argument names review

docs/example_data.jl

@@ -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

docs/literate/tutorials/circTopo.jl

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

docs/literate/tutorials/erp.jl

@@ -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
@@ -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
@@ -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)`.
@@ -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)
@@ -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)

docs/make.jl

@@ -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",
@@ -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")

docs/run_liveserver.jl

@@ -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 = ".",
+)

docs/src/how_to/mult_vis_in_fig.md

@@ -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()`
 
@@ -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)
 
@@ -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
 ```

docs/src/how_to/position2color.md

@@ -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))
 ```