diff --git a/dev/404.html b/dev/404.html index 1d5c6ca..6cdd50a 100644 --- a/dev/404.html +++ b/dev/404.html @@ -8,7 +8,7 @@ - + @@ -16,7 +16,7 @@
- + \ No newline at end of file diff --git a/dev/algorithms.html b/dev/algorithms.html index c45e357..62d53c4 100644 --- a/dev/algorithms.html +++ b/dev/algorithms.html @@ -8,11 +8,11 @@ - + - - - + + + @@ -27,8 +27,8 @@ setproperty!.(jitter_plots, :markersize, 7) setproperty!.(jitter_plots, :alpha, 0.3) Makie.update_state_before_display!(fig) -fig

Documentation ​

# SwarmMakie.SimpleBeeswarm — Type.
julia
SimpleBeeswarm()

A simple implementation like Matplotlib's algorithm. This is the default algorithm used in beeswarm.

This algorithm dodges in x but preserves the exact y coordinate of each point. If you don't want to preserve the y coordinate, check out WilkinsonBeeswarm.

source


# SwarmMakie.WilkinsonBeeswarm — Type.
julia
WilkinsonBeeswarm()

A beeswarm algorithm that implements Leland Wilkinson's original dot-hist algorithm.

This is essentially a histogram with dots, where all dots are binned in the y (non-categorical) direction, and then dodged in the x (categorical) direction.

Original y-coordinates are not preserved, and if you want that try SimpleBeeswarm instead.

source


# SwarmMakie.NoBeeswarm — Type.

A simple no-op algorithm, which causes the scatter plot to be drawn as if you called scatter and not beeswarm.

source


Code exists for the algorithm suggested by Michael Borregaard in this StatsPlots.jl PR, but it is currently nonfunctional. If you'd like to take a crack at getting it working, please do!

# SwarmMakie.UniformJitter — Type.
julia
UniformJitter(; jitter_width = 1.0)

A jitter algorithm that uses a uniform distribution to create the jitter.

source


# SwarmMakie.PseudorandomJitter — Type.
julia
PseudorandomJitter(; jitter_width = 1.0)

A jitter algorithm that uses a pseudorandom distribution to create the jitter. A pseudorandom distribution is a uniform distribution weighted by the PDF of the data.

source


# SwarmMakie.QuasirandomJitter — Type.
julia
QuasirandomJitter(; jitter_width = 1.0)

A jitter algorithm that uses a quasirandom (van der Corput) distribution weighted by the data's pdf to jitter the data points.

source


We also welcome any new algorithms you may have in mind. Just open a PR!

Adding a new algorithm ​

In order to add a new algorithm, you must simply define a struct which subtypes SwarmMakie.BeeswarmAlgorithm.

There must also be a corresponding dispatch on SwarmMakie.calculate!(buffer, alg, positions, markersize) which loads the new positions calculated in pixel space into buffer. Note that buffer must be modified here.

See the Wilkinson source page for a deep dive into how to write a beeswarm algorithm!

- +fig

Documentation ​

# SwarmMakie.SimpleBeeswarm — Type.
julia
SimpleBeeswarm()

A simple implementation like Matplotlib's algorithm. This is the default algorithm used in beeswarm.

This algorithm dodges in x but preserves the exact y coordinate of each point. If you don't want to preserve the y coordinate, check out WilkinsonBeeswarm.

source


# SwarmMakie.WilkinsonBeeswarm — Type.
julia
WilkinsonBeeswarm()

A beeswarm algorithm that implements Leland Wilkinson's original dot-hist algorithm.

This is essentially a histogram with dots, where all dots are binned in the y (non-categorical) direction, and then dodged in the x (categorical) direction.

Original y-coordinates are not preserved, and if you want that try SimpleBeeswarm instead.

source


# SwarmMakie.NoBeeswarm — Type.

A simple no-op algorithm, which causes the scatter plot to be drawn as if you called scatter and not beeswarm.

source


Code exists for the algorithm suggested by Michael Borregaard in this StatsPlots.jl PR, but it is currently nonfunctional. If you'd like to take a crack at getting it working, please do!

# SwarmMakie.UniformJitter — Type.
julia
UniformJitter(; jitter_width = 1.0)

A jitter algorithm that uses a uniform distribution to create the jitter.

source


# SwarmMakie.PseudorandomJitter — Type.
julia
PseudorandomJitter(; jitter_width = 1.0)

A jitter algorithm that uses a pseudorandom distribution to create the jitter. A pseudorandom distribution is a uniform distribution weighted by the PDF of the data.

source


# SwarmMakie.QuasirandomJitter — Type.
julia
QuasirandomJitter(; jitter_width = 1.0)

A jitter algorithm that uses a quasirandom (van der Corput) distribution weighted by the data's pdf to jitter the data points.

source


We also welcome any new algorithms you may have in mind. Just open a PR!

Adding a new algorithm ​

In order to add a new algorithm, you must simply define a struct which subtypes SwarmMakie.BeeswarmAlgorithm.

There must also be a corresponding dispatch on SwarmMakie.calculate!(buffer, alg, positions, markersize) which loads the new positions calculated in pixel space into buffer. Note that buffer must be modified here.

See the Wilkinson source page for a deep dive into how to write a beeswarm algorithm!

+ \ No newline at end of file diff --git a/dev/api.html b/dev/api.html index 2bea13d..db720e8 100644 --- a/dev/api.html +++ b/dev/api.html @@ -8,17 +8,17 @@ - + - - - + + + -
SwarmMakie.jl

Appearance

API Reference ​

# SwarmMakie.JitterAlgorithm — Type.

The abstract type for jitter algorithms, which are markersize-agnostic.

source


# SwarmMakie.NoBeeswarm — Type.

A simple no-op algorithm, which causes the scatter plot to be drawn as if you called scatter and not beeswarm.

source


# SwarmMakie.PseudorandomJitter — Type.
julia
PseudorandomJitter(; jitter_width = 1.0)

A jitter algorithm that uses a pseudorandom distribution to create the jitter. A pseudorandom distribution is a uniform distribution weighted by the PDF of the data.

source


# SwarmMakie.QuasirandomJitter — Type.
julia
QuasirandomJitter(; jitter_width = 1.0)

A jitter algorithm that uses a quasirandom (van der Corput) distribution weighted by the data's pdf to jitter the data points.

source


# SwarmMakie.SeabornBeeswarm — Type.
julia
SeabornBeeswarm()

A beeswarm algorithm based on the seaborn Python package.

More adaptive to marker size than SimpleBeeswarm, but takes longer to compute.

source


# SwarmMakie.SimpleBeeswarm — Type.
julia
SimpleBeeswarm()

A simple implementation like Matplotlib's algorithm. This is the default algorithm used in beeswarm.

This algorithm dodges in x but preserves the exact y coordinate of each point. If you don't want to preserve the y coordinate, check out WilkinsonBeeswarm.

source


# SwarmMakie.UniformJitter — Type.
julia
UniformJitter(; jitter_width = 1.0)

A jitter algorithm that uses a uniform distribution to create the jitter.

source


# SwarmMakie.WilkinsonBeeswarm — Type.
julia
WilkinsonBeeswarm()

A beeswarm algorithm that implements Leland Wilkinson's original dot-hist algorithm.

This is essentially a histogram with dots, where all dots are binned in the y (non-categorical) direction, and then dodged in the x (categorical) direction.

Original y-coordinates are not preserved, and if you want that try SimpleBeeswarm instead.

source


# SwarmMakie.beeswarm — Method.
julia
beeswarm(x, y)
+    
SwarmMakie.jl
Appearance
API Reference ​
# SwarmMakie.JitterAlgorithm — Type. 
The abstract type for jitter algorithms, which are markersize-agnostic.
source

# SwarmMakie.NoBeeswarm — Type. 
A simple no-op algorithm, which causes the scatter plot to be drawn as if you called scatter and not beeswarm.
source

# SwarmMakie.PseudorandomJitter — Type. 
julia
PseudorandomJitter(; jitter_width = 1.0)
A jitter algorithm that uses a pseudorandom distribution to create the jitter. A pseudorandom distribution is a uniform distribution weighted by the PDF of the data.
source

# SwarmMakie.QuasirandomJitter — Type. 
julia
QuasirandomJitter(; jitter_width = 1.0)
A jitter algorithm that uses a quasirandom (van der Corput) distribution weighted by the data's pdf to jitter the data points.
source

# SwarmMakie.SeabornBeeswarm — Type. 
julia
SeabornBeeswarm()
A beeswarm algorithm based on the seaborn Python package.
More adaptive to marker size than SimpleBeeswarm, but takes longer to compute.
source

# SwarmMakie.SimpleBeeswarm — Type. 
julia
SimpleBeeswarm()
A simple implementation like Matplotlib's algorithm. This is the default algorithm used in beeswarm.
This algorithm dodges in x but preserves the exact y coordinate of each point. If you don't want to preserve the y coordinate, check out WilkinsonBeeswarm.
source

# SwarmMakie.UniformJitter — Type. 
julia
UniformJitter(; jitter_width = 1.0)
A jitter algorithm that uses a uniform distribution to create the jitter.
source

# SwarmMakie.WilkinsonBeeswarm — Type. 
julia
WilkinsonBeeswarm()
A beeswarm algorithm that implements Leland Wilkinson's original dot-hist algorithm.
This is essentially a histogram with dots, where all dots are binned in the y (non-categorical) direction, and then dodged in the x (categorical) direction.
Original y-coordinates are not preserved, and if you want that try SimpleBeeswarm instead.
source

# SwarmMakie.beeswarm — Method. 
julia
beeswarm(x, y)
 beeswarm(positions)
beeswarm is a PointBased recipe like scatter, accepting all of scatter's input.
It displaces points which would otherwise overlap in the x-direction by binning in the y direction.
Specific attributes to beeswarm are:
  • algorithm = SimpleBeeswarm(): The algorithm used to lay out the beeswarm markers.
  • side = :both: The side towards which markers should extend. Can be :left, :right, or both.
  • direction = :y: Controls the direction of the beeswarm. Can be :y (vertical) or :x (horizontal).
  • gutter = nothing: Creates a gutter of a desired size around each category. Gutter size is always in data space.
  • gutter_threshold = .5: Emit a warning of the number of points added to a gutter per category exceeds the threshold.
Arguments
Available attributes and their defaults for Plot{SwarmMakie.beeswarm} are:
  algorithm         SimpleBeeswarm()
   alpha             1.0
   clip_planes       MakieCore.Automatic()
@@ -57,8 +57,8 @@
   transparency      false
   uv_offset_width   (0.0, 0.0, 0.0, 0.0)
   visible           true
Example
julia
using Makie, SwarmMakie
-beeswarm(ones(100), randn(100); color = rand(RGBf, 100))
source

# SwarmMakie.could_overlap — Method. 
julia
could_overlap(position, markersize, positions, markersizes)
Check if a point given by position with markersize markersize could overlap with any other point in the swarm. Returns a vector of integer indices.
source

# SwarmMakie.first_non_overlapping_candidate — Method. 
julia
Returns `(position::Point2f, idx::Int)`
source

# SwarmMakie.position_candidates — Method. 
Returns (positions::Vector{Point2f}, idxs::Vector{Int}).
source


-    
+beeswarm(ones(100), randn(100); color = rand(RGBf, 100))

source


# SwarmMakie.could_overlap — Method.
julia
could_overlap(position, markersize, positions, markersizes)

Check if a point given by position with markersize markersize could overlap with any other point in the swarm. Returns a vector of integer indices.

source


# SwarmMakie.first_non_overlapping_candidate — Method.
julia
Returns `(position::Point2f, idx::Int)`

source


# SwarmMakie.position_candidates — Method.

Returns (positions::Vector{Point2f}, idxs::Vector{Int}).

source


+ \ No newline at end of file diff --git a/dev/assets/algorithms.md.Dx4XOb6s.js b/dev/assets/algorithms.md._5FBLjFM.js similarity index 94% rename from dev/assets/algorithms.md.Dx4XOb6s.js rename to dev/assets/algorithms.md._5FBLjFM.js index 4633261..58af5d7 100644 --- a/dev/assets/algorithms.md.Dx4XOb6s.js +++ b/dev/assets/algorithms.md._5FBLjFM.js @@ -1,4 +1,4 @@ -import{_ as a,c as s,a5 as e,o as t}from"./chunks/framework.CLWLLiKQ.js";const r="/SwarmMakie.jl/dev/assets/xwbzrjj.CxvVHO24.png",c=JSON.parse('{"title":"Algorithms","description":"","frontmatter":{},"headers":[],"relativePath":"algorithms.md","filePath":"algorithms.md","lastUpdated":null}'),l={name:"algorithms.md"};function h(n,i,p,o,k,d){return t(),s("div",null,i[0]||(i[0]=[e(`

Algorithms ​

SwarmMakie aims to offer several beeswarm algorithms, which give different results. You can change the algorithm which SwarmMakie uses by passing it as a keyword argument algorithm, or mutating plot.algorithm after the fact.

Currently, it offers the SimpleBeeswarm and WilkinsonBeeswarm algorithms, which are inspired by Matplotlib and Leland Wilkinson's original paper respectively, and a no-op NoBeeswarm algorithm which simply decomposes back to the original data.

In addition, SwarmMakie offers jittered scatter plots as algorithms to beeswarm. These aren't exactly beeswarm plots since they don't guarantee that all points are non-overlapping, but they can still be useful to show distributions, especially for larger numbers of points where all points cannot fit into a beeswarm. These algorithms are accessible as UniformJitter, PseudorandomJitter, and QuasirandomJitter, similar to ggbeeswarm's options.

