PNG output in docs

docs/Project.toml

@@ -3,6 +3,7 @@ ControlSystemIdentification = "3abffc1c-5106-53b7-b354-a47bfc086282"
 ControlSystemsBase = "aaaaaaaa-a6ca-5380-bf3e-84a91bcd477e"
 DataInterpolations = "82cc6244-b520-54b8-b5a6-8a565e85f1d0"
 DelimitedFiles = "8bb1440f-4735-579b-a4ab-409b98df4dab"
+DisplayAs = "0b91fe84-8a4c-11e9-3e1d-67c38462b6d6"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 LeastSquaresOptim = "0fc2ff8b-aaa3-5acd-a817-1944a5e08891"
 LowLevelParticleFilters = "d9d29d28-c116-5dba-9239-57a5fe23875b"

docs/make.jl

@@ -1,4 +1,4 @@
-ENV["GKSwstype"] = 322 # workaround for gr segfault on GH actions
+ENV["GKSwstype"] = 100 # workaround for gr segfault on GH actions
 using Documenter, ControlSystemIdentification, ControlSystemsBase, DelimitedFiles
 # ENV["JULIA_DEBUG"]=Documenter # Enable this for debugging
 
@@ -56,3 +56,5 @@ makedocs(
 deploydocs(
       repo = "github.com/baggepinnen/ControlSystemIdentification.jl.git",
 )
+
+cd(@__DIR__)

docs/src/freq.md

@@ -6,6 +6,7 @@ Frequency-domain estimation refers to estimation of linear systems using frequen
 Non-parametric estimation refers to the estimation of a model without a fixed number of parameters. Instead, the number of estimated parameters typically grows with the size of the data. This form of estimation can be useful to gain an initial understanding of the system, before selecting model orders etc. for a more standard parametric model. We provide non-parametric estimation of transfer functions through spectral estimation. To illustrate, we once again simulate some data:
 ```@example npfreq
 using ControlSystemIdentification, ControlSystemsBase, Plots
+using DisplayAs # hide
 gr(fmt=:png) # hide
 T          = 100000
 h          = 1
@@ -34,6 +35,7 @@ bodeplot([sys,sysn], exp10.(range(-3, stop=log10(pi), length=200)), layout=(1,3)
 coherenceplot!(dn, subplot=3)
 plot!(G, subplot=1, lab="G Est", alpha=0.3, title="Process model")
 plot!(√N, subplot=2, lab="N Est", alpha=0.3, title="Noise model")
+DisplayAs.PNG(current()) # hide
 ```
 
 The left figure displays the Bode magnitude of the true system, together with the estimate (noisy), and the middle figure illustrates the estimated noise model. The right figure displays the coherence function ([`coherenceplot`](@ref)), which is close to 1 everywhere except for at the resonance peak of the noise `log10(sqrt(0.3)) = -0.26`.

docs/src/impulse.md

@@ -21,6 +21,7 @@ plot!(impulse(sys,50), lab="True system", l=:dash)
 MIMO example
 ```@example
 using ControlSystemIdentification, ControlSystemsBase, Plots
+using DisplayAs # hide
 T = 200
 h = 1
 t = 0:h:T-h
@@ -33,6 +34,7 @@ d  = iddata(res)
 H = okid(d, sys.nx)
 plot(impulse(sys,50), lab="True system", layout=sys.ny+sys.nu, sp=(1:4)')
 plot!(reshape(H, sys.nu+sys.ny, :)', lab="OKID Estiamte", seriestype=:steppre, l=:dash)
+DisplayAs.PNG(current()) # hide
 ```
 
 See the [example notebooks](https://github.com/JuliaControl/ControlExamples.jl) for more details.

docs/src/nonlinear.md

@@ -1,6 +1,7 @@
 ```@setup HW
 using ControlSystemIdentification
 using LeastSquaresOptim
+using DisplayAs
 ```
 
