diff --git a/Project.toml b/Project.toml index b6b26bdd0..ba9e5794f 100644 --- a/Project.toml +++ b/Project.toml @@ -1,7 +1,7 @@ name = "UnfoldMakie" uuid = "69a5ce3b-64fb-4f22-ae69-36dd4416af2a" authors = ["Benedikt Ehinger","Vladimir Mikheev", "Daniel Baumgartner", "Sören Döring", "Niklas Gärtner"] -version = "0.3.3" +version = "0.3.4" [deps] AlgebraOfGraphics = "cbdf2221-f076-402e-a563-3d30da359d67" diff --git a/src/nb_plot_erpimage2.jl b/src/nb_plot_erpimage2.jl index 2fad513b8..62265b92d 100644 --- a/src/nb_plot_erpimage2.jl +++ b/src/nb_plot_erpimage2.jl @@ -1,23 +1,59 @@ ### A Pluto.jl notebook ### -# v0.19.26 +# v0.19.27 using Markdown using InteractiveUtils +# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error). +macro bind(def, element) + quote + local iv = try Base.loaded_modules[Base.PkgId(Base.UUID("6e696c72-6542-2067-7265-42206c756150"), "AbstractPlutoDingetjes")].Bonds.initial_value catch; b -> missing; end + local el = $(esc(element)) + global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : iv(el) + el + end +end + +# ╔═╡ 80e36c32-187f-41ba-82eb-d84c22de6d69 +using Pkg + + +# ╔═╡ 7b3eca23-cc2a-4212-abc3-70f941afb4d4 +begin +Pkg.activate(mktempdir()) + Pkg.add(name="micromamba_jll",version="1.3") + Pkg.add(["UnfoldMakie","PyMNE","CairoMakie","Unfold","CSV","DataFrames","CoordinateTransformations","PlutoUI","StaticArrays","Statistics"]) +end + + # ╔═╡ ea3e73be-2bb6-11ee-0b04-43b39a893537 begin using UnfoldMakie using PyMNE using CairoMakie using Unfold - + using CSV + using DataFrames + using CoordinateTransformations + using PlutoUI + using StaticArrays + using Statistics + import StatsBase.mean end -# ╔═╡ f6615908-96a6-4f33-93ea-cc8bed5e7a8b -using CSV +# ╔═╡ e0c29a25-1ccd-455b-b99b-fa93c72310d4 +using TopoPlots + +# ╔═╡ 5451c8fd-c1cf-4088-9e0b-6fed0c388bf7 +TableOfContents() + +# ╔═╡ 256e9785-9f54-4546-ac51-53c2a22e6c79 +md""" +### Load Data +""" + +# ╔═╡ ba178d91-1d03-40f1-97ea-ec03b8bd29c6 -# ╔═╡ d3109971-ac7f-4362-9367-7d161a053b51 -using DataFrames # ╔═╡ c0f10639-eda1-4af5-b877-00406b066de0 begin @@ -31,14 +67,82 @@ evts_fix = subset(evts,:type => x->x.=="fixation") # ╔═╡ 15533746-a53e-4694-b8ca-285bc20f83c5 raw = PyMNE.io.read_raw_eeglab("/store/data/WLFO/derivatives/preproc_agert/sub-20/eeg/sub-20_task-WLFO_eeg.set") +# ╔═╡ 67470dc9-3282-4462-a675-b00d8a9c9241 +begin +pos3= pyconvert(Array,raw._get_channel_positions()); + pos3[:,2] = -pos3[:,2] + pos3[:,1] = -pos3[:,1] +end + # ╔═╡ b2efb84a-1f96-4671-b25e-65001cba12a2 d,times = Unfold.epoch(pyconvert(Array,raw.get_data(units="uV")),evts_fix,(-0.1,1),512) -# ╔═╡ 12b40fba-6b65-4f99-8177-83f8c4aaf61a +# ╔═╡ df5ff6e9-505f-4a25-97aa-e19be6286c8f +md""" +## Synched ChannelImage +""" + +# ╔═╡ 1b025463-936f-432c-91a1-056d0d9b9bcd + +# ╔═╡ 3f0fa6da-3790-4264-beba-4785eb1bb0ee +begin + function chanselectTopoplot(pos::Vector;labels=nothing,label_text=false,markersize=21,obs=nothing) + + labels = isnothing(labels) ? (1:length(pos)) : labels + + f,topoaxis,h = eeg_topoplot(zeros(length(pos)), labels; + positions=pos, + interpolation=NullInterpolator(), + enlarge=1, + label_text, + label_scatter=(markersize=markersize, color=:black) + ) + hidedecorations!