No Plots.jl

Project.toml

@@ -16,10 +16,10 @@ MacroTools = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
 MatrixEquations = "99c1a7ee-ab34-5fd5-8076-27c950a045f4"
 MatrixPencils = "48965c70-4690-11ea-1f13-43a2532b2fa8"
 OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
-Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 Polynomials = "f27b6e38-b328-58d1-80ce-0feddd5e7a45"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+RecipesBase = "3cdcf5f2-1ef4-517c-9805-6587b60abb01"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 
 [compat]
@@ -33,14 +33,15 @@ MacroTools = "0.5"
 MatrixEquations = "1, 2.1"
 MatrixPencils = "1.6"
 OrdinaryDiffEq = "5.2, 6.0"
-Plots = "0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 1.0"
 Polynomials = "1.1.10, 2.0"
+RecipesBase = "1"
 julia = "1.6"
 
 [extras]
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 GR = "28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71"
+Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["Test", "Documenter", "GR"]
+test = ["Test", "Documenter", "GR", "Plots"]

README.md

@@ -103,6 +103,7 @@ CLs = TransferFunction[kp*P/(1 + kp*P) for kp = [1, 5, 15]];
 
 # Plot the step response of the controllers
 # Any keyword arguments supported in Plots.jl can be supplied
+using Plots
 plot(step.(CLs, 5), label=["Kp = 1" "Kp = 5" "Kp = 15"])
 ```
 

docs/src/examples/example.md

@@ -13,6 +13,7 @@ end
 # LQR design
 ```jldoctest; output = false
 using LinearAlgebra # For identity matrix I
+using Plots
 Ts      = 0.1
 A       = [1 Ts; 0 1]
 B       = [0 1]' # To handle bug TODO
@@ -26,7 +27,7 @@ u(x,t)  = -L*x .+ 1.5(t>=2.5)# Form control law (u is a function of t and x), a
 t       =0:Ts:5
 x0      = [1,0]
 y, t, x, uout = lsim(sys,u,t,x0=x0)
-Plots.plot(t,x', lab=["Position" "Velocity"], xlabel="Time [s]")
+plot(t,x', lab=["Position" "Velocity"], xlabel="Time [s]")
 
 save_docs_plot("lqrplot.svg"); # hide
 
@@ -53,13 +54,14 @@ we notice that the sensitivity function is a bit too high around frequencies ω
 function `loopshapingPI` and tell it that we want 60 degrees phase margin at this frequency. The resulting gang of four is plotted for both the constructed controller and for unit feedback.
 
 ```jldoctest PIDDESIGN; output = false
+using Plots
 ωp = 0.8
 kp,ki,C = loopshapingPI(P,ωp,phasemargin=60)
 
 p1 = gangoffourplot(P, [tf(1), C]);
 p2 = nyquistplot([P, P*C], ylims=(-1,1), xlims=(-1.5,1.5));
 
-Plots.plot(p1,p2, layout=(2,1), size=(800,800))
+plot(p1,p2, layout=(2,1), size=(800,800))
 # save_docs_plot("pidgofplot2.svg") # hide
 # save_docs_plot("pidnyquistplot.svg"); # hide
 save_docs_plot("pidgofnyquistplot.svg") # hide
@@ -71,13 +73,14 @@ save_docs_plot("pidgofnyquistplot.svg") # hide
 
 We could also cosider a situation where we want to create a closed-loop system with the bandwidth ω = 2 rad/s, in which case we would write something like
 ```jldoctest PIDDESIGN; output = false
+using Plots
 ωp = 2
 kp,ki,C60 = loopshapingPI(P,ωp,rl=1,phasemargin=60, doplot=true)
 
 p1 = gangoffourplot(P, [tf(1), C60]);
 p2 = nyquistplot([P, P*C60], ylims=(-2,2), xlims=(-3,3));
 
-Plots.plot(p1,p2, layout=(2,1), size=(800,800))
+plot(p1,p2, layout=(2,1), size=(800,800))
 
