### A Pluto.jl notebook ###
# v0.17.7

using Markdown
using InteractiveUtils

# ╔═╡ 3f9f4948-74c3-11ec-2daf-51719517f805
# Import packages
begin
	# Parse HTML
	using Gumbo

	# CSS selector for parsed HTML
	using Cascadia

	# Pluto Notebook utilities
	using PlutoUI

	# Work with tabular data (similar to pandas in Python)
	using DataFrames

	# General purpose linear algebra
	using LinearAlgebra

	# Working with Dates and DateTimes
	using Dates

	# Make plots for data visualization
	using Plots
end

# ╔═╡ 2398b4e5-5920-442e-980b-f75359ba73ae
md"""
# Assignment 1

- Name: Eric Nguyen
- TUID: 915786865
"""

# ╔═╡ 97d595d3-3c44-4112-a7a8-4efe4c1ba943
md"""
# Problem 1

The goal is to provide better data about the top 50 solar flares recorded so far than those shown by [SpaceWeatherLive.com](https://www.spaceweatherlive.com/en/solar-activity/top-50-solar-flares).
Use [this messy NASA data](http://cdaw.gsfc.nasa.gov/CME_list/radio/waves_type2.html) to add more features for the top 50 solar flares.
You need to scrape this information directly from each HTML page.
You can read here more about [Solar Flares](https://en.wikipedia.org/wiki/Solar_flare), [coronal mass ejections](https://www.spaceweatherlive.com/en/help/what-is-a-coronal-mass-ejection-cme), and [the solar flare alphabet soup](http://spaceweather.com/glossary/flareclasses.html).
"""

# ╔═╡ 62ba405d-4d1a-42b7-bacc-1350bf90564e
md"## Part 1: Data scraping and preparation"

# ╔═╡ d6c88a68-c221-417b-bb78-06938fe97407
md"""
### Task 1: Scrape your competitor's data

Scrape data for the top 50 solar flares shown in [SpaceWeatherLive.com](https://www.spaceweatherlive.com/en/solar-activity/top-50-solar-flares).
"""

# ╔═╡ 151282f9-0a35-435c-9cba-e7736efccb3f
swldf() = let
	# Make request to website for HTML data using cookies to gain authorization for web scraping
	data = read(`curl 'https://www.spaceweatherlive.com/en/solar-activity/top-50-solar-flares.html' \
  -H 'authority: www.spaceweatherlive.com' \
  -H 'cache-control: max-age=0' \
  -H 'sec-ch-ua: " Not;A Brand";v="99", "Google Chrome";v="97", "Chromium";v="97"' \
  -H 'sec-ch-ua-mobile: ?0' \
  -H 'sec-ch-ua-platform: "macOS"' \
  -H 'upgrade-insecure-requests: 1' \
  -H 'user-agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/97.0.4692.71 Safari/537.36' \
  -H 'accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9' \
  -H 'sec-fetch-site: same-origin' \
  -H 'sec-fetch-mode: navigate' \
  -H 'sec-fetch-user: ?1' \
  -H 'sec-fetch-dest: document' \
  -H 'referer: https://www.spaceweatherlive.com/en/solar-activity/top-50-solar-flares.html' \
  -H 'accept-language: en-US,en;q=0.9' \
  -H 'cookie: CookieScriptConsent={"action":"accept","categories":"[\"targeting\",\"performance\"]","CMP":"CPSyPX3PSyPX3F2ADBENBdCsAP_AAH_AAAAAH6Nf_X__bX9j-_59__t0eY1f9_7_v-wzjhfdt-8N2P_X_L0X42E7PF36pq4KuR4Eu3LBIQNlHOHUTUmw6okVrTPsak2Mr7NKJ7LEmnMZe2dYGHtfn91TuZKY7_78_9fz3_-v_v___9f3r-3_3__59X---_e_V399zLv9_____9nA_YAkw1L4ALsSxwZJo0qhRAhCsJDoBQAUUAwtE1hAwuCnZWAR6ggYAITUBGBECDEFGLAIABAIAkIiAkAPBAIgCIBAACAFSAhAARMAgsALAwCAAUA0LECKAIQJCDI4KjlMCAiRaKCeysASi72NMIQyiwAoFH9FRgIlCCBYGQkLBzGAAAAA.YAAAAAAAAAAA","key":"cd200ab5-5ddb-4df2-8057-ad9de673f1e6"}; _gid=GA1.2.1779020148.1642128786; SPSI=e3d46e0ee55ab21c983b674967abeb21; SPSE=QtZcE7RE5wJ43mJgik/6IiEWjaVvJvUBWREba0C084quUcv8c2WF9rgKyHChd+zcp+4hVlkLs6x5OJPlBXX6HA==; spcsrf=2db0574ff9f3e8b9fbc45b8804c9f061; sp_lit=/BKwhMbFX5iEuAmhsZlBwA==; UTGv2=h47aacd40fcd612bc33976e2edcd25116129; adOtr=643eeedS055; PRLST=ua/Ry/Ai; _ga_25HHLX0QL3=GS1.1.1642218626.6.1.1642219616.0; _ga=GA1.2.737209106.1642128785' \
  --compressed`, String)

