mirror of
https://gitlab.com/orbital-debris-research/directed-study/report-1.git
synced 2025-06-16 15:06:52 +00:00
71 lines
1.7 KiB
Julia
71 lines
1.7 KiB
Julia
using DataFrames
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using LinearAlgebra
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using CSV
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using DataFramesMeta
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# using Plots
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# using StatsPlots
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using PlotlyJS
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begin
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df = CSV.read("compiled.csv", DataFrame)
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df.box = df.bb_length .* df.bb_width .* df.bb_height
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@eachrow! df begin
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@newcol :Ix::Vector{Float64}
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@newcol :Iy::Vector{Float64}
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@newcol :Iz::Vector{Float64}
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@newcol :Ibar::Vector{Float64}
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I = [
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:Ixx :Ixy :Ixz
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:Iyx :Iyy :Iyz
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:Izx :Izy :Izz
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]
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(:Ix, :Iy, :Iz) = eigvals(I)
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:Ibar = :Ix^2 + :Iy^2 + :Iz^2 |> sqrt
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end
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# Convert material to scalar
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begin
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kv = Dict(reverse.(enumerate(Set(df.material_name))))
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mats = [] |> Vector{Int}
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for material in df.material_name
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push!(mats, kv[material])
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end
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df.material_index = mats
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end
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# Remove outliers
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df = df[df.box.<1e6, :]
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df = df[df.mass.<1000, :]
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end
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# @df df cornerplot(cols(1:7), compact = true)
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features = [:mass, :volume, :density, :area, :box, :Ibar, :material_index]
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p1 = plot(df, dimensions = features, kind = "splom", Layout(title = "Raw Data"))
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CSV.write("prepped.csv", df)
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df.cluster = "Cluster " .* ([1, 3, 2, 1, 2, 1, 1, 3, 1, 3, 2, 3, 1, 1, 2, 2, 1, 3, 1, 3, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 3, 2, 1, 1, 2, 2, 3, 3, 2, 2, 2, 1,] .|> string) # From matlab kmeans idx
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p2 = plot(df, dimensions = features, color = :cluster, kind = "splom", Layout(title = "Clustered Data"))
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savefig(p1, "prepped.svg", width = 1000, height = 1000)
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savefig(p2, "prepped_clustered.svg", width = 1000, height = 1000)
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open("./prepped.html", "w") do io
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PlotlyBase.to_html(io, p1.plot)
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end
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open("./prepped_clustered.html", "w") do io
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PlotlyBase.to_html(io, p2.plot)
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end |