 # gangoffourplot(P, [tf(1), C60]) # hide
 # save_docs_plot("pidgofplot3.svg") # hide

docs/src/man/introduction.md

@@ -49,12 +49,13 @@ TransferFunction{Continuous, ControlSystems.SisoRational{Float64}}
 Continuous-time transfer function model
 ```
 ## Plotting
-Plotting requires some extra care. The ControlSystems package is using `Plots.jl` ([link](https://github.com/tbreloff/Plots.jl)) as interface to generate all the plots. This means that the user is able to freely choose back-end. The plots in this manual are generated using `GR`. If you have several back-ends for plotting then you can select the one you want to use with the corresponding `Plots` call (for `GR` this is `Plots.gr()`, some alternatives are `pyplot(), plotly(), pgfplots()`). A simple example where we generate a plot and save it to a file is
+Plotting requires some extra care. The ControlSystems package is using `RecipesBase.jl` ([link](https://github.com/JuliaPlots/RecipesBase.jl)) as interface to generate all the plots. This means that it is up to the user to choose a plotting library that supports `RecipesBase.jl`, a suggestion would be `Plots.jl` with which the user is also able to freely choose a back-end. The plots in this manual are generated using `Plots.jl` with the `GR` backend. If you have several back-ends for plotting then you can select the one you want to use with the corresponding `Plots` call (for `GR` this is `Plots.gr()`, some alternatives are `pyplot(), plotly(), pgfplots()`). A simple example where we generate a plot and save it to a file is
 ```jldoctest; output=false
+using Plots
 
 fig = bodeplot(tf(1,[1,2,1]))
 
-Plots.savefig(fig, "myfile.svg")
+savefig(fig, "myfile.svg")
 
 save_docs_plot(fig, "intro_bode.svg") # hide
 

example/autodiff.jl

@@ -1,4 +1,4 @@
-using ControlSystems, OrdinaryDiffEq, NLopt, BlackBoxOptim, ForwardDiff
+using ControlSystems, OrdinaryDiffEq, NLopt, BlackBoxOptim, ForwardDiff, Plots
 p0          = [0.2,0.8,1] # Initial guess
 K(kp,ki,kd) = pid(kp=kp, ki=ki, kd=kd)
 K(p)        = K(p...)

example/dc_motor_lqg_design.jl

@@ -1,4 +1,4 @@
-using ControlSystems
+using ControlSystems, Plots
 """
 Example for designing an LQG speed controller for an electrical DC motor.
 """
@@ -29,7 +29,7 @@ function motor(Ke, Kt, L, R, J, b=1e-3)
 end
 
 p60 = motor(Ke, Kt, L, Rel, J)
-f1 = stepplot(p60, 1)
+f1 = plot(step(p60, 1))
 f2 = bodeplot(p60)
 
 # LQR control
@@ -60,5 +60,5 @@ S = 1-T
 
 # 1000 logarithmically spaced values from -3 to 3
 f3 = bodeplot([Gcl, S, T], exp10.(range(-3, stop=3, length=1000)))
-f4 = stepplot(Gcl, 1, label="Closed loop system using LQG")
-Plots.plot(f1, f2, f3, f4, layout=(2,2), size=(800, 600))
+f4 = plot(step(Gcl, 1), label="Closed loop system using LQG")
+plot(f1, f2, f3, f4, layout=(2,2), size=(800, 600))

example/delayed_lti_timeresp.jl

@@ -19,9 +19,9 @@ function u0(out,t)
     return
 end
 
-@time y, t, x = lsim(sys, u0, t)
+@time res = lsim(sys, u0, t)
 
-plot(t, y')
+plot(res)
 
 s = tf("s")
 P = delay(2.6)*ss((s+3.0)/(s^2+0.3*s+1))
@@ -30,8 +30,8 @@ P*C
 T = feedback(P*C,1.0)
 
 t = 0:0.1:70
-y, t, x = lsim(T, t -> (t<0 ? 0 : 1 ), t)
-plot(t, y, c = :blue)
+res = lsim(T, t -> (t<0 ? 0 : 1 ), t)
+plot(res, c = :blue)
 
 w = 10 .^ (-2:0.01:2)
 marginplot(P*C, w)
@@ -41,24 +41,23 @@ notch = ss(tf([1, 0.2, 1],[1, .8, 1]));
 C = ss(0.05 * (1 + 1/s));
 Tnotch = feedback(P*C*notch, 1.0)
 
