diff --git a/Manifest.toml b/Manifest.toml
new file mode 100644
index 0000000000000000000000000000000000000000..91a42cfce85bd943bc4146076cb7a3bf315ebab8
--- /dev/null
+++ b/Manifest.toml
@@ -0,0 +1,599 @@
+[[AbstractFFTs]]
+deps = ["Compat", "LinearAlgebra"]
+git-tree-sha1 = "8d59c3b1463b5e0ad05a3698167f85fac90e184d"
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+[[Base64]]
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+[[BinDeps]]
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+[[Calculus]]
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+[[Plots]]
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+
+[[WinRPM]]
+deps = ["BinDeps", "Compat", "HTTPClient", "LibExpat", "Libdl", "Libz", "URIParser"]
+git-tree-sha1 = "2a889d320f3b77d17c037f295859fe570133cfbf"
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diff --git a/Project.toml b/Project.toml
new file mode 100644
index 0000000000000000000000000000000000000000..a68942256ac3a5375af9627ad107ab810f7ab1c3
--- /dev/null
+++ b/Project.toml
@@ -0,0 +1,11 @@
+[deps]
+ControlSystemIdentification = "0861197f-74c3-5f66-9cd7-8a02bdf51734"
+ControlSystems = "a6e380b2-a6ca-5380-bf3e-84a91bcd477e"
+DSP = "717857b8-e6f2-59f4-9121-6e50c889abd2"
+Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
+GR = "28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71"
+JLD = "4138dd39-2aa7-5051-a626-17a0bb65d9c8"
+LabProcesses = "6929cf19-6a72-5044-8237-19d456703100"
+Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
+Polynomials = "f27b6e38-b328-58d1-80ce-0feddd5e7a45"
+ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca"
diff --git a/README.md b/README.md
index 979f14990697b4cf20396cf3b928c687cb1c2d0d..e8dde868b65176a38b6b92d1f2079e12714bac23 100644
--- a/README.md
+++ b/README.md
@@ -3,26 +3,23 @@
# Installation (specific to lab 1 FRTN35: Frequency response analysis)
0. If you run on your personal computer, you *might* need to install Julia.
-Download and install Julia v0.6+ from [julialang.org](https://julialang.org/downloads/).
+Download and install Julia v1.0+ from [julialang.org](https://julialang.org/downloads/).
If you prefer a "batteries included" approach with an IDE, download and install
the free version of [Julia Pro](https://juliacomputing.com/products/juliapro.html).
1. Open a terminal
2. Type `mkdir FRTN35_lab1; cd FRTN35_lab1`
3. Paste the following in a terminal
```
-printf 'export JULIA_PKGDIR=/var/tmp/$USER\n' > julialab1
+printf 'export JULIA_DEPOT_PATH=/var/tmp/$USER\n' > julialab1
printf 'export JULIA_EDITOR=gedit\n' >> julialab1
printf 'julia\n' >> julialab1
chmod +x julialab1
```
(Feel free to choose another text editor if you don't like gedit.) This step only needs to be done once. Henceforth you can start julia with the environment setup by running `./julialab1` in the current directory.
4. Start `./julialab`
-5. Install `BallAndBeam.jl` using command `Pkg.clone("https://gitlab.control.lth.se/processes/BallAndBeam.jl.git"); using BallAndBeam` Lots of packages will now be installed, this will take some time.
-6. Type `cp(Pkg.dir("BallAndBeam","src","FRTN35_lab1.jl"), "FRTN35_lab1.jl"); edit("FRTN35_lab1.jl")`. The script `FRTN35_lab1.jl` will now open in the chosen text editor, edit it while following the lab manual. If unsure about how a function works, type `?function_name` for help.
-
-printf 'export JULIA_PKGDIR=/var/tmp/$USER; export JULIA_EDITOR=gedit' > julialab
-printf 'julia' >> julialab
-chmod +x julialab
+5. Install `BallAndBeam.jl` using command `using Pkg; Pkg.add("https://gitlab.control.lth.se/processes/BallAndBeam.jl.git"); using BallAndBeam` Lots of packages will now be installed, this will take some time.
+6. Type `cp(joinpath(pathof(BallAndBeam),"src","FRTN35_lab1.jl")), "FRTN35_lab1.jl"); edit("FRTN35_lab1.jl")`. The script `FRTN35_lab1.jl` will now open in the chosen text editor, edit it while following the lab manual. If unsure about how a function works, type `?function_name` for help.