 
@@ -111,6 +112,7 @@ plot(
     plot(U', title="Inputs", lab=["u1" "u2"]),
     plot(Y', title="Outputs", lab=["y1" "y2"]),
 )
+DisplayAs.PNG(current()) # hide
 ```
 
 We package the experimental data into an [`iddata`](@ref) object as usual. Finally, we specify the covariance matrices for the dynamics noise and measurement noise as well as a guess for the initial condition. Since we can measure the level in the first two tanks, we use the true initial condition for those tanks, but we pretend that we are quite off when guessing the initial condition for the last two tanks. 
@@ -136,11 +138,13 @@ simplot(model, d, layout=2)
 x_guess = LowLevelParticleFilters.rollout(discrete_dynamics, x0_guess, u, p_guess)[1:end-1]
 y_guess = measurement.(x_guess, u, 0, 0)
 plot!(reduce(hcat, y_guess)', lab="Initial guess")
+DisplayAs.PNG(current()) # hide
 ```
 
 We can also perform a residual analysis to see if the model is able to capture the dynamics of the system
 ```@example HW
 residualplot(model, d)
+DisplayAs.PNG(current()) # hide
 ```
 
 since we are using simulated data here, the residuals are white and there's nothing to worry about. In practice, one should always inspect the residuals to see if there are any systematic errors in the model.
@@ -268,6 +272,7 @@ The example below identifies a model of a resonant system where the sign of the
 using ControlSystemIdentification, ControlSystemsBase
 using ControlSystemsBase.DemoSystems: resonant
 using Random, Plots
+using DisplayAs # hide
 
 # Generate some data from the system
 Random.seed!(1)
@@ -307,6 +312,7 @@ output_nonlinearity(yh, nothing) # We need to manually apply the output nonlinea
 plot(d.t, [abs.(y); u]', lab=["True nonlinear output" "Input"], seriestype = [:line :steps], layout=(2,1), xlabel="Time")
 scatter!(d.t, ynn', lab="Measured nonlinear output", sp=1)
 plot!(d.t, yh', lab="Simulation", sp=1, l=:dash)
+DisplayAs.PNG(current()) # hide
 ```
 
 

docs/src/ss.md

@@ -16,6 +16,7 @@ There exist several methods for identification of statespace models, [`subspacei
 In this example we will estimate a statespace model using the [`subspaceid`](@ref) method. This function returns an object of type [`N4SIDStateSpace`](@ref) where the model is accessed as `sys.sys`.
 ```@example ss
 using ControlSystemIdentification, ControlSystemsBase, Plots
+using DisplayAs # hide
 gr(fmt=:png) # hide
 Ts = 0.1
 G  = c2d(DemoSystems.resonant(), Ts)
@@ -91,6 +92,7 @@ f2 = bodeplot([G, meanmodel], lab=["True" "" "Combined estimate" ""], l=2)
 bodeplot!(models, lab="Individual estimates", c=:black, alpha=0.5, legend=:bottomleft)
 
 plot(f1, f2)
+DisplayAs.PNG(current()) # hide
 ```
 
 The procedure shown above is equivalent to calling [`era`](@ref) directly with a vector of data sets, in which case the averaging of the impulse responses is done internally.

docs/src/validation.md

@@ -7,6 +7,7 @@ Generate some test data:
 ```@example validation
 using ControlSystemIdentification, ControlSystemsBase, Random
 using ControlSystemIdentification: newpem
+using DisplayAs # hide
 Random.seed!(1)
 T          = 200
 nx         = 2
@@ -50,6 +51,7 @@ bodeplot!((getindex.(res,1)),                     ω, link = :none, balance=fals
 bodeplot!(innovation_form.(getindex.(res,1)),     ω, link = :none, balance=false, plotphase=false, subplot=4, linewidth=2*[4 3 2 1])
 bodeplot!(sys,                                    ω, link = :none, balance=false, plotphase=false, subplot=3, lab="True", l=(:black, :dash), legend = :bottomleft, title="System model")
 bodeplot!(innovation_form(ss(sys),syse=ss(sysn)), ω, link = :none, balance=false, plotphase=false, subplot=4, lab="True", l=(:black, :dash), ylims=(0.1, 100), legend = :bottomleft, title="Noise model")
+DisplayAs.PNG(current()) # hide
 ```
 
 In the figure, simulation output is compared to the true model on the top left and prediction on top right. The system models and noise models are visualized in the bottom plots. All models capture the system dynamics reasonably well, but struggle slightly with capturing the gain of the noise dynamics.