Comparison ​

Here's a comparison of all the available algorithms:

julia
using SwarmMakie, CairoMakie
+import{_ as a,c as s,a5 as e,o as t}from"./chunks/framework.Bx3vxiYB.js";const r="/SwarmMakie.jl/dev/assets/nibppwx.hc6mWVZB.png",m=JSON.parse('{"title":"Algorithms","description":"","frontmatter":{},"headers":[],"relativePath":"algorithms.md","filePath":"algorithms.md","lastUpdated":null}'),l={name:"algorithms.md"};function h(n,i,p,o,d,k){return t(),s("div",null,i[0]||(i[0]=[e(`
Algorithms ​
SwarmMakie aims to offer several beeswarm algorithms, which give different results. You can change the algorithm which SwarmMakie uses by passing it as a keyword argument algorithm, or mutating plot.algorithm after the fact.
Currently, it offers the SimpleBeeswarm and WilkinsonBeeswarm algorithms, which are inspired by Matplotlib and Leland Wilkinson's original paper respectively, and a no-op NoBeeswarm algorithm which simply decomposes back to the original data.
In addition, SwarmMakie offers jittered scatter plots as algorithms to beeswarm. These aren't exactly beeswarm plots since they don't guarantee that all points are non-overlapping, but they can still be useful to show distributions, especially for larger numbers of points where all points cannot fit into a beeswarm. These algorithms are accessible as UniformJitter, PseudorandomJitter, and QuasirandomJitter, similar to ggbeeswarm's options.
Comparison ​
Here's a comparison of all the available algorithms:
julia
using SwarmMakie, CairoMakie
 algorithms = [NoBeeswarm() SimpleBeeswarm() WilkinsonBeeswarm(); UniformJitter() PseudorandomJitter() QuasirandomJitter()]
 fig = Figure(; size = (800, 450))
 xs = rand(1:3, 400); ys = randn(400)
@@ -7,4 +7,4 @@ import{_ as a,c as s,a5 as e,o as t}from"./chunks/framework.CLWLLiKQ.js";const r
 setproperty!.(jitter_plots, :markersize, 7)
 setproperty!.(jitter_plots, :alpha, 0.3)
 Makie.update_state_before_display!(fig)
-fig
Documentation ​
# SwarmMakie.SimpleBeeswarm — Type. 
julia
SimpleBeeswarm()
A simple implementation like Matplotlib's algorithm. This is the default algorithm used in beeswarm.
This algorithm dodges in x but preserves the exact y coordinate of each point. If you don't want to preserve the y coordinate, check out WilkinsonBeeswarm.
source

# SwarmMakie.WilkinsonBeeswarm — Type. 
julia
WilkinsonBeeswarm()
A beeswarm algorithm that implements Leland Wilkinson's original dot-hist algorithm.
This is essentially a histogram with dots, where all dots are binned in the y (non-categorical) direction, and then dodged in the x (categorical) direction.
Original y-coordinates are not preserved, and if you want that try SimpleBeeswarm instead.
source

# SwarmMakie.NoBeeswarm — Type. 
A simple no-op algorithm, which causes the scatter plot to be drawn as if you called scatter and not beeswarm.
source

Code exists for the algorithm suggested by Michael Borregaard in this StatsPlots.jl PR, but it is currently nonfunctional. If you'd like to take a crack at getting it working, please do!
# SwarmMakie.UniformJitter — Type. 
julia
UniformJitter(; jitter_width = 1.0)
A jitter algorithm that uses a uniform distribution to create the jitter.
source

# SwarmMakie.PseudorandomJitter — Type. 
julia
PseudorandomJitter(; jitter_width = 1.0)
A jitter algorithm that uses a pseudorandom distribution to create the jitter. A pseudorandom distribution is a uniform distribution weighted by the PDF of the data.
source

# SwarmMakie.QuasirandomJitter — Type. 
julia
QuasirandomJitter(; jitter_width = 1.0)
A jitter algorithm that uses a quasirandom (van der Corput) distribution weighted by the data's pdf to jitter the data points.
source

We also welcome any new algorithms you may have in mind. Just open a PR!
Adding a new algorithm ​
In order to add a new algorithm, you must simply define a struct which subtypes SwarmMakie.BeeswarmAlgorithm.
There must also be a corresponding dispatch on SwarmMakie.calculate!(buffer, alg, positions, markersize) which loads the new positions calculated in pixel space into buffer. Note that buffer must be modified here.
See the Wilkinson source page for a deep dive into how to write a beeswarm algorithm!
',27)]))}const m=a(l,[["render",h]]);export{c as __pageData,m as default};
+fig

Documentation ​

# SwarmMakie.SimpleBeeswarm — Type.
julia
SimpleBeeswarm()

A simple implementation like Matplotlib's algorithm. This is the default algorithm used in beeswarm.

This algorithm dodges in x but preserves the exact y coordinate of each point. If you don't want to preserve the y coordinate, check out WilkinsonBeeswarm.

source


# SwarmMakie.WilkinsonBeeswarm — Type.
julia
WilkinsonBeeswarm()

A beeswarm algorithm that implements Leland Wilkinson's original dot-hist algorithm.

This is essentially a histogram with dots, where all dots are binned in the y (non-categorical) direction, and then dodged in the x (categorical) direction.

Original y-coordinates are not preserved, and if you want that try SimpleBeeswarm instead.

source


# SwarmMakie.NoBeeswarm — Type.

A simple no-op algorithm, which causes the scatter plot to be drawn as if you called scatter and not beeswarm.

source


Code exists for the algorithm suggested by Michael Borregaard in this StatsPlots.jl PR, but it is currently nonfunctional. If you'd like to take a crack at getting it working, please do!

# SwarmMakie.UniformJitter — Type.
julia
UniformJitter(; jitter_width = 1.0)

A jitter algorithm that uses a uniform distribution to create the jitter.

source


# SwarmMakie.PseudorandomJitter — Type.
julia
PseudorandomJitter(; jitter_width = 1.0)

A jitter algorithm that uses a pseudorandom distribution to create the jitter. A pseudorandom distribution is a uniform distribution weighted by the PDF of the data.

source


# SwarmMakie.QuasirandomJitter — Type.
julia
QuasirandomJitter(; jitter_width = 1.0)

A jitter algorithm that uses a quasirandom (van der Corput) distribution weighted by the data's pdf to jitter the data points.

source


We also welcome any new algorithms you may have in mind. Just open a PR!

Adding a new algorithm ​

In order to add a new algorithm, you must simply define a struct which subtypes SwarmMakie.BeeswarmAlgorithm.

There must also be a corresponding dispatch on SwarmMakie.calculate!(buffer, alg, positions, markersize) which loads the new positions calculated in pixel space into buffer. Note that buffer must be modified here.

See the Wilkinson source page for a deep dive into how to write a beeswarm algorithm!

',27)]))}const c=a(l,[["render",h]]);export{m as __pageData,c as default}; diff --git a/dev/assets/algorithms.md.Dx4XOb6s.lean.js b/dev/assets/algorithms.md._5FBLjFM.lean.js similarity index 94% rename from dev/assets/algorithms.md.Dx4XOb6s.lean.js rename to dev/assets/algorithms.md._5FBLjFM.lean.js index 4633261..58af5d7 100644 --- a/dev/assets/algorithms.md.Dx4XOb6s.lean.js +++ b/dev/assets/algorithms.md._5FBLjFM.lean.js @@ -1,4 +1,4 @@ -import{_ as a,c as s,a5 as e,o as t}from"./chunks/framework.CLWLLiKQ.js";const r="/SwarmMakie.jl/dev/assets/xwbzrjj.CxvVHO24.png",c=JSON.parse('{"title":"Algorithms","description":"","frontmatter":{},"headers":[],"relativePath":"algorithms.md","filePath":"algorithms.md","lastUpdated":null}'),l={name:"algorithms.md"};function h(n,i,p,o,k,d){return t(),s("div",null,i[0]||(i[0]=[e(`

Algorithms ​

SwarmMakie aims to offer several beeswarm algorithms, which give different results. You can change the algorithm which SwarmMakie uses by passing it as a keyword argument algorithm, or mutating plot.algorithm after the fact.

Currently, it offers the SimpleBeeswarm and WilkinsonBeeswarm algorithms, which are inspired by Matplotlib and Leland Wilkinson's original paper respectively, and a no-op NoBeeswarm algorithm which simply decomposes back to the original data.

In addition, SwarmMakie offers jittered scatter plots as algorithms to beeswarm. These aren't exactly beeswarm plots since they don't guarantee that all points are non-overlapping, but they can still be useful to show distributions, especially for larger numbers of points where all points cannot fit into a beeswarm. These algorithms are accessible as UniformJitter, PseudorandomJitter, and QuasirandomJitter, similar to ggbeeswarm's options.

Comparison ​

Here's a comparison of all the available algorithms:

julia
using SwarmMakie, CairoMakie
+import{_ as a,c as s,a5 as e,o as t}from"./chunks/framework.Bx3vxiYB.js";const r="/SwarmMakie.jl/dev/assets/nibppwx.hc6mWVZB.png",m=JSON.parse('{"title":"Algorithms","description":"","frontmatter":{},"headers":[],"relativePath":"algorithms.md","filePath":"algorithms.md","lastUpdated":null}'),l={name:"algorithms.md"};function h(n,i,p,o,d,k){return t(),s("div",null,i[0]||(i[0]=[e(`
Algorithms ​
SwarmMakie aims to offer several beeswarm algorithms, which give different results. You can change the algorithm which SwarmMakie uses by passing it as a keyword argument algorithm, or mutating plot.algorithm after the fact.
Currently, it offers the SimpleBeeswarm and WilkinsonBeeswarm algorithms, which are inspired by Matplotlib and Leland Wilkinson's original paper respectively, and a no-op NoBeeswarm algorithm which simply decomposes back to the original data.
In addition, SwarmMakie offers jittered scatter plots as algorithms to beeswarm. These aren't exactly beeswarm plots since they don't guarantee that all points are non-overlapping, but they can still be useful to show distributions, especially for larger numbers of points where all points cannot fit into a beeswarm. These algorithms are accessible as UniformJitter, PseudorandomJitter, and QuasirandomJitter, similar to ggbeeswarm's options.
Comparison ​
Here's a comparison of all the available algorithms:
julia
using SwarmMakie, CairoMakie
 algorithms = [NoBeeswarm() SimpleBeeswarm() WilkinsonBeeswarm(); UniformJitter() PseudorandomJitter() QuasirandomJitter()]
 fig = Figure(; size = (800, 450))
 xs = rand(1:3, 400); ys = randn(400)
@@ -7,4 +7,4 @@ import{_ as a,c as s,a5 as e,o as t}from"./chunks/framework.CLWLLiKQ.js";const r
 setproperty!.(jitter_plots, :markersize, 7)
 setproperty!.(jitter_plots, :alpha, 0.3)
 Makie.update_state_before_display!(fig)
-fig
Documentation ​
# SwarmMakie.SimpleBeeswarm — Type. 
julia
SimpleBeeswarm()
A simple implementation like Matplotlib's algorithm. This is the default algorithm used in beeswarm.
This algorithm dodges in x but preserves the exact y coordinate of each point. If you don't want to preserve the y coordinate, check out WilkinsonBeeswarm.
source

# SwarmMakie.WilkinsonBeeswarm — Type. 
julia
WilkinsonBeeswarm()
A beeswarm algorithm that implements Leland Wilkinson's original dot-hist algorithm.
This is essentially a histogram with dots, where all dots are binned in the y (non-categorical) direction, and then dodged in the x (categorical) direction.
Original y-coordinates are not preserved, and if you want that try SimpleBeeswarm instead.
source

# SwarmMakie.NoBeeswarm — Type. 
A simple no-op algorithm, which causes the scatter plot to be drawn as if you called scatter and not beeswarm.
source

Code exists for the algorithm suggested by Michael Borregaard in this StatsPlots.jl PR, but it is currently nonfunctional. If you'd like to take a crack at getting it working, please do!
# SwarmMakie.UniformJitter — Type. 
julia
UniformJitter(; jitter_width = 1.0)
A jitter algorithm that uses a uniform distribution to create the jitter.
source

# SwarmMakie.PseudorandomJitter — Type. 
julia
PseudorandomJitter(; jitter_width = 1.0)
A jitter algorithm that uses a pseudorandom distribution to create the jitter. A pseudorandom distribution is a uniform distribution weighted by the PDF of the data.
source

# SwarmMakie.QuasirandomJitter — Type. 
julia
QuasirandomJitter(; jitter_width = 1.0)
A jitter algorithm that uses a quasirandom (van der Corput) distribution weighted by the data's pdf to jitter the data points.
source

We also welcome any new algorithms you may have in mind. Just open a PR!
Adding a new algorithm ​
In order to add a new algorithm, you must simply define a struct which subtypes SwarmMakie.BeeswarmAlgorithm.
There must also be a corresponding dispatch on SwarmMakie.calculate!(buffer, alg, positions, markersize) which loads the new positions calculated in pixel space into buffer. Note that buffer must be modified here.
See the Wilkinson source page for a deep dive into how to write a beeswarm algorithm!
',27)]))}const m=a(l,[["render",h]]);export{c as __pageData,m as default};
+fig

Documentation ​

# SwarmMakie.SimpleBeeswarm — Type.
julia
SimpleBeeswarm()

A simple implementation like Matplotlib's algorithm. This is the default algorithm used in beeswarm.

This algorithm dodges in x but preserves the exact y coordinate of each point. If you don't want to preserve the y coordinate, check out WilkinsonBeeswarm.

source


# SwarmMakie.WilkinsonBeeswarm — Type.
julia
WilkinsonBeeswarm()

A beeswarm algorithm that implements Leland Wilkinson's original dot-hist algorithm.

This is essentially a histogram with dots, where all dots are binned in the y (non-categorical) direction, and then dodged in the x (categorical) direction.

Original y-coordinates are not preserved, and if you want that try SimpleBeeswarm instead.

source


# SwarmMakie.NoBeeswarm — Type.

A simple no-op algorithm, which causes the scatter plot to be drawn as if you called scatter and not beeswarm.

source


Code exists for the algorithm suggested by Michael Borregaard in this StatsPlots.jl PR, but it is currently nonfunctional. If you'd like to take a crack at getting it working, please do!

# SwarmMakie.UniformJitter — Type.
julia
UniformJitter(; jitter_width = 1.0)

A jitter algorithm that uses a uniform distribution to create the jitter.

source


# SwarmMakie.PseudorandomJitter — Type.
julia
PseudorandomJitter(; jitter_width = 1.0)

A jitter algorithm that uses a pseudorandom distribution to create the jitter. A pseudorandom distribution is a uniform distribution weighted by the PDF of the data.

source


# SwarmMakie.QuasirandomJitter — Type.
julia
QuasirandomJitter(; jitter_width = 1.0)

A jitter algorithm that uses a quasirandom (van der Corput) distribution weighted by the data's pdf to jitter the data points.

source


We also welcome any new algorithms you may have in mind. Just open a PR!

Adding a new algorithm ​

In order to add a new algorithm, you must simply define a struct which subtypes SwarmMakie.BeeswarmAlgorithm.

There must also be a corresponding dispatch on SwarmMakie.calculate!(buffer, alg, positions, markersize) which loads the new positions calculated in pixel space into buffer. Note that buffer must be modified here.

See the Wilkinson source page for a deep dive into how to write a beeswarm algorithm!