(current_axis()) + hidespines!(current_axis()) + + obs = isa(obs,Observable) ? obs : Observable(1) + str = lift((obs, labels) -> "$(labels[obs])", obs, labels) + text!(topo_axis, 1, 1, text = str, align = (:center, :center)) + on(events(f).mousebutton, priority = 2) do event + if event.button == Mouse.left && event.action == Mouse.press + plt, p = pick(topo_axis) + obs[] = p + end + end + f + return obs,f + end +end -# ╔═╡ 058f8da3-3ad2-4e59-8757-c15a4b7299e8 +# ╔═╡ 933f3050-4c0e-44b3-89e7-4dc59591128d +@bind chanOrder PlutoUI.Select(["chanorder","cluster","X","Y","Z"]) +# ╔═╡ 93dc3a57-6b20-4fe8-abf8-b54ef7a8ab7e +@bind sorttime PlutoUI.Slider(0:500) + +# ╔═╡ 5c14e245-6b95-4c4d-85f6-bfbff95ca013 + let + d_m = mean(d,dims=3)[:,:,1] + series(d_m,solid_color=:black) + end + +# ╔═╡ d4c2e872-2bad-44ce-8a9a-42b549464678 + + +# ╔═╡ 8c70f1c1-313c-4b3b-b4e7-c14818ec9f57 +md""" +# ERPImage +""" + +# ╔═╡ 6d9c9e59-242e-4021-a972-db57b1b4f58b +heatmap(d[1,:,:]) # channel x time x trials + +# ╔═╡ 23f118e1-ceed-407f-9063-f0c49138077b +@bind sortby PlutoUI.Select(names(evts_fix)) + +# ╔═╡ 16369ac8-6cd4-481c-b2dc-b2d884afbfe1 +evts_fix # ╔═╡ cb8b03b7-6457-4fa1-ab4a-84bd1faffbf4 coalesce.(d[1,:,:],NaN) @@ -111,2217 +215,230 @@ end end # ╔═╡ eb3ed1d4-6ad7-4807-8a7a-10313b11b067 -begin +let f = Figure() -d_nan = coalesce.(d[1,:,:],NaN) +d_nan = coalesce.(d[100,:,:],NaN) + d_nan = d_nan .- mean(d_nan,dims=2)[:,1] + #d_nan = d_nan .+ mean(d_nan,dims=1)[:,1] v = (;colorrange = (-10,10)) + e = (;erpBlur = 5) @show size(d_nan) -plot_erpimage2!(f[1,1],times,d_nan,visual=v) -plot_erpimage2!(f[1,2],times,d_nan;sortvalues = diff(evts_fix.onset./100),visual=v) -plot_erpimage2!(f[2,1],times,d_nan;sortvalues = evts_fix.sac_startpos_x,visual=v) -plot_erpimage2!(f[2,2],times,d_nan;sortvalues=evts_fix.sac_amplitude,visual=v) +plot_erpimage2!(f[1,1],times,d_nan,visual=v,extra=e) +plot_erpimage2!(f[1,2],times,d_nan;extra=e,sortvalues = diff(evts_fix.onset./100),visual=v) +plot_erpimage2!(f[2,1],times,d_nan;extra=e,sortvalues = evts_fix.sac_startpos_x,visual=v) +plot_erpimage2!(f[2,2],times,d_nan;extra=e,sortvalues=evts_fix.sac_amplitude,visual=v) f end -# ╔═╡ 00000000-0000-0000-0000-000000000001 -PLUTO_PROJECT_TOML_CONTENTS = """ -[deps] -CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" -CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0" -DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" -PyMNE = "6c5003b2-cbe8-491c-a0d1-70088e6a0fd6" -Unfold = "181c99d8-e21b-4ff3-b70b-c233eddec679" -UnfoldMakie = "69a5ce3b-64fb-4f22-ae69-36dd4416af2a" - -[compat] -CSV = "~0.10.11" -CairoMakie = "~0.10.7" -DataFrames = "~1.6.1" -PyMNE = "~0.2.1" -Unfold = "~0.5.1" -UnfoldMakie = "~0.3.1" -""" +# ╔═╡ 6539620f-7d85-40c4-b5f1-dda42bc9e3e9 +begin 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= SphericalFromCartesian().(SVector.(x,y,z)) + sph = [vcat(s.r,s.θ,π/2 - s.