running_analysis/RunningAnalysis.jl

### A Pluto.jl notebook ###
# v0.19.23

using Markdown
using InteractiveUtils

# ╔═╡ c649e044-d418-11ed-16c7-7171aa17ea3b
begin
	using DataFrames
	using CSV
	using PyCall
	using Conda
	using Dates
	using Plots
	using Statistics
	using StatsBase
	using StatsPlots
end

# ╔═╡ 9fee4ffb-83c0-46df-b84a-ef902a5e99ab
md"""
# Jake's Ultramarathon Training

Welcome to a typical Pluto notebook! This typically starts with

- Packages needed
- Initial data exploration
"""

# ╔═╡ 8bf03425-5697-4817-93fa-9519d9f184bd
md"""
## Packages Installed

- Pluto maintains it's own package environment per-notebook
- The checkmark means it is installed.
- The little cloud icon means it will be installed when ran
"""

# ╔═╡ 7566ebff-2171-4054-b156-42eccf335b86
begin
	workouts = CSV.read("workouts.csv", DataFrame);
	metrics = CSV.read("metrics.csv", DataFrame);

	# Start with some average time in each stage. Timestamp should be in a more usable
	# format.
	metrics.ParsedTimestamp = DateTime.(metrics.Timestamp, dateformat"y-m-d H:M:S")
	describe(metrics)
end

# ╔═╡ 3c54b5e3-2f95-4e38-89cf-944f15b21ff4
md"""
## First Steps

- Usually a good idea to describe the dataframe that you have
- Peform any cleaning needed

## New Columns

- Defined using the `.` format
- Uses broadcast over the existing Timestamp column that is a string
- Better for plotting and programmatic usage.
"""

# ╔═╡ 9c2dfaba-c7a6-49d3-ad79-ca1ff971fe38
md"""
## Sleep During Training

- How well did I sleep during training?
- Was I consistent in my sleep?
- Hypothesis, probably not
"""

# ╔═╡ ce659d96-c1f9-4556-9171-d2b27604b4ba
begin
	# Sleep data, with outliers trimmed (didn't wear my watch)
	row_selector = (metrics.Type .== "Sleep Hours") .&& (metrics.Value .< 9.) .&& (metrics.Value .> 6)
	
	sleep_hours = metrics[row_selector, [:Value, :ParsedTimestamp]]

	# Plot it
	scatter(sleep_hours.ParsedTimestamp, sleep_hours.Value, xlabel="Date", ylabel="Hours Slept", title="Sleep over Training, Parent Edition")
end

# ╔═╡ eb8d8089-fc7a-460c-808a-fa97e49c186f
md"""
## Answer

- No, I was not consistent in my sleep at all.
- Probably due to toddler
- Or potty training
- Or having aching legs...
- I digress.

### Follow up

Were my averages week to week consistent?
"""

# ╔═╡ a04217e3-d037-4fcb-94db-0ba13f685075
begin
	weeks = length(sleep_hours.Value) ÷ 7
	week_sleeps = [sleep_hours.Value[i:min(i+7, length(sleep_hours.Value))] for i ∈ 1:7:length(sleep_hours.Value)]
	mean_week_sleeps = mean.(week_sleeps)

	scatter(1:length(mean_week_sleeps), mean_week_sleeps, ylims=(6.0, 8.5), xticks=1:1:length(mean_week_sleeps), ylabel="Hours Slept", xlabel="Weeks", title="Mean Sleep Per Week", legend=false)
end

# ╔═╡ 7ce2f271-633a-4bdf-9306-49ec2fd42271
md"""
## Answer

- Yes, my averages week to week were consistent.
- Between 7-7.5 hours of sleep usually.

## Methodology

- Creates segments of max length 7 sleep observations
- Takes the mean of each segment
- Plot those means

## Next Up

- Reading my workout data
"""

# ╔═╡ 342be831-5e47-4c7c-a4fd-0b442ea88fbf
md"""
## Reading Workout Data

- Data is stored in .FIT files
- I failed to write the Garmin FIT SDK in time for Julia
- Use PyCall and Garmin's Python SDK!
"""

# ╔═╡ dfe39216-9c18-4149-9db9-cbd2f6bcf632
begin
	# Install the Garmin SDK into our Notebook environment.
	Conda.pip_interop(true)
	Conda.pip("install", "/Users/jacobwindle/Downloads/FitSDKRelease_21.105.00/py")
end

# ╔═╡ d94c3d18-27c1-498d-91fb-5a8e6dd6b585
md"""
## Python Interop

- Useful because I failed at writing the FIT SDK
- Use Pip interop in this case, `install` with local filepath
- Now time to read all the FIT files
"""