	# Parse HTML data using CSS selectors
	page = parsehtml(data)
	table = first(eachmatch(sel".table-responsive-md > table", page.root))
	thead = first(eachmatch(sel"thead", table))
	tbody = first(eachmatch(sel"tbody", table))

	# Use reshape() to convert list of <td> to 1x8-Matrix
	# Vertically concatenate each <td> Matrix to form 50x8 Matrix
	datarows = reduce(vcat, [reshape([Gumbo.text(td.children[1]) for td ∈ tr.children], (1, :)) for tr ∈ eachmatch(sel"tr", tbody)])

	# Put rows of data into DataFrame
	df = DataFrame(datarows, [:rank, :x_class, :date, :region, :start_time, :max_time, :end_time, :movie])
end

# ╔═╡ 8f922beb-d685-4790-a08b-0d24810c417d
md"#### DataFrame for top 50 solar flares from SpaceWeatherLive.com"

# ╔═╡ c93284d7-0cb2-4991-a44e-a30e53c29a93
swldf()

# ╔═╡ 99a21f98-bd1a-4bc4-a619-668e824bb788
md"### Task 2: Tidy the top 50 solar flare data"

# ╔═╡ 6fa4452d-ddcd-4df7-996a-ba75ae21ba6e
tidy_swldf() = let
	df = swldf()

	# Remove '+' from x_class
	df[!,:x_class][last.(df[!,:x_class]) .== '+'] = chop.(df[!,:x_class][last.(df[!,:x_class]) .== '+'])

	# Drop last column of DataFrame
	select!(df, Not(:movie))

	# Set missing values to missing (if they exist)
	allowmissing!(df, :region)
	df[!,:region][df[!,:region] .== "-"] .= missing
	
	# Convert rank to integer
	df.rank = parse.(Int, df.rank)

	# Convert dates and times to DateTime
	df[!,:date] = Date.(df[:, :date], "yyyy/mm/dd")
	df[!,:start_time] = DateTime.(df[:, :date], Time.(df[:, :start_time]))
	df[!,:max_time] = DateTime.(df[:, :date], Time.(df[:, :max_time]))
	df[!,:end_time] = DateTime.(df[:, :date], Time.(df[:, :end_time]))

	# Drop date columns
	select!(df, Not(:date))

	# Rename columns
	rename!(df, :start_time => :start_datetime)
	rename!(df, :max_time => :max_datetime)
	rename!(df, :end_time => :end_datetime)

	# Reorder columns
	df[!, [:rank, :x_class, :start_datetime, :max_datetime, :end_datetime, :region]]
end

# ╔═╡ e2c62cf7-9c2e-48d7-97a3-0990328e6ec5
md"#### Tidied DataFrame for top 50 solar flares from SpaceWeatherLive.com"

# ╔═╡ ff744877-012a-4edf-bdf9-a9e29744cff3
tidy_swldf()

# ╔═╡ d5bf8c5a-229e-4a2f-bbff-6d1ef6fcf9c6
md"""
### Task 3: Scrape the NASA data

Scrape the [NASA data](http://cdaw.gsfc.nasa.gov/CME_list/radio/waves_type2.html) to get additional features about the solar flares using the [table format description](http://cdaw.gsfc.nasa.gov/CME_list/radio/waves_type2_description.htm).
"""

# ╔═╡ 467168a3-33d0-46e4-acdc-3aea85fd2ff5
nasadf() = let
	# Make request to website for HTML data
	data = read(`curl https://cdaw.gsfc.nasa.gov/CME_list/radio/waves_type2.html`, String)

	# Parse HTML data using CSS selectors
	page = parsehtml(data)
	pre = Gumbo.text(first(eachmatch(sel"pre", page.root)))
	data = reduce(vcat, [reshape(split(row)[1:14], (1,:)) for row ∈ split(pre, '\n')[12:end-1]])

