, "teg6.csv"), DataFrame)
teᵧ′₇ = CSV.read(joinpath(dataout_dir, "teg7.csv"), DataFrame)
teᵧ′₈ = CSV.read(joinpath(dataout_dir, "teg8.csv"), DataFrame)
teᵧ′₉ = CSV.read(joinpath(dataout_dir, "teg9.csv"), DataFrame)
# teᵧ′ₑ = CSV.read(joinpath(dataout_dir, "tege.csv"), DataFrame)
end;

# ╔═╡ a9a616b6-b121-4f84-9261-d3465466ba02
teₐ′₅

# ╔═╡ 9289932b-2af7-4991-971f-96f6ef395b14
begin
test_terms = [teₐ′₀,teₐ′₂,teₐ′₃,teₐ′₄,teₐ′₅,teₐ′₆,teₐ′₇,teₐ′₈,teₐ′₉];
resultsₑ = vcat([ DataFrames.select(x, common_terms) for x in test_terms ]...);
resultsₑ.diff = errfunc.(resultsₑ.prediction .- resultsₑ.recorded);
resultsₑ.fuel = repeat(["Electricity"], nrow(resultsₑ))
resultsₑ
end;

# ╔═╡ 4efca97a-34f4-41dd-bd76-3ffed4262d28
null_namesₑ = names(test_terms[1])

# ╔═╡ c1587eef-ec3e-42d1-ab0b-c5d8a395e5cd
begin
dataset_namesₑ = Dict()
null_terms = filter( x -> ~occursin(r"_f.", x), names(test_terms[1]))
push!(dataset_namesₑ, "Null" => length(null_terms))

for terms in test_terms
	term_names = filter( x -> occursin(r"_f.", x), names(terms))
	model_name = terms.model[1]
	for name in term_names
		push!(dataset_namesₑ, model_name => length(term_names))
	end
end
dataset_namesₑ
end

# ╔═╡ 9679fec5-be9b-4fa8-a699-b18f0ada44b9
unique(resultsₑ.date)

# ╔═╡ 94a4b152-683f-450b-8016-1a3252b8ec56
begin
test_termsᵧ = [teᵧ′₀,teᵧ′₂,teᵧ′₃,teᵧ′₄,teᵧ′₅,teᵧ′₆,teᵧ′₇,teᵧ′₈,teᵧ′₉];
resultsᵧ = vcat([ DataFrames.select(x, common_terms) for x in test_termsᵧ ]...);
resultsᵧ.diff = errfunc.(resultsᵧ.prediction .- resultsᵧ.recorded);
resultsᵧ.fuel = repeat(["Natural Gas"], nrow(resultsᵧ))
resultsᵧ
end;

# ╔═╡ 75a9d962-54bd-498d-a513-035e9a225eff
results = vcat(resultsₑ, resultsᵧ);

# ╔═╡ 9a3fbbb9-0520-4a04-841e-e960928212b2
landsat = CSV.read(joinpath(data_path, "p2_o", "landsat8.csv"), DataFrame; dateformat="yyyy-mm-dd HH:MM:SS")

# ╔═╡ 5a07ce6a-4e09-4983-b0d6-c1a3e9d1d980
dropmissing!(landsat)

# ╔═╡ b1ffd85f-9e55-4b62-bfe5-6811744ca72b
landsat_dates = sort(unique(landsat.date))

# ╔═╡ d2381a47-019d-45df-ab7f-6b18f92ccd54
month_samples = values(countmap(Dates.Date.(Dates.Year.(landsat_dates), Dates.Month.(landsat_dates))))

# ╔═╡ 0c3cb738-f14a-4c3f-9a0a-af3bd1e6e585
mean(month_samples)

# ╔═╡ 19897c1b-8c2a-4ce8-a66e-8522591137b0
landsat_datediff = filter( x -> x >0, Dates.days.((landsat_dates[2:end] .- landsat_dates[1:end-1])))

# ╔═╡ 30c94fe5-fc8d-4ea1-8f0e-9f558905bb5c
Gadfly.plot(
	x=landsat_datediff,
	Geom.histogram,
	Theme(default_color="black")
)

# ╔═╡ de81a406-6215-4396-9052-e4c9cb460351
median(landsat_datediff)

# ╔═╡ 2126d3c7-f73b-42b3-a8cd-9fad2f548786
landsat_dates[2]

# ╔═╡ 0f5c1310-84bb-491e-ac56-dde86b363a42
landsat_dates[1]

# ╔═╡ d3367231-3be3-4853-8639-04444ee32614
sort(unique(landsat.date))

# ╔═╡ ed4e75e7-14a0-41e4-81bf-56d5766135cc
function realnames(x)
	rename(x, :fuel => "\\textbf{Energy Class}", :cvrmse => "\\textbf{CV(RMSE)}", :nmbe => "\\textbf{NMBE}", :cvstd => "\\textbf{CV(STD)}", :rmse => "\\textbf{RMSE}", :model => "\\textbf{Model}", :mae => "\\textbf{MAE}")
end

# ╔═╡ 14a12712-0dce-46ad-9e9b-57462d24b58d
md"""
Aggregated Building UBEM metrics
"""

# ╔═╡ 0df2d858-f0a7-4986-876f-f7c7b908051b
## graphical approach

# ╔═╡ 7288df95-22a7-423b-8821-f5d14742c1a3
results_agg = combine(groupby(results, ["model","month","zone","fuel"]), :diff => mean, renamecols=false);

