explaining text

docs/example_data.jl

@@ -186,7 +186,11 @@ function example_data(example = "TopoPlots.jl")
         generate_events(design)
 
         time1 = vcat(rand(time_padding), component) # 500 msec = randiom 100 msec and 400 msec of n400
-        c = UnfoldSim.LinearModelComponent(; basis = time1, formula = @formula(0 ~ 1), β = [1])
+        c = UnfoldSim.LinearModelComponent(;
+            basis = time1,
+            formula = @formula(0 ~ 1),
+            β = [1],
+        )
 
         hart = headmodel(type = "hartmut") # 227 electrodes
         less_hart = magnitude(hart)[:, 1] # extract 1 lead field and 64 electrodes
@@ -207,16 +211,18 @@ function example_data(example = "TopoPlots.jl")
         # Create the DataFrame
         df = DataFrame(
             :estimate => dat[:],
-            :channel => repeat(1:size(dat, 1), outer = Int(length(dat[:]) / size(dat, 1))),
+            :channel =>
+                repeat(1:size(dat, 1), outer = Int(length(dat[:]) / size(dat, 1))),
             :time => repeat(1:size(dat, 2), outer = Int(length(dat[:]) / size(dat, 2))),
-            :trial => repeat(1:size(dat, 3), outer = Int(length(dat[:]) / size(dat, 3))),
+            :trial =>
+                repeat(1:size(dat, 3), outer = Int(length(dat[:]) / size(dat, 3))),
         )
 
         # chosing positions
         pos3d = hart.electrodes["pos"]
         pos2d = to_positions(pos3d')
         pos2d = [Point2f(p[1] + 0.5, p[2] + 0.5) for p in pos2d]
-    return df, pos2d
+        return df, pos2d
     elseif example == "raw_ch_names"
         return [
             "FP1",

docs/literate/how_to/uncertain_topo.jl

@@ -1,3 +1,7 @@
+# ```@raw html
+# <details>
+# <summary>Click to expand</summary>
+# ```
 using Unfold
 using UnfoldMakie
 using UnfoldSim
@@ -7,11 +11,20 @@ using TopoPlots
 using Statistics
 using Random
 
+# ```@raw html
+# </details >
+# ```
+# Representing uncertainty is one of the most difficult tasks in visualization. It is especially difficult for heatmaps and topoplots. 
+# Here we will present new ways to show uncertainty for topoplots series.
+
 # Data input
 include("../../../example_data.jl")
 dat, positions = TopoPlots.example_data()
 df = UnfoldMakie.eeg_array_to_dataframe(dat[:, :, 1], string.(1:length(positions)));
 df_uncert = UnfoldMakie.eeg_array_to_dataframe(dat[:, :, 2], string.(1:length(positions)));
+
+# Generate data with 227 channels, 50 trials, 500 mseconds for bootstrapping
+df_toposeries, pos_toposeries = data, pos = example_data("bootstrap_toposeries");
 nothing #hide
 
 # # Uncertainty via additional row
@@ -50,15 +63,12 @@ function bootstrap_toposeries(df)
     df1.estimate = tmp
     return df1
 end
-
-# Generate data with 227 channels, 50 trials, 500 mseconds
 # ```@raw html
 # </details >
 # ```
 
 #  To show uncertainty of estimate we will compute 10 different means of bootsrtaped data. 
 # More detailed: 1) create N boostrapped datasets by random sampling with replacement across trials; 2) compute their means; 3) do toposeries animation iterating across these means. 
-df_toposeries, pos_toposeries = data, pos = example_data("bootstrap_toposeries")
 dat_obs = Observable(df_toposeries)
 f = Figure()
 plot_topoplotseries!(
@@ -74,6 +84,6 @@ record(f, "bootstrap_toposeries.gif"; framerate = 10000) do io
         dat_obs[] = bootstrap_toposeries(df_toposeries)
         recordframe!(io)
     end
-end
+end;
 
 # ![](bootstrap_toposeries.gif)