	# Put data into DataFrame
	df = DataFrame(data, [:start_date, :start_time, :end_date, :end_time, :start_frequency, :end_frequency, :flare_location, :flare_region, :flare_classification, :cme_date, :cme_time, :cme_angle, :cme_width, :cme_speed])
end

# ╔═╡ c7e4ee98-8c09-4eb5-aa9d-df7a8e2b51d7
md"#### DataFrame of solar flare data from NASA"

# ╔═╡ 9f986c37-8f68-4348-a587-1cc18f924fee
nasadf()

# ╔═╡ fd3f32e1-29b6-4766-b1a4-60ce09c9e8f9
md"### Task 4: Tidy the NASA table"

# ╔═╡ cc46809e-3436-4748-a29a-2a82cc197d66
tidy_nasadf() = let
	df = nasadf()
	allowmissing!(df)

	# Replace missing data with 'missing'
	df[!,:start_frequency][df[!,:start_frequency] .== "????"] .= missing
	df[!,:end_frequency][df[!,:end_frequency] .== "????"] .= missing
	df[!,:flare_location][df[!,:flare_location] .== "------"] .= missing
	df[!,:flare_region][df[!,:flare_region] .== "-----"] .= missing
	df[!,:flare_classification][df[!,:flare_classification] .== "----"] .= missing
	df[!,:cme_date][df[!,:cme_date] .== "--/--"] .= missing
	df[!,:cme_time][df[!,:cme_time] .== "--:--"] .= missing
	df[!,:cme_angle][df[!,:cme_angle] .== "----"] .= missing
	df[!,:cme_width][df[!,:cme_width] .== "----" .|| df[!,:cme_width] .== "---"] .= missing
	df[!,:cme_speed][df[!,:cme_speed] .== "----"] .= missing

	# Create is_halo column and set Halo angles to missing
	df.is_halo = df[!,:cme_angle] .== "Halo"
	df[!,:cme_angle][coalesce.(df.is_halo, false)] .= missing

	# Indicate lower bound and strip non-numeric parts of width
	df.width_lower_bound = occursin.(">", coalesce.(df[!,:cme_width], ""))
	df[!,:cme_width][df.width_lower_bound] = replace.(df[!,:cme_width][df.width_lower_bound], ">" => "")
	df[!,:cme_width] = replace.(coalesce.(df[!,:cme_width], "-1"), "h" => "")
	
	# Convert dates and times to DateTime
	df[!,:start_date] = Date.(df[:, :start_date], "yyyy/mm/dd")
	df[!,:end_date] = Date.(string.(year.(df[!,:start_date])) .* "/" .* df[:, :end_date], "yyyy/mm/dd")
	
	df[!,:start_time] = DateTime.(df[:, :start_date], Time.(df[:, :start_time]))
	df[!,:end_time][df[!,:end_time] .== "24:00"] .= "23:59" # Fix invalid time format
	df[!,:end_time] = DateTime.(df[:, :end_date], Time.(df[:, :end_time]))
	
	df[!,:cme_date] = Date.(coalesce.(string.(year.(df[!,:start_date])) .* "/" .* df[!,:cme_date], "0"), "yyyy/mm/dd")
	allowmissing!(df, :cme_date)
	df[!,:cme_date][year.(df[!,:cme_date]) .== 0] .= missing
	
	df[!,:cme_time] = DateTime.(coalesce.(df[:, :cme_date], Date(0)), Time.(coalesce.(df[:, :cme_time], "0")))
	allowmissing!(df, :cme_time)
	df[!,:cme_time][year.(df[!,:cme_time]) .== 0] .= missing

	# Parse int columns
	for col ∈ [:start_frequency, :end_frequency, :cme_angle, :cme_width, :cme_speed]
		df[!,col] .= parse.(Int, coalesce.(df[!,col], "-1"))
		allowmissing!(df, col)
		df[!,col][df[!,col] .== -1] .= missing
	end
	
	# Drop date columns
	select!(df, Not(:start_date))
	select!(df, Not(:end_date))
	select!(df, Not(:cme_date))
	
	# Rename columns
	rename!(df, :start_time => :start_datetime)
	rename!(df, :end_time => :end_datetime)
	rename!(df, :cme_time => :cme_datetime)
	rename!(df, :cme_angle => :cpa)
	rename!(df, :cme_width => :width)
	rename!(df, :cme_speed => :speed)

