diff --git a/docs/literate/reference/positions.jl b/docs/literate/explanations/positions.jl
similarity index 100%
rename from docs/literate/reference/positions.jl
rename to docs/literate/explanations/positions.jl
diff --git a/docs/literate/how_to/hide_deco.jl b/docs/literate/how_to/hide_deco.jl
index 4f9f7dbd..86b26b6a 100644
--- a/docs/literate/how_to/hide_deco.jl
+++ b/docs/literate/how_to/hide_deco.jl
@@ -28,7 +28,7 @@ plot_butterfly!(
     data;
     positions = pos,
     topomarkersize = 10,
-    topoheigth = 0.4,
+    topoheight = 0.4,
     topowidth = 0.4,
     axis = (; title = "With decorations"),
 )
@@ -37,7 +37,7 @@ plot_butterfly!(
     data;
     positions = pos,
     topomarkersize = 10,
-    topoheigth = 0.4,
+    topoheight = 0.4,
     topowidth = 0.4,
     axis = (; title = "Without decorations"),
     layout = (; hidedecorations = (:label => true, :ticks => true, :ticklabels => true)),
diff --git a/docs/literate/how_to/mult_vis_in_fig.jl b/docs/literate/how_to/mult_vis_in_fig.jl
index 27f1c949..31a32118 100644
--- a/docs/literate/how_to/mult_vis_in_fig.jl
+++ b/docs/literate/how_to/mult_vis_in_fig.jl
@@ -83,8 +83,8 @@ plot_designmatrix!(f[2, 3], designmatrix(uf))
 plot_topoplot!(f[3, 1], data[:, 150, 1]; positions = positions)
 plot_topoplotseries!(
     f[4, 1:3],
-    d_topo,
-    0.1;
+    d_topo;
+    bin_width = 0.1,
     positions = positions,
     mapping = (; label = :channel),
 )
@@ -157,7 +157,7 @@ plot_butterfly!(
     d_topo;
     positions = pos,
     topomarkersize = 10,
-    topoheigth = 0.4,
+    topoheight = 0.4,
     topowidth = 0.4,
 )
 hlines!(0, color = :gray, linewidth = 1)
@@ -166,8 +166,8 @@ plot_topoplot!(gc, data[:, 340, 1]; positions = positions, axis = (; xlabel = "[
 
 plot_topoplotseries!(
     gd,
-    df,
-    80;
+    df;
+    bin_width = 80,
     positions = positions,
     visual = (label_scatter = false,),
     layout = (; use_colorbar = true),
diff --git a/docs/literate/how_to/position2color.jl b/docs/literate/how_to/position2color.jl
index bdb05f0b..247d3a1f 100644
--- a/docs/literate/how_to/position2color.jl
+++ b/docs/literate/how_to/position2color.jl
@@ -54,3 +54,19 @@ plot_butterfly(
     positions = positions,
     topopositions_to_color = x -> Colors.RGB(0.5),
 )
+
+# Transparency
+# Unlike RGB, RGBA has a fourth channel, alpha, which is responsible for transparency.
+# Here are two examples of how to manipulate it.
+
+plot_butterfly(
+    results;
+    positions = positions,
+    topopositions_to_color = x -> (RGBA(UnfoldMakie.pos_to_color_RomaO(x), 1)),
+)
+
+plot_butterfly(
+    results;
+    positions = positions,
+    topopositions_to_color = x -> (GrayA(UnfoldMakie.pos_to_color_RomaO(x), 0.5)),
+)
diff --git a/docs/literate/intro/code_principles.jl b/docs/literate/intro/code_principles.jl
new file mode 100644
index 00000000..3ac8df4b
--- /dev/null
+++ b/docs/literate/intro/code_principles.jl
@@ -0,0 +1,11 @@
+# # Code principles
+
+
+# Here we will write about principles which we developed through our publication.
+
+#- Code should be clear and concise
+#- Variables inside the code should have meaningful names
+#- Every function exposed to the user should have documentation that specifies all parameters, types, input and output arguments.
+#- Most people will not look at the defaults, so it is very important to nudge users to show important details with a picture or text.
+#- Function naming should be based on some theory and naming conventions.
+#- You should avoid functions longer 50 lines 
diff --git a/docs/literate/intro/plot_types.jl b/docs/literate/intro/plot_types.jl
index 851f8e88..3a5fa0dc 100644
--- a/docs/literate/intro/plot_types.jl
+++ b/docs/literate/intro/plot_types.jl
@@ -1,7 +1,7 @@
 # # The Dilemma of Multidimensionality
 
 # !!! note
-#        Please read the paper [The Art of Brainwaves](https://apertureneuro.org/article/116386-the-art-of-brainwaves-a-survey-on-event-related-potential-visualization-practices), if you want to know more about how we come up with these plot types.
+#        Please read the paper [The Art of Brainwaves](https://apertureneuro.org/article/116386-the-art-of-brainwaves-a-survey-on-event-related-potential-visualization-practices), if you want to know how we come up with these plot types.
 #=
 EEG – multidimensional data and could be presented differently.
 
diff --git a/docs/literate/tutorials/butterfly.jl b/docs/literate/tutorials/butterfly.jl
index 11c6256e..3e30912f 100644
--- a/docs/literate/tutorials/butterfly.jl
+++ b/docs/literate/tutorials/butterfly.jl
@@ -1,5 +1,5 @@
 # # [Butterfly Plot](@id bfp_vis)
-# Butterfly plot is a plot type for visualisation of Event-related potentials. 
+# **Butterfly plot** is a plot type for visualisation of Event-related potentials. 
 # It can fully represent time and channels dimensions using lines. With addition of topoplot inset it can also represent location of channels.
 # It called "butterfly" because the envelope of channels reminds butterfly wings🦋. 
 
@@ -38,7 +38,7 @@ plot_butterfly(df; positions = pos)
 
 # You want to change size of topomarkers and size of topoplot:
 
-plot_butterfly(df; positions = pos, topomarkersize = 10, topoheigth = 0.4, topowidth = 0.4)
+plot_butterfly(df; positions = pos, topomarkersize = 10, topoheight = 0.4, topowidth = 0.4)
 
 # You want to add vline and hline:
 
diff --git a/docs/literate/tutorials/channel_image.jl b/docs/literate/tutorials/channel_image.jl
index 9a56a357..c73a9a7b 100644
--- a/docs/literate/tutorials/channel_image.jl
+++ b/docs/literate/tutorials/channel_image.jl
@@ -1,6 +1,6 @@
 # # Channel image 
 
-# Channel image is a plot type for visualizing EEG activity for all channels. 
+# **Channel image** is a plot type for visualizing EEG activity for all channels. 
 # It can fully represent time and channel  dimensions using a heatmap. 
 # Y-axis represents all channels, x-axis represents time, while color represents voltage. 
 
diff --git a/docs/literate/tutorials/circ_topo.jl b/docs/literate/tutorials/circ_topo.jl
index f6b34d6a..f8be30f5 100644
--- a/docs/literate/tutorials/circ_topo.jl
+++ b/docs/literate/tutorials/circ_topo.jl
@@ -1,5 +1,10 @@
 # # Circular Topoplots
 
+# **Circular topoplot series** is a plot type for visualizing EEG activity in relation to some continous variable arranged on a circluar line. 
+# It can fully represent channel and channel location dimensions using contour lines. It can also partially represent the varaible dimension.
+# Variable could be for instance accadic amplitude or degrees of visual angle. 
+# Basically, it is a series of Topoplots arranged on a circle.
+
 # # Setup
 # ## Package loading
 
diff --git a/docs/literate/tutorials/erp.jl b/docs/literate/tutorials/erp.jl
index 41d39d12..721063ba 100644
--- a/docs/literate/tutorials/erp.jl
+++ b/docs/literate/tutorials/erp.jl
@@ -1,6 +1,6 @@
 # # [ERP Plot](@id erp_vis)
 
-# ERP plot is plot type for visualisation of [Event-related potentials](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3016705/). 
+# **ERP plot** is plot type for visualisation of [Event-related potentials](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3016705/). 
 # It can fully represent time and experimental condition dimensions using lines.
 
 # # Setup
diff --git a/docs/literate/tutorials/erp_grid.jl b/docs/literate/tutorials/erp_grid.jl
index 9e36b1e4..0ebfaccc 100644
--- a/docs/literate/tutorials/erp_grid.jl
+++ b/docs/literate/tutorials/erp_grid.jl
@@ -1,4 +1,8 @@
 # # ERP grid 
+# **ERP grid** is a plot type for visualisation of Event-related potentials. 
+# It can fully represent time, channel, and layout (channel locations) dimensions using lines. It can also partially represent condition dimensions.
+# Lines are displayed on a grid. The location of each axis represents the location of the electrode.
+# This plot type is not as popular because it is too cluttered. 
 
 # # Setup
 # ## Package loading
diff --git a/docs/literate/tutorials/erpimage.jl b/docs/literate/tutorials/erpimage.jl
index c8488bfe..9af55605 100644
--- a/docs/literate/tutorials/erpimage.jl
+++ b/docs/literate/tutorials/erpimage.jl
@@ -1,6 +1,6 @@
 # # ERP image
 
-# ERP image is a plot type for visualizing EEG activity for all trials. 
+# **ERP image** is a plot type for visualizing EEG activity for all trials. 
 # It can fully represent time and trial dimensions using a heatmap. 
 # Y-axis represents all trials, x-axis represents time, while color represents voltage. 
 # The ERP image can also be sorted by specific experimental variables, which helps to reveal important correlations. 
diff --git a/docs/literate/tutorials/parallelcoordinates.jl b/docs/literate/tutorials/parallelcoordinates.jl
index f6241b66..87f3f461 100644
--- a/docs/literate/tutorials/parallelcoordinates.jl
+++ b/docs/literate/tutorials/parallelcoordinates.jl
@@ -1,6 +1,6 @@
 # # Parallel Coordinates
 
-# Parallel Coordinates Plot (PCP) is a plot type used to visualize EEG activity for some channels. 
+# **Parallel Coordinates Plot** (PCP) is a plot type used to visualize EEG activity for some channels. 
 # It can fully represent state and channel dimensions using lines. It can also partially represent time or trials
 # Y-axis represents time points, vertical axes represent channels, while lines show voltage changes. 
 
diff --git a/docs/literate/tutorials/topoplot.jl b/docs/literate/tutorials/topoplot.jl
index 7a87fc1e..8eab2c91 100644
--- a/docs/literate/tutorials/topoplot.jl
+++ b/docs/literate/tutorials/topoplot.jl
@@ -1,5 +1,5 @@
 # # [Topoplot](@id topo_vis)
-# Topoplot (aka topography plot) is a plot type for visualisation of EEG activity in a specific time stemp or time interval. 
+# **Topoplot** (aka topography plot) is a plot type for visualisation of EEG activity in a specific time stemp or time interval. 
 # It can fully represent channel and channel location dimensions using contour lines.
 
 # The topoplot is a 2D projection and interpolation of the 3D distributed sensor activity. The name stems from physical geography, but instead of height, the contour lines represent voltage levels. 
diff --git a/docs/literate/tutorials/topoplotseries.jl b/docs/literate/tutorials/topoplotseries.jl
index e51fac1b..03d9f9fb 100644
--- a/docs/literate/tutorials/topoplotseries.jl
+++ b/docs/literate/tutorials/topoplotseries.jl
@@ -1,6 +1,6 @@
 # # Topoplot Series
 
-# Topoplot Series is a plot type for visualizing EEG activity in a given time frame or time interval. 
+# **Topoplot series** is a plot type for visualizing EEG activity in a given time frame or time interval. 
 # It can fully represent channel and channel location dimensions using contour lines. It can also partially represent the time dimension.
 # Basically, it is a series of Topoplots.
 
