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@vladdez
vladdez committed Nov 16, 2023
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146 changes: 91 additions & 55 deletions docs/example_data.jl
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Expand Up @@ -7,60 +7,96 @@ type ==
"TopoPlots.jl"(default) Returns tidy DataFrame with two conditions, 64 channels and 800ms of data. This is an average over many subjects.
"""

function example_data(example="TopoPlots.jl")
function example_data(example = "TopoPlots.jl")

if example == "UnfoldLinearModel"
# load and generate a simulated Unfold Design
data,evts = UnfoldSim.predef_eeg(;noiselevel=10,return_epoched=true)
data = reshape(data,1,size(data)...)
f = @formula 0~1+condition+continuous
# generate ModelStruct
se_solver =(x,y)->Unfold.solver_default(x,y,stderror=true)
return fit(UnfoldModel,(Dict(Any=>(f,range(0,length=size(data,2),step=1/100)))) ,evts,data;solver=se_solver)
elseif example == "UnfoldLinearModelMultiChannel"
# load and generate a simulated Unfold Design
cAll = DataFrame()
sfreq = 100
for ch = 1:5
data,evts = UnfoldSim.predef_eeg(;p1 = (p100(;sfreq=sfreq), @formula(0~1),[5],Dict()),

n1 = (n170(;sfreq=sfreq), @formula(0~1+condition),[5,-ch*0.5],Dict()),
p3 = (p300(;sfreq=sfreq), @formula(0~1+continuous),[ch,1],Dict()),
return_epoched=true)
data = reshape(data,1,size(data)...)
f = @formula 0~1+condition+continuous
# generate ModelStruct

m= fit(UnfoldModel,(Dict(Any=>(f,range(0,length=size(data,2),step=1/100)))) ,evts,data)
d = coeftable(m)
d.channel .= ch
cAll = append!(cAll,d)
end
return cAll
if example == "UnfoldLinearModel"
# load and generate a simulated Unfold Design
data, evts = UnfoldSim.predef_eeg(; noiselevel = 10, return_epoched = true)
data = reshape(data, 1, size(data)...)
f = @formula 0 ~ 1 + condition + continuous
# generate ModelStruct
se_solver = (x, y) -> Unfold.solver_default(x, y, stderror = true)
return fit(
UnfoldModel,
(Dict(Any => (f, range(0, length = size(data, 2), step = 1 / 100)))),
evts,
data;
solver = se_solver,
)
elseif example == "UnfoldLinearModelMultiChannel"
# load and generate a simulated Unfold Design
cAll = DataFrame()
sfreq = 100
for ch = 1:5
data, evts = UnfoldSim.predef_eeg(;
p1 = (p100(; sfreq = sfreq), @formula(0 ~ 1), [5], Dict()),
n1 = (
n170(; sfreq = sfreq),
@formula(0 ~ 1 + condition),
[5, -ch * 0.5],
Dict(),
),
p3 = (p300(; sfreq = sfreq), @formula(0 ~ 1 + continuous), [ch, 1], Dict()),
return_epoched = true,
)
data = reshape(data, 1, size(data)...)
f = @formula 0 ~ 1 + condition + continuous
# generate ModelStruct

elseif example == "UnfoldLinearModelContinuousTime"
# load and generate a simulated Unfold Design
data,evts = UnfoldSim.predef_eeg(;)
data = reshape(data,1,size(data)...)
f = @formula 0~1+condition+continuous
basis = firbasis([0,0.5],100)
# generate ModelStruct
return fit(UnfoldModel,Dict(Any=>(f,basis)) ,evts,data)

elseif example == "TopoPlots.jl"
data,chanlocs = TopoPlots.example_data();
df = DataFrame(estimate=Float64[],time=Float64[],channel=Int64[],coefname=String[],topoPositions=[],se=Float64[],pval=Float64[])
pos = TopoPlots.points2mat(chanlocs)
for ch = 1:size(data,1)
for t = 1:size(data,2)
append!(df,DataFrame(estimate=data[ch,t,1],se=data[ch,t,2],pval=data[ch,t,3],time=t,channel=ch,coefname="A",topoPositions=(pos[1,ch],pos[2,ch])))


