unfoldtoolbox · vladdez · Oct 24, 2023 · Sep 19, 2023 · Sep 20, 2023 · Sep 20, 2023
diff --git a/CITATION.cff b/CITATION.cff
@@ -0,0 +1,28 @@
+# This CITATION.cff file was generated with cffinit.
+# Visit https://bit.ly/cffinit to generate yours today!
+
+cff-version: 1.2.0
+title: UnfoldMakie
+message: 'If you use this page, please cite it as below.'
+type: software
+authors:
+  - given-names: Benedikt
+    family-names: Ehinger
+    orcid: 'https://orcid.org/0000-0002-6276-3332'
+  - given-names: Sören
+    family-names: Döring
+  - given-names: Niklas
+    family-names: Gärtner
+  - given-names: Vladimir
+    family-names: Mikheev
+    orcid: 'https://orcid.org/0000-0002-4738-6655'
+  - given-names: René
+    family-names: Skukies
+    orcid: 'https://orcid.org/0000-0002-4124-4584'
+
+identifiers:
+  - type: doi
+    value: 10.5281/zenodo.6531996
+repository-code: 'https://github.com/unfoldtoolbox/UnfoldMakie.jl'
+version: 0.3.5
+date-released: '2023-10-24'
diff --git a/docs/src/literate/tutorials/circTopo.jl b/docs/src/literate/tutorials/circTopo.jl
@@ -11,18 +11,30 @@ using DataFrames
 
 # # Generate data
 # Generate a Dataframe. We need to specify the TopoPlot Positions either via position, or via labels (according to TopoPlots.jl)
-data,pos = TopoPlots.example_data();
+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],
-    :time=>100:40:300)
-df = flatten(df,:estimate);
+df = DataFrame(
+    :estimate => eachcol(Float64.(data[:, 100:40:300, 1])),
+    :circularVariable => [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`
-plot_circulareegtopoplot(df;positions=pos,axis=(;label="Sac Incoming"),predictor=:circularVariable)
+plot_circulareegtopoplot(
+    df;
+    positions = pos,
+    axis = (; label = "Sac Incoming"),
+    predictor = :circularVariable,
+)
 
 
 # In case the bounding variable is not between 0 and 360, as here we use actually time, we have to specify it. e.g.
-plot_circulareegtopoplot(df;positions=pos,axis=(;label="Time?!"),predictor=:time,extra=(;predictorBounds=[80,320]))
+plot_circulareegtopoplot(
+    df;
+    positions = pos,
+    axis = (; label = "Time?!"),
+    predictor = :time,
+    predictorBounds = [80, 320],
+)
diff --git a/docs/src/literate/tutorials/circTopo.md b/docs/src/literate/tutorials/circTopo.md
@@ -16,27 +16,39 @@ using DataFrames
 Generate a Dataframe. We need to specify the TopoPlot Positions either via position, or via labels (according to TopoPlots.jl)
 
 ````@example circTopo
-data,pos = TopoPlots.example_data();
+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],
-    :time=>100:40:300)
-df = flatten(df,:estimate);
+df = DataFrame(
+    :estimate => eachcol(Float64.(data[:, 100:40:300, 1])),
+    :circularVariable => [0, 50, 80, 120, 180, 210],
+    :time => 100:40:300,
+)
+df = flatten(df, :estimate);
 nothing #hide
 ````
 
 # Our first plot!
 note how the plots are at the angles of circularVariable`
 
 ````@example circTopo
-plot_circulareegtopoplot(df;positions=pos,axis=(;label="Sac Incoming"),predictor=:circularVariable)
+plot_circulareegtopoplot(
+    df;
+    positions = pos,
+    axis = (; label = "Sac Incoming"),
+    predictor = :circularVariable,
+)
 ````
 
 In case the bounding variable is not between 0 and 360, as here we use actually time, we have to specify it. e.g.
 
 ````@example circTopo
-plot_circulareegtopoplot(df;positions=pos,axis=(;label="Time?!"),predictor=:time,extra=(;predictorBounds=[80,320]))
+plot_circulareegtopoplot(
+    df;
+    positions = pos,
+    axis = (; label = "Time?!"),
+    predictor = :time,
+    predictorBounds = [80, 320],
+)
 ````
 
 ---

diff --git a/docs/src/literate/tutorials/erp.jl b/docs/src/literate/tutorials/erp.jl
@@ -16,17 +16,23 @@ using UnfoldMakie
 
 # ## Setup
 # Let's generate some data and fit a model of a 2-level categorical 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 
-se_solver =(x,y)->Unfold.solver_default(x,y,stderror=true);
-
-m = fit(UnfoldModel, Dict(Any=>(f,range(0,step=1/100,length=size(data,2)))), evts, data,solver=se_solver)
+data, evts = UnfoldSim.predef_eeg(; noiselevel = 12, return_epoched = true)
+data = reshape(data, (1, size(data)...))
+f = @formula 0 ~ 1 + condition + continuous
+se_solver = (x, y) -> Unfold.solver_default(x, y, stderror = true);
+
+m = fit(
+    UnfoldModel,
+    Dict(Any => (f, range(0, step = 1 / 100, length = size(data, 2)))),
+    evts,
+    data,
+    solver = se_solver,
+)
 results = coeftable(m)
-res_effects = effects(Dict(:continuous=>-5:0.5:5),m);
+res_effects = effects(Dict(:continuous => -5:0.5:5), m);
 
