### A Pluto.jl notebook ###
# v0.19.42

using Markdown
using InteractiveUtils

# ╔═╡ fc4454f6-1c07-4706-ac3f-040d9ee83994
using PlutoUI, Dates

# ╔═╡ 9a1222aa-110c-11ef-1d54-eb155df43b97
using HTTP, Gumbo, JSON, AbstractTrees, DataFrames, ReadableRegex, PrettyTables

# ╔═╡ 4741fe85-c011-451b-bef0-51cc487e978a
# ╠═╡ show_logs = false
using CondaPkg, PythonCall

# ╔═╡ e5c28754-3322-4436-9a30-ff5a95ec9ce9
using LightOSM, JuMP, GLPK, Random

# ╔═╡ 91585a4c-e842-4a87-ae02-ce9e86c3da37
using TravelingSalesmanExact, SCIP

# ╔═╡ 915bd223-4b6e-441b-a82c-05d680cc93cb
md"""
[Index](https://titanic.caltech.edu/~tgorordo)
"""

# ╔═╡ 38b70851-9587-457d-adca-50394b3f6a98
md"""
$(Resource("https://originaltommys.com/wp-content/uploads/2020/05/tommys-logo-1-min.png"))
# The Traveling Tommy's Problem
So you want to try the ultimate negative time challenge? Here's the traveling-salesman solution for all current Tommy's locations.
"""

# ╔═╡ f1344329-8d08-4d5a-bb81-13ccc50ca6ba
md"""
## Scraping the Grill
Get a list of [current locations](https://originaltommys.com/locations/).
"""

# ╔═╡ 012f104b-d3a1-4f2a-b224-86d1a711c7f4
url = "https://originaltommys.com/locations/";

# ╔═╡ acb28c38-1914-4398-84b5-1288260845bb
# Fetch the Tommy's location page and parse to scrape
rsp = HTTP.get(url); prs = parsehtml(String(rsp.body));

# ╔═╡ 8d135017-37a8-457b-ab33-e4514e223e09
# define a regex to pick out the operating hours from a table row
hoursregex = @compile look_for(one_or_more(ANY), after="Hours: ")

# ╔═╡ 021af38e-3860-4474-a901-620fc92f5d57
# and a regex to help remove the lobby hours, since that isn't relevant info
lobbyregex = @compile look_for("(Lobby " * one_or_more(ANY) * ")")

# ╔═╡ 9c316980-9940-4714-9ef9-357658a23479
df = let df = DataFrame([:Location => [], :Address => [], :Hours => []]);

	# loop over the current table of locations
	for elem in PreOrderDFS(prs.root) try if tag(elem) == :tr && 
        getattr(elem.parent.parent, "id") in ["tablepress-5", "tablepress-6"]
      
      	# extract the location name, address, and hours; push to our DataFrame
      	push!(df, [elem[1][1], elem[2][1], 
                  match(hoursregex, Gumbo.text(elem[2])).match |> 
                      string |> (s -> replace(s, lobbyregex => "")) |> strip 
      	])
    	end catch end
	end 
	transform!(df, :Address => ByRow(Gumbo.text) => :Address)
end;

# ╔═╡ bcc30610-7e29-4d70-8ae1-752681659e5f
let of = copy(df)
	pretty_table(String, of; backend=Val(:html), allow_html_in_cells=true,
		tf=PrettyTables.tf_html_minimalist, alignment=:l, 
		show_row_number=true, show_subheader=false, linebreaks=true,
		title="<code><a href='$url'>$url</a> last scraped @ $(now())</code>"
	) |> HTML
end

# ╔═╡ d31c3579-f6c1-4959-b5d8-ca38f29d119c
md"""
### Geocode
Locate addresses using [Nominatim](https://nominatim.openstreetmap.org/ui/search.html). A couple addresses need manual intervention before lookup:
"""

# ╔═╡ ada082b3-6615-45e9-818f-26e6f63f12ac
# wrong streetname
df[1,:Address]  = replace(df[1,:Address] , "Road" => "Boulevard") 

# ╔═╡ 1ac39fd7-fab9-4f22-a91b-944a69e67260
# bad city name
df[19,:Address] = replace(df[19,:Address], "North Hollywood" => "Garnsey")

