Implements plotfunction #47

.gitignore

@@ -6,4 +6,5 @@
 test/test_images
 test/Manifest.toml
 docs/Manifest.toml
+docs/.CondaPkg
 deps/build.log

docs/src/eeg.md

@@ -9,20 +9,20 @@ TopoPlots.eeg_topoplot
 
 
 
-So for the standard 10/20 montage, one can drop the `positions` attribute:
+For the standard 10/20 montage, one can drop the `positions` attribute:
 ```@example 1
 using TopoPlots, CairoMakie
 
 labels = TopoPlots.CHANNELS_10_20
-TopoPlots.eeg_topoplot(rand(19), labels; axis=(aspect=DataAspect(),), label_text=true, label_scatter=(markersize=10, strokewidth=2,))
+TopoPlots.eeg_topoplot(rand(19); labels=labels, axis=(aspect=DataAspect(),), label_text=true, label_scatter=(markersize=10, strokewidth=2,))
 ```
 
 If the channels aren't 10/20, one can still plot them, but then the positions need to be passed as well:
 
 ```@example 1
 data, positions = TopoPlots.example_data()
 labels = ["s$i" for i in 1:size(data, 1)]
-TopoPlots.eeg_topoplot(data[:, 340, 1], labels; positions=positions, axis=(aspect=DataAspect(),))
+TopoPlots.eeg_topoplot(data[:, 340, 1]; labels = labels, label_text = true, positions=positions, axis=(aspect=DataAspect(),))
 ```
 
 ```@docs

docs/src/general.md

@@ -42,13 +42,13 @@ using TopoPlots, CairoMakie, ScatteredInterpolation, NaturalNeighbours
 
 data, positions = TopoPlots.example_data()
 
-f = Figure(resolution=(1000, 1250))
+f = Figure(size=(1000, 1250))
 
 interpolators = [
     DelaunayMesh() CloughTocher();
     SplineInterpolator() NullInterpolator();
     ScatteredInterpolationMethod(ThinPlate()) ScatteredInterpolationMethod(Shepard(3));
-    NaturalNeighboursMethod(Sibson(1)) NaturalNeighboursMethod(Triangle());
+    #NaturalNeighboursMethod(Sibson(1)) NaturalNeighboursMethod(Triangle());
     ]
 
 data_slice = data[:, 360, 1]
@@ -146,3 +146,25 @@ TopoPlots.topoplot(
     label_scatter=(; strokewidth=2),
     contours=(linewidth=2, color=:white))
 ```
+
+
+## Different plotfunctions
+
+It is possible to exchange the plotting function, from `heatmap!` to `contourf!` or `surface!`. Due to different keyword arguments, one needs to filter which keywords are passed to the plotting function manually.
+
+```@example 1
+f = Figure()
+
+TopoPlots.topoplot(f[1,1],
+    rand(10), rand(Point2f, 10),
+    axis=(; aspect=DataAspect()),
+    plotfnc! = contourf!, plotfnc_kwargs_filter=[:colormap])
+
+TopoPlots.topoplot(f[1,2],
+    rand(10), rand(Point2f, 10),
+    axis=(; aspect=DataAspect()),
+    plotfnc! = surface!) # surface can take all default kwargs similar to heatmap!
+
+f
+
+```