 # ## Plot the results
-plot_erp(results; extra=(:stderror=>true,))
+plot_erp(results; :stderror => true,)
 
 
 
@@ -42,22 +48,22 @@ plot_erp(results; extra=(:stderror=>true,))
 
 # # Configuration for Line Plots
 
-# ## extra
-# `plot_erp(...;extra=(;<name>=<value>,...)`.
+# ## 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)
 
 # 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),
-    extra=(;showLegend=true,
-                    categoricalColor=false,
-                    categoricalGroup=true),
-    legend  = (;nbanks=2),
-    layout  = (;legendPosition=:right))
+plot_erp(
+    res_effects;
+    mapping = (; y = :yhat, color = :continuous, group = :continuous),
+    legend = (; nbanks = 2),
+    layout = (; legendPosition = :right),
+    showLegend = true, categoricalColor = false, categoricalGroup = true,
+)
 
 
 
@@ -78,18 +84,15 @@ plot_erp(res_effects;
 #		coefname=["(Intercept)","condition: face"] # if coefname not specified, line should be black
 #	)
 #
-# plot_erp(results;extra= (;:pvalue=>pvals))
+# plot_erp(results; :pvalue=>pvals)
 # ### stderror (boolean)
 # Indicating whether the plot should show a colored band showing lower and higher estimates based on the stderror. 
 # Default is `false`.
 #
 # previously we showed `:stderror`- but low/high is possible as well`
 results.se_low = results.estimate .- 0.5
 results.se_high = results.estimate .+ 0.15
-plot_erp(select(results,Not(:stderror));extra= (;stderror=true))
+plot_erp(select(results, Not(:stderror)); stderror = true)
 
 # !!! note
 #        as in the above code,`:stderror` has precedence over `:se_low`/`:se_high`
-
-
-
diff --git a/docs/src/literate/tutorials/erp.md b/docs/src/literate/tutorials/erp.md
@@ -24,37 +24,43 @@ using UnfoldMakie
 Let's generate some data and fit a model of a 2-level categorical and a continuous predictor with interaction.
 
 ````@example erp
-data,evts = UnfoldSim.predef_eeg(;noiselevel=12,return_epoched=true)
-data = reshape(data,(1,size(data)...))
-f = @formula 0 ~ 1+condition+continuous
-se_solver =(x,y)->Unfold.solver_default(x,y,stderror=true);
-
-m = fit(UnfoldModel, Dict(Any=>(f,range(0,step=1/100,length=size(data,2)))), evts, data,solver=se_solver)
+data, evts = UnfoldSim.predef_eeg(; noiselevel = 12, return_epoched = true)
+data = reshape(data, (1, size(data)...))
+f = @formula 0 ~ 1 + condition + continuous
+se_solver = (x, y) -> Unfold.solver_default(x, y, stderror = true);
+
+m = fit(
+    UnfoldModel,
+    Dict(Any => (f, range(0, step = 1 / 100, length = size(data, 2)))),
+    evts,
+    data,
+    solver = se_solver,
+)
 results = coeftable(m)
-res_effects = effects(Dict(:continuous=>-5:0.5:5),m);
+res_effects = effects(Dict(:continuous => -5:0.5:5), m);
 nothing #hide
 ````
 
 ## Plot the results
 
 ````@example erp
-plot_erp(results; extra=(:stderror=>true,))
+plot_erp(results; :stderror=>true,)
 ````
 
 ## Column Mappings for Line Plots
 `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=:myEstimate)`
 
 :x Default is `(:x, :time)`.
 :y Default is `(:y, :estimate, :yhat)`.
 :color Default is `(:color, :coefname)`.
 
 # Configuration for Line Plots
 
-## extra
-`plot_erp(...;extra=(;<name>=<value>,...)`.
+## key variables
+`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
@@ -63,13 +69,13 @@ There are multiple default values, that are checked in that order if they exist
 Using some general configurations we can pretty up the default visualization. Here we use the following configuration:
 
 ````@example erp
-plot_erp(res_effects;
-    mapping = (;y=:yhat,color=:continuous, group=:continuous),
-    extra=(;showLegend=true,
-                    categoricalColor=false,
-                    categoricalGroup=true),
-    legend  = (;nbanks=2),
-    layout  = (;legendPosition=:right))
+plot_erp(
+    res_effects;
+    mapping = (; y = :yhat, color = :continuous, group = :continuous),
+    legend = (; nbanks = 2),
+    layout = (; legendPosition = :right),
+    showLegend = true, categoricalColor = false, categoricalGroup = true,
+)
 ````
 