# ╔═╡ 7cb0daaf-6701-48c8-88ee-49a78d46d0a8
begin
	@pyimport garmin_fit_sdk
	
	fit_files = readdir("./fit_files"; join=true)

	function decode_fit_file(fp::AbstractString)::Tuple{Any,Any}
		stream = garmin_fit_sdk.Stream.from_file(fp)
		decoder = garmin_fit_sdk.Decoder(stream)
		try
			decoder.read()
		finally
			stream.close()
		end
	end

	decoded = []
	for file ∈ fit_files
		push!(decoded, decode_fit_file(file))
	end

	decoded_fit_files = [dc[1] for dc in decoded]
end

# ╔═╡ 065c7811-0203-44fd-bb88-49946d674fd8
md"""
## Reading the FIT files

- Function decode_fit_file will use the Garmin Python SDK to read fit file
- Use combo listcomp and broadcast to decode all files

## Getting my Heartrate Data

- From clicking through data, all heartrate information is in `record_mesgs`
- Convert `record_mesgs` into a dataframe for each date
"""

# ╔═╡ 39a46471-663e-4395-b4bd-2380932d7189
begin
	function string_keys(d::Dict{Any,Any})::Dict{String,Any}
		Dict([k => v for (k, v) in d if k isa AbstractString])
	end
	
	function extract_record_and_timestamp(fit_file::Dict{Any, Any})
		ts = DateTime(fit_file["activity_mesgs"][1]["timestamp"])
		messages = string_keys.(fit_file["record_mesgs"])

		try
			ts, DataFrame(messages)
		catch
			nothing
		end
	end

	decoded_files = extract_record_and_timestamp.(decoded_fit_files)
end


# ╔═╡ ed9e2e1f-cebe-4a0b-969a-93d344578a28
md"""

## Mapping my data to DataFrame

- Extract the data needed
- Write function that pulls data out of PyDict

"""

# ╔═╡ b7854b76-eb0e-4d78-9bd6-9b0258a725ba
describe(decoded_files[1][2])

# ╔═╡ 3e1d6c7b-41b5-435a-88f0-b0f3ab442662
begin
	# A generic graph_run function
	function graph_run(decoded_file::Tuple{DateTime, DataFrame})
		label = "Run on $(decoded_file[1])"
		df = decoded_file[2]
		l = @layout [a b; c d]
		p = plot(df."timestamp", df."heart_rate"; title="Heart Rate", ylabel="BPM", xticks=nothing)
		p2 = plot(df.timestamp, df.altitude; title="Altitude", xticks=nothing)
		p3 = scatter(df.timestamp, df.cadence, title="Cadence", xticks=nothing)
		p4 = plot(df.timestamp, df.speed; title="Speed", xticks=nothing)
		plot(p, p2, p3, p4, layout = l)
	end
end

# ╔═╡ 2f4cdc08-27a1-4e92-8709-8021cae771c6
graph_run(decoded_files[14])

# ╔═╡ a9d1b364-65d5-44bc-965f-61a5f2b47a36
md"""
## Graphing Runs

- Show speed, cadence, heartrate, and altitude
- Trail runs appear to have varied cadence, road runs are more stable
"""

# ╔═╡ 0c5d6cdb-7255-4712-92bc-eb3f3182d7eb
graph_run(decoded_files[19])

# ╔═╡ e4461dae-f648-4c34-a963-17dfb3f0220a
md"""
## Trends across Training

- What was my average heartrate for each run?
- Was my average heartrate effected by sleep?
"""

# ╔═╡ d1588e94-a450-4d8c-9811-69873deed252
begin
	function calc_avgs(data::Union{Nothing, Tuple{DateTime, DataFrame}})
		if isnothing(data)
			return nothing
		end
		try
			return (timestamp=data[1], mean_hr=mean(data[2].heart_rate), mean_cadence=mean(data[2].cadence), mean_alt=mean(data[2].altitude))
		catch
			return nothing
		end
	end
	
	avg_heartrates = DataFrame(filter(f -> !isnothing(f), calc_avgs.(decoded_files)))

	ps = scatter(avg_heartrates.timestamp, avg_heartrates.mean_hr, title="Avg Heartrate over Time", label="hr", xlabel="Timestamp", ylabel="Beats per minute", ylims=(115, 160))
	hspan!(ps, [145, maximum(avg_heartrates.mean_hr)]; color=:red, alpha=0.2, label="endurance run")
	hspan!(ps, [minimum(avg_heartrates.mean_hr), 145]; color=:green, alpha=0.2, label="recovery run")
end

# ╔═╡ 0b6bce6e-22e5-4a8d-95f4-4708deb07cdd
md"""
## Determining if Heartrate was affected by sleep

- Will need to use the workouts to determine which days were endurance runs
- Find sleep data for same day
- Stitch together to see results
"""