# ╔═╡ 8266db80-4cd2-4f94-95f3-77792d149a9c
grouped_results = groupby(results, ["model","month","zone","fuel"]);

# ╔═╡ 80849026-8a25-48b0-8909-fea0aca1c712
results

# ╔═╡ b2215e09-a68e-453b-943b-76568faca5c1
sort(results_agg, [:fuel, :diff, :month])

# ╔═╡ eb9acb6d-2068-4681-8088-f7cb401f2682
sort(results_agg, [:fuel, :month, :zone, :diff])

# ╔═╡ 281893d0-4cc9-47c5-ae8b-468f2fcac2d2
r₁ = Gadfly.plot(
	sort(results_agg, [:fuel, :month, :zone, :model]),
	x=:month,
	y=:model,
	xgroup=:fuel,
	ygroup=:zone,
	color=:diff,
	Geom.subplot_grid(
		Geom.rectbin,
		Guide.xticks(ticks=1:12)
	),
	Scale.ContinuousColorScale(
		palette -> get(ColorSchemes.curl, palette),
		minvalue=0,
		maxvalue=100
	),
	Guide.title("Mean Absolute Error by Month, Model, and Building Class"),
	Guide.colorkey(title="MAE")
)

# ╔═╡ 480c9afb-1103-427b-b534-7df2e60bb8f8


# ╔═╡ e88ed963-0ca0-4dc2-a475-3acb775f26ba
latexify(
	rename(select(unstack(filter(x -> x.zone == "Commercial" && x.fuel == "Electricity", sort(results_agg, :month)), :month, :diff), Not([:zone, :fuel])), :model => Symbol("\\textbf{Model}")),
	latex= false, fmt = FancyNumberFormatter(3), env=:table
)

# ╔═╡ fd8ad0e7-2610-4021-a0cc-b735c10a2e6a
latexify(
	rename(select(unstack(filter(x -> x.zone == "Manufacturing" && x.fuel == "Electricity", sort(results_agg, :month)), :month, :diff), Not([:zone, :fuel])), :model => Symbol("\\textbf{Model}")),
	latex= false, fmt = FancyNumberFormatter(3), env=:table
)

# ╔═╡ 679c5199-a2be-47cc-aefd-1fbcd3ec7126
latexify(
	rename(select(unstack(filter(x -> x.zone == "Residential" && x.fuel == "Electricity", sort(results_agg, :month)), :month, :diff), Not([:zone, :fuel])), :model => Symbol("\\textbf{Model}")),
	latex= false, fmt = FancyNumberFormatter(3), env=:table
)

# ╔═╡ c38e9b9a-7396-41f3-adf3-e3dd01ec6b19
latexify(
	rename(select(unstack(filter(x -> x.zone == "Commercial" && x.fuel == "Natural Gas", sort(results_agg, :month)), :month, :diff), Not([:zone, :fuel])), :model => Symbol("\\textbf{Model}")),
	latex= false, fmt = FancyNumberFormatter(3), env=:table
)

# ╔═╡ 94baa87b-0efa-46be-8ee7-1a9e4a568c37
latexify(
	rename(select(unstack(filter(x -> x.zone == "Manufacturing" && x.fuel == "Natural Gas", sort(results_agg, :month)), :month, :diff), Not([:zone, :fuel])), :model => Symbol("\\textbf{Model}")),
	latex= false, fmt = FancyNumberFormatter(3), env=:table
)

# ╔═╡ a86368bb-6c18-4c58-afbc-6e5e1737b887
latexify(
	rename(select(unstack(filter(x -> x.zone == "Residential" && x.fuel == "Natural Gas", sort(results_agg, :month)), :month, :diff), Not([:zone, :fuel])), :model => Symbol("\\textbf{Model}")),
	latex= false, fmt = FancyNumberFormatter(3), env=:table
)

# ╔═╡ 02a34094-e86c-482e-9fed-23001400a943


# ╔═╡ ac759b4b-aa8a-4645-ba37-d2a3e7c9b5be
draw(
	PNG(
		joinpath(output_dir, "aggregation_results.png"), 
		18cm, 
		16cm,
		dpi=500
	), r₁
)

# ╔═╡ aa362ebf-f169-4e1c-8434-719393731c2e
begin
results_agg_comparison = leftjoin(
	results_agg,
	filter(x->x.model == "EPW", results_agg),
	on=["month","zone","fuel"],
	makeunique=true
)
results_agg_comparison.improvement = 100 .* (results_agg_comparison.diff .- results_agg_comparison.diff_1 ) ./ results_agg_comparison.diff_1

results_agg_comparison.improvement = clamp.(results_agg_comparison.improvement, -Inf, 0)
end;

# ╔═╡ 09f74c7d-5ee3-4529-ae5f-ae2392de44de
sort(results_agg_comparison, [:fuel, :improvement])

# ╔═╡ de070df7-7dd2-4a97-a87b-33170144b896
sort(results_agg_comparison, [:fuel,:improvement])

# ╔═╡ 43a58ec0-95c3-4a9e-9106-c618284116b8
r₂ = Gadfly.plot(
	# filter(x -> x.model ∈ ["EPW","NOAA","CMIP","Landsat8","Sentinel-2"],
	sort(results_agg_comparison, [:fuel,:improvement], rev=true),
	x=:month,
	y=:model,
	xgroup=:fuel,
	ygroup=:zone,
	color=:improvement,
	Geom.subplot_grid(
		Geom.rectbin,
		Guide.xticks(ticks=1:12)
	),
	Guide.colorkey(title="Δ"),
	Scale.ContinuousColorScale(
		palette -> get(ColorSchemes.Greens, palette),
	),
	# Scale.ygroup(
	# 	order=[
	# 		"SAR",
	# 		"VIIRS",
	# 		"Dynamic World",
	# 		"Sentinel-2",
	# 		"Landsat8",
	# 		"NOAA",
	# 		"CMIP",
	# 		"EPW",
	# 		"Null"
	# 	]
	# )
)

# ╔═╡ 4726c365-0295-4816-a86e-6dbafedcc003
draw(
	PNG(
		joinpath(output_dir, "aggregation_results_relative.png"), 
		20cm, 
		14cm,
		dpi=500
	), r₂
)

