**BREAKING** change of sampleplot behaviour and defaults

Project.toml

@@ -1,7 +1,7 @@
 name = "AbstractGPs"
 uuid = "99985d1d-32ba-4be9-9821-2ec096f28918"
 authors = ["JuliaGaussianProcesses Team"]
-version = "0.4.0"
+version = "0.4.1"
 
 [deps]
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"

examples/regression-1d/script.jl

@@ -212,19 +212,18 @@ mean(logpdf(gp_posterior(x_train, y_train, p)(x_test), y_test) for p in samples)
 # We sample 5 functions from each posterior GP given by the final 100 samples of kernel
 # parameters.
 
-plt = scatter(
-    x_train,
-    y_train;
-    xlim=(0, 1),
-    xlabel="x",
-    ylabel="y",
-    title="posterior (AdvancedHMC)",
-    label="Train Data",
-)
-for p in samples[(end - 100):end]
-    sampleplot!(plt, 0:0.02:1, gp_posterior(x_train, y_train, p); samples=5)
+plt = plot(; xlim=(0, 1), xlabel="x", ylabel="y", title="posterior (AdvancedHMC)")
+for (i, p) in enumerate(samples[(end - 100):end])
+    sampleplot!(
+        plt,
+        0:0.02:1,
+        gp_posterior(x_train, y_train, p);
+        samples=5,
+        label=(i == 1 ? "samples" : nothing),
+    )
 end
-scatter!(plt, x_test, y_test; label="Test Data")
+scatter!(plt, x_train, y_train; label="Train Data", markercolor=1)
+scatter!(plt, x_test, y_test; label="Test Data", markercolor=2)
 plt
 
 # #### DynamicHMC

src/util/plotting.jl

@@ -4,7 +4,7 @@
     length(x) == length(gp.x) ||
         throw(DimensionMismatch("length of `x` and `gp.x` has to be equal"))
     scale::Float64 = pop!(plotattributes, :ribbon_scale, 1.0)
-    scale > 0.0 || error("`bandwidth` keyword argument must be non-negative")
+    scale >= 0.0 || error("`ribbon_scale` keyword argument must be non-negative")
 
     # compute marginals
     μ, σ2 = mean_and_var(gp)
@@ -82,16 +82,19 @@ Plot samples from the projection `f` of a Gaussian process versus `x`.
     Make sure to load [Plots.jl](https://github.com/JuliaPlots/Plots.jl) before you use
     this function.
 
+When plotting multiple samples, these are treated as a _single_ series (i.e.,
+only a single entry will be added to the legend when providing a `label`).
+
 # Example
 
 ```julia
 using Plots
 
 gp = GP(SqExponentialKernel())
-sampleplot(gp(rand(5)); samples=10, markersize=5)
+sampleplot(gp(rand(5)); samples=10, linealpha=1.0)
 ```
-The given example plots 10 samples from the projection of the GP `gp`. The `markersize` is modified
-from default of 0.5 to 5.
+The given example plots 10 samples from the projection of the GP `gp`.
+The `linealpha` is modified from default of 0.2 to 1.
 
 ---
     sampleplot(x::AbstractVector, gp::AbstractGP; samples=1, kwargs...)
@@ -115,18 +118,17 @@ SamplePlot((f,)::Tuple{<:FiniteGP}) = SamplePlot((f.x, f))
 SamplePlot((x, gp)::Tuple{<:AbstractVector,<:AbstractGP}) = SamplePlot((gp(x, 1e-9),))
 
 @recipe function f(sp::SamplePlot)
-    nsamples::Int = get(plotattributes, :samples, 1)
+    nsamples::Int = pop!(plotattributes, :samples, 1)
     samples = rand(sp.f, nsamples)
 
+    flat_x = repeat(vcat(sp.x, NaN), nsamples)
+    flat_f = vcat(samples, fill(NaN, 1, nsamples)) |> vec
+
     # Set default attributes
-    seriestype --> :line
+    #seriestype --> :line
     linealpha --> 0.2
-    markershape --> :circle
-    markerstrokewidth --> 0.0
-    markersize --> 0.5
-    markeralpha --> 0.3
     seriescolor --> "red"
     label --> ""
 
-    return sp.x, samples
+    return flat_x, flat_f
 end