docs/src/topo_series.jl

@@ -0,0 +1,180 @@
+### A Pluto.jl notebook ###
+# v0.19.9
+
+using Markdown
+using InteractiveUtils
+
+# ╔═╡ 2fafb0da-f3a9-11ec-0ddf-6725344070fe
+begin
+using Pkg
+Pkg.activate("../../devEnv") # docs
+#Pkg.add("PyMNE")
+#Pkg.add(path="../../../TopoPlotsjl/")
+Pkg.develop(path="../../../TopoPlotsjl/")
+#Pkg.add("DataFrames")
+#Pkg.add("AlgebraOfGraphics")
+	#Pkg.add("StatsBase")
+	#Pkg.add("CategoricalArrays")
+
+	#Pkg.add("JLD2")
+
+	#Pkg.add("CairoMakie")
+end
+
+# ╔═╡ c4a25915-c7f5-453a-a4f0-4b40ebedea4c
+using Revise
+
+# ╔═╡ 59b87673-02d2-4deb-90be-74d923d170eb
+	using TopoPlots
+
+
+# ╔═╡ 452a245c-773a-4303-a970-f2592c3e879f
+begin
+	#using TopoPlots
+	#using ../../../Topoplotsjl
+	using CairoMakie
+	using DataFrames
+
+
+end
+
+
+# ╔═╡ 77dc1ba9-9484-485b-a49d-9aa231ef4983
+using Statistics
+
+# ╔═╡ 311f10ff-deb8-4f82-8b12-d5b643656828
+using PyMNE
+
+# ╔═╡ 9fa5c598-3578-4989-9585-29fd32ae1056
+using Distributions
+
+# ╔═╡ 6cda29dc-7086-4079-83c6-3650204a82ff
+pathof(TopoPlots)
+
+# ╔═╡ e0cc560f-d3e8-415b-b22d-6bca23ef093c
+revise(TopoPlots)
+
+# ╔═╡ f4b81740-d907-42ae-a0df-f46fb2f2cb15
+begin
+
+data = Array{Float32}(undef, 64, 400, 3)
+#read!(TopoPlots.assetpath("example-data.bin"), data)
+	read!(splitdir(pathof(TopoPlots))[1]*"/../assets/example-data.bin",data)
+
+positions = Vector{Point2f}(undef, 64)
+	read!(splitdir(pathof(TopoPlots))[1]*"/../assets/layout64.bin",positions)
+#read!(TopoPlots.assetpath("layout64.bin"), positions)
+
+end;
+
+
+# ╔═╡ 42f7755b-80f4-4185-8d21-42e11730e0fc
+begin
+	using Random
+	pos = positions[1:10]
+eeg_topoplot(rand(MersenneTwister(1),length(pos)), string.(1:length(pos));positions=pos,pad_value=0.)
+end
+
+# ╔═╡ aad784ee-6bb7-4f3c-8444-be050456ddea
+eeg_topoplot(data[:, 340, 1], string.(1:length(positions));positions=positions)
+
+# ╔═╡ 237e4f4a-cdf2-4bac-8096-de8050251745
+eeg_topoplot(data[:, 340, 1], string.(1:length(positions));positions=positions,pad_value=0.1)
+
+# ╔═╡ f522329b-3653-4059-9955-8cd05570e923
+topoplot(rand(MersenneTwister(1),length(pos)),pos)
+
+# ╔═╡ a9d2a2e2-6c8c-4cfc-9fed-b5e082cb44af
+let
+mon = PyMNE.channels.make_standard_montage("standard_1020")
+
+posMat = (Matrix(hcat(pos...)).-0.5).*0.5
+	#pos = PyMNE.channels.make_eeg_layout(mon).pos
+PyMNE.viz.plot_topomap(rand(MersenneTwister(1),length(pos)),posMat',cmap="RdBu_r",extrapolate="box",border=-1)
+end
+
+# ╔═╡ c358633f-8d18-4c5e-80f7-ab972e8860be
+Pkg.status("TopoPlots")
+
+# ╔═╡ c0a2ad2e-ccce-4e80-b52c-75f1428ed182
+e1eg_topoplot(data[:, 340, 1], string.(1:length(positions));positions=positions,interpolation = TopoPlots.NormalMixtureInterpolator() )
+
+# ╔═╡ d7620a42-d54c-4244-a820-d15aecdae626
+@time TopoPlots.eeg_topoplot_series(data[:,:,1],40;topoplotCfg=(positions=positions,label_scatter=false))
+
+# ╔═╡ ec59c704-ae33-4a62-82ce-63acc6b17793
+f, ax, pl = TopoPlots.eeg_topoplot(1:length(TopoPlots.CHANNELS_10_20),TopoPlots.CHANNELS_10_20; interpolation=TopoPlots.NullInterpolator(),)
+
+# ╔═╡ f3d1f3cc-f7c9-4ef4-ba4f-3d32f2509cad
+let
+    # 4 coordinates with one peak
+    positions = Point2f[(-1, 0), (0, -1), (1, 0), (0, 1)]
+    i = 1
+    peak_xy = positions[i]
+    data = zeros(length(positions))
+    data[i] = 1.1
+    fig = topoplot(data, positions)
+    # tighten the limits so that the limits of the axis and the data will match
+    tightlimits!(fig.axis)
+
+    # retrieve the interpolated data
+    m = fig.plot.plots[].color[]
+    # get the limits of the axes and data
+    rect = fig.axis.targetlimits[]
+    minx, miny = minimum(rect)
+    maxx, maxy = maximum(rect)
+    # recreate the coordinates of the data
+    x = range(minx, maxx, length=size(m, 1))
+    y = range(miny, maxy, length=size(m, 2))
+    xys = Point2f.(x, y')
+
+    # find the highest point
+    _, i = findmax(x -> isnan(x) ? -Inf : x, m)
+    xy = xys[i]
+	@show peak_xy
+	@show xy
+    #@test isapprox(xy, peak_xy; atol=0.02)
+	@show isapprox(xy, peak_xy; atol=0.02)
+	fig
+end
+
+# ╔═╡ 872ac6a4-ddaa-4dfb-a40d-9d5ea55bdb3d
+let
+	f = Figure()
+	axis = Axis(f[1, 1], aspect = 1)
+	xlims!(low = -2, high = 2)
+	ylims!(low = -2, high = 2)
+
+	data = [0, 0, 0]
+	pos1 = [Point2f(-1, -1), Point2f(-1.0, 0.0), Point2f(0, -1)]
+	pos2 = [Point2f(1, 1), Point2f(1.0, 0.0), Point2f(0, 1)]
+
+	pos1 = pos1 .- mean(pos1)
+	pos2 = pos2 .- mean(pos2)
+	eeg_topoplot!(axis, data, positions=pos1)
+	eeg_topoplot!(axis, data, positions=pos2)
+	f
+end
+
+# ╔═╡ Cell order:
+# ╠═2fafb0da-f3a9-11ec-0ddf-6725344070fe
+# ╠═6cda29dc-7086-4079-83c6-3650204a82ff
+# ╠═c4a25915-c7f5-453a-a4f0-4b40ebedea4c
+# ╠═e0cc560f-d3e8-415b-b22d-6bca23ef093c
+# ╠═59b87673-02d2-4deb-90be-74d923d170eb
+# ╠═452a245c-773a-4303-a970-f2592c3e879f
+# ╠═f4b81740-d907-42ae-a0df-f46fb2f2cb15
+# ╠═77dc1ba9-9484-485b-a49d-9aa231ef4983
+# ╠═aad784ee-6bb7-4f3c-8444-be050456ddea
+# ╠═237e4f4a-cdf2-4bac-8096-de8050251745
+# ╠═42f7755b-80f4-4185-8d21-42e11730e0fc
+# ╠═f522329b-3653-4059-9955-8cd05570e923
+# ╠═311f10ff-deb8-4f82-8b12-d5b643656828
+# ╠═a9d2a2e2-6c8c-4cfc-9fed-b5e082cb44af
+# ╠═c358633f-8d18-4c5e-80f7-ab972e8860be
+# ╠═9fa5c598-3578-4989-9585-29fd32ae1056
+# ╠═c0a2ad2e-ccce-4e80-b52c-75f1428ed182
+# ╠═d7620a42-d54c-4244-a820-d15aecdae626
+# ╠═ec59c704-ae33-4a62-82ce-63acc6b17793
+# ╠═f3d1f3cc-f7c9-4ef4-ba4f-3d32f2509cad
+# ╠═872ac6a4-ddaa-4dfb-a40d-9d5ea55bdb3d

