reverse ERP image trials and sorted ERP image into combined plot

unfoldtoolbox · Nov 24, 2023 · 3fb638e · 3fb638e
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Showing 4 changed files with 12 additions and 6 deletions.
diff --git a/docs/src/how_to/mult_vis_in_fig.md b/docs/src/how_to/mult_vis_in_fig.md
@@ -17,9 +17,15 @@ uf_deconv = example_data("UnfoldLinearModelContinuousTime")
 uf = example_data("UnfoldLinearModel")
 results = coeftable(uf)
 uf_5chan = example_data("UnfoldLinearModelMultiChannel")
-d_singletrial, _ = UnfoldSim.predef_eeg(; return_epoched=true)
-times = -0.099609375:0.001953125:1.0
 data, positions = TopoPlots.example_data()
+dat, evts = UnfoldSim.predef_eeg(; 
+        onset=LogNormalOnset(μ=3.5, σ=0.4), 
+        noiselevel=5
+    )
+dat_e, times = Unfold.epoch(dat, evts, [-0.1, 1], 100)
+evts, dat_e = Unfold.dropMissingEpochs(evts, dat_e)
+evts.Δlatency =  diff(vcat(evts.latency, 0)) *-1
+dat_e = dat_e[1, :, :]
 nothing #hide
 ```
 This section discusses how users can incorporate multiple plots into a single figure.
@@ -81,7 +87,7 @@ plot_erp!(f[2, 4:5], res_effects; categorical_color=false, categorical_group=tru
 plot_parallelcoordinates!(f[3, 2:3], uf_5chan, [1, 2, 3, 4, 5]; mapping=(; color=:coefname), 
     layout=(; legend_position=:bottom))
 
-plot_erpimage!(f[1, 4:5], times, d_singletrial)
+plot_erpimage!(f[1, 4:5], times, dat_e; sortvalues=evts.Δlatency)
 plot_circulareegtopoplot!(f[3:4, 4:5], d_topo[in.(d_topo.time, Ref(-0.3:0.1:0.5)), :];
     positions=positions, predictor=:time, predictor_bounds=[-0.3, 0.5])
 

diff --git a/docs/src/tutorials/erpimage.md b/docs/src/tutorials/erpimage.md
@@ -51,7 +51,7 @@ First, generate a data. Second, specify the necessary sorting parameter.
     )
     dat_e, times = Unfold.epoch(dat, evts, [-0.1,1], 100)
     evts, dat_e = Unfold.dropMissingEpochs(evts, dat_e)
-    evts.Δlatency =  diff(vcat(evts.latency, 0))
+    evts.Δlatency =  diff(vcat(evts.latency, 0)) *-1
     dat_e = dat_e[1,:,:]
 
     plot_erpimage(times, dat_e; sortvalues=evts.Δlatency)

diff --git a/test/test_complexplots.jl b/test/test_complexplots.jl
@@ -47,7 +47,7 @@
     dat, evts = UnfoldSim.predef_eeg(;onset=LogNormalOnset(μ=3.5, σ=0.4), noiselevel = 5)
     dat_e, times = Unfold.epoch(dat,evts, [-0.1,1], 100)
     evts, dat_e = Unfold.dropMissingEpochs(evts, dat_e)
-    evts.Δlatency =  diff(vcat(evts.latency, 0))
+    evts.Δlatency =  diff(vcat(evts.latency, 0)) *-1
     dat_e = dat_e[1,:,:]
     plot_erpimage!(gf, times, dat_e; sortvalues=evts.Δlatency)
     plot_channelimage!(gg, data[:, :, 1], positions[1:30], raw_ch_names; )

diff --git a/test/test_erpimage.jl b/test/test_erpimage.jl
@@ -27,7 +27,7 @@ end
         UnfoldSim.predef_eeg(; onset = LogNormalOnset(μ = 3.5, σ = 0.4), noiselevel = 5)
     dat_e, times = Unfold.epoch(dat, evts, [-0.1, 1], 100)
     evts, dat_e = Unfold.dropMissingEpochs(evts, dat_e)
-    evts.Δlatency = diff(vcat(evts.latency, 0))
+    evts.Δlatency = diff(vcat(evts.latency, 0)) *-1
     dat_e = dat_e[1, :, :]
     plot_erpimage(times, dat_e; sortvalues = evts.Δlatency)
 end