',27)]))}const c=a(l,[["render",h]]);export{m as __pageData,c as default}; diff --git a/dev/assets/api.md.COLbOG1V.js b/dev/assets/api.md.03JrFq4R.js similarity index 91% rename from dev/assets/api.md.COLbOG1V.js rename to dev/assets/api.md.03JrFq4R.js index 8dbceb8..137bb8f 100644 --- a/dev/assets/api.md.COLbOG1V.js +++ b/dev/assets/api.md.03JrFq4R.js @@ -1,4 +1,4 @@ -import{_ as e,c as i,a5 as s,o as r}from"./chunks/framework.CLWLLiKQ.js";const k=JSON.parse('{"title":"API Reference","description":"","frontmatter":{},"headers":[],"relativePath":"api.md","filePath":"api.md","lastUpdated":null}'),t={name:"api.md"};function n(l,a,o,p,d,c){return r(),i("div",null,a[0]||(a[0]=[s(`

API Reference ​

# SwarmMakie.JitterAlgorithm — Type.

The abstract type for jitter algorithms, which are markersize-agnostic.

source


# SwarmMakie.NoBeeswarm — Type.

A simple no-op algorithm, which causes the scatter plot to be drawn as if you called scatter and not beeswarm.

source


# SwarmMakie.PseudorandomJitter — Type.
julia
PseudorandomJitter(; jitter_width = 1.0)

A jitter algorithm that uses a pseudorandom distribution to create the jitter. A pseudorandom distribution is a uniform distribution weighted by the PDF of the data.

source


# SwarmMakie.QuasirandomJitter — Type.
julia
QuasirandomJitter(; jitter_width = 1.0)

A jitter algorithm that uses a quasirandom (van der Corput) distribution weighted by the data's pdf to jitter the data points.

source


# SwarmMakie.SeabornBeeswarm — Type.
julia
SeabornBeeswarm()

A beeswarm algorithm based on the seaborn Python package.

More adaptive to marker size than SimpleBeeswarm, but takes longer to compute.

source


# SwarmMakie.SimpleBeeswarm — Type.
julia
SimpleBeeswarm()

A simple implementation like Matplotlib's algorithm. This is the default algorithm used in beeswarm.

This algorithm dodges in x but preserves the exact y coordinate of each point. If you don't want to preserve the y coordinate, check out WilkinsonBeeswarm.

source


# SwarmMakie.UniformJitter — Type.
julia
UniformJitter(; jitter_width = 1.0)

A jitter algorithm that uses a uniform distribution to create the jitter.

source


# SwarmMakie.WilkinsonBeeswarm — Type.
julia
WilkinsonBeeswarm()

A beeswarm algorithm that implements Leland Wilkinson's original dot-hist algorithm.

This is essentially a histogram with dots, where all dots are binned in the y (non-categorical) direction, and then dodged in the x (categorical) direction.

Original y-coordinates are not preserved, and if you want that try SimpleBeeswarm instead.

source


# SwarmMakie.beeswarm — Method.
julia
beeswarm(x, y)
+import{_ as e,c as i,a5 as s,o as r}from"./chunks/framework.Bx3vxiYB.js";const k=JSON.parse('{"title":"API Reference","description":"","frontmatter":{},"headers":[],"relativePath":"api.md","filePath":"api.md","lastUpdated":null}'),t={name:"api.md"};function n(l,a,o,p,d,c){return r(),i("div",null,a[0]||(a[0]=[s(`
API Reference ​
# SwarmMakie.JitterAlgorithm — Type. 
The abstract type for jitter algorithms, which are markersize-agnostic.
source

# SwarmMakie.NoBeeswarm — Type. 
A simple no-op algorithm, which causes the scatter plot to be drawn as if you called scatter and not beeswarm.
source

# SwarmMakie.PseudorandomJitter — Type. 
julia
PseudorandomJitter(; jitter_width = 1.0)
A jitter algorithm that uses a pseudorandom distribution to create the jitter. A pseudorandom distribution is a uniform distribution weighted by the PDF of the data.
source

# SwarmMakie.QuasirandomJitter — Type. 
julia
QuasirandomJitter(; jitter_width = 1.0)
A jitter algorithm that uses a quasirandom (van der Corput) distribution weighted by the data's pdf to jitter the data points.
source

# SwarmMakie.SeabornBeeswarm — Type. 
julia
SeabornBeeswarm()
A beeswarm algorithm based on the seaborn Python package.
More adaptive to marker size than SimpleBeeswarm, but takes longer to compute.
source

# SwarmMakie.SimpleBeeswarm — Type. 
julia
SimpleBeeswarm()
A simple implementation like Matplotlib's algorithm. This is the default algorithm used in beeswarm.
This algorithm dodges in x but preserves the exact y coordinate of each point. If you don't want to preserve the y coordinate, check out WilkinsonBeeswarm.
source

# SwarmMakie.UniformJitter — Type. 
julia
UniformJitter(; jitter_width = 1.0)
A jitter algorithm that uses a uniform distribution to create the jitter.
source

# SwarmMakie.WilkinsonBeeswarm — Type. 
julia
WilkinsonBeeswarm()
A beeswarm algorithm that implements Leland Wilkinson's original dot-hist algorithm.
This is essentially a histogram with dots, where all dots are binned in the y (non-categorical) direction, and then dodged in the x (categorical) direction.
Original y-coordinates are not preserved, and if you want that try SimpleBeeswarm instead.
source

# SwarmMakie.beeswarm — Method. 
julia
beeswarm(x, y)
 beeswarm(positions)
beeswarm is a PointBased recipe like scatter, accepting all of scatter's input.
It displaces points which would otherwise overlap in the x-direction by binning in the y direction.
Specific attributes to beeswarm are:
  • algorithm = SimpleBeeswarm(): The algorithm used to lay out the beeswarm markers.
  • side = :both: The side towards which markers should extend. Can be :left, :right, or both.
  • direction = :y: Controls the direction of the beeswarm. Can be :y (vertical) or :x (horizontal).
  • gutter = nothing: Creates a gutter of a desired size around each category. Gutter size is always in data space.
  • gutter_threshold = .5: Emit a warning of the number of points added to a gutter per category exceeds the threshold.
Arguments
Available attributes and their defaults for Plot{SwarmMakie.beeswarm} are:
  algorithm         SimpleBeeswarm()
   alpha             1.0
   clip_planes       MakieCore.Automatic()
@@ -37,4 +37,4 @@ import{_ as e,c as i,a5 as s,o as r}from"./chunks/framework.CLWLLiKQ.js";const k
   transparency      false
   uv_offset_width   (0.0, 0.0, 0.0, 0.0)
   visible           true
Example
julia
using Makie, SwarmMakie
-beeswarm(ones(100), randn(100); color = rand(RGBf, 100))
source

# SwarmMakie.could_overlap — Method. 
julia
could_overlap(position, markersize, positions, markersizes)
Check if a point given by position with markersize markersize could overlap with any other point in the swarm. Returns a vector of integer indices.
source

# SwarmMakie.first_non_overlapping_candidate — Method. 
julia
Returns \`(position::Point2f, idx::Int)\`
source

# SwarmMakie.position_candidates — Method. 
Returns (positions::Vector{Point2f}, idxs::Vector{Int}).
source

`,26)]))}const m=e(t,[["render",n]]);export{k as __pageData,m as default};
+beeswarm(ones(100), randn(100); color = rand(RGBf, 100))

source


# SwarmMakie.could_overlap — Method.
julia
could_overlap(position, markersize, positions, markersizes)

Check if a point given by position with markersize markersize could overlap with any other point in the swarm. Returns a vector of integer indices.

source


# SwarmMakie.first_non_overlapping_candidate — Method.
julia
Returns \`(position::Point2f, idx::Int)\`

source


# SwarmMakie.position_candidates — Method.

Returns (positions::Vector{Point2f}, idxs::Vector{Int}).

source


`,26)]))}const b=e(t,[["render",n]]);export{k as __pageData,b as default}; diff --git a/dev/assets/api.md.COLbOG1V.lean.js b/dev/assets/api.md.03JrFq4R.lean.js similarity index 91% rename from dev/assets/api.md.COLbOG1V.lean.js rename to dev/assets/api.md.03JrFq4R.lean.js index 8dbceb8..137bb8f 100644 --- a/dev/assets/api.md.COLbOG1V.lean.js +++ b/dev/assets/api.md.03JrFq4R.lean.js @@ -1,4 +1,4 @@ -import{_ as e,c as i,a5 as s,o as r}from"./chunks/framework.CLWLLiKQ.js";const k=JSON.parse('{"title":"API Reference","description":"","frontmatter":{},"headers":[],"relativePath":"api.md","filePath":"api.md","lastUpdated":null}'),t={name:"api.md"};function n(l,a,o,p,d,c){return r(),i("div",null,a[0]||(a[0]=[s(`

API Reference ​

# SwarmMakie.JitterAlgorithm — Type.

The abstract type for jitter algorithms, which are markersize-agnostic.

source


# SwarmMakie.NoBeeswarm — Type.

A simple no-op algorithm, which causes the scatter plot to be drawn as if you called scatter and not beeswarm.

source


# SwarmMakie.PseudorandomJitter — Type.
julia
PseudorandomJitter(; jitter_width = 1.0)

A jitter algorithm that uses a pseudorandom distribution to create the jitter. A pseudorandom distribution is a uniform distribution weighted by the PDF of the data.

source


# SwarmMakie.QuasirandomJitter — Type.
julia
QuasirandomJitter(; jitter_width = 1.0)

A jitter algorithm that uses a quasirandom (van der Corput) distribution weighted by the data's pdf to jitter the data points.

source


# SwarmMakie.SeabornBeeswarm — Type.
julia
SeabornBeeswarm()

A beeswarm algorithm based on the seaborn Python package.

More adaptive to marker size than SimpleBeeswarm, but takes longer to compute.

source


# SwarmMakie.SimpleBeeswarm — Type.
julia
SimpleBeeswarm()

A simple implementation like Matplotlib's algorithm. This is the default algorithm used in beeswarm.

This algorithm dodges in x but preserves the exact y coordinate of each point. If you don't want to preserve the y coordinate, check out WilkinsonBeeswarm.

source


# SwarmMakie.UniformJitter — Type.
julia
UniformJitter(; jitter_width = 1.0)

A jitter algorithm that uses a uniform distribution to create the jitter.

source


# SwarmMakie.WilkinsonBeeswarm — Type.
julia
WilkinsonBeeswarm()

A beeswarm algorithm that implements Leland Wilkinson's original dot-hist algorithm.

This is essentially a histogram with dots, where all dots are binned in the y (non-categorical) direction, and then dodged in the x (categorical) direction.

Original y-coordinates are not preserved, and if you want that try SimpleBeeswarm instead.

source


# SwarmMakie.beeswarm — Method.
julia
beeswarm(x, y)
+import{_ as e,c as i,a5 as s,o as r}from"./chunks/framework.Bx3vxiYB.js";const k=JSON.parse('{"title":"API Reference","description":"","frontmatter":{},"headers":[],"relativePath":"api.md","filePath":"api.md","lastUpdated":null}'),t={name:"api.md"};function n(l,a,o,p,d,c){return r(),i("div",null,a[0]||(a[0]=[s(`
API Reference ​
# SwarmMakie.JitterAlgorithm — Type. 
The abstract type for jitter algorithms, which are markersize-agnostic.
source

# SwarmMakie.NoBeeswarm — Type. 
A simple no-op algorithm, which causes the scatter plot to be drawn as if you called scatter and not beeswarm.
source

# SwarmMakie.PseudorandomJitter — Type. 
julia
PseudorandomJitter(; jitter_width = 1.0)
A jitter algorithm that uses a pseudorandom distribution to create the jitter. A pseudorandom distribution is a uniform distribution weighted by the PDF of the data.
source

# SwarmMakie.QuasirandomJitter — Type. 
julia
QuasirandomJitter(; jitter_width = 1.0)
A jitter algorithm that uses a quasirandom (van der Corput) distribution weighted by the data's pdf to jitter the data points.
source

# SwarmMakie.SeabornBeeswarm — Type. 
julia
SeabornBeeswarm()
A beeswarm algorithm based on the seaborn Python package.
More adaptive to marker size than SimpleBeeswarm, but takes longer to compute.
source

# SwarmMakie.SimpleBeeswarm — Type. 
julia
SimpleBeeswarm()
A simple implementation like Matplotlib's algorithm. This is the default algorithm used in beeswarm.
This algorithm dodges in x but preserves the exact y coordinate of each point. If you don't want to preserve the y coordinate, check out WilkinsonBeeswarm.
source

# SwarmMakie.UniformJitter — Type. 
julia
UniformJitter(; jitter_width = 1.0)
A jitter algorithm that uses a uniform distribution to create the jitter.
source

# SwarmMakie.WilkinsonBeeswarm — Type. 
julia
WilkinsonBeeswarm()
A beeswarm algorithm that implements Leland Wilkinson's original dot-hist algorithm.
This is essentially a histogram with dots, where all dots are binned in the y (non-categorical) direction, and then dodged in the x (categorical) direction.
Original y-coordinates are not preserved, and if you want that try SimpleBeeswarm instead.
source

# SwarmMakie.beeswarm — Method. 
julia
beeswarm(x, y)
 beeswarm(positions)
beeswarm is a PointBased recipe like scatter, accepting all of scatter's input.
It displaces points which would otherwise overlap in the x-direction by binning in the y direction.
Specific attributes to beeswarm are:
  • algorithm = SimpleBeeswarm(): The algorithm used to lay out the beeswarm markers.
  • side = :both: The side towards which markers should extend. Can be :left, :right, or both.
  • direction = :y: Controls the direction of the beeswarm. Can be :y (vertical) or :x (horizontal).
  • gutter = nothing: Creates a gutter of a desired size around each category. Gutter size is always in data space.
  • gutter_threshold = .5: Emit a warning of the number of points added to a gutter per category exceeds the threshold.
Arguments
Available attributes and their defaults for Plot{SwarmMakie.beeswarm} are:
  algorithm         SimpleBeeswarm()
   alpha             1.0
   clip_planes       MakieCore.Automatic()
@@ -37,4 +37,4 @@ import{_ as e,c as i,a5 as s,o as r}from"./chunks/framework.CLWLLiKQ.js";const k
   transparency      false
   uv_offset_width   (0.0, 0.0, 0.0, 0.0)
   visible           true
Example
julia
using Makie, SwarmMakie
-beeswarm(ones(100), randn(100); color = rand(RGBf, 100))
source

# SwarmMakie.could_overlap — Method. 
julia
could_overlap(position, markersize, positions, markersizes)
Check if a point given by position with markersize markersize could overlap with any other point in the swarm. Returns a vector of integer indices.
source

# SwarmMakie.first_non_overlapping_candidate — Method. 
julia
Returns \`(position::Point2f, idx::Int)\`
source

# SwarmMakie.position_candidates — Method. 
Returns (positions::Vector{Point2f}, idxs::Vector{Int}).
source

`,26)]))}const m=e(t,[["render",n]]);export{k as __pageData,m as default};
+beeswarm(ones(100), randn(100); color = rand(RGBf, 100))

source


# SwarmMakie.could_overlap — Method.
julia
could_overlap(position, markersize, positions, markersizes)

Check if a point given by position with markersize markersize could overlap with any other point in the swarm. Returns a vector of integer indices.

source


# SwarmMakie.first_non_overlapping_candidate — Method.
julia
Returns \`(position::Point2f, idx::Int)\`

source


# SwarmMakie.position_candidates — Method.

Returns (positions::Vector{Point2f}, idxs::Vector{Int}).

