mirror of
https://gitlab.com/orbital-debris-research/directed-study/report-1.git
synced 2025-06-15 22:46:47 +00:00
71 lines
1.7 KiB
Julia
71 lines
1.7 KiB
Julia
using DataFrames
|
|
using LinearAlgebra
|
|
using CSV
|
|
using DataFramesMeta
|
|
# using Plots
|
|
# using StatsPlots
|
|
using PlotlyJS
|
|
|
|
begin
|
|
df = CSV.read("compiled.csv", DataFrame)
|
|
|
|
df.box = df.bb_length .* df.bb_width .* df.bb_height
|
|
|
|
|
|
@eachrow! df begin
|
|
@newcol :Ix::Vector{Float64}
|
|
@newcol :Iy::Vector{Float64}
|
|
@newcol :Iz::Vector{Float64}
|
|
@newcol :Ibar::Vector{Float64}
|
|
|
|
I = [
|
|
:Ixx :Ixy :Ixz
|
|
:Iyx :Iyy :Iyz
|
|
:Izx :Izy :Izz
|
|
]
|
|
|
|
(:Ix, :Iy, :Iz) = eigvals(I)
|
|
:Ibar = :Ix^2 + :Iy^2 + :Iz^2 |> sqrt
|
|
|
|
end
|
|
|
|
|
|
|
|
# Convert material to scalar
|
|
begin
|
|
kv = Dict(reverse.(enumerate(Set(df.material_name))))
|
|
mats = [] |> Vector{Int}
|
|
for material in df.material_name
|
|
push!(mats, kv[material])
|
|
end
|
|
df.material_index = mats
|
|
end
|
|
|
|
# Remove outliers
|
|
df = df[df.box.<1e6, :]
|
|
df = df[df.mass.<1000, :]
|
|
end
|
|
|
|
# @df df cornerplot(cols(1:7), compact = true)
|
|
features = [:mass, :volume, :density, :area, :box, :Ibar, :material_index]
|
|
|
|
p1 = plot(df, dimensions = features, kind = "splom", Layout(title = "Raw Data"))
|
|
|
|
CSV.write("prepped.csv", df)
|
|
|
|
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
|
|
|
|
|
|
p2 = plot(df, dimensions = features, color = :cluster, kind = "splom", Layout(title = "Clustered Data"))
|
|
|
|
|
|
|
|
savefig(p1, "prepped.svg", width = 1000, height = 1000)
|
|
savefig(p2, "prepped_clustered.svg", width = 1000, height = 1000)
|
|
|
|
open("./prepped.html", "w") do io
|
|
PlotlyBase.to_html(io, p1.plot)
|
|
end
|
|
open("./prepped_clustered.html", "w") do io
|
|
PlotlyBase.to_html(io, p2.plot)
|
|
end |