code to process data before matlab

prep/Project.toml

@@ -0,0 +1,6 @@
+[deps]
+CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
+DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
+DataFramesMeta = "1313f7d8-7da2-5740-9ea0-a2ca25f37964"
+Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
+StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd"

prep/prep.jl

@@ -0,0 +1,60 @@
+using DataFrames
+using LinearAlgebra
+using CSV
+using DataFramesMeta
+using Plots
+using StatsPlots
+
+begin
+    df = CSV.read("compiled.csv", DataFrame)
+
+    df.bb_volume = 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 = []
+        for material in df.material_name
+            push!(mats, kv[material])
+        end
+        df.material_index = mats
+    end
+
+    # Remove columns not needed for analysis
+    df = df[!, [:mass, :volume, :density, :area, :bb_volume, :Ibar]]
+
+    # Remove outliers
+    df = df[df.bb_volume.<1e6, :]
+    df = df[df.mass.<1000, :]
+end
+
+@df df cornerplot(cols(1:4), compact = true)
+
+# plot(df.mass)
+# histogram(df.mass)
+scatter(df.mass, df.volume)
+
+
+
+
+CSV.write("prepped.csv", df)