source


`,26)]))}const b=e(t,[["render",n]]);export{k as __pageData,b as default}; diff --git a/dev/assets/app.CdbdB-oT.js b/dev/assets/app.Dd1wCvQQ.js similarity index 90% rename from dev/assets/app.CdbdB-oT.js rename to dev/assets/app.Dd1wCvQQ.js index 8cb3f7e..b9906f2 100644 --- a/dev/assets/app.CdbdB-oT.js +++ b/dev/assets/app.Dd1wCvQQ.js @@ -1 +1 @@ -import{R as p}from"./chunks/theme.Dm67fLnW.js";import{R as o,a6 as u,a7 as c,a8 as l,a9 as f,aa as d,ab as m,ac as h,ad as g,ae as A,af as v,d as P,u as R,v as w,s as y,ag as C,ah as b,ai as E,a4 as S}from"./chunks/framework.CLWLLiKQ.js";function i(e){if(e.extends){const a=i(e.extends);return{...a,...e,async enhanceApp(t){a.enhanceApp&&await a.enhanceApp(t),e.enhanceApp&&await e.enhanceApp(t)}}}return e}const s=i(p),T=P({name:"VitePressApp",setup(){const{site:e,lang:a,dir:t}=R();return w(()=>{y(()=>{document.documentElement.lang=a.value,document.documentElement.dir=t.value})}),e.value.router.prefetchLinks&&C(),b(),E(),s.setup&&s.setup(),()=>S(s.Layout)}});async function D(){globalThis.__VITEPRESS__=!0;const e=j(),a=_();a.provide(c,e);const t=l(e.route);return a.provide(f,t),a.component("Content",d),a.component("ClientOnly",m),Object.defineProperties(a.config.globalProperties,{$frontmatter:{get(){return t.frontmatter.value}},$params:{get(){return t.page.value.params}}}),s.enhanceApp&&await s.enhanceApp({app:a,router:e,siteData:h}),{app:a,router:e,data:t}}function _(){return g(T)}function j(){let e=o,a;return A(t=>{let n=v(t),r=null;return n&&(e&&(a=n),(e||a===n)&&(n=n.replace(/\.js$/,".lean.js")),r=import(n)),o&&(e=!1),r},s.NotFound)}o&&D().then(({app:e,router:a,data:t})=>{a.go().then(()=>{u(a.route,t.site),e.mount("#app")})});export{D as createApp}; +import{R as p}from"./chunks/theme.S2oaF1u3.js";import{R as o,a6 as u,a7 as c,a8 as l,a9 as f,aa as d,ab as m,ac as 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98% rename from dev/assets/examples_examples.md.YYHIoRWe.js rename to dev/assets/examples_examples.md.CLWQhraz.js index 01d1099..a37754c 100644 --- a/dev/assets/examples_examples.md.YYHIoRWe.js +++ b/dev/assets/examples_examples.md.CLWQhraz.js @@ -1,7 +1,7 @@ -import{_ as i,c as a,a5 as h,o as k}from"./chunks/framework.CLWLLiKQ.js";const n="/SwarmMakie.jl/dev/assets/efoizbr.DI01WaYi.png",l="/SwarmMakie.jl/dev/assets/dltlynj.Cw3NbHO-.png",t="/SwarmMakie.jl/dev/assets/kkpqcxp.C6G2Gu5q.png",p="/SwarmMakie.jl/dev/assets/vpufkag.B5ScXlxt.png",e="/SwarmMakie.jl/dev/assets/rlmnfjt.B_FZHNFE.png",C=JSON.parse('{"title":"Examples","description":"","frontmatter":{},"headers":[],"relativePath":"examples/examples.md","filePath":"examples/examples.md","lastUpdated":null}'),E={name:"examples/examples.md"};function r(d,s,g,y,F,o){return k(),a("div",null,s[0]||(s[0]=[h(`

Examples ​

Multiple variables with colors ​

julia
using CairoMakie, SwarmMakie
+import{_ as i,c as a,a5 as h,o as n}from"./chunks/framework.Bx3vxiYB.js";const k="/SwarmMakie.jl/dev/assets/ftumxjs.BrMXU5vF.png",l="/SwarmMakie.jl/dev/assets/breinsr.CzB55Bad.png",t="/SwarmMakie.jl/dev/assets/riqdgnq.C6G2Gu5q.png",p="/SwarmMakie.jl/dev/assets/zdammed.B5ScXlxt.png",e="/SwarmMakie.jl/dev/assets/iunrknc.B_FZHNFE.png",C=JSON.parse('{"title":"Examples","description":"","frontmatter":{},"headers":[],"relativePath":"examples/examples.md","filePath":"examples/examples.md","lastUpdated":null}'),E={name:"examples/examples.md"};function r(d,s,g,y,F,o){return n(),a("div",null,s[0]||(s[0]=[h(`
Examples ​
Multiple variables with colors ​
julia
using CairoMakie, SwarmMakie
 xs = rand(1:4, 500)
 ys = randn(500) .+ xs
-fig, ax, plt = beeswarm(xs, ys, color = xs, colormap = :isoluminant_cm_70_c39_n256)
Now, we can also pass categorical colormaps:
julia
plt.colormap[] = Makie.Categorical(Makie.wong_colors()[1:4])
Categorical(ColorTypes.RGBA{Float32}[RGBA{Float32}(0.0f0,0.44705883f0,0.69803923f0,1.0f0), RGBA{Float32}(0.9019608f0,0.62352943f0,0.0f0,1.0f0), RGBA{Float32}(0.0f0,0.61960787f0,0.4509804f0,1.0f0), RGBA{Float32}(0.8f0,0.4745098f0,0.654902f0,1.0f0)])
and the figure is:
julia
fig
Palmer Penguins ​
julia
using AlgebraOfGraphics, CairoMakie, SwarmMakie
+fig, ax, plt = beeswarm(xs, ys, color = xs, colormap = :isoluminant_cm_70_c39_n256)
Now, we can also pass categorical colormaps:
julia
plt.colormap[] = Makie.Categorical(Makie.wong_colors()[1:4])
Categorical(ColorTypes.RGBA{Float32}[RGBA{Float32}(0.0f0,0.44705883f0,0.69803923f0,1.0f0), RGBA{Float32}(0.9019608f0,0.62352943f0,0.0f0,1.0f0), RGBA{Float32}(0.0f0,0.61960787f0,0.4509804f0,1.0f0), RGBA{Float32}(0.8f0,0.4745098f0,0.654902f0,1.0f0)])
and the figure is:
julia
fig
Palmer Penguins ​
julia
using AlgebraOfGraphics, CairoMakie, SwarmMakie
 using PalmerPenguins, DataFrames
 
 penguins = dropmissing(DataFrame(PalmerPenguins.load()))
diff --git a/dev/assets/examples_examples.md.YYHIoRWe.lean.js b/dev/assets/examples_examples.md.CLWQhraz.lean.js
similarity index 98%
rename from dev/assets/examples_examples.md.YYHIoRWe.lean.js
rename to dev/assets/examples_examples.md.CLWQhraz.lean.js
index 01d1099..a37754c 100644
--- a/dev/assets/examples_examples.md.YYHIoRWe.lean.js
+++ b/dev/assets/examples_examples.md.CLWQhraz.lean.js
@@ -1,7 +1,7 @@
-import{_ as i,c as a,a5 as h,o as k}from"./chunks/framework.CLWLLiKQ.js";const n="/SwarmMakie.jl/dev/assets/efoizbr.DI01WaYi.png",l="/SwarmMakie.jl/dev/assets/dltlynj.Cw3NbHO-.png",t="/SwarmMakie.jl/dev/assets/kkpqcxp.C6G2Gu5q.png",p="/SwarmMakie.jl/dev/assets/vpufkag.B5ScXlxt.png",e="/SwarmMakie.jl/dev/assets/rlmnfjt.B_FZHNFE.png",C=JSON.parse('{"title":"Examples","description":"","frontmatter":{},"headers":[],"relativePath":"examples/examples.md","filePath":"examples/examples.md","lastUpdated":null}'),E={name:"examples/examples.md"};function r(d,s,g,y,F,o){return k(),a("div",null,s[0]||(s[0]=[h(`
Examples ​
Multiple variables with colors ​
julia
using CairoMakie, SwarmMakie
+import{_ as i,c as a,a5 as h,o as n}from"./chunks/framework.Bx3vxiYB.js";const k="/SwarmMakie.jl/dev/assets/ftumxjs.BrMXU5vF.png",l="/SwarmMakie.jl/dev/assets/breinsr.CzB55Bad.png",t="/SwarmMakie.jl/dev/assets/riqdgnq.C6G2Gu5q.png",p="/SwarmMakie.jl/dev/assets/zdammed.B5ScXlxt.png",e="/SwarmMakie.jl/dev/assets/iunrknc.B_FZHNFE.png",C=JSON.parse('{"title":"Examples","description":"","frontmatter":{},"headers":[],"relativePath":"examples/examples.md","filePath":"examples/examples.md","lastUpdated":null}'),E={name:"examples/examples.md"};function r(d,s,g,y,F,o){return n(),a("div",null,s[0]||(s[0]=[h(`
Examples ​
Multiple variables with colors ​
julia
using CairoMakie, SwarmMakie
 xs = rand(1:4, 500)
 ys = randn(500) .+ xs
-fig, ax, plt = beeswarm(xs, ys, color = xs, colormap = :isoluminant_cm_70_c39_n256)
Now, we can also pass categorical colormaps:
julia
plt.colormap[] = Makie.Categorical(Makie.wong_colors()[1:4])
Categorical(ColorTypes.RGBA{Float32}[RGBA{Float32}(0.0f0,0.44705883f0,0.69803923f0,1.0f0), RGBA{Float32}(0.9019608f0,0.62352943f0,0.0f0,1.0f0), RGBA{Float32}(0.0f0,0.61960787f0,0.4509804f0,1.0f0), RGBA{Float32}(0.8f0,0.4745098f0,0.654902f0,1.0f0)])
and the figure is:
julia
fig
Palmer Penguins ​
julia
using AlgebraOfGraphics, CairoMakie, SwarmMakie
+fig, ax, plt = beeswarm(xs, ys, color = xs, colormap = :isoluminant_cm_70_c39_n256)
Now, we can also pass categorical colormaps:
julia
plt.colormap[] = Makie.Categorical(Makie.wong_colors()[1:4])
Categorical(ColorTypes.RGBA{Float32}[RGBA{Float32}(0.0f0,0.44705883f0,0.69803923f0,1.0f0), RGBA{Float32}(0.9019608f0,0.62352943f0,0.0f0,1.0f0), RGBA{Float32}(0.0f0,0.61960787f0,0.4509804f0,1.0f0), RGBA{Float32}(0.8f0,0.4745098f0,0.654902f0,1.0f0)])
and the figure is:
julia
fig
Palmer Penguins ​
julia
using AlgebraOfGraphics, CairoMakie, SwarmMakie
 using PalmerPenguins, DataFrames
 