end
end
df.time = range(-0.3,0.5,step=1/500)[Int.(df.time)]
return df,chanlocs
else
error("unknown example data")
end
end
m = fit(
UnfoldModel,
(Dict(Any => (f, range(0, length = size(data, 2), step = 1 / 100)))),
evts,
data,
)
d = coeftable(m)
d.channel .= ch
cAll = append!(cAll, d)
end
return cAll

elseif example == "UnfoldLinearModelContinuousTime"
# load and generate a simulated Unfold Design
data, evts = UnfoldSim.predef_eeg(;)
data = reshape(data, 1, size(data)...)
f = @formula 0 ~ 1 + condition + continuous
basis = firbasis([0, 0.5], 100)
# generate ModelStruct
return fit(UnfoldModel, Dict(Any => (f, basis)), evts, data)

elseif example == "TopoPlots.jl"
data, chanlocs = TopoPlots.example_data()
df = DataFrame(
estimate = Float64[],
time = Float64[],
channel = Int64[],
coefname = String[],
topo_positions = [],
se = Float64[],
pval = Float64[],
)
pos = TopoPlots.points2mat(chanlocs)
for ch = 1:size(data, 1)
for t = 1:size(data, 2)
append!(
df,
DataFrame(
estimate = data[ch, t, 1],
se = data[ch, t, 2],
pval = data[ch, t, 3],
time = t,
channel = ch,
coefname = "A",
topo_positions = (pos[1, ch], pos[2, ch]),
),
)


end
end
df.time = range(-0.3, 0.5, step = 1 / 500)[Int.(df.time)]
return df, chanlocs
else
error("unknown example data")
end
end
10 changes: 5 additions & 5 deletions docs/literate/tutorials/circTopo.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -15,18 +15,18 @@ data, pos = TopoPlots.example_data();
dat = data[:, 240, 1]
df = DataFrame(
:estimate => eachcol(Float64.(data[:, 100:40:300, 1])),
:circularVariable => [0, 50, 80, 120, 180, 210],
:circular_variable => [0, 50, 80, 120, 180, 210],
:time => 100:40:300,
)
df = flatten(df, :estimate);

# # Our first plot!
# note how the plots are at the angles of circularVariable`
# note how the plots are at the angles of circular_variable`
plot_circulareegtopoplot(
df;
positions = pos,
axis = (; label = "Sac Incoming"),
predictor = :circularVariable,
axis = (; label = "Incoming saccade"),
predictor = :circular_variable,
)


Expand All @@ -37,6 +37,6 @@ plot_circulareegtopoplot(
positions = pos,
axis = (; label = "Time?!"),
predictor = :time,
predictorBounds = [80, 320],
predictor_bounds = [80, 320],
)

25 changes: 12 additions & 13 deletions docs/literate/tutorials/erp.jl
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
# ## [Line Plot Visualization](@id lp_vis)

# Here we discuss line plot visualization.
# Here we discuss ERP plot visualization.
# Make sure you have looked into the [installation instructions](@ref install_instruct).

# ## Include used Modules
Expand All @@ -15,7 +15,7 @@ using UnfoldSim
using UnfoldMakie

# ## Setup
# Let's generate some data and fit a model of a 2-level categorical and a continuous predictor with interaction.
# Let's generate some data. We'll fit a model with a 2 level categorical predictor and a continuous predictor with interaction.
data, evts = UnfoldSim.predef_eeg(; noiselevel = 12, return_epoched = true)
data = reshape(data, (1, size(data)...))
f = @formula 0 ~ 1 + condition + continuous
Expand All @@ -40,7 +40,7 @@ plot_erp(results; :stderror => true,)
# `plot_erp` use a `DataFrame` as an input, the library needs to know the names of the columns used for plotting.