@@ -23,14 +23,14 @@ nothing #hide
 
 # # Plotting
 
-Δbin = 80
-plot_topoplotseries(df, Δbin; positions = positions)
+bin_width = 80
+plot_topoplotseries(df; bin_width, positions = positions)
 
 # # Additional features
 
 # ## Disabling colorbar
 
-plot_topoplotseries(df, Δbin; positions = positions, layout = (; use_colorbar = false))
+plot_topoplotseries(df; bin_width, positions = positions, layout = (; use_colorbar = false))
 
 # ## Aggregating functions
 # In this example `combinefun` is specified by `mean`, `median` and `std`. 
@@ -38,24 +38,24 @@ plot_topoplotseries(df, Δbin; positions = positions, layout = (; use_colorbar =
 f = Figure(size = (500, 500))
 plot_topoplotseries!(
     f[1, 1],
-    df,
-    Δbin;
+    df;
+    bin_width,
     positions = positions,
     combinefun = mean,
-    axis = (; title = "combinefun = mean"),
+    axis = (; xlabel = "", title = "combinefun = mean"),
 )
 plot_topoplotseries!(
     f[2, 1],
-    df,
-    Δbin;
+    df;
+    bin_width,
     positions = positions,
     combinefun = median,
-    axis = (; title = "combinefun = median"),
+    axis = (; xlabel = "", title = "combinefun = median"),
 )
 plot_topoplotseries!(
     f[3, 1],
-    df,
-    Δbin;
+    df;
+    bin_width,
     positions = positions,
     combinefun = std,
     axis = (; title = "combinefun = std"),
@@ -66,36 +66,54 @@ f
 
 #=
 If you need to plot many topoplots, you should display them in multiple rows. 
-
-Here you can specify:
-- Grouping condition using `mapping.row`.
-- Label the y-axis with `axis.ylabel`.
-- Hide electrode markers with `visual.label_scatter`.
-- Change the color map with `visual.colormap`. The default is `Reverse(:RdBu)`.
-- Adjust the limits of the topoplot boxes with `axis.xlim_topo` and `axis.ylim_topo`. By default both are `(-0.25, 1.25)`.
-- Adjust the size of the figure with `Figure(size = (x, y))`.
-- Adjust the padding between topoplot labels and axis labels using `xlabelpadding` and `ylabelpadding`.
 =#
+
+f = Figure()
 df1 = UnfoldMakie.eeg_matrix_to_dataframe(data[:, :, 1], string.(1:length(positions)))
-df1.condition = repeat(["A", "B", "C", "D", "E"], size(df, 1) ÷ 5)
+plot_topoplotseries!(
+    f[1, 1:5],
+    df1;
+    bin_num = 14,
+    nrows = 4,
+    positions = positions,
+    visual = (; label_scatter = false),
+)
+f
+
+# ## Categorical topoplots
+
+#=
+If you decide to use categorical values instead of time intvervals for sepration of topoplots do this:
+- Do not specify `bin_width` or `bin_num`
+- Put categorical value in `mapping.col`
+=#
+
+df2 = UnfoldMakie.eeg_matrix_to_dataframe(data[:, 1:5, 1], string.(1:length(positions)))
+df2.condition = repeat(["A", "B", "C", "D", "E"], size(df2, 1) ÷ 5)
 
 f = Figure(size = (600, 500))
 
 plot_topoplotseries!(
-    f[1:2, 1:2],
-    df1,
-    Δbin;
+    f[1, 1],
+    df2;
     col_labels = true,
-    mapping = (; row = :condition),
-    axis = (; ylabel = "Conditions"),
+    mapping = (; col = :condition),
+    axis = (; xlabel = "Conditions"),
     positions = positions,
-    visual = (label_scatter = false,),
-    layout = (; use_colorbar = true),
 )
 f
 
 # # Configurations of Topoplot series
 
+#=
+Also you can specify:
+- Label the x-axis with `axis.xlabel`.
+- Hide electrode markers with `visual.label_scatter`.
+- Change the color map with `visual.colormap`. The default is `Reverse(:RdBu)`.
+- Adjust the limits of the topoplot boxes with `axis.xlim_topo` and `axis.ylim_topo`. By default both are `(-0.25, 1.25)`.
+- Adjust the size of the figure with `Figure(size = (x, y))`.
+- Adjust the padding between topoplot labels and axis labels using `xlabelpadding` and `ylabelpadding`.
+=#
 # ```@docs
 # plot_topoplotseries
 # ```
diff --git a/docs/make.jl b/docs/make.jl
index bb67fb2e..015759cf 100644
--- a/docs/make.jl
+++ b/docs/make.jl
@@ -15,7 +15,7 @@ using Glob
 