 penguins = dropmissing(DataFrame(PalmerPenguins.load()))
diff --git a/dev/assets/examples_scales.md.CNGQ6RBo.js b/dev/assets/examples_scales.md.CNGQ6RBo.js
new file mode 100644
index 0000000..f3951ca
--- /dev/null
+++ b/dev/assets/examples_scales.md.CNGQ6RBo.js
@@ -0,0 +1,30 @@
+import{_ as s,c as n,a5 as e,o as p}from"./chunks/framework.Bx3vxiYB.js";const h=JSON.parse('{"title":"Nonlinear beeswarm plots","description":"","frontmatter":{},"headers":[],"relativePath":"examples/scales.md","filePath":"examples/scales.md","lastUpdated":null}'),i={name:"examples/scales.md"};function l(t,a,o,r,c,d){return p(),n("div",null,a[0]||(a[0]=[e(`
Nonlinear beeswarm plots ​
Beeswarm plots can be plotted in any combination of xscale and yscale.
Specifically, beeswarm plots are correct in any separable transform - basically, any transform in which the x and y coordinates are independent of each other. This excludes most geographic transformations, but includes any transformation you can make using xscale and yscale in a Makie Axis.
@example
import CairoMakie # hide
+CairoMakie.activate!() # hide
+import Main.MakieDocsHelpers4 # hide
+var"#result" = begin # hide
+using SwarmMakie, CairoMakie
+data = randn(75) .+ 3
+fig = Figure()
+ax1 = Axis(fig[1, 1]; title = "No transform")
+beeswarm!(ax1, ones(75), data)
+ax2 = Axis(fig[1, 2]; title = "Log y axis", yscale = log10)
+beeswarm!(ax2, ones(75), data)
+fig
+end # hide
+if var"#result" isa Makie.FigureLike # hide
+    MakieDocsHelpers4.AsMIME(MIME"image/png"(), var"#result") # hide
+else # hide
+    var"#result" # hide
+end # hide
Note how the shape of the beeswarm is transformed in the left plot, because of the log scale.
@example
import CairoMakie # hide
+CairoMakie.activate!() # hide
+import Main.MakieDocsHelpers4 # hide
+var"#result" = begin # hide
+ax2.xscale = Makie.pseudolog10
+ax2.title = "Log x and y axes"
+fig
+end # hide
+if var"#result" isa Makie.FigureLike # hide
+    MakieDocsHelpers4.AsMIME(MIME"image/png"(), var"#result") # hide
+else # hide
+    var"#result" # hide
+end # hide
`,6)]))}const m=s(i,[["render",l]]);export{h as __pageData,m as default};
diff --git a/dev/assets/examples_scales.md.CNGQ6RBo.lean.js b/dev/assets/examples_scales.md.CNGQ6RBo.lean.js
new file mode 100644
index 0000000..f3951ca
--- /dev/null
+++ b/dev/assets/examples_scales.md.CNGQ6RBo.lean.js
@@ -0,0 +1,30 @@
+import{_ as s,c as n,a5 as e,o as p}from"./chunks/framework.Bx3vxiYB.js";const h=JSON.parse('{"title":"Nonlinear beeswarm plots","description":"","frontmatter":{},"headers":[],"relativePath":"examples/scales.md","filePath":"examples/scales.md","lastUpdated":null}'),i={name:"examples/scales.md"};function l(t,a,o,r,c,d){return p(),n("div",null,a[0]||(a[0]=[e(`
Nonlinear beeswarm plots ​
Beeswarm plots can be plotted in any combination of xscale and yscale.
Specifically, beeswarm plots are correct in any separable transform - basically, any transform in which the x and y coordinates are independent of each other. This excludes most geographic transformations, but includes any transformation you can make using xscale and yscale in a Makie Axis.
@example
import CairoMakie # hide
+CairoMakie.activate!() # hide
+import Main.MakieDocsHelpers4 # hide
+var"#result" = begin # hide
+using SwarmMakie, CairoMakie
+data = randn(75) .+ 3
+fig = Figure()
+ax1 = Axis(fig[1, 1]; title = "No transform")
+beeswarm!(ax1, ones(75), data)
+ax2 = Axis(fig[1, 2]; title = "Log y axis", yscale = log10)
+beeswarm!(ax2, ones(75), data)
+fig
+end # hide
+if var"#result" isa Makie.FigureLike # hide
+    MakieDocsHelpers4.AsMIME(MIME"image/png"(), var"#result") # hide
+else # hide
+    var"#result" # hide
+end # hide
Note how the shape of the beeswarm is transformed in the left plot, because of the log scale.
@example
import CairoMakie # hide
+CairoMakie.activate!() # hide
+import Main.MakieDocsHelpers4 # hide
+var"#result" = begin # hide
+ax2.xscale = Makie.pseudolog10
+ax2.title = "Log x and y axes"
+fig
+end # hide
+if var"#result" isa Makie.FigureLike # hide
+    MakieDocsHelpers4.AsMIME(MIME"image/png"(), var"#result") # hide
+else # hide
+    var"#result" # hide
+end # hide
`,6)]))}const m=s(i,[["render",l]]);export{h as __pageData,m as default};
diff --git a/dev/assets/examples_scales.md.DaaSMAdS.js b/dev/assets/examples_scales.md.DaaSMAdS.js
deleted file mode 100644
index 646180a..0000000
--- a/dev/assets/examples_scales.md.DaaSMAdS.js
+++ /dev/null
@@ -1,10 +0,0 @@
-import{_ as i,c as a,a5 as n,o as e}from"./chunks/framework.CLWLLiKQ.js";const t="/SwarmMakie.jl/dev/assets/dkaxpxj.CvcTUDeg.png",l="/SwarmMakie.jl/dev/assets/szomdpo.BZxsO4NP.png",c=JSON.parse('{"title":"Nonlinear beeswarm plots","description":"","frontmatter":{},"headers":[],"relativePath":"examples/scales.md","filePath":"examples/scales.md","lastUpdated":null}'),h={name:"examples/scales.md"};function p(k,s,r,d,E,o){return e(),a("div",null,s[0]||(s[0]=[n(`
Nonlinear beeswarm plots ​
Beeswarm plots can be plotted in any combination of xscale and yscale.
Specifically, beeswarm plots are correct in any separable transform - basically, any transform in which the x and y coordinates are independent of each other. This excludes most geographic transformations, but includes any transformation you can make using xscale and yscale in a Makie Axis.
julia
using SwarmMakie, CairoMakie
-data = randn(75) .+ 3
-fig = Figure()
-ax1 = Axis(fig[1, 1]; title = "No transform")
-beeswarm!(ax1, ones(75), data)
-ax2 = Axis(fig[1, 2]; title = "Log y axis", yscale = log10)
-beeswarm!(ax2, ones(75), data)
-fig
Note how the shape of the beeswarm is transformed in the left plot, because of the log scale.
julia
ax2.xscale = Makie.pseudolog10
-ax2.title = "Log x and y axes"
-fig
',8)]))}const y=i(h,[["render",p]]);export{c as __pageData,y as default};
diff --git a/dev/assets/examples_scales.md.DaaSMAdS.lean.js b/dev/assets/examples_scales.md.DaaSMAdS.lean.js
deleted file mode 100644
index 646180a..0000000
--- a/dev/assets/examples_scales.md.DaaSMAdS.lean.js
+++ /dev/null
@@ -1,10 +0,0 @@
-import{_ as i,c as a,a5 as n,o as e}from"./chunks/framework.CLWLLiKQ.js";const t="/SwarmMakie.jl/dev/assets/dkaxpxj.CvcTUDeg.png",l="/SwarmMakie.jl/dev/assets/szomdpo.BZxsO4NP.png",c=JSON.parse('{"title":"Nonlinear beeswarm plots","description":"","frontmatter":{},"headers":[],"relativePath":"examples/scales.md","filePath":"examples/scales.md","lastUpdated":null}'),h={name:"examples/scales.md"};function p(k,s,r,d,E,o){return e(),a("div",null,s[0]||(s[0]=[n(`
Nonlinear beeswarm plots ​
Beeswarm plots can be plotted in any combination of xscale and yscale.
Specifically, beeswarm plots are correct in any separable transform - basically, any transform in which the x and y coordinates are independent of each other. This excludes most geographic transformations, but includes any transformation you can make using xscale and yscale in a Makie Axis.
julia
using SwarmMakie, CairoMakie
-data = randn(75) .+ 3
-fig = Figure()
-ax1 = Axis(fig[1, 1]; title = "No transform")
-beeswarm!(ax1, ones(75), data)
-ax2 = Axis(fig[1, 2]; title = "Log y axis", yscale = log10)
-beeswarm!(ax2, ones(75), data)
-fig
Note how the shape of the beeswarm is transformed in the left plot, because of the log scale.
julia
ax2.xscale = Makie.pseudolog10
-ax2.title = "Log x and y axes"
-fig
',8)]))}const y=i(h,[["render",p]]);export{c as __pageData,y as default};
diff --git a/dev/assets/examples_unconventional.md.MMAyDr8z.lean.js b/dev/assets/examples_unconventional.md.BSnBm1_2.js
similarity index 99%
rename from dev/assets/examples_unconventional.md.MMAyDr8z.lean.js
rename to dev/assets/examples_unconventional.md.BSnBm1_2.js
index 815d1ac..870deed 100644
--- a/dev/assets/examples_unconventional.md.MMAyDr8z.lean.js
+++ b/dev/assets/examples_unconventional.md.BSnBm1_2.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.CLWLLiKQ.js";const k="/SwarmMakie.jl/dev/assets/ouijmxy.D1Kk23_-.png",l="/SwarmMakie.jl/dev/assets/dorqctz.wZLTApW1.png",p="/SwarmMakie.jl/dev/assets/uqaigib.gLfiNtBx.png",o=JSON.parse('{"title":"Unconventional swarm plots","description":"","frontmatter":{},"headers":[],"relativePath":"examples/unconventional.md","filePath":"examples/unconventional.md","lastUpdated":null}'),t={name:"examples/unconventional.md"};function E(e,s,r,g,d,y){return h(),a("div",null,s[0]||(s[0]=[n(`
Unconventional swarm plots ​
You can use swarm plots to simply separate scatter markers which share the same x coordinate, and distinguish them by color and marker type.
The Julia benchmark plot ​
julia
# Load the required Julia packages
+import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.Bx3vxiYB.js";const k="/SwarmMakie.jl/dev/assets/pkuoapx.D1Kk23_-.png",l="/SwarmMakie.jl/dev/assets/fgoudoo.wZLTApW1.png",p="/SwarmMakie.jl/dev/assets/mtgwbdz.gLfiNtBx.png",o=JSON.parse('{"title":"Unconventional swarm plots","description":"","frontmatter":{},"headers":[],"relativePath":"examples/unconventional.md","filePath":"examples/unconventional.md","lastUpdated":null}'),t={name:"examples/unconventional.md"};function E(e,s,r,g,d,y){return h(),a("div",null,s[0]||(s[0]=[n(`
Unconventional swarm plots ​
You can use swarm plots to simply separate scatter markers which share the same x coordinate, and distinguish them by color and marker type.
The Julia benchmark plot ​
julia
# Load the required Julia packages
 using Base.MathConstants
 using CSV
 using DataFrames
diff --git a/dev/assets/examples_unconventional.md.MMAyDr8z.js b/dev/assets/examples_unconventional.md.BSnBm1_2.lean.js
similarity index 99%
rename from dev/assets/examples_unconventional.md.MMAyDr8z.js
rename to dev/assets/examples_unconventional.md.BSnBm1_2.lean.js
index 815d1ac..870deed 100644
--- a/dev/assets/examples_unconventional.md.MMAyDr8z.js
+++ b/dev/assets/examples_unconventional.md.BSnBm1_2.lean.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.CLWLLiKQ.js";const k="/SwarmMakie.jl/dev/assets/ouijmxy.D1Kk23_-.png",l="/SwarmMakie.jl/dev/assets/dorqctz.wZLTApW1.png",p="/SwarmMakie.jl/dev/assets/uqaigib.gLfiNtBx.png",o=JSON.parse('{"title":"Unconventional swarm plots","description":"","frontmatter":{},"headers":[],"relativePath":"examples/unconventional.md","filePath":"examples/unconventional.md","lastUpdated":null}'),t={name:"examples/unconventional.md"};function E(e,s,r,g,d,y){return h(),a("div",null,s[0]||(s[0]=[n(`
Unconventional swarm plots ​
You can use swarm plots to simply separate scatter markers which share the same x coordinate, and distinguish them by color and marker type.
The Julia benchmark plot ​
julia
# Load the required Julia packages
+import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.Bx3vxiYB.js";const k="/SwarmMakie.jl/dev/assets/pkuoapx.D1Kk23_-.png",l="/SwarmMakie.jl/dev/assets/fgoudoo.wZLTApW1.png",p="/SwarmMakie.jl/dev/assets/mtgwbdz.gLfiNtBx.png",o=JSON.parse('{"title":"Unconventional swarm plots","description":"","frontmatter":{},"headers":[],"relativePath":"examples/unconventional.md","filePath":"examples/unconventional.md","lastUpdated":null}'),t={name:"examples/unconventional.md"};function E(e,s,r,g,d,y){return h(),a("div",null,s[0]||(s[0]=[n(`
Unconventional swarm plots ​
You can use swarm plots to simply separate scatter markers which share the same x coordinate, and distinguish them by color and marker type.
The Julia benchmark plot ​
julia
# Load the required Julia packages
 using Base.MathConstants
 using CSV
 using DataFrames
diff --git a/dev/assets/dorqctz.wZLTApW1.png b/dev/assets/fgoudoo.wZLTApW1.png
similarity index 100%
rename from dev/assets/dorqctz.wZLTApW1.png
rename to dev/assets/fgoudoo.wZLTApW1.png
diff --git a/dev/assets/ftumxjs.BrMXU5vF.png b/dev/assets/ftumxjs.BrMXU5vF.png
new file mode 100644
index 0000000..f0a44e0
Binary files /dev/null and b/dev/assets/ftumxjs.BrMXU5vF.png differ
diff --git a/dev/assets/gutters.md.BWh_I-Hj.js b/dev/assets/gutters.md.BWh_I-Hj.js
new file mode 100644
index 0000000..95a5901
--- /dev/null
+++ b/dev/assets/gutters.md.BWh_I-Hj.js
@@ -0,0 +1,8 @@
+import{_ as N,c as D,a5 as I,o as j}from"./chunks/framework.Bx3vxiYB.js";const g="/SwarmMakie.jl/dev/assets/civpvbm.B9fzDNC6.png",i=JSON.parse('{"title":"Gutters","description":"","frontmatter":{},"headers":[],"relativePath":"gutters.md","filePath":"gutters.md","lastUpdated":null}'),z={name:"gutters.md"};function T(A,M,O,y,S,x){return j(),D("div",null,M[0]||(M[0]=[I(`
Gutters ​
julia
using SwarmMakie, CairoMakie
+xs = rand(1:10, 2000)
+beeswarm(xs, rand(2000); gutter = 0.3, color = xs)
Gutters are a threshold on how far the beeswarm plot can extend from the category point, in data space.
They are off by default, but can be set by passing gutter::Float64 as a keyword argument or by setting the gutter attribute of the plot. You can turn off guttering by setting gutter = nothing, which is the default.
A nice gutter size to avoid overlap in neighboring categories ranges between 0.5 and 0.3 (the latter shown in the example above).
Examples ​
julia
using SwarmMakie, CairoMakie
+f, a, p = beeswarm(
+    rand(1:3, 300), randn(300);
+    color = rand(RGBAf, 300), markersize = 20, algorithm = SimpleBeeswarm()
+)
+p.gutter = 0.5
0.5
Note the warning messages printed here! These can be helpful to diagnose when your data is moving too far out of the gutter, but you can turn them off by passing gutter_threshold = false or setting the gutter_threshold to a higher value (must be an Int and >0).
julia
f
Gutters work with all beeswarm plots.
Implementation ​
Gutters are implemented through the gutterize! function, and you can overload gutter handling for your own beeswarm type by defining a new dispatch for your algorithm.
',15)]))}const E=N(z,[["render",T]]);export{i as __pageData,E as default};
diff --git a/dev/assets/gutters.md.BWh_I-Hj.lean.js b/dev/assets/gutters.md.BWh_I-Hj.lean.js
new file mode 100644
index 0000000..95a5901
--- /dev/null
+++ b/dev/assets/gutters.md.BWh_I-Hj.lean.js
@@ -0,0 +1,8 @@
+import{_ as N,c as D,a5 as I,o as j}from"./chunks/framework.Bx3vxiYB.js";const g="/SwarmMakie.jl/dev/assets/civpvbm.B9fzDNC6.png",i=JSON.parse('{"title":"Gutters","description":"","frontmatter":{},"headers":[],"relativePath":"gutters.md","filePath":"gutters.md","lastUpdated":null}'),z={name:"gutters.md"};function T(A,M,O,y,S,x){return j(),D("div",null,M[0]||(M[0]=[I(`
Gutters ​
julia
using SwarmMakie, CairoMakie
+xs = rand(1:10, 2000)
+beeswarm(xs, rand(2000); gutter = 0.3, color = xs)
Gutters are a threshold on how far the beeswarm plot can extend from the category point, in data space.
They are off by default, but can be set by passing gutter::Float64 as a keyword argument or by setting the gutter attribute of the plot. You can turn off guttering by setting gutter = nothing, which is the default.
A nice gutter size to avoid overlap in neighboring categories ranges between 0.5 and 0.3 (the latter shown in the example above).
Examples ​
julia
using SwarmMakie, CairoMakie
+f, a, p = beeswarm(
+    rand(1:3, 300), randn(300);
+    color = rand(RGBAf, 300), markersize = 20, algorithm = SimpleBeeswarm()
+)
+p.gutter = 0.5
0.5
Note the warning messages printed here! These can be helpful to diagnose when your data is moving too far out of the gutter, but you can turn them off by passing gutter_threshold = false or setting the gutter_threshold to a higher value (must be an Int and >0).
julia
f
Gutters work with all beeswarm plots.
Implementation ​
Gutters are implemented through the gutterize! function, and you can overload gutter handling for your own beeswarm type by defining a new dispatch for your algorithm.
',15)]))}const E=N(z,[["render",T]]);export{i as __pageData,E as default};
diff --git a/dev/assets/gutters.md.quAAtSD6.js b/dev/assets/gutters.md.quAAtSD6.js
deleted file mode 100644
index c4d0b77..0000000
--- a/dev/assets/gutters.md.quAAtSD6.js
+++ /dev/null
@@ -1,8 +0,0 @@
-import{_ as N,c as D,a5 as I,o as j}from"./chunks/framework.CLWLLiKQ.js";const g="/SwarmMakie.jl/dev/assets/bnvymtv.p9AHmL4N.png",L=JSON.parse('{"title":"Gutters","description":"","frontmatter":{},"headers":[],"relativePath":"gutters.md","filePath":"gutters.md","lastUpdated":null}'),z={name:"gutters.md"};function T(A,M,O,y,S,x){return j(),D("div",null,M[0]||(M[0]=[I(`
Gutters ​
julia
using SwarmMakie, CairoMakie
-xs = rand(1:10, 2000)
-beeswarm(xs, rand(2000); gutter = 0.3, color = xs)
Gutters are a threshold on how far the beeswarm plot can extend from the category point, in data space.
They are off by default, but can be set by passing gutter::Float64 as a keyword argument or by setting the gutter attribute of the plot. You can turn off guttering by setting gutter = nothing, which is the default.
A nice gutter size to avoid overlap in neighboring categories ranges between 0.5 and 0.3 (the latter shown in the example above).
Examples ​
julia
using SwarmMakie, CairoMakie
-f, a, p = beeswarm(
-    rand(1:3, 300), randn(300);
-    color = rand(RGBAf, 300), markersize = 20, algorithm = SimpleBeeswarm()
-)
-p.gutter = 0.5
0.5
Note the warning messages printed here! These can be helpful to diagnose when your data is moving too far out of the gutter, but you can turn them off by passing gutter_threshold = false or setting the gutter_threshold to a higher value (must be an Int and >0).
julia
f
Gutters work with all beeswarm plots.
Implementation ​
Gutters are implemented through the gutterize! function, and you can overload gutter handling for your own beeswarm type by defining a new dispatch for your algorithm.
',15)]))}const E=N(z,[["render",T]]);export{L as __pageData,E as default};
diff --git a/dev/assets/gutters.md.quAAtSD6.lean.js b/dev/assets/gutters.md.quAAtSD6.lean.js
deleted file mode 100644
index c4d0b77..0000000
--- a/dev/assets/gutters.md.quAAtSD6.lean.js
+++ /dev/null
@@ -1,8 +0,0 @@
-import{_ as N,c as D,a5 as I,o as j}from"./chunks/framework.CLWLLiKQ.js";const g="/SwarmMakie.jl/dev/assets/bnvymtv.p9AHmL4N.png",L=JSON.parse('{"title":"Gutters","description":"","frontmatter":{},"headers":[],"relativePath":"gutters.md","filePath":"gutters.md","lastUpdated":null}'),z={name:"gutters.md"};function T(A,M,O,y,S,x){return j(),D("div",null,M[0]||(M[0]=[I(`
Gutters ​
julia
using SwarmMakie, CairoMakie
-xs = rand(1:10, 2000)
-beeswarm(xs, rand(2000); gutter = 0.3, color = xs)
Gutters are a threshold on how far the beeswarm plot can extend from the category point, in data space.
They are off by default, but can be set by passing gutter::Float64 as a keyword argument or by setting the gutter attribute of the plot. You can turn off guttering by setting gutter = nothing, which is the default.
A nice gutter size to avoid overlap in neighboring categories ranges between 0.5 and 0.3 (the latter shown in the example above).
Examples ​
julia
using SwarmMakie, CairoMakie
-f, a, p = beeswarm(
-    rand(1:3, 300), randn(300);
-    color = rand(RGBAf, 300), markersize = 20, algorithm = SimpleBeeswarm()
-)
-p.gutter = 0.5
0.5
Note the warning messages printed here! These can be helpful to diagnose when your data is moving too far out of the gutter, but you can turn them off by passing gutter_threshold = false or setting the gutter_threshold to a higher value (must be an Int and >0).
julia
f
Gutters work with all beeswarm plots.
Implementation ​
Gutters are implemented through the gutterize! function, and you can overload gutter handling for your own beeswarm type by defining a new dispatch for your algorithm.
',15)]))}const E=N(z,[["render",T]]);export{L as __pageData,E as default};
diff --git a/dev/assets/index.md.D309uB29.js b/dev/assets/index.md.Dvnn5-Dd.js
similarity index 97%
rename from dev/assets/index.md.D309uB29.js
rename to dev/assets/index.md.Dvnn5-Dd.js
index 476ec53..6d81584 100644
--- a/dev/assets/index.md.D309uB29.js
+++ b/dev/assets/index.md.Dvnn5-Dd.js
@@ -1,4 +1,4 @@
-import{_ as s,c as a,a5 as e,o as t}from"./chunks/framework.CLWLLiKQ.js";const c=JSON.parse('{"title":"What is SwarmMakie.jl?","description":"","frontmatter":{"layout":"home","hero":{"name":"SwarmMakie.jl","text":null,"tagline":"Beeswarm plots for Makie.jl","image":{"src":"/logo.png","alt":"SwarmMakie"},"actions":[{"theme":"brand","text":"Introduction","link":"/introduction"},{"theme":"alt","text":"View on Github","link":"https://github.com/MakieOrg/SwarmMakie.jl"},{"theme":"alt","text":"API Reference","link":"/api"}]},"features":[{"icon":"\\"Julia","title":"Pure Julia code","details":"Fast, understandable, extensible functions","link":"/introduction"},{"icon":"","title":"Literate programming","details":"Documented source code with examples!","link":"/source/methods/clipping/cut"},{"icon":"","title":"Full integration with Makie","details":"Use any Makie.jl scatter input!","link":"https://docs.makie.org/stable/"}]},"headers":[],"relativePath":"index.md","filePath":"index.md","lastUpdated":null}'),n={name:"index.md"};function l(h,i,k,p,r,d){return t(),a("div",null,i[0]||(i[0]=[e(`
What is SwarmMakie.jl? ​
SwarmMakie makes beeswarm plots for Makie through the beeswarm recipe.
Quick start ​
The entry point to this package is the beeswarm recipe, which accepts input the same way scatter does in all respects -- plus a keyword algorithm, which specifies the beeswarm algorithm!
julia
using SwarmMakie, CairoMakie
+import{_ as s,c as a,a5 as e,o as t}from"./chunks/framework.Bx3vxiYB.js";const c=JSON.parse('{"title":"What is SwarmMakie.jl?","description":"","frontmatter":{"layout":"home","hero":{"name":"SwarmMakie.jl","text":null,"tagline":"Beeswarm plots for Makie.jl","image":{"src":"/logo.png","alt":"SwarmMakie"},"actions":[{"theme":"brand","text":"Introduction","link":"/introduction"},{"theme":"alt","text":"View on Github","link":"https://github.com/MakieOrg/SwarmMakie.jl"},{"theme":"alt","text":"API Reference","link":"/api"}]},"features":[{"icon":"\\"Julia","title":"Pure Julia code","details":"Fast, understandable, extensible functions","link":"/introduction"},{"icon":"","title":"Literate programming","details":"Documented source code with examples!","link":"/source/methods/clipping/cut"},{"icon":"","title":"Full integration with Makie","details":"Use any Makie.jl scatter input!","link":"https://docs.makie.org/stable/"}]},"headers":[],"relativePath":"index.md","filePath":"index.md","lastUpdated":null}'),n={name:"index.md"};function l(h,i,k,p,r,d){return t(),a("div",null,i[0]||(i[0]=[e(`
What is SwarmMakie.jl? ​
SwarmMakie makes beeswarm plots for Makie through the beeswarm recipe.
Quick start ​
The entry point to this package is the beeswarm recipe, which accepts input the same way scatter does in all respects -- plus a keyword algorithm, which specifies the beeswarm algorithm!
julia
using SwarmMakie, CairoMakie
 ys = rand(150)
 beeswarm(ones(length(ys)), ys)
Single, simple beeswarm
julia
using SwarmMakie, CairoMakie
 xs = rand(1:4, 500)
diff --git a/dev/assets/index.md.D309uB29.lean.js b/dev/assets/index.md.Dvnn5-Dd.lean.js
similarity index 97%
rename from dev/assets/index.md.D309uB29.lean.js
rename to dev/assets/index.md.Dvnn5-Dd.lean.js
index 476ec53..6d81584 100644
--- a/dev/assets/index.md.D309uB29.lean.js
+++ b/dev/assets/index.md.Dvnn5-Dd.lean.js
@@ -1,4 +1,4 @@
-import{_ as s,c as a,a5 as e,o as t}from"./chunks/framework.CLWLLiKQ.js";const c=JSON.parse('{"title":"What is SwarmMakie.jl?","description":"","frontmatter":{"layout":"home","hero":{"name":"SwarmMakie.jl","text":null,"tagline":"Beeswarm plots for Makie.jl","image":{"src":"/logo.png","alt":"SwarmMakie"},"actions":[{"theme":"brand","text":"Introduction","link":"/introduction"},{"theme":"alt","text":"View on Github","link":"https://github.com/MakieOrg/SwarmMakie.jl"},{"theme":"alt","text":"API Reference","link":"/api"}]},"features":[{"icon":"\\"Julia","title":"Pure Julia code","details":"Fast, understandable, extensible functions","link":"/introduction"},{"icon":"","title":"Literate programming","details":"Documented source code with examples!","link":"/source/methods/clipping/cut"},{"icon":"","title":"Full integration with Makie","details":"Use any Makie.jl scatter input!","link":"https://docs.makie.org/stable/"}]},"headers":[],"relativePath":"index.md","filePath":"index.md","lastUpdated":null}'),n={name:"index.md"};function l(h,i,k,p,r,d){return t(),a("div",null,i[0]||(i[0]=[e(`
What is SwarmMakie.jl? ​
SwarmMakie makes beeswarm plots for Makie through the beeswarm recipe.
Quick start ​
The entry point to this package is the beeswarm recipe, which accepts input the same way scatter does in all respects -- plus a keyword algorithm, which specifies the beeswarm algorithm!
julia
using SwarmMakie, CairoMakie
+import{_ as s,c as a,a5 as e,o as t}from"./chunks/framework.Bx3vxiYB.js";const c=JSON.parse('{"title":"What is SwarmMakie.jl?","description":"","frontmatter":{"layout":"home","hero":{"name":"SwarmMakie.jl","text":null,"tagline":"Beeswarm plots for Makie.jl","image":{"src":"/logo.png","alt":"SwarmMakie"},"actions":[{"theme":"brand","text":"Introduction","link":"/introduction"},{"theme":"alt","text":"View on Github","link":"https://github.com/MakieOrg/SwarmMakie.jl"},{"theme":"alt","text":"API Reference","link":"/api"}]},"features":[{"icon":"\\"Julia","title":"Pure Julia code","details":"Fast, understandable, extensible functions","link":"/introduction"},{"icon":"","title":"Literate programming","details":"Documented source code with examples!","link":"/source/methods/clipping/cut"},{"icon":"","title":"Full integration with Makie","details":"Use any Makie.jl scatter input!","link":"https://docs.makie.org/stable/"}]},"headers":[],"relativePath":"index.md","filePath":"index.md","lastUpdated":null}'),n={name:"index.md"};function l(h,i,k,p,r,d){return t(),a("div",null,i[0]||(i[0]=[e(`
What is SwarmMakie.jl? ​
SwarmMakie makes beeswarm plots for Makie through the beeswarm recipe.
Quick start ​
The entry point to this package is the beeswarm recipe, which accepts input the same way scatter does in all respects -- plus a keyword algorithm, which specifies the beeswarm algorithm!
julia
using SwarmMakie, CairoMakie
 ys = rand(150)
 beeswarm(ones(length(ys)), ys)
Single, simple beeswarm
julia
using SwarmMakie, CairoMakie
 xs = rand(1:4, 500)
diff --git a/dev/assets/introduction.md.BY6rcP0y.js b/dev/assets/introduction.md.BhTeXRCX.js
similarity index 98%
rename from dev/assets/introduction.md.BY6rcP0y.js
rename to dev/assets/introduction.md.BhTeXRCX.js
index 6d8cde6..d6f7820 100644
--- a/dev/assets/introduction.md.BY6rcP0y.js
+++ b/dev/assets/introduction.md.BhTeXRCX.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a5 as t,o as e}from"./chunks/framework.CLWLLiKQ.js";const h="/SwarmMakie.jl/dev/assets/zogshfk.OgIUaXoz.png",n="/SwarmMakie.jl/dev/assets/tryhbsf.B8wH5TFm.png",c=JSON.parse('{"title":"SwarmMakie","description":"","frontmatter":{},"headers":[],"relativePath":"introduction.md","filePath":"introduction.md","lastUpdated":null}'),k={name:"introduction.md"};function l(p,s,r,d,E,g){return e(),a("div",null,s[0]||(s[0]=[t(`
SwarmMakie ​
SwarmMakie implements beeswarm or swarm plots in Makie. These are scatter plots which are categorical (or singular) in the x-axis, where the markers are nudged so that each marker is visible and avoids overlap.
The main entry point to the package is the beeswarm recipe, which takes the same arguments as Makie's scatter plots, and transforms them into a beautiful beeswarm plot!
Being a Makie recipe, you can also use this with AlgebraOfGraphics.
Quick start ​
Here's a quick example to get you started:
julia
using CairoMakie, SwarmMakie
+import{_ as i,c as a,a5 as t,o as e}from"./chunks/framework.Bx3vxiYB.js";const h="/SwarmMakie.jl/dev/assets/sblfyxu.CvP2aw-6.png",n="/SwarmMakie.jl/dev/assets/sppjqhf.B8wH5TFm.png",c=JSON.parse('{"title":"SwarmMakie","description":"","frontmatter":{},"headers":[],"relativePath":"introduction.md","filePath":"introduction.md","lastUpdated":null}'),k={name:"introduction.md"};function l(p,s,r,d,E,g){return e(),a("div",null,s[0]||(s[0]=[t(`
SwarmMakie ​
SwarmMakie implements beeswarm or swarm plots in Makie. These are scatter plots which are categorical (or singular) in the x-axis, where the markers are nudged so that each marker is visible and avoids overlap.
The main entry point to the package is the beeswarm recipe, which takes the same arguments as Makie's scatter plots, and transforms them into a beautiful beeswarm plot!
Being a Makie recipe, you can also use this with AlgebraOfGraphics.
Quick start ​
Here's a quick example to get you started:
julia
using CairoMakie, SwarmMakie
 xs = rand(1:3, 40)
 ys = randn(40)
 f, a, p = scatter(xs, ys; color = xs, axis = (; title = "Scatter plot"))
diff --git a/dev/assets/introduction.md.BY6rcP0y.lean.js b/dev/assets/introduction.md.BhTeXRCX.lean.js
similarity index 98%
rename from dev/assets/introduction.md.BY6rcP0y.lean.js
rename to dev/assets/introduction.md.BhTeXRCX.lean.js
index 6d8cde6..d6f7820 100644
--- a/dev/assets/introduction.md.BY6rcP0y.lean.js
+++ b/dev/assets/introduction.md.BhTeXRCX.lean.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a5 as t,o as e}from"./chunks/framework.CLWLLiKQ.js";const h="/SwarmMakie.jl/dev/assets/zogshfk.OgIUaXoz.png",n="/SwarmMakie.jl/dev/assets/tryhbsf.B8wH5TFm.png",c=JSON.parse('{"title":"SwarmMakie","description":"","frontmatter":{},"headers":[],"relativePath":"introduction.md","filePath":"introduction.md","lastUpdated":null}'),k={name:"introduction.md"};function l(p,s,r,d,E,g){return e(),a("div",null,s[0]||(s[0]=[t(`
SwarmMakie ​
SwarmMakie implements beeswarm or swarm plots in Makie. These are scatter plots which are categorical (or singular) in the x-axis, where the markers are nudged so that each marker is visible and avoids overlap.
The main entry point to the package is the beeswarm recipe, which takes the same arguments as Makie's scatter plots, and transforms them into a beautiful beeswarm plot!
Being a Makie recipe, you can also use this with AlgebraOfGraphics.
Quick start ​
Here's a quick example to get you started:
julia
using CairoMakie, SwarmMakie
+import{_ as i,c as a,a5 as t,o as e}from"./chunks/framework.Bx3vxiYB.js";const h="/SwarmMakie.jl/dev/assets/sblfyxu.CvP2aw-6.png",n="/SwarmMakie.jl/dev/assets/sppjqhf.B8wH5TFm.png",c=JSON.parse('{"title":"SwarmMakie","description":"","frontmatter":{},"headers":[],"relativePath":"introduction.md","filePath":"introduction.md","lastUpdated":null}'),k={name:"introduction.md"};function l(p,s,r,d,E,g){return e(),a("div",null,s[0]||(s[0]=[t(`
SwarmMakie ​
SwarmMakie implements beeswarm or swarm plots in Makie. These are scatter plots which are categorical (or singular) in the x-axis, where the markers are nudged so that each marker is visible and avoids overlap.
The main entry point to the package is the beeswarm recipe, which takes the same arguments as Makie's scatter plots, and transforms them into a beautiful beeswarm plot!
Being a Makie recipe, you can also use this with AlgebraOfGraphics.
Quick start ​
Here's a quick example to get you started:
julia
using CairoMakie, SwarmMakie
 xs = rand(1:3, 40)
 ys = randn(40)
 f, a, p = scatter(xs, ys; color = xs, axis = (; title = "Scatter plot"))
diff --git a/dev/assets/rlmnfjt.B_FZHNFE.png b/dev/assets/iunrknc.B_FZHNFE.png
similarity index 100%
rename from dev/assets/rlmnfjt.B_FZHNFE.png
rename to dev/assets/iunrknc.B_FZHNFE.png
diff --git a/dev/assets/uqaigib.gLfiNtBx.png b/dev/assets/mtgwbdz.gLfiNtBx.png
similarity index 100%
rename from dev/assets/uqaigib.gLfiNtBx.png
rename to dev/assets/mtgwbdz.gLfiNtBx.png
diff --git a/dev/assets/nibppwx.hc6mWVZB.png b/dev/assets/nibppwx.hc6mWVZB.png
new file mode 100644
index 0000000..6b04bd3
Binary files /dev/null and b/dev/assets/nibppwx.hc6mWVZB.png differ
diff --git a/dev/assets/ouijmxy.D1Kk23_-.png b/dev/assets/pkuoapx.D1Kk23_-.png
similarity index 100%
rename from dev/assets/ouijmxy.D1Kk23_-.png
rename to dev/assets/pkuoapx.D1Kk23_-.png
diff --git a/dev/assets/kkpqcxp.C6G2Gu5q.png b/dev/assets/riqdgnq.C6G2Gu5q.png
similarity index 100%
rename from dev/assets/kkpqcxp.C6G2Gu5q.png
rename to dev/assets/riqdgnq.C6G2Gu5q.png
diff --git a/dev/assets/sblfyxu.CvP2aw-6.png b/dev/assets/sblfyxu.CvP2aw-6.png
new file mode 100644
index 0000000..ec27b96
Binary files /dev/null and b/dev/assets/sblfyxu.CvP2aw-6.png differ
diff --git a/dev/assets/source_SwarmMakie.md.B36at8SY.js b/dev/assets/source_SwarmMakie.md.B3s4EFqA.js
similarity index 96%
rename from dev/assets/source_SwarmMakie.md.B36at8SY.js
rename to dev/assets/source_SwarmMakie.md.B3s4EFqA.js
index 5d58e78..a9c317a 100644
--- a/dev/assets/source_SwarmMakie.md.B36at8SY.js
+++ b/dev/assets/source_SwarmMakie.md.B3s4EFqA.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a5 as n,o as l}from"./chunks/framework.CLWLLiKQ.js";const o=JSON.parse('{"title":"SwarmMakie.jl","description":"","frontmatter":{},"headers":[],"relativePath":"source/SwarmMakie.md","filePath":"source/SwarmMakie.md","lastUpdated":null}'),t={name:"source/SwarmMakie.md"};function e(p,s,h,k,r,E){return l(),a("div",null,s[0]||(s[0]=[n(`
SwarmMakie.jl ​
julia
module SwarmMakie
+import{_ as i,c as a,a5 as n,o as l}from"./chunks/framework.Bx3vxiYB.js";const o=JSON.parse('{"title":"SwarmMakie.jl","description":"","frontmatter":{},"headers":[],"relativePath":"source/SwarmMakie.md","filePath":"source/SwarmMakie.md","lastUpdated":null}'),t={name:"source/SwarmMakie.md"};function e(p,s,h,k,r,E){return l(),a("div",null,s[0]||(s[0]=[n(`
SwarmMakie.jl ​
julia
module SwarmMakie
 
