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code to process data before matlab
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prep/Manifest.toml
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prep/Manifest.toml
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prep/Project.toml
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prep/Project.toml
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[deps]
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CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
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DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
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DataFramesMeta = "1313f7d8-7da2-5740-9ea0-a2ca25f37964"
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Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
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StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd"
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prep/prep.jl
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prep/prep.jl
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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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begin
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df = CSV.read("compiled.csv", DataFrame)
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df.bb_volume = 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 = []
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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]]
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# Remove outliers
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df = df[df.bb_volume.<1e6, :]
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df = df[df.mass.<1000, :]
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end
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@df df cornerplot(cols(1:4), compact = true)
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# plot(df.mass)
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# histogram(df.mass)
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scatter(df.mass, df.volume)
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CSV.write("prepped.csv", df)
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prepped.csv
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prepped.csv
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mass,volume,density,area,bb_volume,Ibar
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1.066,995.9,0.001,2069.301,7176.96,153.72874402012403
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108.079,13768.029,0.008,19215.837,40069.26,207734.78101312218
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433.406,55210.971,0.008,57969.555,210407.28048000002,683995.5502520726
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0.847,108.651,0.008,274.826,1081.5593375840003,14.168204014623734
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238.488,30380.637,0.008,36346.664,67608.51299999999,1.3486211992376125e6
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11.338,1444.311,0.008,1210.691,2211.0,1738.215640169539
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11.338,1444.311,0.008,1210.691,2211.0,1738.215640169539
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51.926,19232.035,0.003,5129.026,41817.6,12690.25031274084
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3.615,460.535,0.008,1181.069,5533.789063138,445.2408284299633
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51.926,19232.035,0.003,5129.026,41817.6,12690.25031274084
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238.488,30380.637,0.008,36346.664,67608.51299999999,1.3486211992376125e6
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141.036,17966.31,0.008,33980.334,860155.86,314358.9114140484
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12.203,1554.512,0.008,2351.908,2936.6708,690.0074899607974
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12.203,1554.512,0.008,2351.908,2936.6708,690.0074899607974
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238.488,30380.637,0.008,36346.664,67608.51299999999,1.3486211992376125e6
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365.628,46576.871,0.008,55492.134,103890.15,4.701074854900372e6
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58.053,7395.244,0.008,2990.011,10164.0,42239.105195730794
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51.926,19232.035,0.003,5129.026,41817.6,12690.250312740842
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4.211,536.42,0.008,874.34,1860.2788,402.35623195745336
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133.418,16995.941,0.008,30952.471,72293.1,90508.51809910338
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1.066,995.9,0.001,2069.301,7176.96,153.72874402012403
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20.543,2633.756,0.008,6078.184,53128.416,8520.54886629764
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238.488,30380.637,0.008,36346.664,67608.51299999999,1.3486211992376125e6
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11.338,1444.311,0.008,1210.691,2211.0,1738.215640169539
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11.338,1444.311,0.008,1210.691,2211.0,1738.215640169539
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11.338,1444.311,0.008,1210.691,2211.0,1738.215640169539
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0.847,108.651,0.008,274.826,1081.5593375840003,14.168204014623734
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46.103,5872.947,0.008,13143.977,47481.0,16394.45274151644
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238.488,30380.637,0.008,36346.664,67608.51299999999,1.3486211992376125e6
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58.053,7395.244,0.008,2990.011,10164.0,42239.105195730794
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0.847,108.651,0.008,274.826,1081.5593375840003,14.168204014623734
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9.266,3431.699,0.003,3369.751,5062.5,3063.7696834076805
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141.036,17966.31,0.008,33980.334,860155.86,314358.9114140484
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365.628,46576.871,0.008,55492.134,103890.15,4.701074854900372e6
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58.053,7395.244,0.008,2990.011,10164.0,42239.105195730794
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11.338,1444.311,0.008,1210.691,2211.0,1738.215640169539
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505.292,64368.346,0.008,9976.414,67240.0,233826.99232060715
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365.638,46578.15,0.008,74736.144,205264.512,584912.3744381818
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108.079,13768.029,0.008,19215.837,40069.26,207734.78101312218
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0.0,12245.003,0.0,18268.074,234646.24052758503,0.0
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238.488,30380.637,0.008,36346.664,67608.51299999999,1.3486211992376125e6
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249.853,31828.4,0.008,7087.864,45938.0,88826.10011105421
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317.415,56671.244,0.006,46801.002,342330.694810351,390547.77702129295
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0.847,108.651,0.008,274.826,1081.5593375840003,14.168204014623734
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