ϕ) for s in sph] + sph = hcat(sph...)' + return sph + end + + +# ╔═╡ 03462e09-692b-47e0-96c0-bc730fcaf200 +pos3D_to_layout(pos3::AbstractMatrix;kwargs...) =pos3D_to_layout(pos3[:,1],pos3[:,2],pos3[:,3];kwargs...) + +# ╔═╡ 8513fc7f-cd5a-4ea3-b131-c1bcbcfb4878 +function pos3D_to_layout(x,y,z;sphere=[0,0,0.]) + #cart3d_to_spherical(x,y,z) + +# translate to sphere origin + x .-= sphere[1] + y .-= sphere[2] + z .-= sphere[3] + + # convert to spherical coordinates + sph = cart3d_to_spherical(x,y,z) + + # get rid of of the radius for now + pol_a = sph[:,3] + pol_b = sph[:,2] + + # use only theta & phi, convert back to cartesian coordinates + p_x = pol_a .* cos.(pol_b) + p_y = pol_a .* sin.(pol_b) + + # scale by the radius + p_x .*= sph[:,1] ./(π/2) + p_y .*= sph[:,1] ./(π/2) + + # move back by the sphere coordinates + p_x .+= sphere[1] + p_y .+= sphere[2] + + diff(m::Tuple) = [m[1],m[2]] + p_x .= p_x ./ (2* diff(extrema(p_x))[1]) + p_y .= p_y ./ (2* diff(extrema(p_y))[1]) + + p_x = p_x .+ 0.5 + p_y = p_y .+ 0.5 + return Point2f.(p_x,p_y) +end + +# ╔═╡ 461827a5-8597-4a05-9201-4cf94af01b58 +begin +pos2 = pos3D_to_layout(pos3) + +end + +# ╔═╡ 01c13032-2b18-4cc3-8e62-975448e390a1 +extrema([p[1] for p in pos2]) + +# ╔═╡ ca9835eb-0a68-461e-884f-9ca75a584d92 +begin +obs,f = chanselectTopoplot(pos2) + f +end + +# ╔═╡ 85de4fff-8eb3-4193-b11d-a545d4f60a05 +begin + global obs_pluto +on(obs) do val + global obs_pluto = val +end +end + +# ╔═╡ a91e1f64-fd06-4648-9352-4533eff5aaaa +obs_pluto + +# ╔═╡ ec1915aa-8c07-400c-ad09-2637a74aceee +begin + +f2 = Figure(resolution=(1000,500)) + d_m = mean(d,dims=3)[:,:,1] + d_m = disallowmissing(d_m) + + if chanOrder == "chanorder" + + sortix =(1:length(pos2)) + elseif chanOrder == "cluster" + sortix = sortperm(d_m[:,sorttime]) + elseif chanOrder == "X" + sortix = sortperm(pos3[:,1]) + elseif chanOrder == "Y" +sortix = sortperm(pos3[:,2]) + elseif chanOrder =="Z" + sortix = sortperm(pos3[:,3]) + end + + axzoom = (;limits=((-0.25, 1.25), (-0.25, 1.25))) + + ax,h = heatmap(f2[1,2:7],d_m[sortix,:]',colormap=Reverse("RdBu"),colorrange = [-15,15]) + Colorbar(f2[1,8],h) + vlines!(current_axis(),[sorttime]) +d_tmp = UnfoldMakie.eeg_matrix_to_dataframe(d_m,string.(1:size(d_m,1))) + d_tmp.channel = parse.(Int,d_tmp.label) + + +h = plot_topoplotseries!(f2[2,2:7],d_tmp,75;positions=pos2,visual=(;interp_resolution=(128,128),enlarge=1,label_scatter=false,contours=false),labels=nothing) + + hLeg = plot_topoplot!(f2[2,1],Float32.(1:length(pos2));positions=pos2,visual=(;interpolation=TopoPlots.NullInterpolator(),enlarge=0.9),layout=(;showLegend=false),axis=(;aspect=1,axzoom...)) + +# colgap!(h.layout.content[2].content,0) + #rowgap!(h.layout.content[2].content,1100) +rowsize!(f2.layout, 2, Relative(1/4)) + +f2 +end + +# ╔═╡ 23dca8b4-9166-41fc-bf42-9d6d57f29da6 +begin +f3 = Figure() +#GridLayout(f3[1,1]) +for (ix,chO) = enumerate(["chanorder","cluster","X","Y","Z"]) + if chO == "chanorder" + + sortix =(1:length(pos2)) + elseif chO == "cluster" + sortix = sortperm(d_m[:,100]) + elseif chO == "X" + sortix = sortperm(pos3[:,1]) + elseif chO == "Y" +sortix = sortperm(pos3[:,2]) + elseif chO =="Z" + sortix = sortperm(pos3[:,3]) + end + @show mod(ix-1,2) + @show ix + + heatmap(f3[mod(ix-1,2)+1,ix÷2],d_m[sortix,:]',axis=(;title=chO)) +end + f3 + +end + +# ╔═╡ 6f3fb600-013c-4b18-92e8-fe6c0990f0a1 +plot_topoplotseries!