# ╔═╡ c9b841da-1974-467d-b360-40118082cb19
md"""
#### RQ 1 
Weather Station Distance
"""

# ╔═╡ d7fabb28-8d25-46b9-8348-146c54c4ea8e
begin
meter_resolution = 750
results_metadata = filter(x-> x.model ∈ ["EPW","NOAA","Landsat8"], results);
results_metadata.distance_bucket = (results_metadata.weather_station_distance .- (results_metadata.weather_station_distance .% meter_resolution)) ./ 1000;

results_metadata.area_bucket = results_metadata.area .- (results_metadata.area .% 2500);
results_metadata.heightroof_bucket = results_metadata.heightroof .- (results_metadata.heightroof .% 100);
end;

# ╔═╡ 19d528ac-b099-4e9d-8037-b98cb39c731d
epw_results = filter(x -> x.model == "EPW", select(results, ["Property Id", "date", "model", "diff", "weather_station_distance","fuel"]))

# ╔═╡ 02dac397-1a6e-4523-9016-d7db811555cb
group_results = filter(x -> x.model ∈ ["Landsat8","NOAA"], select(results, ["Property Id", "date", "model", "diff","fuel"]))

# ╔═╡ 0531d20a-929d-4f0e-a999-5a5fba5a66d2
begin
resso = leftjoin(
	group_results,
	epw_results,
	on=["Property Id","date","fuel"],
	makeunique=true
)
resso.improvement = 100 .* (resso.diff .- resso.diff_1) ./ resso.diff_1
resso
end

# ╔═╡ 5b8c6173-11ee-4484-910f-1aec2e756ab6
# Gadfly.plot(
# 	resso,
# 	x=:weather_station_distance,
# 	y=:improvement,
# 	color=:model,
# 	ygroup=:fuel,
# 	Geom.subplot_grid(
# 		Geom.smooth(smoothing=0.1),
# 		Guide.xrug
# 	)
# )

# ╔═╡ 99b00d40-b112-40b5-af21-19f762c7bf99
rectbin_results = combine(groupby(results_metadata, [:area_bucket, :distance_bucket, :fuel, :heightroof_bucket]), :diff => mean, renamecols=false);

# ╔═╡ 86912ac4-6ad2-420d-a8a0-5680aa1d2c24
geomhair_results = combine(groupby(results_metadata, [:distance_bucket, :fuel,:model]), :diff => mean, renamecols=false);

# ╔═╡ 95645a95-d129-4ab2-965c-6fd28da6d211
begin
baseline_comparison = leftjoin(
	geomhair_results,
	filter(x -> x.model == "EPW", geomhair_results),
	on=["distance_bucket","fuel"],
	makeunique=true
)

baseline_comparison.random_dist = baseline_comparison.distance_bucket .+ (rand(nrow(baseline_comparison)) .- 0.5)
baseline_comparison.improvement = 100 .* (baseline_comparison.diff .- baseline_comparison.diff_1 ) ./ baseline_comparison.diff_1
end;

# ╔═╡ d04db8e6-784d-40c0-87dd-f42638c6fe89
baseline_comparison.distance_bucket

# ╔═╡ 0985c9d2-6705-4682-9d31-67f05dc8f81b
baseline_comparison.random_dist

# ╔═╡ 9ead69f4-4ce6-49e3-835f-47c88793a5c9
histogram(filter(x -> x.model == "Landsat8", baseline_comparison).improvement, bins=20)

# ╔═╡ 380a19ab-8219-45fe-aeff-6eba347c6806
results_epwdistance = Gadfly.plot(
	filter(x -> x.model != "EPW", baseline_comparison),
	x=:distance_bucket,
	y=:improvement,
	yintercept=[0],
	y_group=:fuel,
	# x_group=:model,
	color=:model,
	Geom.subplot_grid(
		Geom.point,
		# Geom.hair,
		Geom.smooth(smoothing=0.5),
		# Geom.line,
		# Geom.step,
		# Geom.smooth(smoothing=0.4),
		# Geom.line,
		Guide.xticks(ticks=collect(0:5:20)),
		# Guide.xrug,
		Guide.yticks(ticks=-20:15:30),
		Geom.hline(color="pink"),
		# Coord.cartesian(ymin=25, ymax=75),
		free_y_axis=true
	),
	Theme(
		point_size=1.75pt,
		line_width=1.0pt,
		key_position=:bottom, 
		default_color="gray",
		major_label_font_size=12pt,
		minor_label_font_size=10pt,
		# highlight_width=0.5pt,
		alphas=[0.7]
		# color=[colorant"black", colorant"red"]
	),
	Guide.colorkey(title="Model"),
	Guide.xlabel("Distance to Weather Station (km)"),
	Guide.ylabel("Percent Difference MAE"),
	Scale.color_discrete_manual(
		colorant"#FF483B",
		colorant"#83B592"
	),
	# Scale.y_log,
	Guide.title("Prediction Error Relative to EPW (%) vs Distance")
)

# ╔═╡ 299d1227-6803-4f32-aa05-65b73817c3e3
draw(
	PNG(
		joinpath(output_dir, "distance_error_epw.png"), 
		13cm, 
		10cm,
		dpi=500
	), results_epwdistance
)

# ╔═╡ 3527d303-01ae-4b41-a4f5-f0cad5dba29c


# ╔═╡ a82232c7-4904-435c-ad1c-b797d79232ba
md"""
#### Hypothesis 2 
Seasonal Benefits
"""

# ╔═╡ 20b0a476-3b73-4893-bc31-c9142b1a2531
Gadfly.plot(
	results,
	x=:date,
	y=:diff,
	color=:model,
	ygroup=:fuel,
	Geom.subplot_grid(
		Geom.smooth(smoothing=0.2),
		free_y_axis=true,
			Guide.xticks(
		ticks=DateTime("2018-01-1"):Month(3):DateTime("2021-01-01"),
		orientation=:vertical
	),
	),
)