# ╔═╡ c9f6c975-d9d0-4ad3-9f9a-4dacda0bb79c
# can't find address, so take the one across the street
df[22,:Address] = replace(df[22,:Address], "9301 E. Whittier Blvd" => "9298 Speedway")

# ╔═╡ 30812787-81a0-4f3d-9cd2-2df00c7c4ebd
# really just doesn't know this address
df[26,:Address] = replace(df[26,:Address], 
	"28116 N. The Old Road" => "Original Tommy's Golden State Freeway", 
	"91355" => "91310")

# ╔═╡ 4fc5b1a8-9b33-486d-950a-4647d9993aff
md"""
query the api:
"""

# ╔═╡ 47126b71-1f25-4d73-bf91-22e39a84d2cc
let api = "https://nominatim.openstreetmap.org/search?"
	places = []
	latlons = []
	for l in eachrow(df) 
		try
			res = HTTP.get(api, 
						headers=["User-Agent" => 
							"titanic.caltech.edu/1.0.0-DEV (HTTP.jl; tcgorordo@gmail.com)"],
                        query= ["q" => l[:Address], "format" => "json"]);
			@info l.Address res
    		jres = res.body |> String |> JSON.parse
			push!(places, jres[1]["place_id"])
			push!(latlons, (parse(Float64, jres[1]["lat"]), parse(Float64, jres[1]["lon"])))
			sleep(1)
  		catch e
    		@warn ("No result for " * l[:Address]) e
  		end
	end
	df[!,:placeid] = places
	df[!,:latlon] = latlons
end;

# ╔═╡ 951bfa51-da39-437c-9be2-e44648a1b868
# ╠═╡ show_logs = false
CondaPkg.add("folium"); const flm = pyimport("folium");

# ╔═╡ 715a5564-81b1-4189-84ed-c9775f438b0f
minlat, minlon = 32.363396239603740, -119.77393355557084;

# ╔═╡ 0f31dd19-bb4e-47fc-8566-645062b209c0
maxlat, maxlon = 37.178653972331674, -113.97315230557084;

# ╔═╡ 8af5b693-f17d-44b4-b49f-7786b6e9f636
boxbnd = [(minlat, minlon), (maxlat, maxlon)]

# ╔═╡ 8a55f364-495e-467f-9ca4-fa35e0318388
polybnd = [ [34.248800, -119.377205],
			[35.138756, -117.045934],
			[36.442690, -115.020941],
			[35.755994, -115.004204],
			[34.606297, -116.752006],
			[33.540679, -117.146023],
			[33.427632, -117.598244]];

# ╔═╡ 7bf4ae79-5ba5-46ae-8cfa-a198fd6b4312
let fm = flm.Map()
	flm.Polygon(polybnd, color="grey", opacity=0.8, weight=3).add_to(fm)
	flm.Rectangle(boxbnd, color="black", weight=2).add_to(fm)
	for r in eachrow(df)
		flm.Marker(r[:latlon],
			tooltip="$(rownumber(r)) $(r[:Location]): $(r[:Address])").add_to(fm)
	end
	@py fm.fit_bounds([(minlat, minlon), (maxlat, maxlon)])
	fm._repr_html_() |> HTML
end

# ╔═╡ 2f12547c-4be1-4644-85b7-c3fbdfcedf20
LightOSM.download_osm_network(
	:polygon, network_type=:drive, metadata=true, download_format=:osm,
	polygon=(collect∘map)(reverse, polybnd), # bbox = boxbnd,
	save_to_file_location="LAVegas.osm"
);

# ╔═╡ 568cc195-023c-4df3-a9fb-65f27ea534c5
md"""
[`LAVegas.osm`](https://titanic.caltech.edu/~tgorordo/LAVegas.osm)
"""

# ╔═╡ 4401458c-3535-47c6-b219-c62c101920af
g = LightOSM.graph_from_file("LAVegas.osm", network_type=:drive, weight_type=:time, largest_connected_component=true)

# ╔═╡ efcf7447-bec3-4199-a54c-d8cf1c9874ad
md"""
Route a distance matrix:
"""

# ╔═╡ 542b52d9-9f83-49d4-98e1-be2d4c5870a9
LightOSM.total_path_weight(::LightOSM.OSMGraph{U, T, W}, ::Nothing) where {U, T, W} = 0 # for a connected graph, `nothing` indicates we've asked for the path from a node to itself.