(Figure(),d_tmp,100;positions=pos2,visual=(;interp_resolution=(128,128),enlarge=1)) + + +# ╔═╡ 95426b22-deb7-4b58-bb43-67c368cb9ab9 +let +f = Figure() +plot_topoplot!(f[1,1],Float32.(1:length(pos2));positions=pos2,visual=(;interpolation=TopoPlots.NullInterpolator(),enlarge=0.9,markersize=20),layout=(;showLegend=false)) + plot_topoplot!(f[1,2],Float32.(zeros(length(pos2)).+0.1*rand(length(pos2)));positions=pos2,visual=(;colorrange=[-1,1],interpolation=TopoPlots.NullInterpolator(),enlarge=0.9,markersize=20),layout=(;showLegend=false)) + f +end + +# ╔═╡ 10d2ee3b-7708-4ad8-af36-6071b21e0cc9 +pos3D_to_layout(pos3) + # ╔═╡ Cell order: +# ╠═5451c8fd-c1cf-4088-9e0b-6fed0c388bf7 +# ╠═80e36c32-187f-41ba-82eb-d84c22de6d69 +# ╠═7b3eca23-cc2a-4212-abc3-70f941afb4d4 # ╠═ea3e73be-2bb6-11ee-0b04-43b39a893537 -# ╠═f6615908-96a6-4f33-93ea-cc8bed5e7a8b -# ╠═d3109971-ac7f-4362-9367-7d161a053b51 +# ╠═256e9785-9f54-4546-ac51-53c2a22e6c79 +# ╠═ba178d91-1d03-40f1-97ea-ec03b8bd29c6 # ╠═c0f10639-eda1-4af5-b877-00406b066de0 +# ╠═67470dc9-3282-4462-a675-b00d8a9c9241 +# ╠═461827a5-8597-4a05-9201-4cf94af01b58 +# ╠═01c13032-2b18-4cc3-8e62-975448e390a1 # ╠═a8cf7a53-6d5e-4400-a3bd-80034a7ed5f6 # ╠═15533746-a53e-4694-b8ca-285bc20f83c5 # ╠═b2efb84a-1f96-4671-b25e-65001cba12a2 +# ╠═df5ff6e9-505f-4a25-97aa-e19be6286c8f +# ╠═ca9835eb-0a68-461e-884f-9ca75a584d92 +# ╠═85de4fff-8eb3-4193-b11d-a545d4f60a05 +# ╠═a91e1f64-fd06-4648-9352-4533eff5aaaa +# ╠═1b025463-936f-432c-91a1-056d0d9b9bcd +# ╠═3f0fa6da-3790-4264-beba-4785eb1bb0ee +# ╠═933f3050-4c0e-44b3-89e7-4dc59591128d +# ╠═93dc3a57-6b20-4fe8-abf8-b54ef7a8ab7e +# ╠═5c14e245-6b95-4c4d-85f6-bfbff95ca013 +# ╠═ec1915aa-8c07-400c-ad09-2637a74aceee +# ╠═23dca8b4-9166-41fc-bf42-9d6d57f29da6 +# ╠═d4c2e872-2bad-44ce-8a9a-42b549464678 +# ╠═e0c29a25-1ccd-455b-b99b-fa93c72310d4 +# ╠═6f3fb600-013c-4b18-92e8-fe6c0990f0a1 +# ╠═10d2ee3b-7708-4ad8-af36-6071b21e0cc9 +# ╠═8c70f1c1-313c-4b3b-b4e7-c14818ec9f57 +# ╠═6d9c9e59-242e-4021-a972-db57b1b4f58b +# ╠═95426b22-deb7-4b58-bb43-67c368cb9ab9 # ╠═eb3ed1d4-6ad7-4807-8a7a-10313b11b067 -# ╠═12b40fba-6b65-4f99-8177-83f8c4aaf61a -# ╠═058f8da3-3ad2-4e59-8757-c15a4b7299e8 +# ╠═23f118e1-ceed-407f-9063-f0c49138077b +# ╠═6539620f-7d85-40c4-b5f1-dda42bc9e3e9 +# ╠═16369ac8-6cd4-481c-b2dc-b2d884afbfe1 # ╠═cb8b03b7-6457-4fa1-ab4a-84bd1faffbf4 # ╠═d42c85dd-ecb7-4e97-87fa-7dbd1746102c -# ╟─00000000-0000-0000-0000-000000000001 -# ╟─00000000-0000-0000-0000-000000000002 +# ╠═5548c28e-f67f-4873-9f90-d91ba0aaa42b +# ╠═776e0206-91ee-4800-83ec-8b6bdb06505a +# ╠═03462e09-692b-47e0-96c0-bc730fcaf200 +# ╠═8513fc7f-cd5a-4ea3-b131-c1bcbcfb4878 diff --git a/test/test_topoplot.jl b/test/test_topoplot.jl index 8f5c09b07..4dfefb76b 100644 --- a/test/test_topoplot.jl +++ b/test/test_topoplot.jl @@ -1,4 +1,3 @@ - @testset "testing topoplot" begin data, positions = TopoPlots.example_data() plot_topoplot(data[:, 150, 1]; positions=positions, t=150)