# ╔═╡ 470ea684-8977-43ef-aaa0-eab98907b93d
begin
Ξ = copy(results)
Ξ′ = combine(groupby(Ξ, [:model,:month,:fuel]), :diff => mean, renamecols=false)
Ξ′.color = map(x->seasons[x],Ξ′.month)
Ξ′.model = convert.(String, Ξ′.model)
end;

# ╔═╡ 2d6815ea-762f-4ff4-a632-715653c0c89a
# p₄ = Gadfly.plot(
# 	filter(x->x.model ∈ ["Null","EPW","Landsat8"], Ξ′),
# 	x=:month,
# 	y=:diff,
# 	color=:color,
# 	xgroup=:model,
# 	ygroup=:fuel,
# 	yintercept=[0],
# 	Guide.ylabel("Δ Prediction - Recorded"),
# 	Guide.xlabel("Month"),
# 	Guide.title("Mean Electricity Error by Season"),
# 	Guide.colorkey(title="Season"),
# 	Geom.subplot_grid(
# 		# Guide.yticks(ticks=0.0:0.001:0.01),
# 		Guide.xticks(ticks=1:2:12),
# 		# Geom.hair,
# 		Geom.point,
# 		# Geom.line,
# 		Geom.hline(color=["pink"], style=:solid),
# 		# Geom.point,
# 		free_y_axis=true,
# 	),	# Coord.cartesian(ymin=-0.1, ymax=0.1, aspect_ratio=1.5),
# 	Theme(key_position = :bottom),
# 	# Scale.color_discrete_manual(
# 	# 	colorant"skyblue",
# 	# 	colorant"lightgreen",
# 	# 	colorant"indianred",
# 	# 	colorant"coral"
# 	# ),
# )

# ╔═╡ 7980fbcd-9e53-481f-95ed-18818ecf0c49
begin
epw_comparison = leftjoin(
	filter(x -> x.model == "EPW", Ξ′),
	Ξ′,
	on=[:month,:fuel],
	makeunique=true
)
epw_comparison.improvement = 100 .* ( epw_comparison.diff_1 .- epw_comparison.diff ) ./ epw_comparison.diff

epw_comparison.improvement = clamp.(epw_comparison.improvement, -Inf, 0)
end;

# ╔═╡ aa8dcf2e-6776-403a-abcf-9dee17839c8f
p₄ = Gadfly.plot(
	filter(x->x.model_1 ∈ ["Landsat8","NOAA"], epw_comparison),
	x=:month,
	y=:improvement,
	color=:color_1,
	xgroup=:model_1,
	ygroup=:fuel,
	yintercept=[0],
	Guide.ylabel("Percent Difference to EPW - MAE"),
	Guide.xlabel("Months by Model"),
	Guide.title("Relative Benefits to EPW by Season - MAE"),
	Guide.colorkey(title="Season"),
	Geom.subplot_grid(
		# Guide.yticks(ticks=0.0:0.001:0.01),
		Guide.xticks(ticks=1:2:12),
		Guide.yticks(ticks=-10:2.5:0),
		Geom.hair,
		Geom.point,
		# Geom.line,
		Geom.hline(color=["pink"], style=:solid),
		# Geom.point,
		# free_y_axis=true,
	),	# Coord.cartesian(ymin=-0.1, ymax=0.1, aspect_ratio=1.5),
	Theme(
		key_position = :bottom,
		line_width=1.7pt,
		point_size=3.0pt,
		# minor_label_font_size,
		major_label_font_size=12pt,
		minor_label_font_size=10pt
	),
	Scale.color_discrete_manual(
		colorant"#55134E",
		colorant"#12817B",
		colorant"#FF483B",
		colorant"#83B592"
	),
)

# ╔═╡ f193ade3-9ed2-493d-b97f-cdeb919acaeb
draw(
	PNG(
		joinpath(output_dir, "seasonality_benefits.png"), 
		12cm, 
		12cm,
		dpi=500
	), p₄
)

# ╔═╡ 575b3d7d-2484-4eb0-9f4d-720c3c550dae
mae

# ╔═╡ fdcef3ad-2692-4de2-b578-e30acf11842e
begin
function aggregation_results(individual_results, aggterms = ["date","zipcode","fuel","model"])
	ṫ = combine(groupby(individual_results, aggterms), [:prediction, :recorded] .=> sum, nrow => :count,  renamecols=false)
	
	res = combine(groupby(ṫ, filter( x -> x ≠ "date", aggterms))) do vᵢ
		(
		cvrmse = cvrmse(vᵢ.prediction, vᵢ.recorded),
		nmbe = nmbe(vᵢ.prediction, vᵢ.recorded),
		cvstd = cvstd(vᵢ.prediction, vᵢ.recorded),
		rmse = rmse(vᵢ.prediction, vᵢ.recorded),
		mae = mae(vᵢ.prediction, vᵢ.recorded),
		model = first(vᵢ.model),
		count = first(vᵢ.count)
		)
		end
	
	return res, combine(
		groupby(res, [:fuel, :model]), 
		names(res, Float64) .=> median, 
		renamecols=false
	)
end
end

# ╔═╡ 2808cc24-c3bb-4b69-bc4d-8503c1503a46
begin
res, aggresults = aggregation_results(results);
res.random_count = res.count .+ rand(nrow(res))
	
sort(aggresults, [:fuel, :rmse])
end

# ╔═╡ 2eb802aa-a9da-4f7e-8ca5-d3897c5e2874
resultsₐ = realnames(sort(select(aggresults, [:fuel, :model, :cvrmse, :nmbe, :mae], :), [:fuel, :cvrmse]))