 GENERATED = joinpath(@__DIR__, "src", "generated")
 SOURCE = joinpath(@__DIR__, "literate")
-for subfolder ∈ ["how_to", "intro", "tutorials", "reference"]
+for subfolder ∈ ["how_to", "intro", "tutorials", "explanations"]
     local SOURCE_FILES = Glob.glob(subfolder * "/*.jl", SOURCE)
     foreach(fn -> Literate.markdown(fn, GENERATED * "/" * subfolder), SOURCE_FILES)
 end
@@ -38,6 +38,7 @@ makedocs(;
         "Intro" => [
             "Installation" => "generated/intro/installation.md",
             "Plot types" => "generated/intro/plot_types.md",
+            "Code principles" => "generated/intro/code_principles.md",
         ],
         "Visualization Types" => [
             "ERP plot" => "generated/tutorials/erp.md",
@@ -56,8 +57,8 @@ makedocs(;
             "Hide Decorations and Axis Spines" => "generated/how_to/hide_deco.md",
             "Include multiple figures in one" => "generated/how_to/mult_vis_in_fig.md",
         ],
-        "Reference" => [
-            "Convert electrode positions from 3D to 2D" => "generated/reference/positions.md",
+        "Explanations" => [
+            "Convert electrode positions from 3D to 2D" => "generated/explanations/positions.md",
         ],
         "API / DocStrings" => "api.md",
         "Utilities" => "helper.md",
diff --git a/docs/src/index.md b/docs/src/index.md
index 4a9615c6..0aae8e04 100644
--- a/docs/src/index.md
+++ b/docs/src/index.md
@@ -4,17 +4,19 @@
 <img src="assets/complex_plot.png" width="300" align="right"/>
 ```
 
-This is the documentation of the UnfoldMakie.jl module (aka library) for the Julia programming language. 
+This is the documentation of the UnfoldMakie.jl package for the Julia programming language. 
 
-## Benefits of UnfoldMakie.jl
+## Highlights of UnfoldMakie.jl
 
 - **10 plot functions for displaying ERPs.**
 Each plot emphasizes certain dimensions while collapsing others.
+- **Fast plotting**
+Plot one figure with 20 topoplots in 1 second? No problemo!
 - **Highly adaptable.**
-The module is based on the [Unfold](https://github.com/unfoldtoolbox/unfold.jl/) and [Makie](https://makie.juliaplots.org/stable/) modules, so you can use configurations from these modules to add new features to your figures.
+The package is primarily based on [Unfold.jl](https://github.com/unfoldtoolbox/unfold.jl/) and [Makie.jl](https://makie.juliaplots.org/stable/).
 - **Many usage examples**
-Here in documentation you can find user-friendly examples of how to use plots.
-- **Scientific colormaps as default**
-According to our study (Mikheev, 2024), 40% of EEG researchers do not know about the issue of scientific color maps. To protect the scientific integrity, we used `Reverse(:RdBu)` and `Roma` as default color maps. 
+You can find many user-friendly examples of how to use and adapt the plots in this documentation.
+- **Scientific colormaps by default**
+According to our study [(Mikheev, 2024)](https://apertureneuro.org/article/116386-the-art-of-brainwaves-a-survey-on-event-related-potential-visualization-practices),, 40% of EEG researchers do not know about the issue of scientific color maps. By default, we use `Reverse(:RdBu)` (based on [colorbrewer](https://colorbrewer2.org/#type=sequential&scheme=BuGn&n=3)) and `Roma` (based on [Sceintific Colormaps](https://www.fabiocrameri.ch/colourmaps/) by Fabio Crameri) as default color maps. 
 - **Interactivity** 
-Several plots use Observables and have interactive mode so you can click on them and change their layout. Check `plot_topoplotseries` and `plot_erpimage`.
\ No newline at end of file
+Several plots make use of `Observables.jl` which allows fast updating of the underlying data. Several plots already have predfined interactive features, e.g. you can click on labels to enable / disable them. See `plot_topoplotseries` and `plot_erpimage` for examples.
diff --git a/src/eeg_series.jl b/src/eeg_series.jl
index 0350ce51..48489fa3 100644
--- a/src/eeg_series.jl
+++ b/src/eeg_series.jl
@@ -16,9 +16,10 @@ end
 
 """
     eeg_topoplot_series(data::DataFrame,
-        fig,
-        data::DataFrame,
-        Δbin;
+        f,
+        data::DataFrame;
+        bin_width,
+        bin_num,
         y = :erp,
         label = :label,
         col = :time,
@@ -31,16 +32,16 @@ end
         ylim_topo,
         topoplot_attributes...,
     )
-    eeg_topoplot_series!(fig, data::DataFrame, Δbin; kwargs..)
+    eeg_topoplot_series!(fig, data::DataFrame, bin_width; kwargs..)
 
 Plot a series of topoplots. 
-The function automatically takes the `combinefun = mean` over the `:time` column of `data` in `Δbin` steps.
-- `fig` \\
+The function automatically takes the `combinefun = mean` over the `:time` column of `data` in `bin_width` steps.
+- `f` \\
     Figure object. \\
 - `data::DataFrame`\\
     Needs the columns `:time` and `y(=:erp)`, and `label(=:label)`. \\
     If `data` is a matrix, it is automatically cast to a dataframe, time bins are in samples, labels are `string.(1:size(data,1))`.
-- `Δbin = :time` \\
+- `bin_width = :time` \\
     In `:time` units, specifying the time steps. All other keyword arguments are passed to the `EEG_TopoPlot` recipe. \\
     In most cases, the user should specify the electrode positions with `positions = pos`.
 - `col`, `row = :time` \\
@@ -61,36 +62,47 @@ eeg_topoplot_series(df, 5; positions = pos)
 **Return Value:** `Tuple{Figure, Vector{Any}}`.
 """
 function eeg_topoplot_series(
-    data::Union{<:Observable,<:DataFrame,<:AbstractMatrix},
-    Δbin;
+    data::Union{<:Observable,<:DataFrame,<:AbstractMatrix};
     figure = NamedTuple(),
     kwargs...,
 )
-    return eeg_topoplot_series!(Figure(; figure...), data, Δbin; kwargs...)
+    return eeg_topoplot_series!(Figure(; figure...), data; kwargs...)
 end
-# allow to specify Δbin as an keyword for nicer readability
-eeg_topoplot_series(
+# allow to specify bin_width as an keyword for nicer readability
+
+#eeg_topoplot_series(data::Union{<:Observable,<:DataFrame,<:AbstractMatrix}; kwargs...) =
+#    eeg_topoplot_series(data; kwargs...)
+# AbstractMatrix
+function eeg_topoplot_series!(
+    fig,
     data::Union{<:Observable,<:DataFrame,<:AbstractMatrix};
-    Δbin,
     kwargs...,
-) = eeg_topoplot_series(data, Δbin; kwargs...)
-# AbstractMatrix
-function eeg_topoplot_series!(fig, data::AbstractMatrix, Δbin; kwargs...)
-    return eeg_topoplot_series!(fig, data, string.(1:size(data, 1)), Δbin; kwargs...)
+)
+    return eeg_topoplot_series!(fig, data, string.(1:size(data, 1)); kwargs...)
 end
 
 # convert a 2D Matrix to the dataframe
-function eeg_topoplot_series(data::AbstractMatrix, labels, Δbin; kwargs...)
-    return eeg_topoplot_series(eeg_matrix_to_dataframe(data, labels), Δbin; kwargs...)
+function eeg_topoplot_series(
+    data::Union{<:Observable,<:DataFrame,<:AbstractMatrix},
+    labels;
+    kwargs...,
+)
+    return eeg_topoplot_series(eeg_matrix_to_dataframe(data, labels); kwargs...)
 end
-function eeg_topoplot_series!(fig, data::AbstractMatrix, labels, Δbin; kwargs...)
-    return eeg_topoplot_series!(fig, eeg_matrix_to_dataframe(data, labels), Δbin; kwargs...)
+function eeg_topoplot_series!(
+    fig,
+    data::Union{<:Observable,<:DataFrame,<:AbstractMatrix},
+    labels;
+    kwargs...,
+)
+    return eeg_topoplot_series!(fig, eeg_matrix_to_dataframe(data, labels); kwargs...)
 end
 
 function eeg_topoplot_series!(
     fig,
-    data::Union{<:Observable{<:DataFrame},<:DataFrame},
-    Δbin;
+    data::Union{<:Observable{<:DataFrame},<:DataFrame};
+    bin_width = nothing,
+    bin_num = nothing,
     y = :erp,
     label = :label,
     col = :time,
@@ -102,47 +114,20 @@ function eeg_topoplot_series!(
     xlim_topo = (-0.25, 1.25),
     ylim_topo = (-0.25, 1.25),
     interactive_scatter = nothing,
-    highlight_scatter = false,#Observable([0]),
+    highlight_scatter = false,
     topoplot_attributes...,
 )
-
-    # cannot be made easier right now, but Simon promised a simpler solution "soonish"
-    axisOptions = (
-        aspect = 1,
-        xgridvisible = false,
-        xminorgridvisible = false,
-        xminorticksvisible = false,
-        xticksvisible = false,
-        xticklabelsvisible = false,
-        xlabelvisible = false,
-        ygridvisible = false,
-        yminorgridvisible = false,
-        yminorticksvisible = false,
-        yticksvisible = false,
-        yticklabelsvisible = false,
-        ylabelvisible = false,
-        leftspinevisible = false,
-        rightspinevisible = false,
-        topspinevisible = false,
-        bottomspinevisible = false,
-        xpanlock = true,
-        ypanlock = true,
-        xzoomlock = true,
-        yzoomlock = true,
-        xrectzoom = false,
-        yrectzoom = false,
-        limits = (xlim_topo, ylim_topo),
-    )
+    axis_options = create_axis_options(xlim_topo, ylim_topo)
     # aggregate the data over time bins
     # using same colormap + contour levels for all plots
 
     data = _as_observable(data)
     if eltype(to_value(data)[!, col]) <: Number
-
         data_mean = @lift(
             df_timebin(
-                $data,
-                Δbin;
+                $data;
+                bin_width = bin_width,
+                bin_num = bin_num,
                 col_y = y,
                 fun = combinefun,
                 grouping = [label, col, row],
@@ -157,13 +142,12 @@ function eeg_topoplot_series!(
         (
             colorrange = (q_min, q_max),
             interp_resolution = (128, 128),
-            contours = (levels = range(q_min, q_max; length = 7),),
+            contours = (levels = range(q_min, q_max; length = 7)),
         ),
         topoplot_attributes,
     )
 
     # do the col/row plot
-
     select_col = isnothing(col) ? 1 : unique(to_value(data_mean)[:, col])
     select_row = isnothing(row) ? 1 : unique(to_value(data_mean)[:, row])
 
@@ -171,126 +155,160 @@ function eeg_topoplot_series!(
         @assert isa(interactive_scatter, Observable)
     end
 
-
     axlist = []
     for r = 1:length(select_row)
         for c = 1:length(select_col)
-            ax = Axis(fig[:, :][r, c]; axisOptions...)
-            # select one topoplot
-            sel = 1 .== ones(size(to_value(data_mean), 1)) # select all
-            if !isnothing(col)
-                sel = sel .&& (to_value(data_mean)[:, col] .== select_col[c]) # subselect
-            end
-            if !isnothing(row)
-                sel = sel .&& (to_value(data_mean)[:, row] .== select_row[r]) # subselect
-            end
-
-            df_single = @lift($data_mean[sel, :])
-
-            # select labels
-            labels = to_value(df_single)[:, label]
-            # select data
-            d_vec = @lift($df_single[:, y])
-            # plot it
-            if highlight_scatter != false || interactive_scatter != nothing
-
-                #    pos = @lift topoplot_attributes[:positions][highlight_scatter]
-                strokecolor = Observable(repeat([:black], length(to_value(d_vec))))
-
-
-                highlight_feature = (; strokecolor = strokecolor)
-                if :label_scatter ∈ keys(topoplot_attributes)
-                    topoplot_attributes = merge(
-                        topoplot_attributes,
-                        (;
-                            label_scatter = merge(
-                                topoplot_attributes[:label_scatter],
-                                highlight_feature,
-                            )
-                        ),
-                    )
-                else
-                    topoplot_attributes =
-                        merge(topoplot_attributes, (; label_scatter = highlight_feature))
-                end
-
-
-            end
-            h_topo = eeg_topoplot!(ax, d_vec, labels; topoplot_attributes...)