 using Makie
 using Random
diff --git a/dev/assets/source_SwarmMakie.md.B36at8SY.lean.js b/dev/assets/source_SwarmMakie.md.B3s4EFqA.lean.js
similarity index 96%
rename from dev/assets/source_SwarmMakie.md.B36at8SY.lean.js
rename to dev/assets/source_SwarmMakie.md.B3s4EFqA.lean.js
index 5d58e78..a9c317a 100644
--- a/dev/assets/source_SwarmMakie.md.B36at8SY.lean.js
+++ b/dev/assets/source_SwarmMakie.md.B3s4EFqA.lean.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a5 as n,o as l}from"./chunks/framework.CLWLLiKQ.js";const o=JSON.parse('{"title":"SwarmMakie.jl","description":"","frontmatter":{},"headers":[],"relativePath":"source/SwarmMakie.md","filePath":"source/SwarmMakie.md","lastUpdated":null}'),t={name:"source/SwarmMakie.md"};function e(p,s,h,k,r,E){return l(),a("div",null,s[0]||(s[0]=[n(`
SwarmMakie.jl ​
julia
module SwarmMakie
+import{_ as i,c as a,a5 as n,o as l}from"./chunks/framework.Bx3vxiYB.js";const o=JSON.parse('{"title":"SwarmMakie.jl","description":"","frontmatter":{},"headers":[],"relativePath":"source/SwarmMakie.md","filePath":"source/SwarmMakie.md","lastUpdated":null}'),t={name:"source/SwarmMakie.md"};function e(p,s,h,k,r,E){return l(),a("div",null,s[0]||(s[0]=[n(`
SwarmMakie.jl ​
julia
module SwarmMakie
 
