Useful Packages and Programs¶
This page includes details of the packages and programs that I am using in OCP.
Adding and Removing Packages in julia¶
A useful description of the syntax for adding and remove packages in julia can be found here.
All packages that I have on julia¶
I have many packages and to configure them all can be tricky, so I include a list of commands that you can copy and past into julia to get started. Make sure that you are restarting the both julia and the terminal after things are installed.
Basics¶
Pkg.add("DataFrames")
Pkg.add("IJulia")
Pkg.add("Parameters")
Pkg.add("PkgDev")
Pkg.add("AmplNLWriter")
#Pkg.clone("https://github.com/JunoLab/Juno.jl")
Pkg.add("HDF5")
Pkg.build("HDF5")
Pkg.add("SymPy")
Pkg.add("Jacobi")
Math¶
I use:
Pkg.add("DiffBase")
Pkg.clone("https://github.com/Keno/Polynomials.jl")
Pkg.add("DifferentialEquations")
Pkg.add("Dierckx")
Pkg.add("ImplicitEquations")
Pkg.add("Interpolations")
Optimization¶
I use:
Pkg.add("JuMP") # adds MathProgBase automatically
Pkg.add("Ipopt")
Pkg.add("CoinOptServices")
Pkg.add("NLopt")
Pkg.build("NLopt")
Plotting¶
Basically I use Plots.jl to interface with some of these:
Pkg.add("Conda")
ENV["PYTHON"]=""
Pkg.add("PyPlot")
Pkg.add("Plots")
Pkg.build("PyPlot")
Pkg.add("ImageMagick")
Pkg.add("GR")
Pkg.add("Plotly")
Pkg.add("StatPlots")
Pkg.add("PlotRecipes")
Pkg.add("UnicodePlots")
Pkg.add("Gadfly")
Pkg.add("RDatasets")
Pkg.add("Winston")
Pkg.add("PGFPlots")
Pkg.build("PGFPlots")
Blink.AtomShell.install()
import Conda # to fix pyplot!!!
Conda.add("qt=4.8.5")
My Packages¶
I started these:
Pkg.clone("https://github.com/huckl3b3rry87/VehicleModels.jl")
Pkg.clone("https://github.com/huckl3b3rry87/NLOptControl.jl")
Miscellaneous¶
probably do not need:
Pkg.clone("https://github.com/pwl/MovcolN.jl")
Pkg.add("Devectorize")
Pkg.add("FactCheck")
Customizing Keybindings and Tab Completion¶
type:
import Base: LineEdit, REPL
const mykeys = Dict{Any,Any}(
# Up Arrow
"\e[A" => (s,o...)->(LineEdit.edit_move_up(s) || LineEdit.history_prev(s, LineEdit.mode(s).hist)),
# Down Arrow
"\e[B" => (s,o...)->(LineEdit.edit_move_up(s) || LineEdit.history_next(s, LineEdit.mode(s).hist))
)
function customize_keys(repl)
repl.interface = REPL.setup_interface(repl; extra_repl_keymap = mykeys)
end
atreplinit(customize_keys)
Basic Pkg. Usage Notes¶
Optimization¶
Currently the only optimization tool that is being tested it IPOPT.
- IPOPT
It is very easy to get going using IPOPT in julia using IPOPT.jl.
Derivatives¶
JuMP.jl is one of the most useful packages for solving the OCP because it takes very fast automatic derivatives and it allows the user to easily set up optimization problems. So, with this tool there is no need to write out the complicated Jacobian and Hessian functions.
The documentation for this package can be found JuMP docs.
Some useful Methods are found by clicking.
Some useful commands Query upper and lower bounds of all constraints in the model:
JuMP.constraintbound(m::Model)
MathProgBase.jl¶
Polynomial Division¶
There was an issue when JuMP was sent a term with polynomial division. This section deals with the attempt to use the Polynomials.jl to take care of the polynomial division on the front end, before the expressions are sent to JuMP.
Basic Functionality:
using Ploynomials
Poly([1,0,3,4])
Poly(1 + 3x^2 + 4x^3)
Division Functionality:
P1 = Poly([1,2,3,5,7])
P2 = Poly([1,0,3])
P3 = div(P1,P2)
Poly(0.22222222222222218 + 1.6666666666666667x + 2.3333333333333335x^2)
Plots.jl¶
A very powerful plotting tool is Plots.jl. It took some time to get everything working because I was not using the same versions of the packages as the developers, but in the end it was work the time. It you run the code that I have listed below you should not have to deal with the setup issues that I had.
With PGFPlots:
sudo apt-get install pdf2svg
http://nbviewer.jupyter.org/github/sisl/PGFPlots.jl/blob/master/doc/PGFPlots.ipynb
Problem 1
I wasted a bunch if my time, could not recreate in a simpler example, but basically, when plot() should have worked it failed and when I changed the order of the terms in plot() it works.
Solution 1
EX:
for i in 1:k
# plot!(dfs[i][:t],dfs[i][:SA]*180/pi,w=w2,label=label_string[i])
plot!(dfs[i][:t],dfs[i][:SA]*180/pi,label=label_string[i],w=w2)
end
Problem 2
Segfault when attempting to plot after Conda update to Segfault with qt >=4.8.6 on ubuntu
I was running into an issue with Plots.jl after doing a Pkg.update():
_ _ _(_)_ | A fresh approach to technical computing
(_) | (_) (_) | Documentation: http://docs.julialang.org
_ _ _| |_ __ _ | Type "?help" for help.
| | | | | | |/ _` | |
| | |_| | | | (_| | | Version 0.6.0-dev.508 (2016-09-06 20:36 UTC)
_/ |\__'_|_|_|\__'_| | Commit b1f1525 (26 days old master)
|__/ | x86_64-linux-gnu
julia> using Plots
julia> plot(rand(4,100))
signal (11): Segmentation fault
while loading no file, in expression starting on line 0
unknown function (ip: 0x32735)
Allocations: 14606607 (Pool: 14605017; Big: 1590); GC: 25
Segmentation fault (core dumped)
Solution 2
Change back to old Conda:
import Conda
Conda.add("qt=4.8.5")
using PyPlot
plot(rand(10))
Check out this link.
DifferentialEquations.jl¶
Useful Commands
all of the timepoints:
sol[:]
Many times you might want to use a good interpolation. For example, the plots use something like:
[sol(t) for t in linspace(t0,tend,100)]
To get 100 points from time t0 to tend for the first component:
[sol(t)[1] for t in linspace(t0,tend,100)]
The array of timepoints for component j:
[sol[i][j] for i in 1:length(sol)]