+7. Run `Pkg.activate(pathof(BallAndBeam))` to install further required packages.
# Documentation
diff --git a/REQUIRE b/REQUIRE
index bbfa24c275b6d2ad670f5a605eedd702d199d806..ca27e579296d1a5631849568697076b656ec0c32 100644
--- a/REQUIRE
+++ b/REQUIRE
@@ -1,4 +1,4 @@
-julia 0.7
+julia 1.0
Plots
GR
ControlSystems
diff --git a/src/BallAndBeam.jl b/src/BallAndBeam.jl
index b668b9a400e33a5b52e8feaf3390dc346275103d..fcd40a7d1a28d602858e68b74338c9682651f7ee 100644
--- a/src/BallAndBeam.jl
+++ b/src/BallAndBeam.jl
@@ -1,8 +1,4 @@
using Pkg
-installed_packages = Pkg.installed()
-if "LabProcesses" ∉ keys(installed_packages)
- Pkg.clone("https://gitlab.control.lth.se/processes/LabProcesses.jl.git")
-end
#__precompile__()
diff --git a/src/FRTN35_lab1.jl b/src/FRTN35_lab1.jl
index ebafbc7df1c737e2b367cdae0a9aba72434d820e..b0342cc4b85c8b62dcfd34623e074f47e7c0856c 100644
--- a/src/FRTN35_lab1.jl
+++ b/src/FRTN35_lab1.jl
@@ -1,4 +1,4 @@
-using BallAndBeam, ControlSystems, LabProcesses, Plots, JLD
+using BallAndBeam, ControlSystems, ControlSystemIdentification, LabProcesses, Plots, JLD
# @load "workspace.jld" # Run this command to restore a saved workspace
P = LabProcesses.Beam(bias = 0.) # Replace for BeamSimulator() to run simulations
diff --git a/src/arx.jl b/src/arx.jl
deleted file mode 100644
index faa71a9d2f0c4be0b32f1e196244776246d40cbc..0000000000000000000000000000000000000000
--- a/src/arx.jl
+++ /dev/null
@@ -1,182 +0,0 @@
-export toeplitz, getARXregressor, find_na, arx, bopl_confidence, bopl_confidence!
-
-
-## Helper functions
-rms(x) = sqrt(mean(x.^2))
-sse(x) = sum(x.^2)
-fit(y,yh) = 100 * (1-rms(y-yh)./rms(y-mean(y)));
-aic(x,d) = log(sse(x)) + 2d/length(x)
-
-"""
- toeplitz(c::AbstractArray,r::AbstractArray)
-Returns a Toeplitz matrix where `c` is the first column and `r` is the first row.
-"""
-function toeplitz(c::AbstractVector{T},r::AbstractVector{T}) where T
- nc = length(c)
- nr = length(r)
- A = zeros(T, nc, nr)
- A[:,1] = c
- A[1,:] = r
- for i in 2:nr
- A[2:end,i] = A[1:end-1,i-1]
- end
- A
-end
-
-"""
- getARXregressor(y::AbstractVector,u::AbstractVecOrMat, na, nb)
-Returns a shortened output signal `y` and a regressor matrix `A` such that the least-squares ARX model estimate of order `na,nb` is `y\\A`
-Return a regressor matrix used to fit an ARX model on, e.g., the form
-`A(z)y = B(z)f(u)`
-with output `y` and input `u` where the order of autoregression is `na` and
-the order of input moving average is `nb`
-# Example
-Here we test the model with the Function `f(u) = √(|u|)`
-```julia
-A = [1,2*0.7*1,1] # A(z) coeffs
-B = [10,5] # B(z) coeffs
-u = randn(100) # Simulate 100 time steps with Gaussian input
-y = filt(B,A,u)
-yr,A = getARXregressor(y,u,3,2) # We assume that we know the system order 3,2
-x = A\\yr # Estimate model polynomials
-plot([yr A*x], lab=["Signal" "Prediction"])
-```
-For nonlinear ARX-models, see [BasisFunctionExpansions.jl](https://github.com/baggepinnen/BasisFunctionExpansions.jl/)
-"""
-function getARXregressor(y::AbstractVector,u::AbstractVecOrMat, na, nb)
- @assert(length(nb) == size(u,2))
- m = max(na+1,maximum(nb))
- n = length(y) - m+1
- offs = m-na-1
- A = toeplitz(y[offs+na+1:n+na+offs],y[offs+na+1:-1:1])
- y = copy(A[:,1])
- A = A[:,2:end]
- for i = 1:length(nb)
- offs = m-nb[i]-1
- A = [A toeplitz(u[nb[i]+offs:n+nb[i]+offs-1,i],u[nb[i]+offs:-1:1+offs,i])]
- end
- return y,A
-end
-
-"""
- find_na(y::AbstractVector,n::Int)
-Plots the RMSE and AIC For model orders up to `n`. Useful for model selection
-"""
-function find_na(y::AbstractVector,n::Int)
- error = zeros(n,2)
- for i = 1:n
- w,e = ar(y,i)
- error[i,1] = rms(e)
- error[i,2] = aic(e,i)
- print(i,", ")
- end
- println("Done")
- scatter(error, show=true)
-end
-
-"""
- Gtf, Σ = arx(h,y, u, na, nb; λ = 0)
-Fit a transfer Function to data using an ARX model.