-            @debug typeof(h_topo) typeof(ax)
-
-            if rasterize_heatmaps
-                h_topo.plots[1].plots[1].rasterize = true
-            end
-            if r == length(select_row) && col_labels
-                ax.xlabel = string(to_value(df_single)[1, col])
-                ax.xlabelvisible = true
-            end
-            if c == 1 && length(select_row) > 1 && row_labels
-                #@show df_single
-                ax.ylabel = string(to_value(df_single)[1, row])
-                ax.ylabelvisible = true
-            end
-
-            if interactive_scatter != false
-
-                on(events(h_topo).mousebutton) do event
-                    if event.button == Mouse.left && event.action == Mouse.press
-                        plt, p = pick(h_topo)
-
-                        if isa(plt, Makie.Scatter) && plt == h_topo.plots[1].plots[3]
-
-                            plt.strokecolor[] .= :black
-                            plt.strokecolor[][p] = :white
-                            notify(plt.strokecolor) # not sure why this is necessary, but oh well..
-
-                            interactive_scatter[] = (r, c, p)
-                        end
-
-                    end
-                end
-            end
-
+            ax = single_topoplot(
+                fig,
+                r,
+                c,
+                row,
+                col,
+                select_row,
+                select_col,
+                y,
+                label,
+                axis_options,
+                data_mean,
+                highlight_scatter,
+                interactive_scatter,
+                topoplot_attributes,
+                col_labels,
+                row_labels,
+                rasterize_heatmaps,
+            )
             push!(axlist, ax)
+
         end
     end
     if typeof(fig) != GridLayout && typeof(fig) != GridLayoutBase.GridSubposition
         colgap!(fig.layout, 0)
     end
-
     return fig, axlist
 end
 
-"""
-    df_timebin(df, Δbin; col_y = :erp, fun = mean, grouping = [])
-Split or combine `DataFrame` according to equally spaced time bins.
-
-Arguments:
-- `df::AbstractTable`\\
-    With columns `:time` and `col_y` (default `:erp`), and all columns in `grouping`;
-- `Δbin`\\
-    Bin size in `:time` units;
-- `col_y = :erp` \\
-    The column to combine over (with `fun`);
-- `fun = mean()`\\
-    Function to combine.
-- `grouping = []`\\
-    Vector of symbols or strings, columns to group the data by before aggregation. Values of `nothing` are ignored.
-
-**Return Value:** `DataFrame`.
-"""
-function df_timebin(df, Δbin; col_y = :erp, fun = mean, grouping = [])
-    tmin = minimum(df.time)
-    tmax = maximum(df.time)
+function single_topoplot(
+    fig,
+    r,
+    c,
+    row,
+    col,
+    select_row,
+    select_col,
+    y,
+    label,
+    axis_options,
+    data_mean,
+    highlight_scatter,
+    interactive_scatter,
+    topoplot_attributes,
+    col_labels,
+    row_labels,
+    rasterize_heatmaps,
+)
+    ax = Axis(fig[:, :][r, c]; axis_options...)
+    # select one topoplot
+    sel = 1 .== ones(size(to_value(data_mean), 1)) # select all
+    if !isnothing(col)
+        sel = sel .&& (to_value(data_mean)[:, col] .== select_col[c]) # subselect
+    end
+    if !isnothing(row)
+        sel = sel .&& (to_value(data_mean)[:, row] .== select_row[r]) # subselect
+    end
+    df_single = @lift($data_mean[sel, :])
+
+    # select labels
+    labels = to_value(df_single)[:, label]
+    # select data
+    d_vec = @lift($df_single[:, y])
+    # plot it
+    if highlight_scatter != false || interactive_scatter != nothing
+        strokecolor = Observable(repeat([:black], length(to_value(d_vec))))
+        highlight_feature = (; strokecolor = strokecolor)
+
+        if :label_scatter ∈ keys(topoplot_attributes)
+            topoplot_attributes = merge(
+                topoplot_attributes,
+                (;
+                    label_scatter = if isa(topoplot_attributes[:label_scatter], NamedTuple)
+                        merge(topoplot_attributes[:label_scatter], highlight_feature)
+                    else
+                        highlight_feature
+                    end
+                ),
+            )
+        else
+            topoplot_attributes =
+                merge(topoplot_attributes, (; label_scatter = highlight_feature))
+        end
+    end
+    if isempty(to_value(d_vec))
+        return
+    end
+    single_topoplot = eeg_topoplot!(ax, d_vec, labels; topoplot_attributes...)
+    if rasterize_heatmaps
+        single_topoplot.plots[1].plots[1].rasterize = true
+    end
 
-    bins = range(; start = tmin, step = Δbin, stop = tmax)
-    df = deepcopy(df) # cut seems to change stuff inplace
-    df.time = cut(df.time, bins; extend = true)
+    # to put column and row labels
+    if col_labels == true
+        if r == length(select_row) && col_labels
+            ax.xlabel = string(to_value(df_single)[1, col])
+            ax.xlabelvisible = true
+        end
+        if c == 1 && length(select_row) > 1 && row_labels
+            ax.ylabel = string(to_value(df_single)[1, row])
+            ax.ylabelvisible = true
+        end
+    else
+        ax.xlabelvisible = true
+        ax.xlabel = string(to_value(df_single).time[1, :][])
+    end
+    interctive_toposeries(interactive_scatter, single_topoplot)
+    return ax
+end
 
-    grouping = grouping[.!isnothing.(grouping)]
+function interctive_toposeries(interactive_scatter, single_topoplot)
+    if interactive_scatter != false
+        on(events(single_topoplot).mousebutton) do event
+            if event.button == Mouse.left && event.action == Mouse.press
+                plt, p = pick(single_topoplot)
+                if isa(plt, Makie.Scatter) && plt == single_topoplot.plots[1].plots[3]
+                    plt.strokecolor[] .= :black
+                    plt.strokecolor[][p] = :white
+                    notify(plt.strokecolor) # not sure why this is necessary, but oh well..
+                    interactive_scatter[] = (r, c, p)
+                end
+            end
+        end
+    end
+end
 
-    df_m = combine(groupby(df, unique([:time, grouping...])), col_y => fun)
-    #df_m = combine(groupby(df, Not(y)), y=>fun)
-    rename!(df_m, names(df_m)[end] => col_y) # remove the _fun part of the new column
-    return df_m
+function create_axis_options(xlim_topo, ylim_topo)
+    return (
+        aspect = 1,
+        xgridvisible = false,
+        xminorgridvisible = false,
+        xminorticksvisible = false,
+        xticksvisible = false,
+        xticklabelsvisible = false,
+        xlabelvisible = false,
+        ygridvisible = false,
+        yminorgridvisible = false,
+        yminorticksvisible = false,
+        yticksvisible = false,
+        yticklabelsvisible = false,
+        ylabelvisible = false,
+        leftspinevisible = false,
+        rightspinevisible = false,
+        topspinevisible = false,
+        bottomspinevisible = false,
+        xpanlock = true,
+        ypanlock = true,
+        xzoomlock = true,
+        yzoomlock = true,
+        xrectzoom = false,
+        yrectzoom = false,
+        limits = (xlim_topo, ylim_topo),
+    )
 end
diff --git a/src/layout_helper.jl b/src/layout_helper.jl
index 1aeb9fc0..183e27f6 100644
--- a/src/layout_helper.jl
+++ b/src/layout_helper.jl
@@ -53,14 +53,6 @@ function apply_layout_settings!(
     if :hidedecorations ∈ keys(config.layout) && !isnothing(config.layout.hidedecorations)
         hidedecorations!(ax; config.layout.hidedecorations...)
     end
-
-    # automatic labels
-    #if !isnothing(config.layout.xlabelFromMapping)
-    #    ax.xlabel = string(config.mapping[config.layout.xlabelFromMapping])
-    #end
-    #if !isnothing(config.layout.ylabelFromMapping)
-    #    ax.ylabel = string(config.mapping[config.layout.ylabelFromMapping])
-    #end
 end
 Makie.hidedecorations!(ax::Matrix{AxisEntries}; kwargs...) =
     Makie.hidedecorations!.(ax; kwargs...)
diff --git a/src/plot_circular_topoplots.jl b/src/plot_circular_topoplots.jl
index e0690a61..a22f4452 100644
--- a/src/plot_circular_topoplots.jl
+++ b/src/plot_circular_topoplots.jl
@@ -20,13 +20,13 @@ Plot a circular EEG topoplot.
     Positions of the [`plot_topoplot`](@ref topo_vis).
 - `center_label::String = ""`\\
     The text in the center of the cricle.
+- `plot_radius::String = 0.8`\\
+    The radius of the circular topoplot series plot calucalted by formula: `radius = (minwidth * plot_radius) / 2`.
 - `labels::Vector{String} = nothing`\\
     Labels for the [`plot_topoplots`](@ref topo_vis).
 
 $(_docstring(:circtopos))
 
-
-
 **Return Value:** `Figure` displaying the Circular topoplot series.
 
 """
@@ -42,6 +42,7 @@ function plot_circular_topoplots!(
     positions = nothing,
     labels = nothing,
     center_label = "",
+    plot_radius = 0.8,
     kwargs...,
 )
     config = PlotConfig(:circtopos)
@@ -89,7 +90,7 @@ function plot_circular_topoplots!(
         height = @lift Fixed($(pixelarea(ax.scene)).widths[2])
     )
     plot_topo_plots!(
-        ax,
+        f[1, 1],
         data[:, config.mapping.y],
         positions,
         predictor_values,
@@ -97,6 +98,7 @@ function plot_circular_topoplots!(
         min,
         max,
         labels,
+        plot_radius,
     )
 
     apply_layout_settings!(config; ax = ax)
@@ -119,7 +121,6 @@ end
 function plot_circular_axis!(ax, predictor_bounds, center_label)
     # The axis position is always the middle of the screen 
     # It uses the GridLayout's full size
-
     lines!(
         ax,
         1 * cos.(LinRange(0, 2 * pi, 500)),
@@ -184,33 +185,22 @@ function plot_topo_plots!(
     globalmin,
     globalmax,
     labels,
+    plot_radius,
 )
     df = DataFrame(:e => data, :p => predictor_values)
     gp = groupby(df, :p)
     i = 0
     for g in gp
         i += 1
-        bbox = calculate_BBox([0, 0], [1, 1], g.p[1], predictor_bounds)
-
-        # convert BBox to rect
-        rect = (
-            Float64.([
-                bbox.origin[1],
-                bbox.origin[1] + bbox.widths[1],
-                bbox.origin[2],
-                bbox.origin[2] + bbox.widths[2],
-            ])...,
-        )
-
-        b = rel_to_abs_bbox(f.scene.viewport[], rect)
+        bbox = calculate_BBox([0, 0], [1, 1], g.p[1], predictor_bounds, plot_radius)
         eeg_axis = Axis(
-            get_figure(f);
+            f, # this creates an axis at the same grid location of the current axis
             aspect = 1,
-            width = Relative(0.99),
-            height = Relative(0.99),
-            halign = b.origin[1],
-            valign = b.origin[2],
-            backgroundcolor = :white,
+            width = Relative(0.2), # size of bboxes
+            height = Relative(0.2), # size of bboxes
+            halign = bbox.origin[1] + bbox.widths[1] / 2, # coordinates 
+            valign = bbox.origin[2] + bbox.widths[2] / 2,
+            #backgroundcolor = nothing,
         )
 
         if !isnothing(labels)
@@ -229,11 +219,10 @@ function plot_topo_plots!(
     end
 end
 
-function calculate_BBox(origin, widths, predictor_value, bounds)
-
+function calculate_BBox(origin, widths, predictor_value, bounds, plot_radius)
     minwidth = minimum(widths)
     predictor_ratio = (predictor_value - bounds[1]) / (bounds[2] - bounds[1])
-    radius = (minwidth * 0.7) / 2
+    radius = (minwidth * plot_radius) / 2 # radius of the position circle of a circular topoplot 
     size_of_BBox = minwidth / 5
     # the middle point of the circle for the topoplot positions
     # has to be moved a bit into the direction of the longer axis
@@ -251,6 +240,9 @@ function calculate_BBox(origin, widths, predictor_value, bounds)