# There are multiple default values, that are checked in that order if they exist in the `DataFrame`, a custom name can be chosen using
# `plot_erp(...;mapping=(; :y=:myEstimate)`
# `plot_erp(...; mapping=(; :y=:my_estimate)`

# :x Default is `(:x, :time)`.
# :y Default is `(:y, :estimate, :yhat)`.
Expand All @@ -50,24 +50,23 @@ plot_erp(results; :stderror => true,)

# ## key values
# `plot_erp(...; <name>=<value>,...)`.
# - categoricalColor (boolean, true) - in case of numeric `:color` column, is color a continuous or categorical variable?
# - categoricalGroup (boolean, true) - in case of numeric `:group` column, treat `:group` as categorical variable by default
# - stderror (boolean, false) - add an error-ribbon based on the `:stderror` column
# - pvalue (see below)
# - categorical_color (boolean, true) - in case of numeric `:color` column, treat `:color` as continuous or categorical variable.
# - categorical_group (boolean, true) - in case of numeric `:group` column, treat `:group` as categorical variable by default.
# - `topolegend` (bool, `false`): add an inlay topoplot with corresponding electrodes.
# - `stderror` (bool, `false`): add an error ribbon, with lower and upper limits based on the `:stderror` column.
# - `pvalue` (Array, `[]`): show a pvalue (see below).

# Using some general configurations we can pretty up the default visualization. Here we use the following configuration:

plot_erp(
res_effects;
mapping = (; y = :yhat, color = :continuous, group = :continuous),
legend = (; nbanks = 2),
layout = (; showLegend = true, legendPosition = :right),
categoricalColor = false, categoricalGroup = true,
layout = (; showLegend = true, legend_position = :right),
categorical_color = false, categorical_group = true,
)




# In the following we will use this "pretty" line plot as a basis for looking into configuration options.

# ## pvalue (array)
Expand All @@ -77,11 +76,11 @@ plot_erp(
#
# Is an array of p-values. If array not empty, plot shows colored lines under the plot representing the p-values.
# Default is `[]` (an empty array).
# Shown below is an example in which `pvalue` are given:
# Below is an example in which `pvalue` are given:
# pvals = DataFrame(
# from=[0.1,0.3],
# to=[0.5,0.7],
# coefname=["(Intercept)","condition: face"] # if coefname not specified, line should be black
# coefname=["(Intercept)", "condition: face"] # if coefname not specified, line should be black
# )
#
# plot_erp(results; :pvalue=>pvals)
Expand Down
31 changes: 13 additions & 18 deletions docs/make.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -22,20 +22,20 @@ for subfolder ∈ ["explanations", "HowTo", "tutorials", "reference"]

end

DocMeta.setdocmeta!(UnfoldMakie, :DocTestSetup, :(using UnfoldMakie); recursive=true)
DocMeta.setdocmeta!(UnfoldMakie, :DocTestSetup, :(using UnfoldMakie); recursive = true)

makedocs(;
modules=[UnfoldMakie],
authors="Benedikt Ehinger, Vladimir Mikheev, Daniel Baumgartner, Niklas Gärtner, Sören Döring",
repo="https://github.com/unfoldtoolbox/UnfoldMakie.jl/blob/{commit}{path}#{line}",
sitename="UnfoldMakie.jl",
warnonly = :cross_references,
format=Documenter.HTML(;
prettyurls=get(ENV, "CI", "false") == "true",
canonical="https://unfoldtoolbox.github.io/UnfoldMakie.jl",
assets=String[]
modules = [UnfoldMakie],
authors = "Benedikt Ehinger, Vladimir Mikheev, Daniel Baumgartner, Niklas Gärtner, Sören Döring",
repo = "https://github.com/unfoldtoolbox/UnfoldMakie.jl/blob/{commit}{path}#{line}",
sitename = "UnfoldMakie.jl",
warnonly = :cross_references,
format = Documenter.HTML(;
prettyurls = get(ENV, "CI", "false") == "true",
canonical = "https://unfoldtoolbox.github.io/UnfoldMakie.jl",
assets = String[],
),
pages=[
pages = [
"UnfoldMakie Documentation" => "index.md",
"Visualizations-Types" => [
"ERP plot" => "generated/tutorials/erp.md",
Expand All @@ -55,16 +55,11 @@ makedocs(;
"Show out of Bounds Label" => "how_to/show_oob_labels.md",
],
"Reference" => [
"Convert 3D positions / montages to 2D layouts" => "generated/reference/positions.md"