# ╔═╡ 01ecc218-b2e6-4fec-8c7c-5898d20f769e
resultsₐ

# ╔═╡ 94769e0b-6649-4ba5-87e3-e4dd7603423e
resultsₐ

# ╔═╡ 33ce90bf-5c8e-496a-b7a4-514f1550989a
latexify(resultsₐ, latex=false, fmt = FancyNumberFormatter(4), env=:table)

# ╔═╡ 3322ff22-f2cd-467e-929a-238186ab7184
percentile(unique(select(res, [:zipcode, :count]), :zipcode).count, 75)

# ╔═╡ 9a0f2fb6-372a-4658-be76-d9562f478f31
begin
resᵪ, aggresultsᵪ = aggregation_results(results, ["date","zipcode","fuel","model","zone"]);
end

# ╔═╡ cbd5410b-5d3e-41aa-9c63-7d089617f54a
aggresultsᵪ

# ╔═╡ 4b6e88dc-82e1-43d7-a92a-6b3a5b01f01d
begin
function individual_results(df, aggterms = ["fuel","model"])
	res = combine(groupby(df, aggterms)) do vᵢ
		(
		cvrmse = cvrmse(vᵢ.prediction, vᵢ.recorded),
		nmbe = nmbe(vᵢ.prediction, vᵢ.recorded),
		cvstd = cvstd(vᵢ.prediction, vᵢ.recorded),
		rmse = rmse(vᵢ.prediction, vᵢ.recorded),
		mae = mae(vᵢ.prediction, vᵢ.recorded),
		model = first(vᵢ.model)
		)
		end
	
	return res
end
end

# ╔═╡ 81af87a5-aba3-4c14-a2ec-57e49af69066
resultsᵢ = realnames(sort(select(individual_results(results), [:fuel, :model, :cvrmse, :nmbe, :mae], :), [:fuel, :rmse]))

# ╔═╡ 4936a25d-90a9-44f7-8213-0831b47b5953
latexify(resultsᵢ, latex=false, fmt = FancyNumberFormatter(4), env=:table)

# ╔═╡ 9172ed67-695c-4818-90d0-ee5e3104feae
resultsᵪᵢ = rename(realnames(sort(individual_results(results, ["fuel","zone","model"]), [:fuel,:zone,:cvrmse])), :zone => "\\textbf{Zone}")

# ╔═╡ 08d901c6-f7ce-4070-b41c-ec3661868861
latexify(resultsᵪᵢ, latex=false, fmt = FancyNumberFormatter(4), env=:table)

# ╔═╡ edc350d5-e505-4487-a4a6-cfb680711ac1
begin
joining_terms = ["Property Id", "date"]
extra_omission_features = [
	"weather_station_distance",
	"zone",
	"zipcode",
	"month",
	"model",
	"recorded",
	"prediction"
]

exclusion_termsₑ = Not([
	joining_terms..., 
	extra_omission_features...,
	"electricity_mwh"
])

exclusion_termsᵧ = Not([
	joining_terms..., 
	extra_omission_features...,
	"naturalgas_mwh"
])
end;

# ╔═╡ 33f58d0f-bcb6-4528-a10d-548fdf429196
# Gadfly.plot(
# 	x=filter(x -> x>-1, res.count),
# 	Geom.histogram(bincount=40),
# 	Guide.xticks(ticks=0:5:50),
# 	Guide.title("Count of buildings in each zipcode"),
# 	Guide.xlabel("Count of Zipcodes"),
# 	Guide.ylabel("Building Count"),
# 	Theme(default_color="black")
# )

# ╔═╡ 506850d0-30dd-4eba-83fb-42c9a00b37d6
md"""
#### Hypothesis 3 
Aggregated Benefits
"""

# ╔═╡ f5482c86-da26-4b37-bd22-78367726ccb6
combine(groupby(res, [:fuel, :model]), :cvrmse => median)

# ╔═╡ 5516e2ca-abc9-47fe-89b3-57ff36b003c6
median(filter(x -> x < 300, res.cvrmse))

# ╔═╡ a06bf7f0-b234-4a59-a92b-18be3cfb8f10
Gadfly.plot(
	x=filter(x -> x < 300, res.cvrmse),
	Guide.xticks(ticks=0:25:300),
	Geom.histogram,
	Guide.title("Histogram of CVRMSE scores"),
	Guide.ylabel("Count of Buildings"),
	Guide.xlabel("CV(RMSE)")
)

# ╔═╡ a9e93d79-b97b-4fc1-9304-2a5f3ec2f4bf
begin
modelchoice = ["EPW","Landsat8","NOAA"]
scalingplot = Gadfly.plot(
	stack(
		filter(x -> x.model ∈ modelchoice && x.count > 2, 
			res
		), [:cvrmse, :nmbe, :rmse]), #:cvstd, :rmse
	x=:random_count,
	y=:value,
	color=:model,
	yintercept=[0],
	ygroup=:variable,
	# xgroup=:fuel,
	Geom.subplot_grid(
		# Geom.point,
		Geom.smooth,
		# Geom.smooth(smoothing=0.5, color="red"),
		Geom.hline(color="pink"),
		Guide.xrug,
		Guide.xticks(ticks=collect(0:5:40), label=true),
		free_y_axis=true
	),
	# Scale.x_log,
	Guide.title("Metric Scaling Behavior"),
	Guide.xlabel("Number of Buildings"),
	Guide.ylabel("Error Metric"),
	# Scale.x_log,
	Theme(
		line_width=1.2pt,
		# rug_size=8pt,
		point_size=0.5pt,
		# alphas=[0.3],
		default_color="gray", 
		highlight_width = 0pt,
		key_position=:bottom,
	),
	Scale.color_discrete_manual(
		# colorant"#55134E",
		colorant"#12817B",
		colorant"#FF483B",
		colorant"#83B592"
	),
)
end

# ╔═╡ 55d0c30f-8253-42c6-a531-66cb18cedd9a
draw(
	PNG(
		joinpath(output_dir, "scaling-behavior.png"), 
		12cm, 
		20cm,
		dpi=500
	), scalingplot
)

