Manually create legend in Plots.jl

Hi all,

I have a plot with a gradient build from a large series of subplots. I would like to manually add a legend independent of the underlying series data. The legend might show a colored line paired with a label:

(blue line) x1
(red line) x2

I have been struggling to get this to work. Any help would be greatly appreciated.

A simple workaround to do this is to plot!() red and blue lines outside the current view area, by imposing in the plot command xlims=xlims(p), ylims=ylims(p) obtained from the previous plot object p.

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As always, thanks for the suggestion. For some reason the legend includes labels from the data series of the first plot. Here is an example based on your code:

Summary
using Distributions, SmoothingSplines, Plots; 
pyplot()

f(x) = sin(x)    # baseline function for this example

# GENERATE INPUT DATA:
σy = 1.0    # stdev relative to curve f(x)
npoints = 100
nsamples = 50
X = vcat([rand(Uniform(0, 4π), npoints) for _ in 1:nsamples]...)
Y = @. f.(X) + rand(Normal(0, σy*abs(cos(X))+0.1))    # variable stdev

# FIT NORMAL DISTRIBUTIONS WITH PARAMETERS: μ(x) and σ(x)
nx = 100;  # number of divisions of x-axis to estimate distribution
x = LinRange(0, 4π, nx)
xᵢ = [(x[i], x[i+1]) for i in 1:length(x)-1]
x₀ = mean.(xᵢ)
μ = similar(x₀); σ = similar(x₀)
for (i,xᵢ) in pairs(xᵢ)
    ix = xᵢ[1] .<= X .<= xᵢ[2]
    h = fit(Normal, Y[ix])
    μ[i] = h.μ;  σ[i] = h.σ
end

# FIT SMOOTHING SPLINES TO ABOVE
using SmoothingSplines
splμ = fit(SmoothingSpline,x₀, μ, 0.05)      # λ=0.05
splσ = fit(SmoothingSpline,x₀, σ, 0.02)      # λ=0.02
μp = SmoothingSplines.predict(splμ, x₀)
σp = SmoothingSplines.predict(splσ, x₀)

# PLOT DATA:
p1 = scatter(X,Y, ms=1, msw=-0, mc=:blue, label=false)
plot!(f, lc=:cyan, lw=1.5, label="sin(x) baseline")
plot!(x₀, μ, lc=:red, lw=1, ls=:dash, label="mean μ(x)")
plot!(x₀, σ, lc=:lime, lw=1, ls=:dash, label="stdev σ(x)")
plot!(x₀, μp, lc=:red, lw=1, label="spline μp(x)")
plot!(x₀, σp, lc=:lime, lw=1, label="spline σp(x)")

# PLOT RIBBONS WITH ALPHA TRANSPARENCY SCALED:
N = 300  # number of ribbon slices
qq = @. quantile(Normal(μp, σp), LinRange(0.01, 0.99,N))
α = [LinRange(0.01,0.4,N÷2); LinRange(0.4,0.01,N÷2)]
p2 = plot(legend=false)
for i in 2:N-1
    yᵢ = getindex.(qq, i)
    dy = yᵢ - getindex.(qq, i-1)
    plot!(x₀, yᵢ - dy/2, lw=0, color=:blue, fillalpha=α[i], ribbon=dy)
end
p2
temp = [-10 -20; -30 -40 ]
plot!(p2, temp, ylims = (-3,3), leg=true, label = ["a" "b"])

Is there any way to suppress the legend from the first data series?

Try setting label=false in the previous plots.

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Thanks for your help. I didn’t know that label can be set to false. I put together a MWE based on your solution.

using Plots
x = rand(10)
plot(x, label = false)
temp = [-10 -20; -30 -40 ]
plot!(temp, ylims=(0,1), color=[:green :red], label=["a" "b"])
1 Like