	df
end

# ╔═╡ 8710210a-2dcf-4d63-a274-4ab44445a17f
md"#### Tidied DataFrame of solar flare data from NASA"

# ╔═╡ 12328375-5e07-49c1-83da-bdae88fcc8d4
tidy_nasadf()

# ╔═╡ 1e97263b-5fa9-497b-b610-7f3ad30b5520
md"## Part 2: Analysis"

# ╔═╡ 238002f4-c32b-41bc-8342-ac238828f74a
md"### Task 5: Replication"

# ╔═╡ 3156a7d1-d1a3-414e-8794-c895ed08f3b0
replicate_nasadf() = let
	df = tidy_nasadf()

	# Sort DataFrame by flare classification in descending order
	df = sort(subset(dropmissing(df, :flare_classification), :flare_classification => x -> occursin.('X', x)), order(:flare_classification, by = x -> parse(Float64, x[2:end])), rev=true)
	
	# Rename columns
	rename!(df, :cme_datetime => :max_datetime)
	rename!(df, :flare_classification => :x_class)
	rename!(df, :flare_region => :region)

	# Truncate regions
	df[!,:region][length.(coalesce.(df[!,:region], "")) .> 4] .= chop.(df[!,:region][length.(coalesce.(df[!,:region], "")) .> 4], head = 1, tail = 0)

	# Remove trailing dots from x_class
	df[!,:x_class][last.(df[!,:x_class]) .== '.'] .= chop.(df[!,:x_class][last.(df[!,:x_class]) .== '.'])

	# Reorder the columns
	df[!, [:x_class, :start_datetime, :max_datetime, :end_datetime, :region]]
end

# ╔═╡ b35a3d34-628e-470d-96b2-076a1e3d5ba5
md"#### DataFrame replication for top 50 solar flares using NASA data"

# ╔═╡ 6048f590-30e0-41ea-bcb9-a283d700668b
replicate_nasadf()[1:50,:]

# ╔═╡ ec522eda-7c21-4691-863e-4b9adf1414b3
md"""
**Task 5 result.** Using the NASA table, I cannot produce an exact replicate due to discrepancies between the tables, e.g., some of the dates/times are different between the tables, and there are some records in the tables that don't exist in the other table.
"""

# ╔═╡ 0509b052-02c7-4cf5-9c1f-fa5cf584360c
md"### Task 6: Integration"

# ╔═╡ 9a30c019-fc18-492c-b147-14c234c8ff12
integrated_nasadf() = let
	df = replicate_nasadf()
	df_t50 = tidy_swldf()

	# Find most similar row from SpaceWeatherLive.com
	similarities = [
		findmin([
			sqrt.(
				# Compare x_class
				(parse.(Float64, chop.(row[:x_class], head = 1, tail = 0)) - parse.(Float64, chop.(t50row[:x_class], head = 1, tail = 0))).^2 +

				# Compare start_datetime
				Dates.value.(Dates.Minute.(row[:start_datetime] - t50row[:start_datetime])).^2 +

				# Compare max_datetime
				Dates.value.(Dates.Minute.(coalesce.(row[:max_datetime], DateTime(0)) - t50row[:max_datetime])).^2 .*

				# Ignore missing max_datetimes
				Dates.value.(Dates.Minute.(coalesce.(row[:max_datetime], DateTime(0)))) +

				# Compare end_datetime
				Dates.value.(Dates.Minute.(row[:end_datetime] - t50row[:end_datetime])).^2 +

				# Compare region
				(parse.(Int, coalesce.(row[:region], "0")) - parse.(Int, t50row[:region])).^2 .*

				# Ignore missing regions
				parse.(Int, coalesce.(row[:region], "0"))
			) for t50row ∈ eachrow(df_t50)]) for row ∈ eachrow(df)
	]

	# Add rank column
	insertcols!(df, 1, :rank => [similarities[x][2] for x ∈ 1:length(similarities)])
	allowmissing!(df, :rank)
	buf = Set()
	min_xclass = minimum(parse.(Float64, chop.(df_t50[!,:x_class], head = 1, tail = 0)))
	for row ∈ eachrow(df)
		if row[:rank] ∈ buf || parse(Float64, chop(row[:x_class], head = 1, tail = 0)) < min_xclass
			row[:rank] = missing
		else
			push!(buf, row[:rank])
		end
	end