# ╔═╡ b65ab7f0-ab57-4e8a-813f-87d54c77f60d
md"""
###### temperature and error
"""

# ╔═╡ c0073981-495d-4352-b745-04511d02d5fd
resultsₜ = filter(x-> x.model ∈ ["EPW","Landsat8"], results);

# ╔═╡ 06228a5c-7bd5-41f4-910e-aca4ee21fbd1
begin
tₑ = select(teₐ′₅, Not(:electricity_mwh))
tₑ.fuel = repeat(["Electricity"], nrow(tₑ))

tᵧ = select(teᵧ′₆, Not(:naturalgas_mwh))
tᵧ.fuel = repeat(["Natural Gas"], nrow(tᵧ))
	
landsat_predictions = vcat(
	tₑ,
	tᵧ
)
landsat_predictions.diff = abs.(landsat_predictions.prediction .- landsat_predictions.recorded)

landsat_predictions.temperature_bucket = (landsat_predictions.ST_B10 .- (landsat_predictions.ST_B10 .% 2.5));

temperature_estimates = combine(groupby(landsat_predictions, [:fuel, :temperature_bucket]), 
	:diff => (x -> quantile(x, 0.25)) => :q25,
	:diff => median => :q50,
	:diff => (x -> quantile(x, 0.75)) => :q75,
	:diff => (x -> length(x) / 50) => :nrow,
	:diff => (x -> 0.0) => :zeroterms
);end

# ╔═╡ 1c68d5f7-fab6-4564-8184-106a2abf39e4
tₑ

# ╔═╡ f7de4fc1-5ba5-431e-808f-50b89b346bba
lsample = landsat_predictions[sample(1:nrow(landsat_predictions), 1000),:];

# ╔═╡ ce34731a-cf8f-4e17-b721-82cefd53e859
maximum(temperature_estimates.q75)

# ╔═╡ 771a46a8-bc0f-4582-afea-6e481704b95a
t₀ = Gadfly.plot(
	temperature_estimates,
	x=:temperature_bucket,
	y=:nrow,
	Geom.bar,
	# Scale.y_log,
	Guide.xticks(ticks=-15:5:55, label=false),
	# Guide.yticks(label=false),
	# Guide.yticks(ticks=0:1000:2000),
	Guide.xlabel(""),
	Guide.ylabel(""),
	Theme(default_color="black")
)

# ╔═╡ f8d68092-ce96-454a-ae36-a6b94673b04f
t₁ = Gadfly.plot(
	temperature_estimates,
	x=:temperature_bucket,
	y=:q50,
	ymin=:q25,
	ymax=:q75,
	yintercept=[0],
	xgroup=:fuel,
	Geom.subplot_grid(
		# Geom.point,
		Geom.step,
		# Geom.smooth(smoothing=0.5),
		# Guide.xrug,
		Geom.ribbon,
		Geom.hline(color="gray"),
		Guide.xticks(ticks=-15:20:55),
		# free_y_axis=true,
			# Guide.yticks(ticks=0:20:100),
		#Coord.Cartesian(ymin=0, ymax=150),
	),
	# Geom.label,
	# Geom.line,
	# Guide.xticks(ticks=-15:5:55),
	# Scale.y_log10,
	Theme(
		alphas=[0.5],
		default_color="black",
		point_size=2pt,
		line_width=1pt,
		major_label_font_size=12pt,
		minor_label_font_size=10pt,
	),
	Guide.xlabel("Landsat8 LST - °C"),
	Guide.ylabel("Monthly MAE - MWh"),
	Guide.title("Extreme Temperature Predictions - Error Profile"),
	Guide.ylabel(""),
	# Geom.smooth(smoothing=0.5)
)

# ╔═╡ 298e9181-6f03-4b73-9ebb-2f54adfccf8d
draw(
	PNG(
		joinpath(output_dir, "temperature_error.png"), 
		12cm, 
		10cm,
		dpi=500
	), t₁
)

# ╔═╡ 98e438b6-d504-44a9-8fbe-45c9fc19e8c2
Plots.plot

# ╔═╡ 417470dc-aa97-4b49-b4bb-3e1c54b2f3b5
teₐ′₂

# ╔═╡ 39ba2144-604c-4be7-a404-ef7af6495fb0
begin
ep = copy(teₐ′₂)
ep.diff = abs.(ep.prediction .- ep.recorded)
ep.temperature_bucket = (ep.TMP .- (ep.TMP .% 2.5));

ep_estimates = combine(groupby(ep, :temperature_bucket), 
	:diff => (x -> quantile(x, 0.25)) => :q25,
	:diff => median => :q50,
	:diff => (x -> quantile(x, 0.75)) => :q75
);end

# ╔═╡ 50acba26-503c-431e-b3fe-7e455867531c
input_dir_environmental = joinpath(data_path, "p2_o")


# ╔═╡ 96cfb872-621e-466f-8d04-879dd31bb3a7
landsat8_p = joinpath(input_dir_environmental, "landsat8.csv")

# ╔═╡ 1485f5a0-be89-4ab9-abce-5f0638aa1c1b
date_f = "yyyy-mm-dd HH:MM:SS"

# ╔═╡ cf42f453-e140-453f-a348-a11be682f9b7
begin
landsat8_r = CSV.read(landsat8_p, DataFrame; dateformat=date_f);
landsat8_r.ST_B10 = (landsat8_r.ST_B10 .* 0.00341802 .+ 149.0) .- 273.15
landsat8_r.ST_QA = (landsat8_r.ST_QA .* 0.01) ./ 2
end

# ╔═╡ f1afa899-4119-42bf-88a5-01c20a58356d
landsat8_r

# ╔═╡ 103d08f8-4fce-43f0-9f79-6579a07e5f41
epw_sample = CSV.read(joinpath(data_path, "p1_o", "epw_sample.csv"), DataFrame);