src/TopoPlots.jl

@@ -63,8 +63,8 @@ export GeomExtrapolation, NullExtrapolation
     @compile_workload begin
         # all calls in this block will be precompiled, regardless of whether
         # they belong to your package or not (on Julia 1.8 and higher)
-        eeg_topoplot(view(data, :, 340, 1); positions)
-        eeg_topoplot(data[:, 340, 1]; positions)
+       eeg_topoplot(view(data, :, 340, 1); positions)
+       eeg_topoplot(data[:, 340, 1]; positions)
     end
 end
 

src/core-recipe.jl

@@ -13,7 +13,9 @@
         labels = nothing,
         label_text = false,
         label_scatter = false,
-        contours = false
+        contours = false,
+        plotfnc! = heatmap!,
+        plotfnc_kwargs_filter=  [:colorrange, :colormap, :interpolate],
     )
 end
 
@@ -39,6 +41,8 @@ Creates an irregular interpolation for each `data[i]` point at `positions[i]`.
     * true: add point for each position with default attributes
     * NamedTuple: Attributes get passed to the Makie.scatter! call.
 * `markersize = 5`: size of the points defined by positions, shortcut for label_scatter=(markersize=5,)
+* `plotfnc! = heatmap!`: function to use for plotting the interpolation
+* `plotfnc_kwargs_filter = [:colorrange, :colormap, :interpolate]`: different `plotfnc` support different kwargs, this array contains the keys to filter the full list which is [:colorrange, :colormap, :interpolate]
 
 * `contours = false`:
     * true: add scatter point for each position
@@ -116,8 +120,9 @@ function Makie.plot!(p::TopoPlot)
 #            z[mask] .= NaN
             return z
         end
-
-        heatmap!(p, xg, yg, data, colormap=p.colormap, colorrange=colorrange, interpolate=true)
+        kwargs_all = Dict(:colorrange => colorrange, :colormap => p.colormap, :interpolate => true)
+
+        p.plotfnc![](p, xg, yg, data;  (p.plotfnc_kwargs_filter[].=>getindex.(Ref(kwargs_all),p.plotfnc_kwargs_filter[]))...)
         contours = to_value(p.contours)
         attributes = @plot_or_defaults contours Attributes(color=(:black, 0.5), linestyle=:dot, levels=6)
         if !isnothing(attributes) && !(p.interpolation[] isa NullInterpolator)