 using Makie
 using Random
diff --git a/dev/assets/source_algorithms_jitter.md.CwVkr6fT.js b/dev/assets/source_algorithms_jitter.md.BpG3cFsL.js
similarity index 99%
rename from dev/assets/source_algorithms_jitter.md.CwVkr6fT.js
rename to dev/assets/source_algorithms_jitter.md.BpG3cFsL.js
index 8181578..89e13e2 100644
--- a/dev/assets/source_algorithms_jitter.md.CwVkr6fT.js
+++ b/dev/assets/source_algorithms_jitter.md.BpG3cFsL.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a5 as t,o as n}from"./chunks/framework.CLWLLiKQ.js";const g=JSON.parse('{"title":"Jitter algorithms","description":"","frontmatter":{},"headers":[],"relativePath":"source/algorithms/jitter.md","filePath":"source/algorithms/jitter.md","lastUpdated":null}'),h={name:"source/algorithms/jitter.md"};function k(l,s,p,e,r,d){return n(),a("div",null,s[0]||(s[0]=[t(`
Jitter algorithms ​
julia
export UniformJitter, PseudorandomJitter, QuasirandomJitter
Jitter is a way for scatterplots to receive a bit of randomness in the points, so that overlapping points are visible.
It's not exactly a beeswarm plot, since there are no guarantees that all points are shown, and nor is there any consideration of marker size.
Still, it's good enough for government work!
The algorithms in this file were provided by Benedikt Ehinger and Vladimir Mikheev, in (https://github.com/MakieOrg/Makie.jl/pull/2872)[PR 2872 to Makie.jl].
Allow to globally set jitter RNG for testing.
A bit of a lazy solution, but it doesn't seem to be desirable to pass the RNG through the plotting command.
julia
const JITTER_RNG = Ref{Random.AbstractRNG}(Random.GLOBAL_RNG)
+import{_ as i,c as a,a5 as t,o as n}from"./chunks/framework.Bx3vxiYB.js";const g=JSON.parse('{"title":"Jitter algorithms","description":"","frontmatter":{},"headers":[],"relativePath":"source/algorithms/jitter.md","filePath":"source/algorithms/jitter.md","lastUpdated":null}'),h={name:"source/algorithms/jitter.md"};function k(l,s,p,e,r,d){return n(),a("div",null,s[0]||(s[0]=[t(`
Jitter algorithms ​
julia
export UniformJitter, PseudorandomJitter, QuasirandomJitter
Jitter is a way for scatterplots to receive a bit of randomness in the points, so that overlapping points are visible.
It's not exactly a beeswarm plot, since there are no guarantees that all points are shown, and nor is there any consideration of marker size.
Still, it's good enough for government work!
The algorithms in this file were provided by Benedikt Ehinger and Vladimir Mikheev, in (https://github.com/MakieOrg/Makie.jl/pull/2872)[PR 2872 to Makie.jl].
Allow to globally set jitter RNG for testing.
A bit of a lazy solution, but it doesn't seem to be desirable to pass the RNG through the plotting command.
julia
const JITTER_RNG = Ref{Random.AbstractRNG}(Random.GLOBAL_RNG)
 
 "The abstract type for jitter algorithms, which are markersize-agnostic."
 abstract type JitterAlgorithm <: BeeswarmAlgorithm end
diff --git a/dev/assets/source_algorithms_jitter.md.CwVkr6fT.lean.js b/dev/assets/source_algorithms_jitter.md.BpG3cFsL.lean.js
similarity index 99%
rename from dev/assets/source_algorithms_jitter.md.CwVkr6fT.lean.js
rename to dev/assets/source_algorithms_jitter.md.BpG3cFsL.lean.js
index 8181578..89e13e2 100644
--- a/dev/assets/source_algorithms_jitter.md.CwVkr6fT.lean.js
+++ b/dev/assets/source_algorithms_jitter.md.BpG3cFsL.lean.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a5 as t,o as n}from"./chunks/framework.CLWLLiKQ.js";const g=JSON.parse('{"title":"Jitter algorithms","description":"","frontmatter":{},"headers":[],"relativePath":"source/algorithms/jitter.md","filePath":"source/algorithms/jitter.md","lastUpdated":null}'),h={name:"source/algorithms/jitter.md"};function k(l,s,p,e,r,d){return n(),a("div",null,s[0]||(s[0]=[t(`
Jitter algorithms ​
julia
export UniformJitter, PseudorandomJitter, QuasirandomJitter
Jitter is a way for scatterplots to receive a bit of randomness in the points, so that overlapping points are visible.
It's not exactly a beeswarm plot, since there are no guarantees that all points are shown, and nor is there any consideration of marker size.
Still, it's good enough for government work!
The algorithms in this file were provided by Benedikt Ehinger and Vladimir Mikheev, in (https://github.com/MakieOrg/Makie.jl/pull/2872)[PR 2872 to Makie.jl].
Allow to globally set jitter RNG for testing.
A bit of a lazy solution, but it doesn't seem to be desirable to pass the RNG through the plotting command.
julia
const JITTER_RNG = Ref{Random.AbstractRNG}(Random.GLOBAL_RNG)
+import{_ as i,c as a,a5 as t,o as n}from"./chunks/framework.Bx3vxiYB.js";const g=JSON.parse('{"title":"Jitter algorithms","description":"","frontmatter":{},"headers":[],"relativePath":"source/algorithms/jitter.md","filePath":"source/algorithms/jitter.md","lastUpdated":null}'),h={name:"source/algorithms/jitter.md"};function k(l,s,p,e,r,d){return n(),a("div",null,s[0]||(s[0]=[t(`
Jitter algorithms ​
julia
export UniformJitter, PseudorandomJitter, QuasirandomJitter
Jitter is a way for scatterplots to receive a bit of randomness in the points, so that overlapping points are visible.
It's not exactly a beeswarm plot, since there are no guarantees that all points are shown, and nor is there any consideration of marker size.
Still, it's good enough for government work!
The algorithms in this file were provided by Benedikt Ehinger and Vladimir Mikheev, in (https://github.com/MakieOrg/Makie.jl/pull/2872)[PR 2872 to Makie.jl].
Allow to globally set jitter RNG for testing.
A bit of a lazy solution, but it doesn't seem to be desirable to pass the RNG through the plotting command.
julia
const JITTER_RNG = Ref{Random.AbstractRNG}(Random.GLOBAL_RNG)
 