 In the following we will use this "pretty" line plot as a basis for looking into configuration options.
@@ -91,7 +97,8 @@ pvals = DataFrame(
 #	)
 ````
 
-plot_erp(results;extra= (;:pvalue=>pvals))
+plot_erp(results; :pvalue=>pvals)
+
 ### stderror (boolean)
 Indicating whether the plot should show a colored band showing lower and higher estimates based on the stderror.
 Default is `false`.
@@ -101,7 +108,7 @@ previously we showed `:stderror`- but low/high is possible as well`
 ````@example erp
 results.se_low = results.estimate .- 0.5
 results.se_high = results.estimate .+ 0.15
-plot_erp(select(results,Not(:stderror));extra= (;stderror=true))
+plot_erp(select(results, Not(:stderror)); stderror=true)
 ````
 
 !!! note

diff --git a/docs/src/tutorials/butterfly.md b/docs/src/tutorials/butterfly.md
@@ -19,7 +19,7 @@ We filter the data to make it more clearly represented:
 ```@example main
 include("../../example_data.jl")
 df, pos = example_data("TopoPlots.jl")
-first(df,3)
+first(df, 3)
 ```
 
 ## Plot Butterfly Plots
@@ -37,7 +37,7 @@ plot_butterfly(df; positions=pos)
 
 ## Column Mappings for Butterfly Plots
 
-Since butterfly plots use a `DataFrame` as input, the library needs to know the names of the columns used for plotting. You can set these mapping values by calling `plot_butterfly(...; mapping=(;:x=:time,))`, that is, by specifying a `NamedTuple` (note the `;` right after the opening parentheses).
+Since butterfly plots use a `DataFrame` as input, the library needs to know the names of the columns used for plotting. You can set these mapping values by calling `plot_butterfly(...; mapping=(; :x=:time,))`, that is, by specifying a `NamedTuple` (note the `;` right after the opening parentheses).
 
 For more information on mapping values, see the [Mapping Data](@ref config_mapping) section of the documentation.
 
@@ -56,8 +56,10 @@ Default is `(:labels, :label, :topoLabels, :sensor, :nothing)`
 
 ## Configurations for Butterfly Plots
 
-Here we look into possible options for configuring the butterfly plot visualization using `(...; extra=(<name>=<value>, ...)`.
-This is the list of unique configuration (extraData):
+
+Here we look into possible options for configuring the butterfly plot visualization using `(...; <name>=<value>, ...)`.
+This is the list of unique configuration (key values):
+
 
 - topoLegend (boolean)
 

diff --git a/docs/src/tutorials/designmatrix.md b/docs/src/tutorials/designmatrix.md
@@ -28,20 +28,20 @@ The following code will result in the default configuration.
 plot_designmatrix(designmatrix(uf))
 ```
 
-# `plot_designmatrix(...; extra=(<name>=<value>, ...)`.
+# kwargs `plot_designmatrix(...; ...)`.
+
 
 - sortData (boolean,false)  - Indicating whether the data is sorted; using sortslices() of Base Julia. 
 
 
 In order to make the designmatrix easier to read, you may want to sort it.
 ```
-plot_designmatrix(designmatrix(uf);extra=(;sortData=true))
+plot_designmatrix(designmatrix(uf); sortData=true)
 ```
 
-- standardizeData (boolean,false) - Indicating whether the data is standardized, mapping the values between 0 and 1. 
-
+- standardizeData (boolean, false) - Indicating whether the data is standardized, mapping the values between 0 and 1. 
+- xTicks (number, nothing)
 
-- xTicks (number,nothing)
 Indicating the number of labels on the x-axis. Behavior if specified in configuration:
     - xTicks = 0: no labels are placed.
     - xTicks = 1: first possible label is placed.

diff --git a/docs/src/tutorials/erpimage.md b/docs/src/tutorials/erpimage.md
@@ -29,14 +29,14 @@ Since ERP images use a `Matrix` as an input, the library does not need any infor
 ## extra=(;)
 - erpBlur (number, 10) - Is a number indicating how much blur is applied to the image; using Gaussian blur of the ImageFiltering module. Negative values deactivate the blur.
 
-- sortData (boolean, false) - Indicating whether the data is sorted; using sortperm() of Base Julia 
+- sortvalues - Indicating whether the data is sorted; using sortperm() of Base Julia 
 (sortperm() computes a permutation of the array's indices that puts the array into sorted order). 
 
-- ploterp (bool, false) - Indicating whether the plot should add a line plot below the ERP image, showing the mean of the data. If limits are set in the axis values both plots will be aligned.
+- meanPlot (bool, false) - Indicating whether the plot should add a line plot below the ERP image, showing the mean of the data. If limits are set in the axis values both plots will be aligned.
 
 ```@example main
 plot_erpimage(data;
-    extra = (ploterp = true,),
+    meanPlot = true,
     colorbar = (label = "Voltage [µV]",),
     visual = (colormap = :viridis, colorrange = (-40, 40)))
 

diff --git a/src/UnfoldMakie.jl b/src/UnfoldMakie.jl
@@ -30,7 +30,7 @@ import AlgebraOfGraphics.hidedecorations!
 
 include("plotconfig.jl")
 
-include("eeg-series.jl")
+include("eeg_series.jl")
 include("plot_topoplotseries.jl")
 
 include("plot_erp.jl")