# ╔═╡ a868a31f-2cc7-4283-b0e6-27c50f43a3be
d = let nodes = map(l -> nearest_node(g, l)[1], collect.(df[!,:latlon]))
	[ total_path_weight(g, shortest_path(g, a, b)) for a=nodes, b=nodes]
end

# ╔═╡ 63333464-4a87-4b14-9507-57cbcdd3d734
n = size(d, 1)

# ╔═╡ 5a635ea7-a3e7-4608-b7db-065588a0d732
function build_tsp_model(m)
	n = size(m)[1]
	model = Model(GLPK.Optimizer)
	@variable(model, x[1:n, 1:n], Bin, Symmetric)
	@objective(model, Min, sum(m .* x) / 2)
	@constraint(model, [i in 1:n], sum(x[i, :]) == 2)
	@constraint(model, [i in 1:n], x[i, i] == 0.)
	return model
end

# ╔═╡ 0bf52f3a-cacd-4310-9526-2610ef1235bb
function subtour(edges::Vector{Tuple{Int,Int}}, n)
    shortest_subtour, unvisited = collect(1:n), Set(collect(1:n))
    while !isempty(unvisited)
        this_cycle, neighbors = Int[], unvisited
        while !isempty(neighbors)
            current = pop!(neighbors)
            push!(this_cycle, current)
            if length(this_cycle) > 1
                pop!(unvisited, current)
            end
            neighbors =
                [j for (i, j) in edges if i == current && j in unvisited]
        end
        if length(this_cycle) < length(shortest_subtour)
            shortest_subtour = this_cycle
        end
    end
    return shortest_subtour
end

# ╔═╡ 39fe4d23-0e0c-4203-a026-db8fbe275815
function selected_edges(x::Matrix{Float64}, n)
    return Tuple{Int,Int}[(i, j) for i in 1:n, j in 1:n if x[i, j] > 0.5]
end

# ╔═╡ 013924bf-76ff-451b-b45b-965d020a9564
subtour(x::Matrix{Float64}) = subtour(selected_edges(x, size(x, 1)), size(x, 1))

# ╔═╡ e7b7eddc-ddde-4554-8b11-1785e381fd0e
subtour(x::AbstractMatrix{VariableRef}) = subtour(value.(x))

# ╔═╡ 26fa54e2-864c-417d-a44b-fb20550cebb2
mapsol(ps) = let fm = flm.Map()
	flm.Polygon(polybnd, color="grey", opacity=0.8, weight=3).add_to(fm)
	flm.Rectangle(boxbnd, color="black", weight=2).add_to(fm)
	#for r in eachrow(df)
	#	flm.Marker(r[:latlon]).add_to(fm)
	#end
	for p in ps
		flm.PolyLine(locations=[df[p[1], :latlon], df[p[2], :latlon]]).add_to(fm)
	end
	@py fm.fit_bounds([(minlat, minlon), (maxlat, maxlon)])
	fm._repr_html_() |> HTML
end

# ╔═╡ b136349f-c4b2-4cc8-88c2-99e2bb1688c2
begin 
	iterative_model = build_tsp_model(d)
	set_optimizer_attribute(iterative_model, "msg_lev", GLPK.GLP_MSG_ALL)
	optimize!(iterative_model)
	@assert is_solved_and_feasible(iterative_model)
	time_iterated = solve_time(iterative_model)
	cycle = subtour(iterative_model[:x])
	while 1 < length(cycle) < n
    	@info "Found cycle of length $(length(cycle))"
    	S = [(i, j) for (i, j) in Iterators.product(cycle, cycle) if i < j]
    	@constraint(
        	iterative_model,
        	sum(iterative_model[:x][i, j] for (i, j) in S) <= length(cycle) - 1,
    	)
    	optimize!(iterative_model)
    	@assert is_solved_and_feasible(iterative_model)
		a = value.(iterative_model[:x])
		p = selected_edges(a, size(a, 1))
		@info "Map:" mapsol(p)
    	global time_iterated += solve_time(iterative_model)
    	global cycle = subtour(iterative_model[:x])
	end
end