# ╔═╡ 5063fa17-46ad-41d7-8086-83a1b4162e51
epw_sample

# ╔═╡ b91114dd-908c-463b-9984-8b785f714ab4
begin
selected_weather_station = 725053
single_building = filter( x-> x["Property Id"] == 8841, landsat8_r );
dropmissing!(single_building, [:ST_B10, :ST_QA])

sample_epw_region = filter( x -> x.weather_station_id == selected_weather_station, epw_sample )
sample_epw_region.year = Dates.Year.(sample_epw_region.date)
sample_epw_region.month = Dates.Month.(sample_epw_region.date)

sample_epw_aggregation = combine(groupby(sample_epw_region, [:month, :year]),
	names(sample_epw_region, Real) .=> mean,
	renamecols=false
)
sample_epw_aggregation.date = Dates.Date.(sample_epw_aggregation.year, sample_epw_aggregation.month)
sample_epw_aggregation
end

# ╔═╡ 931144a6-98bc-43db-9a30-2d60d983edbc
property_map = CSV.read(joinpath(data_path, "p1_o", "property_mapping.csv"), DataFrame);

# ╔═╡ d73ac47f-3eed-4b53-9858-5eef7662ddba
weatherstation_buildings = filter( x -> x.weather_station_id == selected_weather_station, property_map)[:,"Property Id"]

# ╔═╡ 84cbb29d-d709-48b1-aa92-00cfd1f8686b
unique(landsat8_r[:,"Property Id"])

# ╔═╡ 3ca311f4-3b93-40f4-bbaf-0eab9562ae60
sample_sars = sample(MersenneTwister(1), filter( x -> x ∈ weatherstation_buildings, unique(landsat8_r[:,"Property Id"])), 100)

# ╔═╡ 15dfb4bd-e06a-4c17-acd5-52ac3dcaad4f
begin
sample_filtered = filter( x-> x["Property Id"] in sample_sars, landsat8_r );
dropmissing!(sample_filtered, :ST_B10)
end

# ╔═╡ 56e32a7f-b4ee-48b8-af5e-6bf4799a397e
temp_profile = Gadfly.plot(
	Gadfly.layer(
		single_building,
		x=:date,
		y=:ST_B10,
		ymin=single_building.ST_B10 .- single_building.ST_QA,
		ymax=single_building.ST_B10 .+ single_building.ST_QA,
		Geom.point,
		Geom.errorbar,
		# Geom.smooth(smoothing=0.1),
		# Geom.line,
		# Geom.line,
		Theme(default_color="lightcoral", line_width=0.7pt, point_size=2pt)
	),
	Gadfly.layer(
		sample_epw_aggregation,
		x=:date,
		y=:median_temp,
		# ymin=:min_temp,
		# ymax=:max_temp,
		# Geom.point,
		Geom.line,
		Theme(
			default_color="gray", 
			line_width=1.1pt, 
			point_size=2pt, 
			alphas=[0.8],
			# Theme(lowlight_color=c->RGBA{Float32}(c.r, c.g, c.b, 0.4))
		),
	),
	Gadfly.layer(
		sample_filtered,
		x=:date,
		y=:ST_B10,
		color="Property Id",
		Geom.smooth(smoothing=0.15),
		Theme(line_width=0.08pt, alphas=[0.2])
	),
	Scale.discrete_color_hue,
	Guide.ylabel("Temperature °C"),
	Guide.xlabel("Date"),
	Guide.yticks(ticks=-10:5:50),
	Guide.xticks(ticks=DateTime("2018-01-1"):Month(1):DateTime("2020-12-31"), orientation=:vertical),
	Guide.title("Temperature Discrepancies in Manhattan - Landsat8 vs. EPW"),
	Theme(default_color="black", key_position = :none),
	Guide.manual_color_key(
		"Legend", ["Landsat8", "EPW"], ["lightcoral", "gray"]
	),
	# Guide.colorkey(pos=:bottom)
)

# ╔═╡ e9c543ec-997c-48fc-ae4c-565da4a2d062
Gadfly.plot(
	sample_filtered,
	x=:date,
	y=sample_filtered.ST_URAD ./ sample_filtered.ST_DRAD,
	color=Symbol("Property Id"),
	Geom.smooth(smoothing=0.3),
)

# ╔═╡ ce660527-a38e-4e89-8690-393bc2b07442
draw(
	PNG(
		joinpath(output_dir, "temperature_nonlinearity.png"), 
		20cm, 
		10cm,
		dpi=500
	), temp_profile
)

# ╔═╡ 00000000-0000-0000-0000-000000000001
PLUTO_PROJECT_TOML_CONTENTS = """
[deps]
CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
Cairo = "159f3aea-2a34-519c-b102-8c37f9878175"
ColorSchemes = "35d6a980-a343-548e-a6ea-1d62b119f2f4"
Compose = "a81c6b42-2e10-5240-aca2-a61377ecd94b"
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
Fontconfig = "186bb1d3-e1f7-5a2c-a377-96d770f13627"
Gadfly = "c91e804a-d5a3-530f-b6f0-dfbca275c004"
Latexify = "23fbe1c1-3f47-55db-b15f-69d7ec21a316"
MLJ = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7"
Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
YAML = "ddb6d928-2868-570f-bddf-ab3f9cf99eb6"

[compat]
CSV = "~0.10.9"
Cairo = "~1.0.5"
ColorSchemes = "~3.20.0"
Compose = "~0.9.4"
DataFrames = "~1.4.4"
Fontconfig = "~0.4.1"
Gadfly = "~1.3.4"
Latexify = "~0.15.18"
MLJ = "~0.19.1"
Plots = "~1.38.4"
StatsBase = "~0.33.21"
YAML = "~0.4.8"
"""