     # right point of the axis. This means that you have to 
     # move the bbox to the bottom left by size_of_bbox/2 to move
     # the center of the axis to a point. 
+    if abs(y) < 1
+        y = round(y, digits = 2)
+    end
     return BBox(
         (origin[1] + widths[1]) / 2 - size_of_BBox / 2 + x,
         (origin[1] + widths[1]) / 2 + size_of_BBox - size_of_BBox / 2 + x,
diff --git a/src/plot_designmatrix.jl b/src/plot_designmatrix.jl
index c605efd5..bea1fb62 100644
--- a/src/plot_designmatrix.jl
+++ b/src/plot_designmatrix.jl
@@ -48,7 +48,7 @@ function plot_designmatrix!(
 )
     config = PlotConfig(:designmat)
     config_kwargs!(config; kwargs...)
-    designmat = UnfoldMakie.modelmatrices(data)
+    designmat = UnfoldMakie.modelmatrix(data)
     if standardize_data
         designmat = designmat ./ std(designmat, dims = 1)
         designmat[isinf.(designmat)] .= 1.0
@@ -59,49 +59,75 @@ function plot_designmatrix!(
             @warn "Sorting does not make sense for time-expanded designmatrices. sort_data has been set to `false`"
             sort_data = false
         end
-        designmat = Matrix(designmat[end÷2-2000:end÷2+2000, :])
+        designmat = Matrix(designmat[end÷2-2000:end÷2+2000, :]) # needs a size(designmat) of at least 4000 x Any
     end
 
     if sort_data
         designmat = Base.sortslices(designmat, dims = 1)
     end
-    labels = Unfold.get_coefnames(data)
-
-    lLength = length(labels)
+    labels0 = replace.(Unfold.get_coefnames(data), r"\s*:" => ":")
+
+    if length(split(labels0[1], ": ")) > 1
+        labels = map(x -> join(split(x, ": ")[3]), labels0)
+        labels_top1 = Unfold.extract_coef_info(Unfold.get_coefnames(data), 2)
+        unique_names = String[]
+        labels_top2 = String[""]
+        for el in labels_top1
+            if !in(el, unique_names)
+                push!(unique_names, el)
+                push!(labels_top2, el)
+            else
+                push!(labels_top2, "")
+            end
+        end
+    end
+    lLength = length(labels0)
     # only change xticks if we want less then all
     if (xticks !== nothing && xticks < lLength)
         @assert(xticks >= 0, "xticks shouldn't be negative")
         # sections between xticks
-        sectionSize = (lLength - 2) / (xticks - 1)
-        newLabels = []
+        section_size = (lLength - 2) / (xticks - 1)
+        new_labels = []
 
         # first tick. Empty if 0 ticks
         if xticks >= 1
-            push!(newLabels, labels[1])
+            push!(new_labels, labels0[1])
         else
-            push!(newLabels, "")
+            push!(new_labels, "")
         end
 
         # fill in ticks in the middle
         for i = 1:(lLength-2)
             # checks if we're at the end of a section, but NO tick on the very last section
-            if i % sectionSize < 1 && i < ((xticks - 1) * sectionSize)
-                push!(newLabels, labels[i+1])
+            if i % section_size < 1 && i < ((xticks - 1) * section_size)
+                push!(new_labels, labels0[i+1])
             else
-                push!(newLabels, "")
+                push!(new_labels, "")
             end
         end
 
         # last tick at the end
         if xticks >= 2
-            push!(newLabels, labels[lLength-1])
+            push!(new_labels, labels0[lLength-1])
         else
-            push!(newLabels, "")
+            push!(new_labels, "")
         end
 
-        labels = newLabels
+        labels0 = new_labels
+    end
+    if length(split(labels0[1], ": ")) > 1
+        ax2 = Axis(
+            f[1, 1],
+            xticklabelcolor = :red,
+            xaxisposition = :top;
+            xticks = (1:length(labels_top2), labels_top2),
+        )
+        hidespines!(ax2)
+        hidexdecorations!(ax2, ticklabels = false, ticks = false)
+        hm = heatmap!(ax2, designmat'; config.visual...)
+    else
+        labels = labels0
     end
-
 
     # plot Designmatrix
     config.axis = merge(config.axis, (; xticks = (1:length(labels), labels)))
@@ -112,7 +138,13 @@ function plot_designmatrix!(
         ax.yreversed = true
     end
 
+
+
+
     apply_layout_settings!(config; fig = f, hm = hm)
 
     return f
 end
+# Unfold.extract_coef_info.(Unfold.get_coefnames.(designmatrix(td)),3)
+# use it!
+# vcat(Unfold.extract_coef_info.(Unfold.get_coefnames.(designmatrix(td)),3)...)
diff --git a/src/plot_erp.jl b/src/plot_erp.jl
index 62a5b334..573e0b30 100644
--- a/src/plot_erp.jl
+++ b/src/plot_erp.jl
@@ -56,7 +56,7 @@ Plot a Butterfly plot.
     Change the size of the electrode markers in topoplot.
 - `topowidth::Real = 0.25` \\
     Change the width of inlay topoplot.
-- `topoheigth::Real = 0.25` \\
+- `topoheight::Real = 0.25` \\
     Change the height of inlay topoplot.
 - `topopositions_to_color::x -> pos_to_color_RomaO(x)`\\
     Change the line colors.
@@ -80,7 +80,10 @@ plot_butterfly!(
     topolegend = true,
     topomarkersize = 10,
     topowidth = 0.25,
-    topoheigth = 0.25,
+    topoheight = 0.25,
+    topohalign = 0.05,
+    topovalign = 0.95,
+    topoaspect = 1,
     topopositions_to_color = x -> pos_to_color_RomaO(x),
     kwargs...,
 )
@@ -98,8 +101,11 @@ function plot_erp!(
     topolegend = nothing,
     topomarkersize = nothing,
     topowidth = nothing,
-    topoheigth = nothing,
+    topoheight = nothing,
     topopositions_to_color = nothing,
+    topohalign = 0.05,
+    topovalign = 0.95,
+    topoaspect = 1,
     mapping = (;),
     kwargs...,
 )
@@ -140,13 +146,13 @@ function plot_erp!(
             topolegend = false
             colors = nothing
         else
-            allPositions = get_topo_positions(; positions = positions, labels = labels)
+            all_positions = get_topo_positions(; positions = positions, labels = labels)
             if (config.visual.colormap !== nothing)
                 colors = config.visual.colormap
                 un = length(unique(plot_data[:, config.mapping.color]))
                 colors = cgrad(config.visual.colormap, un, categorical = true)
             else
-                colors = get_topo_color(allPositions, topopositions_to_color)
+                colors = get_topo_color(all_positions, topopositions_to_color)
             end
         end
     end
@@ -183,7 +189,7 @@ function plot_erp!(
     end
 
     # remove x / y
-    mappingOthers = deleteKeys(config.mapping, [:x, :y])
+    mappingOthers = deleteKeys(config.mapping, [:x, :y, :positions, :lables])
 
     xy_mapp =
         AlgebraOfGraphics.mapping(config.mapping.x, config.mapping.y; mappingOthers...)
@@ -202,21 +208,20 @@ function plot_erp!(
         basic = basic + addPvalues(plot_data, pvalue, config)
     end
 
-    plotEquation = basic * mapp
+    plot_equation = basic * mapp
 
     f_grid = f[1, 1]
     # butterfly plot is drawn slightly different
     if butterfly
-        # no extra legend
         # add topolegend
         if (topolegend)
             topoAxis = Axis(
                 f_grid,
                 width = Relative(topowidth),
-                height = Relative(topoheigth),
-                halign = 0.05,
-                valign = 0.95,
-                aspect = 1,
+                height = Relative(topoheight),
+                halign = topohalign,
+                valign = topovalign,
+                aspect = topoaspect,
             )
             ix = unique(i -> plot_data[:, config.mapping.group[1]][i], 1:size(plot_data, 1))
             topoplotLegend(
@@ -224,22 +229,22 @@ function plot_erp!(
                 topomarkersize,
                 plot_data[ix, config.mapping.color[1]],
                 colors,
-                allPositions,
+                all_positions,
             )
         end
         if isnothing(colors)
-            drawing = draw!(f_grid, plotEquation; axis = config.axis)
+            drawing = draw!(f_grid, plot_equation; axis = config.axis)
         else
             drawing = draw!(
                 f_grid,
-                plotEquation;
+                plot_equation;
                 axis = config.axis,
                 palettes = (color = colors,),
             )
         end
     else
         # draw a normal ERP lineplot      
-        drawing = draw!(f_grid, plotEquation; axis = config.axis)
+        drawing = draw!(f_grid, plot_equation; axis = config.axis)
     end
     apply_layout_settings!(config; fig = f, ax = drawing, drawing = drawing)
     return f
@@ -258,10 +263,10 @@ function eegHeadMatrix(positions, center, radius)
 end
 
 # topopositions_to_color = colors?
-function topoplotLegend(axis, topomarkersize, unique_val, colors, allPositions)
-    allPositions = unique(allPositions)
+function topoplotLegend(axis, topomarkersize, unique_val, colors, all_positions)
+    all_positions = unique(all_positions)
 
-    topoMatrix = eegHeadMatrix(allPositions, (0.5, 0.5), 0.5)
+    topoMatrix = eegHeadMatrix(all_positions, (0.5, 0.5), 0.5)
 
     un = unique(unique_val)
     specialColors = ColorScheme(
@@ -272,11 +277,11 @@ function topoplotLegend(axis, topomarkersize, unique_val, colors, allPositions)
     ylims!(low = -0.2, high = 1.2)
     topoplot = eeg_topoplot!(
         axis,
-        1:length(allPositions), # go from 1:npos
-        string.(1:length(allPositions));
-        positions = allPositions,
+        1:length(all_positions), # go from 1:npos
+        string.(1:length(all_positions));
+        positions = all_positions,
         interpolation = NullInterpolator(), # inteprolator that returns only 0, which is put to white in the specialColorsmap
-        colorrange = (0, length(allPositions)), # add the 0 for the white-first color
+        colorrange = (0, length(all_positions)), # add the 0 for the white-first color
         colormap = specialColors,
         head = (color = :black, linewidth = 1, model = topoMatrix),
         label_scatter = (markersize = topomarkersize, strokewidth = 0.5),
diff --git a/src/plot_erpimage.jl b/src/plot_erpimage.jl
index 7d6af6ef..f0637aef 100644
--- a/src/plot_erpimage.jl
+++ b/src/plot_erpimage.jl
@@ -125,7 +125,6 @@ function plot_erpimage!(
             xlabel = "Time [s]",
             xlabelpadding = 0,
             xautolimitmargin = (0, 0),
-            #xticks = @lift([round(minimum($sortvalues), digits=2), round(mean($sortvalues), digits=2), round(maximum($sortvalues), digits=2)]),
             limits = @lift((
                 minimum($times),
                 maximum($times),
diff --git a/src/plot_parallelcoordinates.jl b/src/plot_parallelcoordinates.jl
index 614de497..e5323f2b 100644
--- a/src/plot_parallelcoordinates.jl
+++ b/src/plot_parallelcoordinates.jl
@@ -185,7 +185,7 @@ function parallelcoordinates(
             color_ix = [findfirst(un_c .== c) for c in color]
             #@assert length(un_c) == 1 "Only single color found, please don't specify color, "
             if length(un_c) == 1
-                @warn "only a single unique value found in specified color-vec"
+                @warn "The only a single unique value found in the specified color vector"
                 color = cgrad(colormap, 2)[color_ix]
             else
                 color = cgrad(colormap, length(un_c))[color_ix]
@@ -259,20 +259,19 @@ function parallelcoordinates(
         ax_pcp = Makie.LineAxis(
             scene;
             limits = limits,
+            dim_convert = Makie.NoDimConversion(),
             ticks = PCPTicks(),
             endpoints = axis_endpoints,
             tickformat = tickformater,
             axesOptions...,
         )
+
         pcp_title!(
             scene,
             ax_pcp.attributes.endpoints,
             ax_labels[i];
             titlegap = def[:titlegap],
         )
-
-
-
         append!(axlist, [ax_pcp])