# ╔═╡ a67b7b91-3447-4874-864e-5cae0edf4d96
objective_value(iterative_model)

# ╔═╡ e10130dc-8720-4920-b13e-a3f849e238b1
sol = let a = value.(iterative_model[:x])
	s = selected_edges(a, size(a, 1))
	trail = [7] # start at eaglerock
	while length(trail) < 32
		bs = s[findall(map(p -> trail[end] in p, s))] |> Iterators.flatten |> collect |> unique |> l -> filter(x -> !in(x, trail), l)
		append!(trail, first(bs))
	end
	t = sum(total_path_weight(g, shortest_path(g, 
	nearest_node(g, collect(df[p[1], :latlon]))[1],
	nearest_node(g, collect(df[p[2], :latlon]))[1]
			)) for p in collect(zip(trail, vcat(trail[2:end], [7]))))
	rt = sum(total_path_weight(g, shortest_path(g, 
	nearest_node(g, collect(df[p[1], :latlon]))[1],
	nearest_node(g, collect(df[p[2], :latlon]))[1]
			)) for p in collect(zip(reverse(trail), vcat(reverse(trail)[2:end], [7]))))
	if rt < t
		reverse!(trail)
	end
	trail
end

# ╔═╡ e1eabfe7-ae82-4007-abb7-59681bb7bf90
let of = copy(df)[sol, :]
	pretty_table(String, of; backend=Val(:html), allow_html_in_cells=true,
		tf=PrettyTables.tf_html_minimalist, alignment=:l, 
		show_row_number=true, show_subheader=false, linebreaks=true,
		title="<code><a href='$url'>$url</a> last scraped @ $(now())</code>"
	) |> HTML
end

# ╔═╡ 35ae7286-7037-4551-ae18-557811fb9488
let fm = flm.Map()
	flm.Polygon(polybnd, color="grey", opacity=0.8, weight=3).add_to(fm)
	flm.Rectangle(boxbnd, color="black", weight=2).add_to(fm)
	for r in eachrow(df)
		flm.Marker(r[:latlon],
			tooltip="$(r[:Location]): $(r[:Address])").add_to(fm)
	end
	for p in zip(sol, vcat(sol[2:end], [7])) |> collect
		s = shortest_path(g, 
				nearest_node(g, collect(df[p[1], :latlon]))[1],
				nearest_node(g, collect(df[p[2], :latlon]))[1]
			)
		if !isnothing(s)
			ns = []
			for i in s
				l = g.nodes[i].location
				try
					push!(ns, (l.lat, l.lon))
				catch e
					@warn l e
				end
			end
			flm.PolyLine(locations=ns).add_to(fm)
		end
	end
	@py fm.fit_bounds([(minlat, minlon), (maxlat, maxlon)])
	fm._repr_html_() |> HTML
end

# ╔═╡ 00000000-0000-0000-0000-000000000001
PLUTO_PROJECT_TOML_CONTENTS = """
[deps]
AbstractTrees = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"
CondaPkg = "992eb4ea-22a4-4c89-a5bb-47a3300528ab"
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
GLPK = "60bf3e95-4087-53dc-ae20-288a0d20c6a6"
Gumbo = "708ec375-b3d6-5a57-a7ce-8257bf98657a"
HTTP = "cd3eb016-35fb-5094-929b-558a96fad6f3"
JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
LightOSM = "d1922b25-af4e-4ba3-84af-fe9bea896051"
PlutoUI = "7f904dfe-b85e-4ff6-b463-dae2292396a8"
PrettyTables = "08abe8d2-0d0c-5749-adfa-8a2ac140af0d"
PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d"
Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
ReadableRegex = "cbbcb084-453d-4c4c-b292-e315607ba6a4"
SCIP = "82193955-e24f-5292-bf16-6f2c5261a85f"
TravelingSalesmanExact = "737fac7d-4440-55ef-927e-002196e95561"

