mirror of
https://gitlab.com/orbital-debris-research/directed-study/report-1.git
synced 2025-06-15 22:46:47 +00:00
69 lines
1.5 KiB
Julia
69 lines
1.5 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 columns not needed for analysis
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# df = df[!, [:mass, :volume, :density, :area, :bb_volume, :Ibar, :material_index]]
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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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# plot(df.mass)
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# histogram(df.mass)
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# scatter(df.mass, df.Ibar)
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features = [:mass, :volume, :density, :area, :box, :Ibar]
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plot(df, dimensions = features, kind = "splom", Layout(title = "Raw Data"))
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corner(df)
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CSV.write("prepped.csv", df)
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df = dataset(DataFrame, "iris")
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features = [:sepal_width, :sepal_length, :petal_width, :petal_length]
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plot(df, dimensions = features, color = :species, kind = "splom") |