# ╔═╡ 4561f42b-2658-4c74-b642-6b2f92cd3598
begin
	# Copy the existing dataframe.
	hr_sleep_df = deepcopy(avg_heartrates)

	# Get the days that we have an endurance run
	my_endurance_days = workouts[:, :WorkoutDay]

	my_endurance_selector = [d in my_endurance_days for d in Date.(hr_sleep_df.timestamp)]

	my_endurance_days_df = hr_sleep_df[my_endurance_selector, :]
	my_endurance_days_df.Date = Date.(my_endurance_days_df.timestamp)
	# sleep_hours = metrics[metrics.Type .== "Sleep Hours", [:Value, :ParsedTimestamp]]
	sleep_on_endurance_days = [d in my_endurance_days for d in Date.(sleep_hours.ParsedTimestamp)]
	sleep_on_endurance_days_df = sleep_hours[sleep_on_endurance_days, :]
	sleep_on_endurance_days_df.Date = Date.(sleep_on_endurance_days_df.ParsedTimestamp)

	sleep_hr_df = leftjoin(my_endurance_days_df, sleep_on_endurance_days_df; on = :Date)
	ns = scatter(sleep_hr_df.mean_hr, sleep_hr_df.Value; xlims=(120, 160), title="Heartrate over Sleep", xlabel="BPM", ylabel="Hours Slept")
	vspan!(ns, [140, maximum(sleep_hr_df.mean_hr)]; color=:red, alpha=0.2, label="endurance run")
	vspan!(ns, [minimum(sleep_hr_df.mean_hr), 140]; color=:green, alpha=0.2, label="recovery run")
end

# ╔═╡ 27a59d46-f838-4d8f-9c66-a7edc1eea8dd
md"""
## Findings

- No discernible relationship
- Appears to have clusters, left half is recovery, right half is endurance

## Next Question

- Does Cadence Affect HR?
"""

# ╔═╡ 504ed9c8-ff54-4a25-84b9-d832fc435486
begin
	cadence_hr_df = deepcopy(avg_heartrates)
	# cadence_hr_df.Date = Date.(cadence_hr_df.timestamp)
	scatter(cadence_hr_df.mean_hr, cadence_hr_df.mean_cadence; xlabel="HR", ylabel="Cadence", title="HR vs. Cadence")
end

# ╔═╡ 873bcfa3-a989-426a-8402-6b81a8bbb46a
md"""
## Findings

- No relationship, because I always ran with the same cadence!

## Next Question

- Does altitude affect HR?
"""

# ╔═╡ b10b4931-e60f-4791-8dae-d6d58cd71e66
begin
	altitude_hr_df = deepcopy(avg_heartrates)
	scatter(altitude_hr_df.mean_hr, altitude_hr_df.mean_alt; xlabel="HR", ylabel="Mean Altitude", title="HR vs. Altitude")
end

# ╔═╡ b14e7de2-b91f-40d9-8aa8-237d82f25286
md"""
# Findings

- Altitude does appear to affect heart rate.
- Majority of runs were not at altitude, show normal distribution
- Runs at higher altitudes appear to cluster at higher HR

# Next Questions

- Do my data follow the normal distribution?
"""

# ╔═╡ 1fd645ee-0680-4de8-b1eb-1c94c2069ae9
begin
	d = fit(UnitRangeTransform, avg_heartrates.mean_hr)
	hr_normalized = StatsBase.transform(d, avg_heartrates.mean_hr)

	avg_heartrates.hr_normalized = hr_normalized
	@df avg_heartrates histogram(:mean_hr; bins=25, title="HR Histogram")
end

# ╔═╡ c3286275-3cff-4771-9ae6-78a960ba0484
md"""
## Findings

- Kind of normally distributed, but have two clusters
- Recovery vs. Endurance again!
"""

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version = "1.1.34+0"

[[deps.Xorg_libX11_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxcb_jll", "Xorg_xtrans_jll"]
git-tree-sha1 = "5be649d550f3f4b95308bf0183b82e2582876527"
uuid = "4f6342f7-b3d2-589e-9d20-edeb45f2b2bc"
version = "1.6.9+4"

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

[[deps.Xorg_libXcursor_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXfixes_jll", "Xorg_libXrender_jll"]
git-tree-sha1 = "12e0eb3bc634fa2080c1c37fccf56f7c22989afd"
uuid = "935fb764-8cf2-53bf-bb30-45bb1f8bf724"
version = "1.2.0+4"

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[[deps.Zstd_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "49ce682769cd5de6c72dcf1b94ed7790cd08974c"
uuid = "3161d3a3-bdf6-5164-811a-617609db77b4"
version = "1.5.5+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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