[compat]
AbstractTrees = "~0.4.5"
CondaPkg = "~0.2.22"
DataFrames = "~1.6.1"
GLPK = "~1.2.0"
Gumbo = "~0.8.2"
HTTP = "~1.10.6"
JSON = "~0.21.4"
JuMP = "~1.21.1"
LightOSM = "~0.3.1"
PlutoUI = "~0.7.59"
PrettyTables = "~2.3.1"
PythonCall = "~0.9.20"
ReadableRegex = "~0.3.2"
SCIP = "~0.11.14"
TravelingSalesmanExact = "~0.3.11"
"""

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

julia_version = "1.10.3"
manifest_format = "2.0"
project_hash = "25430a92596131b89c522a979165c0cd9a5804b0"

[[deps.ASL_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "6252039f98492252f9e47c312c8ffda0e3b9e78d"
uuid = "ae81ac8f-d209-56e5-92de-9978fef736f9"
version = "0.1.3+0"

[[deps.AbstractPlutoDingetjes]]
deps = ["Pkg"]
git-tree-sha1 = "6e1d2a35f2f90a4bc7c2ed98079b2ba09c35b83a"
uuid = "6e696c72-6542-2067-7265-42206c756150"
version = "1.3.2"

[[deps.AbstractTrees]]
git-tree-sha1 = "2d9c9a55f9c93e8887ad391fbae72f8ef55e1177"
uuid = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"
version = "0.4.5"

[[deps.ArgTools]]
uuid = "0dad84c5-d112-42e6-8d28-ef12dabb789f"
version = "1.1.1"

[[deps.ArnoldiMethod]]
deps = ["LinearAlgebra", "Random", "StaticArrays"]
git-tree-sha1 = "d57bd3762d308bded22c3b82d033bff85f6195c6"
uuid = "ec485272-7323-5ecc-a04f-4719b315124d"
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[[deps.ArrayTools]]
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uuid = "1dc0ca97-c5ce-4e77-ac6d-c576ac9d7f27"
version = "0.2.7"

[[deps.Artifacts]]
uuid = "56f22d72-fd6d-98f1-02f0-08ddc0907c33"

[[deps.Base64]]
uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"

[[deps.BenchmarkTools]]
deps = ["JSON", "Logging", "Printf", "Profile", "Statistics", "UUIDs"]
git-tree-sha1 = "f1dff6729bc61f4d49e140da1af55dcd1ac97b2f"
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version = "1.5.0"

[[deps.BitFlags]]
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[[deps.Bzip2_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "9e2a6b69137e6969bab0152632dcb3bc108c8bdd"
uuid = "6e34b625-4abd-537c-b88f-471c36dfa7a0"
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[[deps.CEnum]]
git-tree-sha1 = "389ad5c84de1ae7cf0e28e381131c98ea87d54fc"
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version = "0.5.0"

[[deps.Clustering]]
deps = ["Distances", "LinearAlgebra", "NearestNeighbors", "Printf", "Random", "SparseArrays", "Statistics", "StatsBase"]
git-tree-sha1 = "9ebb045901e9bbf58767a9f34ff89831ed711aae"
uuid = "aaaa29a8-35af-508c-8bc3-b662a17a0fe5"
version = "0.15.7"

[[deps.CodecBzip2]]
deps = ["Bzip2_jll", "Libdl", "TranscodingStreams"]
git-tree-sha1 = "9b1ca1aa6ce3f71b3d1840c538a8210a043625eb"
uuid = "523fee87-0ab8-5b00-afb7-3ecf72e48cfd"
version = "0.8.2"

[[deps.CodecZlib]]
deps = ["TranscodingStreams", "Zlib_jll"]
git-tree-sha1 = "59939d8a997469ee05c4b4944560a820f9ba0d73"
uuid = "944b1d66-785c-5afd-91f1-9de20f533193"
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[[deps.ColorTypes]]
deps = ["FixedPointNumbers", "Random"]
git-tree-sha1 = "b10d0b65641d57b8b4d5e234446582de5047050d"
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[[deps.ColorVectorSpace]]
deps = ["ColorTypes", "FixedPointNumbers", "LinearAlgebra", "SpecialFunctions", "Statistics", "TensorCore"]
git-tree-sha1 = "600cc5508d66b78aae350f7accdb58763ac18589"
uuid = "c3611d14-8923-5661-9e6a-0046d554d3a4"
version = "0.9.10"