     end
 
diff --git a/src/plot_topoplotseries.jl b/src/plot_topoplotseries.jl
index d1c8342c..612b3102 100644
--- a/src/plot_topoplotseries.jl
+++ b/src/plot_topoplotseries.jl
@@ -1,6 +1,6 @@
 """
-    plot_topoplotseries(f::Union{GridPosition, GridLayout, Figure}, data::DataFrame, Δbin::Real; kwargs...)
-    plot_topoplotseries!(data::DataFrame, Δbin::Real; kwargs...)
+    plot_topoplotseries(f::Union{GridPosition, GridLayout, Figure}, data::DataFrame; kwargs...)
+    plot_topoplotseries!(data::DataFrame; kwargs...)
         
 Multiple miniature topoplots in regular distances. 
 ## Arguments  
@@ -8,22 +8,25 @@ Multiple miniature topoplots in regular distances.
 - `f::Union{GridPosition, GridLayout, GridLayoutBase.GridSubposition, Figure}`\\
     `Figure`, `GridLayout`, `GridPosition`, or GridLayoutBase.GridSubposition to draw the plot.
 - `data::Union{<:Observable{<:DataFrame},DataFrame}`\\
-    DataFrame with data or Observable DataFrame. Requires a `time` column. 
-- `Δbin::Real`\\
-    A number for how large one time bin should be.\\
-    `Δbin` is in units of the `data.time` column.\\
-    Should be `0` if `mapping.col` or `mapping.row` are categorical.
+    DataFrame with data or Observable DataFrame. Requires a `time` column, but could be also specified in mapping.x. 
 
 ## Keyword arguments (kwargs)
+- `bin_width::Real = nothing`\\
+    Number specifing the width of time bin.\\
+    `bin_width` is in units of the `data.time` column.\\
+- `bin_num::Real = nothing`\\
+    Number of topoplots.\\
+    Either `bin_width`, or `bin_num` should be specified. Error if they are both specified\\
+    If `mapping.col` or `mapping.row` are categorical `bin_width` and `bin_num` should be `nothing`.
 - `combinefun::Function = mean`\\
-    Specify how the samples within `Δbin` are summarised.\\
+    Specify how the samples within `bin_width` are summarised.\\
     Example functions: `mean`, `median`, `std`. 
 - `rasterize_heatmaps::Bool = true`\\
     Force rasterization of the plot heatmap when saving in `svg` format.\\
     Except for the interpolated heatmap, all lines/points are vectors.\\
     This is typically what you want, otherwise you get ~128x128 vectors per topoplot, which makes everything super slow.
 - `col_labels::Bool`, `row_labels::Bool = true`\\
-    Shows column and row labels. 
+    Shows column and row labels for categorical values. 
 - `labels::Vector{String} = nothing`\\
     Show labels for each electrode.
 - `positions::Vector{Point{2, Float32}} = nothing`\\
@@ -32,22 +35,31 @@ Multiple miniature topoplots in regular distances.
     Enable interactive mode. \\ 
     If you create `obs_tuple = Observable((0, 0, 0))` and pass it into `interactive_scatter` you can change observable indecies by clicking topopplot markers.\\
     `(0, 0, 0)` corresponds to the indecies of row of topoplot layout, column of topoplot layout and channell. 
+- `mapping.x = :time`\\
+    Specification of x value, could be any contionous variable. 
+- `mapping.layout = nothing`\\
+    When equals `:time` arrange topoplots by rows. 
+
 
 $(_docstring(:topoplotseries))
 
 **Return Value:** `Figure` displaying the Topoplot series.
 """
-plot_topoplotseries(data::DataFrame, Δbin::Real; kwargs...) =
-    plot_topoplotseries!(Figure(), data, Δbin; kwargs...)
+plot_topoplotseries(data::DataFrame; kwargs...) =
+    plot_topoplotseries!(Figure(), data; kwargs...)
+
+#@deprecate plot_topoplotseries(data::DataFrame, Δbin; kwargs...)  plot_topoplotseries(data::DataFrame; bin_width, kwargs...) 
 
 function plot_topoplotseries!(
     f::Union{GridPosition,GridLayout,Figure,GridLayoutBase.GridSubposition},
-    data::Union{<:Observable{<:DataFrame},DataFrame},
-    Δbin;
+    data::Union{<:Observable{<:DataFrame},DataFrame};
+    bin_width = nothing,
+    bin_num = nothing,
     positions = nothing,
-    labels = nothing,
+    nrows = 1,
+    labels = nothing, # rename to channel_labels?
     combinefun = mean,
-    col_labels = true,
+    col_labels = false,
     row_labels = true,
     rasterize_heatmaps = true,
     interactive_scatter = nothing,
@@ -55,6 +67,8 @@ function plot_topoplotseries!(
 )
 
     data = _as_observable(data)
+    positions = get_topo_positions(; positions = positions, labels = labels)
+    chan_or_label = "label" ∉ names(to_value(data)) ? :channel : :label
 
     config = PlotConfig(:topoplotseries)
     # overwrite all defaults by user specified values
@@ -62,24 +76,51 @@ function plot_topoplotseries!(
 
     # resolve columns with data
     config.mapping = resolve_mappings(to_value(data), config.mapping)
-
     cat_or_cont_columns =
         eltype(to_value(data)[!, config.mapping.col]) <: Number ? "cont" : "cat"
+    data = (to_value(data))
     if cat_or_cont_columns == "cat"
         # overwrite Time windows [s] default if categorical
         config_kwargs!(config; axis = (; xlabel = string(config.mapping.col)))
         config_kwargs!(config; kwargs...) # add the user specified once more, just if someone specifies the xlabel manually  
-        # overkll as we would only need to check the xlabel ;)
-    end
-
-    positions = get_topo_positions(; positions = positions, labels = labels)
+    # overkll as we would only need to check the xlabel ;)
+    else
+        # arrangment of topoplots by rows and cols
+        bins = bins_estimation(data.time; bin_width, bin_num, cat_or_cont_columns)
+        n_topoplots = number_of_topoplots(data; bin_width, bin_num, bins, config.mapping)
 
-    chan_or_label = "label" ∉ names(to_value(data)) ? :channel : :label
+        data.timecuts = cut(data.time, bins; extend = true)
+        unique_cuts = unique(data.timecuts)
+        ix = findall.(isequal.(unique_cuts), [data.timecuts])
+        if :layout ∈ keys(config.mapping)
+            n_cols = Int(ceil(sqrt(n_topoplots)))
+            n_rows = Int(ceil(n_topoplots / n_cols))
+        else
+            n_rows = nrows
+            if 0 > n_topoplots / nrows
+                @warn "Impossible number of rows, set to 1 row"
+                n_rows = 1
+            elseif n_topoplots / nrows < 1
+                @warn "Impossible number of rows, set to $(n_topoplots) rows"
+            end
+            n_cols = Int(ceil(n_topoplots / n_rows))
+        end
+        _col = repeat(1:n_cols, outer = n_rows)[1:n_topoplots]
+        _row = repeat(1:n_rows, inner = n_cols)[1:n_topoplots]
+        data._col .= 1
+        data._row .= 1
+        for topo = 1:n_topoplots
+            data._col[ix[topo]] .= _col[topo]
+            data._row[ix[topo]] .= _row[topo]
+        end
+        config_kwargs!(config; mapping = (; row = :_row, col = :_col))
+    end
 
     ftopo, axlist = eeg_topoplot_series!(
         f,
-        data,
-        Δbin;
+        data;
+        bin_width = bin_width,
+        bin_num = bin_num,
         y = config.mapping.y,
         label = chan_or_label,
         col = config.mapping.col,
@@ -99,8 +140,9 @@ function plot_topoplotseries!(
     else
         data_mean = if cat_or_cont_columns == "cont"
             df_timebin(
-                to_value(data),
-                Δbin;
+                to_value(data);
+                bin_width,
+                bin_num,
                 col_y = config.mapping.y,
                 fun = combinefun,
                 grouping = [chan_or_label, config.mapping.col, config.mapping.row],
@@ -119,24 +161,92 @@ function plot_topoplotseries!(
     if !config.layout.use_colorbar
         config_kwargs!(config, layout = (; use_colorbar = false, show_legend = false))
     end
-
     ax = Axis(
-        f[1, 1],
-        xlabel = config.axis.xlabel,
-        ylabel = config.axis.ylabel,
-        title = config.axis.title,
-        titlesize = config.axis.titlesize,
-        titlefont = config.axis.titlefont,
-        ylabelpadding = config.axis.ylabelpadding,
-        xlabelpadding = config.axis.xlabelpadding,
-        xpanlock = config.axis.xpanlock,
-        ypanlock = config.axis.ypanlock,
-        xzoomlock = config.axis.xzoomlock,
-        yzoomlock = config.axis.yzoomlock,
-        xrectzoom = config.axis.xrectzoom,
-        yrectzoom = config.axis.yrectzoom,
+        f[1, 1];
+        (p for p in pairs(config.axis) if p[1] != :xlim_topo && p[1] != :ylim_topo)...,
     )
     apply_layout_settings!(config; fig = f, ax = ax)
     return f
+end
+
+function bins_estimation(
+    time;
+    bin_width = nothing,
+    bin_num = nothing,
+    cat_or_cont_columns = "cont",
+)
+    tmin = minimum(time)
+    tmax = maximum(time)
+    if (!isnothing(bin_width) && !isnothing(bin_num))
+        error("Ambigious parameters: specify only `bin_width` or `bin_num`.")
+    elseif (isnothing(bin_width) && isnothing(bin_num) && cat_or_cont_columns == "cont")
+        error(
+            "You haven't specified `bin_width` or `bin_num`. Such option is available only with categorical `mapping.col` or `mapping.row`.",
+        )
+    end
+    if isnothing(bin_width)
+        bins = range(; start = tmin, length = bin_num + 1, stop = tmax)
+    else
+        bins = range(; start = tmin, step = bin_width, stop = tmax)
+    end
+    return bins
+end
+
+function number_of_topoplots(
+    df::DataFrame;
+    bin_width = nothing,
+    bin_num = nothing,
+    bins,
+    mapping = config.mapping,
+)
+    if !isnothing(bin_width)
+        time_new = cut(df.time, bins; extend = true)
+        n = length(unique(time_new))
+    elseif !isnothing(bin_num)
+        time_new = cut(df.time, bins; extend = true)
+        n = length(unique(time_new))
+    else
+        n = unique(df[:, mapping.col])
+    end
+    return n
+end
+
+
+"""
+    df_timebin(df, bin_width; col_y = :erp, fun = mean, grouping = [])
+Split or combine `DataFrame` according to equally spaced time bins.
+
+Arguments:
+- `df::AbstractTable`\\
+    With columns `:time` and `col_y` (default `:erp`), and all columns in `grouping`;
+- `bin_width::Real = nothing`\\
+    Bin width in `:time` units;
+- `bin_num::Real = nothing`\\
+    Number of topoplots;
+- `col_y = :erp` \\
+    The column to combine over (with `fun`);
+- `fun = mean()`\\
+    Function to combine.
+- `grouping = []`\\
+    Vector of symbols or strings, columns to group the data by before aggregation. Values of `nothing` are ignored.
+
+**Return Value:** `DataFrame`.
+"""
+function df_timebin(
+    df;
+    bin_width = nothing,
+    bin_num = nothing,
+    col_y = :erp,
+    fun = mean,
+    grouping = [],
+)
+    bins = bins_estimation(df.time; bin_width, bin_num, cat_or_cont_columns = "cont")
+    df = deepcopy(df) # cut seems to change stuff inplace
+    df.time = cut(df.time, bins; extend = true)
+
+    grouping = grouping[.!isnothing.(grouping)]
+    df_m = combine(groupby(df, unique([:time, grouping...])), col_y => fun)
+    rename!(df_m, names(df_m)[end] => col_y) # remove the fun part of the new column
 
+    return df_m
 end
diff --git a/src/plotconfig.jl b/src/plotconfig.jl
index 52c913dc..aaefc906 100644
--- a/src/plotconfig.jl
+++ b/src/plotconfig.jl
@@ -105,14 +105,6 @@ function PlotConfig(T::Val{:circtopos})
     return cfg
 end
 
-
-function PlotConfig(T::Val{:topoarray})
-    cfg = PlotConfig(:erp)
-
-    config_kwargs!(cfg; layout = (;), colorbar = (;), mapping = (;), axis = (;))
-    return cfg
-end
-
 function PlotConfig(T::Val{:topoplot})
     cfg = PlotConfig()
 
@@ -120,8 +112,6 @@ function PlotConfig(T::Val{:topoplot})
         cfg;
         layout = (
             show_legend = true,
-            xlabelFromMapping = nothing,
-            ylabelFromMapping = nothing,
             use_colorbar = true,
             hidespines = (),
             hidedecorations = (Dict(:label => false)),
diff --git a/test/runtests.jl b/test/runtests.jl