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

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manifest_format = "2.0"
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deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4fe47bd2247248125c428978740e18a681372dd4"
uuid = "a3789734-cfe1-5b06-b2d0-1dd0d9d62d05"
version = "1.1.3+4"

[[deps.Xorg_libXext_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "b7c0aa8c376b31e4852b360222848637f481f8c3"
uuid = "1082639a-0dae-5f34-9b06-72781eeb8cb3"
version = "1.3.4+4"

[[deps.Xorg_libXfixes_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "0e0dc7431e7a0587559f9294aeec269471c991a4"
uuid = "d091e8ba-531a-589c-9de9-94069b037ed8"
version = "5.0.3+4"

[[deps.Xorg_libXi_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll", "Xorg_libXfixes_jll"]
git-tree-sha1 = "89b52bc2160aadc84d707093930ef0bffa641246"
uuid = "a51aa0fd-4e3c-5386-b890-e753decda492"
version = "1.7.10+4"

[[deps.Xorg_libXinerama_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll"]
git-tree-sha1 = "26be8b1c342929259317d8b9f7b53bf2bb73b123"
uuid = "d1454406-59df-5ea1-beac-c340f2130bc3"
version = "1.1.4+4"

[[deps.Xorg_libXrandr_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll", "Xorg_libXrender_jll"]
git-tree-sha1 = "34cea83cb726fb58f325887bf0612c6b3fb17631"
uuid = "ec84b674-ba8e-5d96-8ba1-2a689ba10484"
version = "1.5.2+4"

[[deps.Xorg_libXrender_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "19560f30fd49f4d4efbe7002a1037f8c43d43b96"
uuid = "ea2f1a96-1ddc-540d-b46f-429655e07cfa"
version = "0.9.10+4"

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

[[deps.Xorg_libxcb_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "XSLT_jll", "Xorg_libXau_jll", "Xorg_libXdmcp_jll", "Xorg_libpthread_stubs_jll"]
git-tree-sha1 = "daf17f441228e7a3833846cd048892861cff16d6"
uuid = "c7cfdc94-dc32-55de-ac96-5a1b8d977c5b"
version = "1.13.0+3"

[[deps.Xorg_libxkbfile_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "926af861744212db0eb001d9e40b5d16292080b2"
uuid = "cc61e674-0454-545c-8b26-ed2c68acab7a"
version = "1.1.0+4"

[[deps.Xorg_xcb_util_image_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "0fab0a40349ba1cba2c1da699243396ff8e94b97"
uuid = "12413925-8142-5f55-bb0e-6d7ca50bb09b"
version = "0.4.0+1"

[[deps.Xorg_xcb_util_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxcb_jll"]
git-tree-sha1 = "e7fd7b2881fa2eaa72717420894d3938177862d1"
uuid = "2def613f-5ad1-5310-b15b-b15d46f528f5"
version = "0.4.0+1"

[[deps.Xorg_xcb_util_keysyms_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "d1151e2c45a544f32441a567d1690e701ec89b00"
uuid = "975044d2-76e6-5fbe-bf08-97ce7c6574c7"
version = "0.4.0+1"

[[deps.Xorg_xcb_util_renderutil_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "dfd7a8f38d4613b6a575253b3174dd991ca6183e"
uuid = "0d47668e-0667-5a69-a72c-f761630bfb7e"
version = "0.3.9+1"

[[deps.Xorg_xcb_util_wm_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "e78d10aab01a4a154142c5006ed44fd9e8e31b67"
uuid = "c22f9ab0-d5fe-5066-847c-f4bb1cd4e361"
version = "0.4.1+1"

[[deps.Xorg_xkbcomp_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxkbfile_jll"]
git-tree-sha1 = "4bcbf660f6c2e714f87e960a171b119d06ee163b"
uuid = "35661453-b289-5fab-8a00-3d9160c6a3a4"
version = "1.4.2+4"

[[deps.Xorg_xkeyboard_config_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xkbcomp_jll"]
git-tree-sha1 = "5c8424f8a67c3f2209646d4425f3d415fee5931d"
uuid = "33bec58e-1273-512f-9401-5d533626f822"
version = "2.27.0+4"

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

[[deps.YAML]]
deps = ["Base64", "Dates", "Printf", "StringEncodings"]
git-tree-sha1 = "dbc7f1c0012a69486af79c8bcdb31be820670ba2"
uuid = "ddb6d928-2868-570f-bddf-ab3f9cf99eb6"
version = "0.4.8"

[[deps.Zlib_jll]]
deps = ["Libdl"]
uuid = "83775a58-1f1d-513f-b197-d71354ab007a"
version = "1.2.12+3"

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

[[deps.fzf_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "868e669ccb12ba16eaf50cb2957ee2ff61261c56"
uuid = "214eeab7-80f7-51ab-84ad-2988db7cef09"
version = "0.29.0+0"

[[deps.libaom_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "3a2ea60308f0996d26f1e5354e10c24e9ef905d4"
uuid = "a4ae2306-e953-59d6-aa16-d00cac43593b"
version = "3.4.0+0"

[[deps.libass_jll]]
deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "HarfBuzz_jll", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
git-tree-sha1 = "5982a94fcba20f02f42ace44b9894ee2b140fe47"
uuid = "0ac62f75-1d6f-5e53-bd7c-93b484bb37c0"
version = "0.15.1+0"

[[deps.libblastrampoline_jll]]
deps = ["Artifacts", "Libdl", "OpenBLAS_jll"]
uuid = "8e850b90-86db-534c-a0d3-1478176c7d93"
version = "5.1.1+0"

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

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

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

[[deps.nghttp2_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"
version = "1.48.0+0"

[[deps.p7zip_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"
version = "17.4.0+0"

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

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

[[deps.xkbcommon_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"]
git-tree-sha1 = "9ebfc140cc56e8c2156a15ceac2f0302e327ac0a"
uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd"
version = "1.4.1+0"
"""

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