	# Remove rows with missing rank
	df = df[(!ismissing).(df[!,:rank]), :]
	
	# Sort rows by rank
	sort(df)
end

# ╔═╡ 074eb18b-05eb-456e-89c7-39b7b122e14e
md"#### Integrated DataFrame of top 50 solar flares from NASA"

# ╔═╡ 53a2adeb-4d89-41e5-8a38-bb0abb2466eb
integrated_nasadf()

# ╔═╡ f9ac8b88-2383-4ef2-a9b9-01435654ebf8
md"### Task 7: Attributes visualization"

# ╔═╡ e389aaa7-82c8-4569-ace4-d13e2e3fb3ec
dataviz(feature) = let
	df = tidy_nasadf()
	df_t50 = integrated_nasadf()
	t50 = filter(row -> row[:start_datetime] ∈ df_t50[!,:start_datetime], df)
	nott50 = filter(row -> row[:start_datetime] ∉ df_t50[!,:start_datetime], df)
	scatter(nott50[!,:start_datetime], nott50[!,feature], label="Other")
	scatter!(t50[!,:start_datetime], t50[!,feature], label="Top 50", marker=:star, markersize=8)
	xlabel!("Starting DateTime")
end

# ╔═╡ c4311bd3-6cd0-4bf9-8210-8b6555d3175f
let
	dataviz(:start_frequency)
	ylabel!("Starting frequencies (kHz)")
	title!("Starting frequencies over time")
end

# ╔═╡ 8f8db2f8-bf75-4828-862c-2b361eb33692
let
	dataviz(:end_frequency)
	ylabel!("Ending frequencies (kHz)")
	title!("Ending frequencies over time")
end

# ╔═╡ 8e6d61eb-923d-47c3-9306-237529a60672
let
	dataviz(:width)
	ylabel!("CME width in the sky plane (degrees)")
	title!("CME width over time")
end

# ╔═╡ b5eb2437-a13e-4b69-a378-e977ecf0fba7
let
	dataviz(:speed)
	xlabel!("Starting DateTime")
	ylabel!("CME Speed in the sky plane (km/s)")
	title!("CME Speeds over time")
end

# ╔═╡ 55ec5071-e9a5-4acf-9e15-9947cc9be2b5
md"### Task 8: Attributes comparison"

# ╔═╡ 046bd1e5-e6c8-46b4-bace-11fe8ef95b50
let
	df = tidy_nasadf()
	df_t50 = integrated_nasadf()
	t50 = filter(row -> row[:start_datetime] ∈ df_t50[!,:start_datetime], df)
	nott50 = filter(row -> row[:start_datetime] ∉ df_t50[!,:start_datetime], df)
	t50ratio = sum(coalesce.(t50[!,:is_halo], false)) / length(t50[!,:is_halo])
	nott50ratio = sum(coalesce.(nott50[!,:is_halo], false)) / length(nott50[!,:is_halo])
	bar(["Top 50", "Other"], [t50ratio, nott50ratio], color = [theme_palette(:auto)[1], theme_palette(:auto)[2]], legend = false)
	ylabel!("Ratio of Halo CMEs vs. Number of CMEs")
	title!("Comparision of ratios of Halo CMEs between the\n top 50 solar flares and the rest of the solar flares")
end

# ╔═╡ e6c5c88c-1d13-4785-bdea-6048cc905dcc
md"### Task 9: Events distribution"

# ╔═╡ d1ffbd03-3454-4e95-8c8f-3466a0d3ecbc
let
	df = tidy_nasadf()
	df_t50 = integrated_nasadf()
	t50 = filter(row -> row[:start_datetime] ∈ df_t50[!,:start_datetime], df)
	nott50 = filter(row -> row[:start_datetime] ∉ df_t50[!,:start_datetime], df)

	# Plot the number of years instead of months to make it easier to visualize
	numyears = length(df[!,:start_datetime][1]:Year(1):df[!,:start_datetime][end])
	h = histogram(nott50[!,:start_datetime], label="Other", nbins = numyears)
	histogram!(h, t50[!,:start_datetime], label="Top 50", nbins = numyears)
	plot(h, xticks = [df[!,:start_datetime][1], df[!,:start_datetime][end÷2], df[!,:start_datetime][end]], xformatter = x -> Date(Dates.epochms2datetime(x)))
	title!("Number of solar flares per month")
end

# ╔═╡ bd510480-984e-44ef-ac45-2fbfa909eb9e
md"There appears to be a cluster of strong flares between the years 2001-2003 however there are no other clusters apparent in the data."

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

[[deps.Zstd_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "cc4bf3fdde8b7e3e9fa0351bdeedba1cf3b7f6e6"
uuid = "3161d3a3-bdf6-5164-811a-617609db77b4"
version = "1.5.0+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"

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

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

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

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

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

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

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

[[deps.xkbcommon_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"]
git-tree-sha1 = "ece2350174195bb31de1a63bea3a41ae1aa593b6"
uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd"
version = "0.9.1+5"
"""

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