color grouping for density2d (#1508)

NEWS.md

@@ -1,6 +1,11 @@
 This is a log of major changes in Gadfly between releases. It is not exhaustive.
 Each release typically has a number of minor bug fixes beyond what is listed here.
 
+# Version 1.x
+
+
+ * Enable `color` grouping for `Geom.density2d` (#1508)
+
 # Version 1.3.1
 
  * Better define what scales are included in the (internal) `scales` dict (#1468)

docs/src/gallery/geometries.md

@@ -177,21 +177,24 @@ plot(layer(x=xs, Geom.density, color=["auto"]),
 
 ```@example
 using Gadfly, Distributions, RDatasets
-set_default_plot_size(21cm, 8cm)
-iris = dataset("datasets", "iris")
+set_default_plot_size(21cm, 18cm)
+geyser, iris = dataset("datasets", "faithful"), dataset("datasets", "iris")
+geyser.g = geyser.Eruptions.>3
 X = rand(Rayleigh(2), 1000,2)
 levelf(x) = maximum(x)*0.5.^collect(1:2:8)
-p1 = plot(x=X[:,1], y=X[:,2], Geom.density2d(levels=levelf), 
-    Geom.point, Scale.color_continuous(colormap=c->colorant"red"),
-    Theme(key_position=:none))
+p1 = plot(x=X[:,1], y=X[:,2], layer(Geom.density2d(levels=levelf), color=[colorant"red"],
+        linestyle=[:solid]), Geom.point)
+p2 = plot(geyser, x=:Eruptions, y=:Waiting, color=:g, Geom.density2d(levels=4),
+        Geom.point, alpha=[0.4])
+p3 = plot(iris, x=:SepalLength, y=:SepalWidth, color=:Species, Geom.density2d(levels=4),
+        Geom.point, alpha=[0.8])
 cs = repeat(Scale.default_discrete_colors(3), inner=50)
-p2 = plot(iris, x=:SepalLength, y=:SepalWidth,
-    layer(x=:SepalLength, y=:SepalWidth, color=cs),
+p4 = plot(iris, x=:SepalLength, y=:SepalWidth,  layer(color=cs, Geom.point),
     layer(Geom.density2d(levels=[0.1:0.1:0.4;]),  order=1),
-    Scale.color_continuous, Guide.colorkey(title=""),
-    Guide.manual_color_key("Iris", unique(iris.Species)),
-    Theme(point_size=3pt, line_width=1.5pt))
-hstack(p1, p2)
+    Scale.color_continuous,   Guide.colorkey(title=""),
+    Theme(point_size=3pt, line_width=1.5pt),
+    Guide.manual_color_key("Iris", unique(iris.Species)))
+gridstack([p1 p2; p3 p4])
 ```
 
 
@@ -245,9 +248,8 @@ set_default_plot_size(21cm, 8cm)
 salaries = dataset("car","Salaries")
 salaries.Salary /= 1000.0
 salaries.Discipline = ["Discipline $(x)" for x in salaries.Discipline]
-fn1(x, u=mean(x), s=std(x)) = (Salary=u, ymin=u-s, ymax=u+s, 
-    label="$(round.(Int,u))")
-df = combine(:Salary=>fn1, groupby(salaries, [:Rank, :Discipline]))
+fn1(x, u=mean(x), s=std(x)) = (Salary=u, ymin=u-s, ymax=u+s, label="$(round.(Int,u))")
+df = combine(groupby(salaries, [:Rank, :Discipline]), :Salary=>fn1=>AsTable)
 
 p1 = plot(df, x=:Discipline, y=:Salary, color=:Rank,
     Scale.x_discrete(levels=["Discipline A", "Discipline B"]),

src/geom/line.jl

@@ -65,7 +65,8 @@ density(; bandwidth::Real=-Inf) =
     Geom.density2d[(; bandwidth=(-Inf,-Inf), levels=15)]
 
 Draw a set of contours showing the density estimate of the `x` and `y`
-aesthetics.  This geometry is equivalent to [`Geom.line`](@ref) with
+aesthetics.  If grouped by `color`, then contour lines are mapped to `linestyle`.
+This geometry is equivalent to [`Geom.line`](@ref) with
 [`Stat.density2d`](@ref); see the latter for more information.
 """
 density2d(; bandwidth::Tuple{Real,Real}=(-Inf,-Inf), levels=15) =

src/statistics.jl

@@ -513,18 +513,53 @@ is controlled by `bandwidth`.  Calls [`Stat.contour`](@ref) to compute the
 const density2d = Density2DStatistic
 
 function apply_statistic(stat::Density2DStatistic,
-                         scales::Dict{Symbol, Gadfly.ScaleElement},
-                         coord::Gadfly.CoordinateElement,
-                         aes::Gadfly.Aesthetics)
-    Gadfly.assert_aesthetics_defined("Density2DStatistic", aes, :x, :y)
+    scales::Dict{Symbol, Gadfly.ScaleElement},
+    coord::Gadfly.CoordinateElement,
+    aes::Gadfly.Aesthetics)
 
     window = (stat.bw[1] <= 0.0 ? KernelDensity.default_bandwidth(aes.x) : stat.bw[1],
               stat.bw[2] <= 0.0 ? KernelDensity.default_bandwidth(aes.y) : stat.bw[2])
-    k = KernelDensity.kde((aes.x,aes.y), bandwidth=window, npoints=stat.n)
-    aes.z = k.density
-    aes.x = collect(k.x)
-    aes.y = collect(k.y)
-    apply_statistic(ContourStatistic(levels=stat.levels), scales, coord, aes)
+
+    Dat = [aes.x aes.y]
+    linestyleflag = aes.linestyle ≠ nothing
+    colorflag = aes.color ≠ nothing
+    aes_color = colorflag ? aes.color : [nothing] 
+    aes_group = (aes.group ≠ nothing) ? aes.group : [nothing]
+    CT, GT = eltype(aes_color), eltype(aes_group)
+
+    groups = collect(Tuple{CT, GT}, Compose.cyclezip(aes_color, aes_group))
+    ugroups = unique(groups)
+    nugroups = length(ugroups)
+
+    K, V = Tuple{CT, GT}, Matrix{eltype(Dat)}
+
+    grouped_xy = if nugroups==1
+        Dict{K, V}(ugroups[1]=>Dat)
+    elseif nugroups>1
+        Dict{K, V}(g=>Dat[groups.==[g],:] for g in ugroups)
+    end
+
+    aess = Gadfly.Aesthetics[]
+    for (g, data) in grouped_xy
+        aes1 = Gadfly.Aesthetics()
+        k = KernelDensity.kde(data, bandwidth=window, npoints=stat.n)
+        aes1.x, aes1.y, aes1.z = k.x, k.y, k.density
+        apply_statistic(ContourStatistic(levels=stat.levels), scales, coord, aes1)
+        colorflag && (aes1.color = fill(g[1], length(aes1.x)))
+        push!(aess, aes1)
+    end
+
+    aes2 = Gadfly.concat(aess...)
+    aes.x, aes.y, aes.color = aes2.x, aes2.y, aes2.color
+    colorflag && (linestyleflag ? (aes.group = aes2.group) : (aes.linestyle = aes2.group.+1))
+    if !colorflag
+        aes.group = aes2.group
+        aes.color_function = aes2.color_function
+        aes.color_label = aes2.color_label
+        aes.color_key_colors = aes2.color_key_colors
+        aes.color_key_continuous = aes2.color_key_continuous
+    end
+
 end