-`nb` and `na` are the orders of the numerator and denominator polynomials.
-"""
-function arx(h,y::AbstractVector{Float64}, u::AbstractVector{Float64}, na, nb; λ = 0)
- na -= 1
- y_train, A = getARXregressor(y,u, na, nb)
-
- if λ == 0
- w = A\y_train
- else
- w = (A'A + λ*I)\A'y_train
- end
- a,b = params2poly(w,na,nb)
- model = tf(b,a,h)
- Σ = parameter_covariance(y_train, A, w, λ)
- return model, Σ
-end
-
-"""
- a,b = params2poly(params,na,nb)
-"""
-function params2poly(w,na,nb)
- a = [1; -w[1:na]]
- b = w[na+1:end]
- a,b
-end
-
-"""
- Σ = parameter_covariance(y_train, A, w, λ=0)
-"""
-function parameter_covariance(y_train, A, w, λ=0)
- σ² = var(y_train .- A*w)
- iATA = if λ == 0
- inv(A'A)
- else
- ATA = A'A
- ATAλ = factorize(ATA + λ*I)
- ATAλ\ATA/ATAλ
- end
- iATA = (iATA+iATA')/2
- Σ = σ²*iATA + sqrt(eps())*I
-end
-
-"""
- bodeconfidence(arxtf::TransferFunction, Σ::Matrix; ω = exp10.(LinRange(0,3,200)))
-Plot a bode diagram of a transfer function estimated with [`arx`](@ref) with confidence bounds on magnitude and phase.
-"""
-bodeconfidence
-
-@userplot BodeConfidence
-
-@recipe function BodeConfidence(p::BodeConfidence; ω = exp10.(LinRange(-2,3,200)))
- arxtfm = p.args[1]
- Σ = p.args[2]
- L = cholesky(Hermitian(Σ))
- am, bm = -reverse(denpoly(arxtfm)[1].a[1:end-1]), reverse(arxtfm.matrix[1].num.a)
- wm = [am; bm]
- na,nb = length(am), length(bm)
- mc = 100
- res = map(1:mc) do _
- w = L.L*randn(size(L,1)) .+ wm
- a,b = params2poly(w,na,nb)
- arxtf = tf(b,a,arxtfm.Ts)
- mag, phase, _ = bode(arxtf, ω)
- mag[:], phase[:]
- end
- magmc = hcat(getindex.(res,1)...)
- phasemc = hcat(getindex.(res,2)...)
- mag = mean(magmc,dims=2)[:]
- phase = mean(phasemc,dims=2)[:]
- uppermag = getpercentile(magmc,0.95)
- lowermag = getpercentile(magmc,0.05)
- upperphase = getpercentile(phasemc,0.95)
- lowerphase = getpercentile(phasemc,0.05)
-
- layout := (2,1)
-
- @series begin
- subplot := 1
- title --> "ARX estimate"
- ylabel --> "Magnitude"
- ribbon := (lowermag, uppermag)
- yscale --> :log10
- xscale --> :log10
- fillalpha --> 0.2
- ω, mag
- end
- @series begin
- subplot := 2
- ribbon := (lowerphase, upperphase)
- ylabel --> "Phase [deg]"
- xlabel --> "Frequency [rad/s]"
- xscale --> :log10
- fillalpha --> 0.2
- ω, phase
- end
- nothing
-end
-
-function getpercentile(mag,p)
- uppermag = mapslices(mag, dims=2) do magω
- sort(magω)[round(Int,length(magω)*p)]
- end
-end