[[deps.Colors]]
deps = ["ColorTypes", "FixedPointNumbers", "Reexport"]
git-tree-sha1 = "362a287c3aa50601b0bc359053d5c2468f0e7ce0"
uuid = "5ae59095-9a9b-59fe-a467-6f913c188581"
version = "0.12.11"

[[deps.CommonSubexpressions]]
deps = ["MacroTools", "Test"]
git-tree-sha1 = "7b8a93dba8af7e3b42fecabf646260105ac373f7"
uuid = "bbf7d656-a473-5ed7-a52c-81e309532950"
version = "0.3.0"

[[deps.Compat]]
deps = ["TOML", "UUIDs"]
git-tree-sha1 = "b1c55339b7c6c350ee89f2c1604299660525b248"
uuid = "34da2185-b29b-5c13-b0c7-acf172513d20"
version = "4.15.0"
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uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"

[[deps.UnicodePlots]]
deps = ["ColorTypes", "Contour", "Crayons", "Dates", "FileIO", "FreeTypeAbstraction", "LazyModules", "LinearAlgebra", "MarchingCubes", "NaNMath", "Printf", "SparseArrays", "StaticArrays", "StatsBase", "Unitful"]
git-tree-sha1 = "ae67ab0505b9453655f7d5ea65183a1cd1b3cfa0"
uuid = "b8865327-cd53-5732-bb35-84acbb429228"
version = "2.12.4"

[[deps.Unitful]]
deps = ["Dates", "LinearAlgebra", "Random"]
git-tree-sha1 = "dd260903fdabea27d9b6021689b3cd5401a57748"
uuid = "1986cc42-f94f-5a68-af5c-568840ba703d"
version = "1.20.0"

    [deps.Unitful.extensions]
    ConstructionBaseUnitfulExt = "ConstructionBase"
    InverseFunctionsUnitfulExt = "InverseFunctions"

    [deps.Unitful.weakdeps]
    ConstructionBase = "187b0558-2788-49d3-abe0-74a17ed4e7c9"
    InverseFunctions = "3587e190-3f89-42d0-90ee-14403ec27112"

[[deps.UnsafePointers]]
git-tree-sha1 = "c81331b3b2e60a982be57c046ec91f599ede674a"
uuid = "e17b2a0c-0bdf-430a-bd0c-3a23cae4ff39"
version = "1.0.0"

[[deps.XML2_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Libiconv_jll", "Zlib_jll"]
git-tree-sha1 = "532e22cf7be8462035d092ff21fada7527e2c488"
uuid = "02c8fc9c-b97f-50b9-bbe4-9be30ff0a78a"
version = "2.12.6+0"

[[deps.Zlib_jll]]
deps = ["Libdl"]
uuid = "83775a58-1f1d-513f-b197-d71354ab007a"
version = "1.2.13+1"

[[deps.bliss_jll]]
deps = ["Artifacts", "GMP_jll", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "f8b75e896a326a162a4f6e998990521d8302c810"
uuid = "508c9074-7a14-5c94-9582-3d4bc1871065"
version = "0.77.0+1"

[[deps.boost_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
git-tree-sha1 = "72f8f9628f7f008e2616fe4c32ceb96bc82da733"
uuid = "28df3c45-c428-5900-9ff8-a3135698ca75"
version = "1.79.0+0"

[[deps.libblastrampoline_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "8e850b90-86db-534c-a0d3-1478176c7d93"
version = "5.8.0+1"

[[deps.micromamba_jll]]
deps = ["Artifacts", "JLLWrappers", "LazyArtifacts", "Libdl"]
git-tree-sha1 = "b4a5a3943078f9fd11ae0b5ab1bdbf7718617945"
uuid = "f8abcde7-e9b7-5caa-b8af-a437887ae8e4"
version = "1.5.8+0"

[[deps.nghttp2_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"
version = "1.52.0+1"

[[deps.oneTBB_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "7d0ea0f4895ef2f5cb83645fa689e52cb55cf493"
uuid = "1317d2d5-d96f-522e-a858-c73665f53c3e"
version = "2021.12.0+0"

[[deps.p7zip_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"
version = "17.4.0+2"
"""

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