index 41566155..9d9ed5c1 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -26,8 +26,12 @@ end
     include("test_topoplot.jl")
 end
 
-@testset "Topoplot series" begin
-    include("test_toposeries.jl")
+@testset "Topoplot series simple" begin
+    include("test_toposeries1.jl")
+end
+
+@testset "Topoplot series advanced" begin
+    include("test_toposeries2.jl")
 end
 
 @testset "Parallel coordinates plot" begin
diff --git a/test/setup.jl b/test/setup.jl
index bee46077..821377de 100644
--- a/test/setup.jl
+++ b/test/setup.jl
@@ -1,6 +1,6 @@
+using CairoMakie
 using UnfoldSim
 using Test
-using CairoMakie
 using GeometryBasics
 using DataFrames
 using TopoPlots
diff --git a/test/test_butterfly.jl b/test/test_butterfly.jl
index b59ef198..c4734e6d 100644
--- a/test/test_butterfly.jl
+++ b/test/test_butterfly.jl
@@ -2,21 +2,32 @@
 include("../docs/example_data.jl")
 df, pos = example_data("TopoPlots.jl")
 
-@testset "butterfly basic" begin
-    plot_butterfly(df; positions = pos)
+@testset "butterfly default" begin
+    plot_butterfly(df; positions = pos, visual = (; transparency = true))
+    #save("dev/UnfoldMakie/default_butterfly.png", f)
 end
 
-@testset "butterfly basic with GridLayout" begin
+@testset "butterfly default with GridLayout" begin
     f = Figure()
     plot_butterfly!(f[1, 1], df; positions = pos)
 end
 
+@testset "butterfly basic" begin
+    plot_butterfly(
+        df;
+        positions = pos,
+        topopositions_to_color = x -> Colors.RGB(0.1),
+        topolegend = false,
+    )
+    #save("dev/UnfoldMakie/basic_butterfly.png", f)
+end
+
 @testset "butterfly with change of topomarkersize" begin
     plot_butterfly(
         df;
         positions = pos,
         topomarkersize = 10,
-        topoheigth = 0.4,
+        topoheight = 0.4,
         topowidth = 0.4,
     )
 end
@@ -47,7 +58,7 @@ end
         df;
         positions = pos,
         topomarkersize = 10,
-        topoheigth = 0.4,
+        topoheight = 0.4,
         topowidth = 0.4,
         layout = (;
             hidedecorations = (:label => true, :ticks => true, :ticklabels => true)
@@ -151,9 +162,8 @@ end
 end
 
 #TO DO
-
 # not working
-@testset "butterfly with two size highlighted channels" begin
+#= @testset "butterfly with two size highlighted channels" begin
     df.highlight = in.(df.channel, Ref([10, 12]))
     plot_butterfly(df; positions = pos, mapping = (; linesize = :highlight))
-end
+end =#
diff --git a/test/test_circular_topoplots.jl b/test/test_circular_topoplots.jl
index 8856a3b2..26f6e1f4 100644
--- a/test/test_circular_topoplots.jl
+++ b/test/test_circular_topoplots.jl
@@ -56,12 +56,12 @@ end
 end
 
 @testset "testing calculate_BBox" begin
-    @test UnfoldMakie.calculate_BBox([0, 0], [1000, 1000], 180, [0, 360]) ==
-          BBox(50.0, 250.0, 400.0, 600.0)
-    @test UnfoldMakie.calculate_BBox([0, 0], [1000, 1000], -45, [0, 360]) ==
-          BBox(647.48737, 847.48737, 152.51262, 352.51262)
-    @test UnfoldMakie.calculate_BBox([0, 0], [1000, 1000], -180, [-180, 180]) ==
-          BBox(750.0, 950.0, 400.0, 600.0)
+    @test UnfoldMakie.calculate_BBox([0, 0], [1000, 1000], 180, [0, 360], 0.8) ==
+          BBox(0.0, 200.0, 400.0, 600)
+    @test UnfoldMakie.calculate_BBox([0, 0], [1000, 1000], -45, [0, 360], 0.8) ==
+          BBox(682.842712474619, 882.842712474619, 117.15728752538104, 317.15728752538104)
+    @test UnfoldMakie.calculate_BBox([0, 0], [1000, 1000], -180, [-180, 180], 0.8) ==
+          BBox(800.0, 1000.0, 400.0, 600.0)
 end
 
 @testset "circularplot plot basic" begin
diff --git a/test/test_complexplots.jl b/test/test_complexplots.jl
index bedb17ed..cb74ad4d 100644
--- a/test/test_complexplots.jl
+++ b/test/test_complexplots.jl
@@ -34,7 +34,7 @@
         d_topo;
         positions = pos,
         topomarkersize = 10,
-        topoheigth = 0.4,
+        topoheight = 0.4,
         topowidth = 0.4,
     )
     hlines!(0, color = :gray, linewidth = 1)
@@ -48,8 +48,8 @@
 
     plot_topoplotseries!(
         gd,
-        df,
-        80;
+        df;
+        bin_width = 80,
         positions = positions,
         visual = (label_scatter = false,),
         layout = (; use_colorbar = true),
@@ -130,8 +130,8 @@ end
     plot_topoplot!(f[2, 1], data[:, 150, 1]; positions = positions)
     plot_topoplotseries!(
         f[2, 2],
-        d_topo,
-        0.1;
+        d_topo;
+        bin_width = 0.1,
         positions = positions,
         visual = (label_scatter = false,),
         layout = (; use_colorbar = true),
@@ -200,8 +200,8 @@ end
     plot_topoplot!(f[3, 1], data[:, 150, 1]; positions = positions)
     plot_topoplotseries!(
         f[4, 1:3],
-        d_topo,
-        0.1;
+        d_topo;
+        bin_width = 0.1,
         positions = positions,
         mapping = (; label = :channel),
     )
diff --git a/test/test_dm.jl b/test/test_dm.jl
index 867022a7..5fe401e2 100644
--- a/test/test_dm.jl
+++ b/test/test_dm.jl
@@ -24,9 +24,13 @@ end
 @testset "hierarchical labels (bugged)" begin
     df, evts = UnfoldSim.predef_eeg()
     f = @formula 0 ~ 1 + condition + continuous
-    #basisfunction = firbasis(τ = (-0.4, 0.8), sfreq = 100, name = "stimulus")
-    basisfunction = firbasis(τ = (-0.4, -0.3), sfreq = 10, name = "")
+    basisfunction = firbasis(τ = (-0.4, 0.8), sfreq = 5, name = "stimulus")
+    #basisfunction = firbasis(τ = (-0.4, -0.3), sfreq = 10, name = "")
     bfDict = [Any => (f, basisfunction)]
     td = fit(UnfoldModel, bfDict, evts, df)
     plot_designmatrix(designmatrix(td))
 end
+
+
+#Unfold.SimpleTraits.istrait(Unfold.ContinuousTimeTrait{typeof(td)})
+#Unfold.SimpleTraits.istrait(Unfold.ContinuousTimeTrait{typeof(uf)})
diff --git a/test/test_erpimage.jl b/test/test_erpimage.jl
index 81008003..bfb914f7 100644
--- a/test/test_erpimage.jl
+++ b/test/test_erpimage.jl
@@ -125,12 +125,13 @@ end
     sortval = Observable(evts_e.continuous)
 end
 
+@testset "check error of empty sortvalues" begin
+    err1 = nothing
+    t() = error(plot_erpimage(times, dat_e; show_sortval = true))
+    try
+        t()
+    catch err1
+    end
 
-
-#= @testset "ERP image with show_sortval" begin
-    plot_erpimage(
-        times,
-        dat_e;
-        show_sortval = true,
-    ) #should learn that it must be Error
-=#
+    @test err1 == ErrorException("`show_sortval` needs non-empty `sortvalues` argument")
+end
diff --git a/test/test_toposeries.jl b/test/test_toposeries.jl
deleted file mode 100644
index 4bc06270..00000000
--- a/test/test_toposeries.jl
+++ /dev/null
@@ -1,280 +0,0 @@
-dat, positions = TopoPlots.example_data()
-df = UnfoldMakie.eeg_matrix_to_dataframe(dat[:, :, 1], string.(1:length(positions)))
-Δbin = 80
-
-@testset "toposeries basic" begin
-    plot_topoplotseries(df, Δbin; positions = positions)
-end
-
-@testset "toposeries basic with channel names" begin
-    plot_topoplotseries(df, Δbin; positions = positions, labels = raw_ch_names)
-end
-
-@testset "toposeries with xlabel" begin
-    f = Figure()
-    ax = Axis(f[1, 1])
-    plot_topoplotseries!(f[1, 1], df, Δbin; positions = positions)
-    text!(ax, 0, 0, text = "Time [ms] ", align = (:center, :center), offset = (0, -120))
-    hidespines!(ax) # delete unnecessary spines (lines)
-    hidedecorations!(ax, label = false)
-    f
-end
-
-@testset "toposeries for one time point" begin
-    plot_topoplotseries(df, Δbin; positions = positions, combinefun = x -> x[end÷2])
-end
-
-@testset "toposeries with differend comb functions " begin
-    f = Figure(size = (500, 500))
-    plot_topoplotseries!(
-        f[1, 1],
-        df,
-        Δbin;
-        positions = positions,
-        combinefun = mean,
-        axis = (; title = "combinefun = mean"),
-    )
-    plot_topoplotseries!(
-        f[2, 1],
-        df,
-        Δbin;
-        positions = positions,
-        combinefun = median,
-        axis = (; title = "combinefun = median"),
-    )
-    plot_topoplotseries!(
-        f[3, 1],
-        df,
-        Δbin;
-        positions = positions,
-        combinefun = std,
-        axis = (; title = "combinefun = std"),
-    )
-    f
-end
-
-@testset "toposeries without colorbar" begin
-    plot_topoplotseries(df, Δbin; positions = positions, layout = (; use_colorbar = false))
-end
-
-@testset "GridPosition with a title" begin
-    f = Figure()
-    ax = Axis(f[1:2, 1:5], aspect = DataAspect(), title = "Just a title")
-
-    df = UnfoldMakie.eeg_matrix_to_dataframe(dat[:, :, 1], string.(1:length(positions)))
-
-    Δbin = 80
-    a = plot_topoplotseries!(
-        f[1:2, 1:5],
-        df,
-        Δbin;
-        positions = positions,
-        layout = (; use_colorbar = true),
-    )
-    hidespines!(ax)
-    hidedecorations!(ax, label = false)
-
-    f
-end
-
-
-@testset "14 topoplots and GridPosition" begin # horrific
-    f = Figure()
-    df = UnfoldMakie.eeg_matrix_to_dataframe(dat[:, :, 1], string.(1:length(positions)))
-    Δbin = 30
-    plot_topoplotseries!(
-        f[1, 1:5],
-        df,
-        Δbin;
-        positions = positions,
-        visual = (label_scatter = false,),
-    )
-    f
-end
-
-
-@testset "row faceting, 2 conditions" begin
-    f = Figure()
-    df = UnfoldMakie.eeg_matrix_to_dataframe(dat[:, :, 1], string.(1:length(positions)))
-    df.condition = repeat(["A", "B"], size(df, 1) ÷ 2)
-
-    plot_topoplotseries!(
-        f[1:2, 1:2],
-        df,
-        Δbin;
-        col_labels = true,
-        mapping = (; row = :condition),
-        positions = positions,
-        visual = (label_scatter = false,),
-        layout = (; use_colorbar = true),
-    )
-    f
-end
-
-@testset "row faceting, 5 conditions" begin
-    df = UnfoldMakie.eeg_matrix_to_dataframe(dat[:, :, 1], string.(1:length(positions)))
-    df.condition = repeat(["A", "B", "C", "D", "E"], size(df, 1) ÷ 5)
-
-    f = Figure(size = (600, 500))
-    plot_topoplotseries!(
-        f[1:2, 1:2],
-        df,
-        Δbin;
-        col_labels = true,
-        mapping = (; row = :condition),
-        axis = (; ylabel = "Conditions"),
-        positions = positions,
-        visual = (label_scatter = false,),
-        layout = (; use_colorbar = true),
-    )
-    f
-end
-
-@testset "toposeries with xlabel" begin
-    plot_topoplotseries(df, Δbin; positions = positions, axis = (; xlabel = "test"))
-end
-@testset "toposeries with adjustable colorrange" begin
-    plot_topoplotseries(
-        df,
-        Δbin;
-        positions = positions,
-        colorbar = (; colorrange = (-1, 1)),
-    )
-end
-@testset "toposeries with xlabel" begin
-    plot_topoplotseries(df, Δbin; positions = positions, axis = (; ylim_topo = (0, 0.7)))
-end
-
-@testset "basic eeg_topoplot_series" begin
-    df = DataFrame(
-        :erp => repeat(1:63, 100),
-        :time => repeat(1:20, 5 * 63),
-        :label => repeat(1:63, 100),
-    ) # simulated data
-    a = (sin.(range(-2 * pi, 2 * pi, 63)))
-    b = [(1:63) ./ 63 .* a (1:63) ./ 63 .* cos.(range(-2 * pi, 2 * pi, 63))]
-    pos = b .* 0.5 .+ 0.5 # simulated electrode positions
-    pos = [Point2.(pos[k, 1], pos[k, 2]) for k = 1:size(pos, 1)]
-    UnfoldMakie.eeg_topoplot_series(df, 5; positions = pos)
-end
-
-@testset "toposeries with GridSubposition" begin
-    f = Figure(size = (500, 500))
-    plot_topoplotseries!(
-        f[2, 1][1, 1],
-        df,
-        Δbin;
-        positions = positions,
-        combinefun = mean,
-        axis = (; title = "combinefun = mean"),
-    )
-end
-
-
-@testset "observables" begin
-    f = Figure()
-    df = UnfoldMakie.eeg_matrix_to_dataframe(dat[:, :, 1], string.(1:length(positions)))
-    df.condition = repeat(["A", "B"], size(df, 1) ÷ 2)
-
-    df_obs = Observable(df)
-
-    plot_topoplotseries!(
-        f[1:2, 1:2],
-        df_obs,
-        Δbin;
-        col_labels = true,