"Convert 3D positions / montages to 2D layouts" => "generated/reference/positions.md",
],
"API" => "api.md",
"Utilities" => "helper.md",
],

)

deploydocs(;
repo="github.com/unfoldtoolbox/UnfoldMakie.jl",
devbranch="main"
)
deploydocs(; repo = "github.com/unfoldtoolbox/UnfoldMakie.jl", devbranch = "main")
8 changes: 6 additions & 2 deletions docs/run_liveserver.jl
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@@ -1,3 +1,7 @@
using LiveServer
servedocs(skip_dir=joinpath("src","generated"),literate_dir=joinpath("literate"),literate=joinpath("literate"),foldername=".")

servedocs(
skip_dir = joinpath("src", "generated"),
literate_dir = joinpath("literate"),
literate = joinpath("literate"),
foldername = ".",
)
15 changes: 8 additions & 7 deletions docs/src/how_to/mult_vis_in_fig.md
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Original file line number Diff line number Diff line change
Expand Up @@ -26,7 +26,7 @@ This section discusses how users can incorporate multiple plots into a single fi

By using the !-version of the plotting function and inserting a grid position instead of an entire figure, we can create multiple coordinated views.

We start by creating a figure with Makie.Figure.
We will start by creating a figure with Makie.Figure.

`f = Figure()`

Expand Down Expand Up @@ -60,8 +60,8 @@ pvals = DataFrame(
coefname=["(Intercept)", "category: face"]
)
plot_erp!(f[2, 1:2], results,
categoricalColor=false,
categoricalGroup=false,
categorical_color=false,
categorical_group=false,
pvalue=pvals,
stderror=true)
Expand All @@ -73,16 +73,17 @@ plot_topoplotseries!(f[4, 1:3], d_topo, 0.1; positions=positions, mapping=(; lab
res_effects = effects(Dict(:continuous => -5:0.5:5), uf_deconv)
plot_erp!(f[2, 4:5], res_effects; categoricalColor=false, categoricalGroup=true,
plot_erp!(f[2, 4:5], res_effects; categorical_color=false, categorical_group=true,
mapping=(; y=:yhat, color=:continuous, group=:continuous),
legend=(; nbanks=2),
layout=(; showLegend=true, legendPosition=:right))
layout=(; show_legend=true, legend_position=:right))
plot_parallelcoordinates!(f[3, 2:3], uf_5chan, [1, 2, 3, 4, 5]; mapping=(; color=:coefname), layout=(; legendPosition=:bottom))
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_circulareegtopoplot!(f[3:4, 4:5], d_topo[in.(d_topo.time, Ref(-0.3:0.1:0.5)), :];
positions=positions, predictor=:time, predictorBounds=[-0.3, 0.5])
positions=positions, predictor=:time, predictor_bounds=[-0.3, 0.5])
f
```
Expand Down
11 changes: 7 additions & 4 deletions docs/src/how_to/position2color.md
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Original file line number Diff line number Diff line change
Expand Up @@ -21,17 +21,20 @@ We can switch the colorscale of the position-map, by giving a function that maps

### Similar to MNE
```@example main
plot_butterfly(results; positions=positions, extra=(; topoPositionToColorFunction = pos -> UnfoldMakie.posToColorRGB(pos)))
plot_butterfly(results;
positions=positions,
topopositions_to_color = pos -> UnfoldMakie.posToColorRGB(pos)
)
```

### HSV-Space
```@example main
plot_butterfly(results; positions=positions, extra=(; topoPositionToColorFunction=UnfoldMakie.posToColorHSV))
plot_butterfly(results; positions=positions, topopositions_to_color=UnfoldMakie.posToColorHSV)
```

### Uniform Color
To highlight the flexibility, we can also make all lines `gray`, or any other arbitrary color, or function of electrode-`position`.

```@example main
using Colors
plot_butterfly(results; positions=positions, extra=(; topoPositionToColorFunction = x -> Colors.RGB(0.5)))
plot_butterfly(results; positions=positions, topopositions_to_color = x -> Colors.RGB(0.5))
```
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