-stepplot(Tnotch)
+plot(step(Tnotch))
 
 y, t, x = step(C, method=:zoh)
 
 y2, t2, x2 = step(Tnotch)
-stepplot(Tnotch)
+plot(step(Tnotch))
 
-stepplot(Tnotch, 40, 0.1)
+plot(step(Tnotch, 40))
 
-stepplot(T, 100)
+plot(step(T, 100))
 
 G = delay(5)/(s+1)
 T = feedback(G, 0.5)
 w = 10 .^ (-2:0.01:3)
 bodeplot(T, w, plotphase=false)
 
 # Test conversion, promotion
-delay(1,Int64) + 3.5
 
 G = 1 + 0.5 * delay(3)
 w = 10 .^(-2:0.001:2)
@@ -67,14 +66,13 @@ bodeplot(G, w, plotphase=false)
 G = delay(1) * ((0.8*s^2+s+2)/(s^2+s))
 T = feedback(G,1)
 # Not possible with direct term
-stepplot(T)
+plot(step(T))
 
 bodeplot(T)
 
 G = 1/(s+1) + delay(4)
 T = feedback(1,G)
 # Not possible to lsim with direct term
-stepplot(T)
+plot(step(T))
 bodeplot(T)
 
-s = tf("s")

src/ControlSystems.jl

@@ -99,11 +99,10 @@ export  LTISystem,
 
 
 # QUESTION: are these used? LaTeXStrings, Requires, IterTools
-using Plots, LaTeXStrings, LinearAlgebra
+using RecipesBase, LaTeXStrings, LinearAlgebra
 import Polynomials
 import Polynomials: Polynomial, coeffs
 using OrdinaryDiffEq
-export Plots
 import Base: +, -, *, /, (==), (!=), isapprox, convert, promote_op
 import Base: getproperty, getindex
 import Base: exp # for exp(-s)

src/pid_design.jl

@@ -225,7 +225,7 @@ See also `Leadlink, leadlinkat`
 function leadlinkcurve(start=1)
     N = range(start, stop=10, length=50)
     dph = 180/pi*map(Ni->atan(sqrt(Ni))-atan(1/sqrt(Ni)), N)
-    Plots.plot(N,dph, xlabel="N", ylabel="Phase advance [deg]")
+    RecipesBase.plot(N,dph, xlabel="N", ylabel="Phase advance [deg]")
 end
 
 
@@ -253,7 +253,7 @@ function stabregionPID(P, ω = _default_freq_vector(P,Val{:bode}()); kd=0, doplo
     phi = angle.(Pv)
     kp  = -cos.(phi)./r
     ki  = kd.*ω.^2 .- ω.*sin.(phi)./r
-    Plots.plot(kp,ki,linewidth = 1.5, xlabel=L"k_p", ylabel=L"k_i", title="Stability region of P, k_d = $(round(kd, digits=4))"), kp, ki
+    RecipesBase.plot(kp,ki,linewidth = 1.5, xlabel=L"k_p", ylabel=L"k_i", title="Stability region of P, k_d = $(round(kd, digits=4))"), kp, ki
 end
 
 
@@ -263,7 +263,7 @@ function stabregionPID(P::Function, ω = exp10.(range(-3, stop=1, length=50)); k
     phi     = angle.(Pv)
     kp      = -cos.(phi)./r
     ki      = kd.*ω.^2 .- ω.*sin.(phi)./r
-    Plots.plot(kp,ki,linewidth = 1.5, xlabel=L"k_p", ylabel=L"k_i", title="Stability region of P, k_d = $(round(kd, digits=4))"), kp, ki
+    RecipesBase.plot(kp,ki,linewidth = 1.5, xlabel=L"k_p", ylabel=L"k_i", title="Stability region of P, k_d = $(round(kd, digits=4))"), kp, ki
 end