-        mapping = (; row = :condition),
-        positions = positions,
-        visual = (label_scatter = false,),
-        layout = (; use_colorbar = true),
-    )
-    f
-    df = to_value(df_obs)
-    df.estimate .= rand(length(df.estimate))
-    df_obs[] = df
-end
-
-@testset "categorical cols" begin
-    f = Figure()
-    df = UnfoldMakie.eeg_matrix_to_dataframe(dat[:, 1:2, 1], string.(1:length(positions)))
-    df.condition = repeat(["A", "B"], size(df, 1) ÷ 2)
-
-    plot_topoplotseries!(
-        f[1:2, 1:2],
-        df,
-        0;
-        col_labels = true,
-        mapping = (; col = :condition),
-        positions = positions,
-        visual = (label_scatter = false,),
-        layout = (; use_colorbar = true),
-    )
-    f
-
-end
-
-@testset "observables on scatterpoints" begin
-    ##--
-    df = UnfoldMakie.eeg_matrix_to_dataframe(dat[:, 1:2, 1], string.(1:length(positions)))
-    df.condition = repeat(["A", "B"], size(df, 1) ÷ 2)
-
-    obs_tuple = Observable((0, 0, 0))
-    plot_topoplotseries(
-        df,
-        0;
-        col_labels = true,
-        mapping = (; col = :condition),
-        positions = positions,
-        visual = (label_scatter = (markersize = 15, strokewidth = 2),),
-        layout = (; use_colorbar = true),
-        interactive_scatter = obs_tuple,
-    )
-
-
-end
-
-
-#= 
-using CSV
-tmp = CSV.read("dev/UnfoldMakie/test/output2.csv", DataFrame)
-tmp = stack(tmp, 1:21)
-rename!(tmp, :variable => :condition, :value => :estimate)
-tmp.time = 1:nrow(tmp)
-
-Δbin = 140
-
-tmp2 = filter(x -> x.condition == "type" || x.condition == "duration", tmp)
-
-plot_topoplotseries(
-    tmp2,
-    Δbin;
-    positions = rand(Point2f, 128),
-    combinefun = x -> x,
-    mapping = (; :col => :condition),
-)
-
-
-tmp3 = filter(x -> x.condition != "type", tmp)
-n = size(tmp3, 1) ÷ 4
-tmp3.row = vcat(fill("A", n), fill("B", n), fill("C", n), fill("D", n))
-
-Δbin = 134
-plot_topoplotseries(
-    tmp3,
-    Δbin;
-    positions = rand(Point2f, 128),
-    combinefun = x -> x,
-    mapping = (; :col => :condition, :row => :row),
-)
-
-using Images
-
-function filt(img)
-    filtered_data = UnfoldMakie.imfilter(img, UnfoldMakie.Kernel.gaussian((1, max(30, 0))))
-end
-
-Images.entropy((dat[:, :, shuffle(1:end)]))
-
-
-map((x) -> Images.entropy((dat[x, :, shuffle(1:end)])) * 2, 1:size(dat, 1))
- =#
diff --git a/test/test_toposeries1.jl b/test/test_toposeries1.jl
new file mode 100644
index 00000000..86e802f0
--- /dev/null
+++ b/test/test_toposeries1.jl
@@ -0,0 +1,179 @@
+# simple checks
+
+dat, positions = TopoPlots.example_data()
+df = UnfoldMakie.eeg_matrix_to_dataframe(dat[:, :, 1], string.(1:length(positions)))
+bin_width = 80
+
+@testset "toposeries basic with bin_width" begin
+    plot_topoplotseries(df; bin_width, positions = positions)
+end
+
+@testset "toposeries basic with bin_num" begin
+    plot_topoplotseries(df; bin_num = 5, positions = positions)
+end
+
+@testset "toposeries basic: checking mapping" begin
+    plot_topoplotseries(df; bin_num = 5, positions = positions, mapping = (; x = df.time))
+end
+
+#= @testset "toposeries with Δbin deprecated" begin #fail
+    plot_topoplotseries(df, Δbin; positions = positions)
+end =#
+
+@testset "toposeries basic with nrows specified" begin
+    plot_topoplotseries(df; bin_num = 5, nrows = 2, positions = positions)
+end
+
+@testset "toposeries basic with nrows specified" begin
+    plot_topoplotseries(df; bin_num = 5, nrows = 3, positions = positions)
+end
+
+@testset "toposeries basic with nrows specified" begin
+    plot_topoplotseries(df; bin_num = 5, nrows = -6, positions = positions)
+end
+
+@testset "error checking: bin_width and bin_num specified" begin
+    err1 = nothing
+    t() = error(plot_topoplotseries(df; bin_width, bin_num = 5, positions = positions))
+    try
+        t()
+    catch err1
+    end
+    @test err1 ==
+          ErrorException("Ambigious parameters: specify only `bin_width` or `bin_num`.")
+end
+
+@testset "error checking: bin_width and bin_num not specified" begin
+    err1 = nothing
+    t() = error(plot_topoplotseries(df; positions = positions))
+    try
+        t()
+    catch err1
+    end
+    @test err1 == ErrorException(
+        "You haven't specified `bin_width` or `bin_num`. Such option is available only with categorical `mapping.col` or `mapping.row`.",
+    )
+end
+
+@testset "toposeries basic with channel names" begin
+    plot_topoplotseries(df; bin_width, positions = positions, labels = raw_ch_names)
+end # doesnt work rn
+
+@testset "toposeries with xlabel" begin
+    f = Figure()
+    ax = Axis(f[1, 1])
+    plot_topoplotseries!(f[1, 1], df; bin_width, positions = positions)
+    text!(ax, 0, 0, text = "Time [ms] ", align = (:center, :center), offset = (0, -120))
+    hidespines!(ax) # delete unnecessary spines (lines)
+    hidedecorations!(ax, label = false)
+    f
+end
+
+@testset "toposeries for one time point (?)" begin
+    plot_topoplotseries(df; bin_width, positions = positions, combinefun = x -> x[end÷2])
+end
+
+@testset "toposeries with differend comb functions " begin
+    f = Figure(size = (500, 500))
+    plot_topoplotseries!(
+        f[1, 1],
+        df;
+        bin_width,
+        positions = positions,
+        combinefun = mean,
+        axis = (; xlabel = "", title = "combinefun = mean"),
+    )
+    plot_topoplotseries!(
+        f[2, 1],
+        df;
+        bin_width,
+        positions = positions,
+        combinefun = median,
+        axis = (; xlabel = "", title = "combinefun = median"),
+    )
+    plot_topoplotseries!(
+        f[3, 1],
+        df;
+        bin_width,
+        positions = positions,
+        combinefun = std,
+        axis = (; title = "combinefun = std"),
+    )
+    f
+end
+
+@testset "toposeries without colorbar" begin
+    plot_topoplotseries(
+        df;
+        bin_width,
+        positions = positions,
+        layout = (; use_colorbar = false),
+    )
+end
+
+@testset "GridPosition with a title" begin
+    f = Figure()
+    ax = Axis(f[1:2, 1:5], aspect = DataAspect(), title = "Just a title")
+
+    df = UnfoldMakie.eeg_matrix_to_dataframe(dat[:, :, 1], string.(1:length(positions)))
+
+    bin_width = 80
+    a = plot_topoplotseries!(
+        f[1:2, 1:5],
+        df;
+        bin_width,
+        positions = positions,
+        layout = (; use_colorbar = true),
+    )
+    hidespines!(ax)
+    hidedecorations!(ax, label = false)
+
+    f
+end
+
+@testset "toposeries with specified xlabel" begin
+    plot_topoplotseries(df; bin_width, positions = positions, axis = (; xlabel = "test"))
+end
+
+@testset "toposeries with adjustable colorrange" begin
+    plot_topoplotseries(
+        df;
+        bin_width,
+        positions = positions,
+        colorbar = (; colorrange = (-1, 1)),
+    )
+end
+
+@testset "toposeries with adjusted ylim_topo" begin
+    plot_topoplotseries(
+        df;
+        bin_width,
+        positions = positions,
+        axis = (; ylim_topo = (0, 0.7)),
+    )
+end
+
+#= @testset "basic eeg_topoplot_series" begin
+    df = DataFrame(
+        :erp => repeat(1:63, 100),
+        :time => repeat(1:20, 5 * 63),
+        :label => repeat(1:63, 100),
+    ) # simulated data
+    a = (sin.(range(-2 * pi, 2 * pi, 63)))
+    b = [(1:63) ./ 63 .* a (1:63) ./ 63 .* cos.(range(-2 * pi, 2 * pi, 63))]
+    pos = b .* 0.5 .+ 0.5 # simulated electrode positions
+    pos = [Point2.(pos[k, 1], pos[k, 2]) for k = 1:size(pos, 1)]
+    UnfoldMakie.eeg_topoplot_series(df; bin_width = 5, positions = pos)
+end =#
+
+@testset "toposeries with GridSubposition" begin
+    f = Figure(size = (500, 500))
+    plot_topoplotseries!(
+        f[2, 1][1, 1],
+        df;
+        bin_width,
+        positions = positions,
+        combinefun = mean,
+        axis = (; title = "combinefun = mean"),
+    )
+end
diff --git a/test/test_toposeries2.jl b/test/test_toposeries2.jl
new file mode 100644
index 00000000..83a0acf8
--- /dev/null
+++ b/test/test_toposeries2.jl
@@ -0,0 +1,136 @@
+# advanced features: facetting and interactivity
+
+dat, positions = TopoPlots.example_data()
+df = UnfoldMakie.eeg_matrix_to_dataframe(dat[:, :, 1], string.(1:length(positions)))
+bin_width = 80
+
+@testset "14 topoplots, 4 rows" begin # horrific
+    f = Figure()
+    df = UnfoldMakie.eeg_matrix_to_dataframe(dat[:, :, 1], string.(1:length(positions)))
+    plot_topoplotseries!(
+        f[1, 1:5],
+        df;
+        bin_num = 14,
+        nrows = 4,
+        positions = positions,
+        visual = (; label_scatter = false),
+    )
+    f
+end
+
+@testset "facetting by layout" begin # could be changed to nrwos = "auto"
+    df = UnfoldMakie.eeg_matrix_to_dataframe(
+        dat[:, 200:1:206, 1],
+        string.(1:length(positions)),
+    )
+
+    f = Figure(size = (600, 500))
+    plot_topoplotseries!(
+        f[1:2, 1:2],
+        df;
+        bin_width = 1,
+        mapping = (; layout = :time),
+        positions = positions,
+    )
+    f
+end
+
+@testset "categorical columns" begin
+    f = Figure()
+    df = UnfoldMakie.eeg_matrix_to_dataframe(dat[:, 1:2, 1], string.(1:length(positions)))
+    df.condition = repeat(["A", "B"], size(df, 1) ÷ 2)
+
+    plot_topoplotseries!(
+        f[1, 1],
+        df;
+        col_labels = true,
+        mapping = (; col = :condition),
+        positions = positions,
+    )
+    f
+end
+
+@testset "4 conditions" begin
+    df = UnfoldMakie.eeg_matrix_to_dataframe(dat[:, 1:4, 1], string.(1:length(positions)))
+    df.condition = repeat(["A", "B", "C", "D"], size(df, 1) ÷ 4)
+
+    f = Figure(size = (600, 500))
+    plot_topoplotseries!(
+        f[1, 1],
+        df;
+        positions = positions,
+        col_labels = true,
+        axis = (; ylabel = "Conditions"),
+        mapping = (; col = :condition),
+    )
+    f
+end
+
+@testset "4 conditions in 2 rows" begin # TBD
+    df = UnfoldMakie.eeg_matrix_to_dataframe(dat[:, 1:4, 1], string.(1:length(positions)))
+    df.condition = repeat(["A", "B", "C", "D"], size(df, 1) ÷ 4)
+
+    f = Figure(size = (600, 500))
+    plot_topoplotseries!(
+        f[1, 1],
+        df;
+        nrows = 2,
+        positions = positions,
+        col_labels = true,
+        axis = (; ylabel = "Conditions"),
+        mapping = (; col = :condition),
+    )
+    f
+end
+
+@testset "change xlabel" begin
+    f = Figure()
+    df = UnfoldMakie.eeg_matrix_to_dataframe(dat[:, 1:2, 1], string.(1:length(positions)))
+    df.condition = repeat(["A", "B"], size(df, 1) ÷ 2)
+
+    plot_topoplotseries!(
+        f[1, 1],
+        df;
+        col_labels = true,
+        mapping = (; col = :condition),
+        axis = (; xlabel = "test"),
+        positions = positions,
+    )
+    f
+end
+
+# use with WGlMakie
+@testset "interactive data" begin
+    df = UnfoldMakie.eeg_matrix_to_dataframe(dat[:, 1:2, 1], string.(1:length(positions)))
+    df.condition = repeat(["A", "B"], size(df, 1) ÷ 2)
+
+    df_obs = Observable(df)
+
+    f = Figure()
+    plot_topoplotseries!(
+        f[1, 1],
+        df_obs;
+        col_labels = true,
+        mapping = (; col = :condition),
+        positions = positions,
+    )
+    f
+    df = to_value(df_obs)
+    df.estimate .= rand(length(df.estimate))
+    df_obs[] = df
+end
+
+@testset "interactive scatter markers" begin
+    df = UnfoldMakie.eeg_matrix_to_dataframe(dat[:, 1:2, 1], string.(1:length(positions)))
+    df.condition = repeat(["A", "B"], size(df, 1) ÷ 2)
+
+    obs_tuple = Observable((0, 0, 0))
+    plot_topoplotseries(
+        df;
+        col_labels = true,
+        mapping = (; col = :condition),
+        positions = positions,
+        visual = (; label_scatter = (markersize = 15, strokewidth = 2)),
+        interactive_scatter = obs_tuple,
+    )
+end