 "The abstract type for jitter algorithms, which are markersize-agnostic."
 abstract type JitterAlgorithm <: BeeswarmAlgorithm end
diff --git a/dev/assets/source_algorithms_mkborregaard.md.CskHhlIR.js b/dev/assets/source_algorithms_mkborregaard.md.BjI3XOVC.js
similarity index 99%
rename from dev/assets/source_algorithms_mkborregaard.md.CskHhlIR.js
rename to dev/assets/source_algorithms_mkborregaard.md.BjI3XOVC.js
index 8a0e720..da1e472 100644
--- a/dev/assets/source_algorithms_mkborregaard.md.CskHhlIR.js
+++ b/dev/assets/source_algorithms_mkborregaard.md.BjI3XOVC.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.CLWLLiKQ.js";const g=JSON.parse('{"title":"Michael Borregaard beeswarm","description":"","frontmatter":{},"headers":[],"relativePath":"source/algorithms/mkborregaard.md","filePath":"source/algorithms/mkborregaard.md","lastUpdated":null}'),k={name:"source/algorithms/mkborregaard.md"};function l(p,s,t,e,E,r){return h(),a("div",null,s[0]||(s[0]=[n(`
Michael Borregaard beeswarm ​
julia
export MKBorregaardBeeswarm
+import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.Bx3vxiYB.js";const g=JSON.parse('{"title":"Michael Borregaard beeswarm","description":"","frontmatter":{},"headers":[],"relativePath":"source/algorithms/mkborregaard.md","filePath":"source/algorithms/mkborregaard.md","lastUpdated":null}'),k={name:"source/algorithms/mkborregaard.md"};function l(p,s,t,e,E,r){return h(),a("div",null,s[0]||(s[0]=[n(`
Michael Borregaard beeswarm ​
julia
export MKBorregaardBeeswarm
 
 struct MKBorregaardBeeswarm <: BeeswarmAlgorithm end
 
diff --git a/dev/assets/source_algorithms_mkborregaard.md.CskHhlIR.lean.js b/dev/assets/source_algorithms_mkborregaard.md.BjI3XOVC.lean.js
similarity index 99%
rename from dev/assets/source_algorithms_mkborregaard.md.CskHhlIR.lean.js
rename to dev/assets/source_algorithms_mkborregaard.md.BjI3XOVC.lean.js
index 8a0e720..da1e472 100644
--- a/dev/assets/source_algorithms_mkborregaard.md.CskHhlIR.lean.js
+++ b/dev/assets/source_algorithms_mkborregaard.md.BjI3XOVC.lean.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.CLWLLiKQ.js";const g=JSON.parse('{"title":"Michael Borregaard beeswarm","description":"","frontmatter":{},"headers":[],"relativePath":"source/algorithms/mkborregaard.md","filePath":"source/algorithms/mkborregaard.md","lastUpdated":null}'),k={name:"source/algorithms/mkborregaard.md"};function l(p,s,t,e,E,r){return h(),a("div",null,s[0]||(s[0]=[n(`
Michael Borregaard beeswarm ​
julia
export MKBorregaardBeeswarm
+import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.Bx3vxiYB.js";const g=JSON.parse('{"title":"Michael Borregaard beeswarm","description":"","frontmatter":{},"headers":[],"relativePath":"source/algorithms/mkborregaard.md","filePath":"source/algorithms/mkborregaard.md","lastUpdated":null}'),k={name:"source/algorithms/mkborregaard.md"};function l(p,s,t,e,E,r){return h(),a("div",null,s[0]||(s[0]=[n(`
Michael Borregaard beeswarm ​
julia
export MKBorregaardBeeswarm
 
 struct MKBorregaardBeeswarm <: BeeswarmAlgorithm end
 
diff --git a/dev/assets/source_algorithms_seaborn.md.BHYLmSO1.js b/dev/assets/source_algorithms_seaborn.md.BT7ccLLi.js
similarity index 99%
rename from dev/assets/source_algorithms_seaborn.md.BHYLmSO1.js
rename to dev/assets/source_algorithms_seaborn.md.BT7ccLLi.js
index 8fe6fcf..ba96657 100644
--- a/dev/assets/source_algorithms_seaborn.md.BHYLmSO1.js
+++ b/dev/assets/source_algorithms_seaborn.md.BT7ccLLi.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.CLWLLiKQ.js";const g=JSON.parse('{"title":"Seaborn algorithm","description":"","frontmatter":{},"headers":[],"relativePath":"source/algorithms/seaborn.md","filePath":"source/algorithms/seaborn.md","lastUpdated":null}'),t={name:"source/algorithms/seaborn.md"};function p(k,s,l,e,r,E){return h(),a("div",null,s[0]||(s[0]=[n(`
Seaborn algorithm ​
julia
export SeabornBeeswarm
This code is adapted from the seaborn Python package, which is licensed under the BSD-3 license below:
md
Copyright (c) 2012-2023, Michael L. Waskom All rights reserved.
+import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.Bx3vxiYB.js";const g=JSON.parse('{"title":"Seaborn algorithm","description":"","frontmatter":{},"headers":[],"relativePath":"source/algorithms/seaborn.md","filePath":"source/algorithms/seaborn.md","lastUpdated":null}'),t={name:"source/algorithms/seaborn.md"};function p(k,s,l,e,r,E){return h(),a("div",null,s[0]||(s[0]=[n(`
Seaborn algorithm ​
julia
export SeabornBeeswarm
This code is adapted from the seaborn Python package, which is licensed under the BSD-3 license below:
md
Copyright (c) 2012-2023, Michael L. Waskom All rights reserved.
 
 Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
 
diff --git a/dev/assets/source_algorithms_seaborn.md.BHYLmSO1.lean.js b/dev/assets/source_algorithms_seaborn.md.BT7ccLLi.lean.js
similarity index 99%
rename from dev/assets/source_algorithms_seaborn.md.BHYLmSO1.lean.js
rename to dev/assets/source_algorithms_seaborn.md.BT7ccLLi.lean.js
index 8fe6fcf..ba96657 100644
--- a/dev/assets/source_algorithms_seaborn.md.BHYLmSO1.lean.js
+++ b/dev/assets/source_algorithms_seaborn.md.BT7ccLLi.lean.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.CLWLLiKQ.js";const g=JSON.parse('{"title":"Seaborn algorithm","description":"","frontmatter":{},"headers":[],"relativePath":"source/algorithms/seaborn.md","filePath":"source/algorithms/seaborn.md","lastUpdated":null}'),t={name:"source/algorithms/seaborn.md"};function p(k,s,l,e,r,E){return h(),a("div",null,s[0]||(s[0]=[n(`
Seaborn algorithm ​
julia
export SeabornBeeswarm
This code is adapted from the seaborn Python package, which is licensed under the BSD-3 license below:
md
Copyright (c) 2012-2023, Michael L. Waskom All rights reserved.
+import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.Bx3vxiYB.js";const g=JSON.parse('{"title":"Seaborn algorithm","description":"","frontmatter":{},"headers":[],"relativePath":"source/algorithms/seaborn.md","filePath":"source/algorithms/seaborn.md","lastUpdated":null}'),t={name:"source/algorithms/seaborn.md"};function p(k,s,l,e,r,E){return h(),a("div",null,s[0]||(s[0]=[n(`
Seaborn algorithm ​
julia
export SeabornBeeswarm
This code is adapted from the seaborn Python package, which is licensed under the BSD-3 license below:
md
Copyright (c) 2012-2023, Michael L. Waskom All rights reserved.
 
 Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
 
diff --git a/dev/assets/source_algorithms_simple.md.H5dL6DhE.js b/dev/assets/source_algorithms_simple.md.DZ0L0gdY.js
similarity index 99%
rename from dev/assets/source_algorithms_simple.md.H5dL6DhE.js
rename to dev/assets/source_algorithms_simple.md.DZ0L0gdY.js
index ddb5eee..99358d2 100644
--- a/dev/assets/source_algorithms_simple.md.H5dL6DhE.js
+++ b/dev/assets/source_algorithms_simple.md.DZ0L0gdY.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.CLWLLiKQ.js";const g=JSON.parse('{"title":"Simple beeswarm","description":"","frontmatter":{},"headers":[],"relativePath":"source/algorithms/simple.md","filePath":"source/algorithms/simple.md","lastUpdated":null}'),l={name:"source/algorithms/simple.md"};function k(p,s,t,e,r,d){return h(),a("div",null,s[0]||(s[0]=[n(`
Simple beeswarm ​
julia
export SimpleBeeswarm
This is a simple beeswarm implementation as used in Matplotlib.
julia
"""
+import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.Bx3vxiYB.js";const g=JSON.parse('{"title":"Simple beeswarm","description":"","frontmatter":{},"headers":[],"relativePath":"source/algorithms/simple.md","filePath":"source/algorithms/simple.md","lastUpdated":null}'),l={name:"source/algorithms/simple.md"};function k(p,s,t,e,r,d){return h(),a("div",null,s[0]||(s[0]=[n(`
Simple beeswarm ​
julia
export SimpleBeeswarm
This is a simple beeswarm implementation as used in Matplotlib.
julia
"""
     SimpleBeeswarm()
 
 A simple implementation like Matplotlib's algorithm.  This is the
diff --git a/dev/assets/source_algorithms_simple.md.H5dL6DhE.lean.js b/dev/assets/source_algorithms_simple.md.DZ0L0gdY.lean.js
similarity index 99%
rename from dev/assets/source_algorithms_simple.md.H5dL6DhE.lean.js
rename to dev/assets/source_algorithms_simple.md.DZ0L0gdY.lean.js
index ddb5eee..99358d2 100644
--- a/dev/assets/source_algorithms_simple.md.H5dL6DhE.lean.js
+++ b/dev/assets/source_algorithms_simple.md.DZ0L0gdY.lean.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.CLWLLiKQ.js";const g=JSON.parse('{"title":"Simple beeswarm","description":"","frontmatter":{},"headers":[],"relativePath":"source/algorithms/simple.md","filePath":"source/algorithms/simple.md","lastUpdated":null}'),l={name:"source/algorithms/simple.md"};function k(p,s,t,e,r,d){return h(),a("div",null,s[0]||(s[0]=[n(`
Simple beeswarm ​
julia
export SimpleBeeswarm
This is a simple beeswarm implementation as used in Matplotlib.
julia
"""
+import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.Bx3vxiYB.js";const g=JSON.parse('{"title":"Simple beeswarm","description":"","frontmatter":{},"headers":[],"relativePath":"source/algorithms/simple.md","filePath":"source/algorithms/simple.md","lastUpdated":null}'),l={name:"source/algorithms/simple.md"};function k(p,s,t,e,r,d){return h(),a("div",null,s[0]||(s[0]=[n(`
Simple beeswarm ​
julia
export SimpleBeeswarm
This is a simple beeswarm implementation as used in Matplotlib.
julia
"""
     SimpleBeeswarm()
 
 A simple implementation like Matplotlib's algorithm.  This is the
diff --git a/dev/assets/source_algorithms_wilkinson.md._K56EWAL.js b/dev/assets/source_algorithms_wilkinson.md.BxECk7D0.js
similarity index 99%
rename from dev/assets/source_algorithms_wilkinson.md._K56EWAL.js
rename to dev/assets/source_algorithms_wilkinson.md.BxECk7D0.js
index 4c8ef7a..e7709d3 100644
--- a/dev/assets/source_algorithms_wilkinson.md._K56EWAL.js
+++ b/dev/assets/source_algorithms_wilkinson.md.BxECk7D0.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.CLWLLiKQ.js";const o=JSON.parse('{"title":"Wilkinson beeswarm","description":"","frontmatter":{},"headers":[],"relativePath":"source/algorithms/wilkinson.md","filePath":"source/algorithms/wilkinson.md","lastUpdated":null}'),e={name:"source/algorithms/wilkinson.md"};function t(l,s,k,p,r,d){return h(),a("div",null,s[0]||(s[0]=[n(`
Wilkinson beeswarm ​
julia
export WilkinsonBeeswarm
This is a beeswarm implementation as described in Leland Wilkinson's original paper on dot plots.
This is probably one of the simplest beeswarm algorithms to implement, so this file is heavily documented to serve as a reference for other implementations.
Boilerplate ​
In order to define a beeswarm algorithm, we need to define a struct that is a subtype of BeeswarmAlgorithm.
This may optionally contain fields which control the algorithm, but in this case, we don't need any.
julia
"""
+import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.Bx3vxiYB.js";const o=JSON.parse('{"title":"Wilkinson beeswarm","description":"","frontmatter":{},"headers":[],"relativePath":"source/algorithms/wilkinson.md","filePath":"source/algorithms/wilkinson.md","lastUpdated":null}'),e={name:"source/algorithms/wilkinson.md"};function t(l,s,k,p,r,d){return h(),a("div",null,s[0]||(s[0]=[n(`
Wilkinson beeswarm ​
julia
export WilkinsonBeeswarm
This is a beeswarm implementation as described in Leland Wilkinson's original paper on dot plots.
This is probably one of the simplest beeswarm algorithms to implement, so this file is heavily documented to serve as a reference for other implementations.
Boilerplate ​
In order to define a beeswarm algorithm, we need to define a struct that is a subtype of BeeswarmAlgorithm.
This may optionally contain fields which control the algorithm, but in this case, we don't need any.
julia
"""
     WilkinsonBeeswarm()
 
 A beeswarm algorithm that implements Leland Wilkinson's original dot-hist algorithm.
diff --git a/dev/assets/source_algorithms_wilkinson.md._K56EWAL.lean.js b/dev/assets/source_algorithms_wilkinson.md.BxECk7D0.lean.js
similarity index 99%
rename from dev/assets/source_algorithms_wilkinson.md._K56EWAL.lean.js
rename to dev/assets/source_algorithms_wilkinson.md.BxECk7D0.lean.js
index 4c8ef7a..e7709d3 100644
--- a/dev/assets/source_algorithms_wilkinson.md._K56EWAL.lean.js
+++ b/dev/assets/source_algorithms_wilkinson.md.BxECk7D0.lean.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.CLWLLiKQ.js";const o=JSON.parse('{"title":"Wilkinson beeswarm","description":"","frontmatter":{},"headers":[],"relativePath":"source/algorithms/wilkinson.md","filePath":"source/algorithms/wilkinson.md","lastUpdated":null}'),e={name:"source/algorithms/wilkinson.md"};function t(l,s,k,p,r,d){return h(),a("div",null,s[0]||(s[0]=[n(`
Wilkinson beeswarm ​
julia
export WilkinsonBeeswarm
This is a beeswarm implementation as described in Leland Wilkinson's original paper on dot plots.
This is probably one of the simplest beeswarm algorithms to implement, so this file is heavily documented to serve as a reference for other implementations.
Boilerplate ​
In order to define a beeswarm algorithm, we need to define a struct that is a subtype of BeeswarmAlgorithm.
This may optionally contain fields which control the algorithm, but in this case, we don't need any.
julia
"""
+import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.Bx3vxiYB.js";const o=JSON.parse('{"title":"Wilkinson beeswarm","description":"","frontmatter":{},"headers":[],"relativePath":"source/algorithms/wilkinson.md","filePath":"source/algorithms/wilkinson.md","lastUpdated":null}'),e={name:"source/algorithms/wilkinson.md"};function t(l,s,k,p,r,d){return h(),a("div",null,s[0]||(s[0]=[n(`
Wilkinson beeswarm ​
julia
export WilkinsonBeeswarm
This is a beeswarm implementation as described in Leland Wilkinson's original paper on dot plots.
This is probably one of the simplest beeswarm algorithms to implement, so this file is heavily documented to serve as a reference for other implementations.
Boilerplate ​
In order to define a beeswarm algorithm, we need to define a struct that is a subtype of BeeswarmAlgorithm.
This may optionally contain fields which control the algorithm, but in this case, we don't need any.
julia
"""
     WilkinsonBeeswarm()
 
 A beeswarm algorithm that implements Leland Wilkinson's original dot-hist algorithm.
diff --git a/dev/assets/source_recipe.md.prk7WZ6I.js b/dev/assets/source_recipe.md.CZDLR43C.js
similarity index 99%
rename from dev/assets/source_recipe.md.prk7WZ6I.js
rename to dev/assets/source_recipe.md.CZDLR43C.js
index b0bcc8c..a24675e 100644
--- a/dev/assets/source_recipe.md.prk7WZ6I.js
+++ b/dev/assets/source_recipe.md.CZDLR43C.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.CLWLLiKQ.js";const g=JSON.parse('{"title":"Beeswarm recipe","description":"","frontmatter":{},"headers":[],"relativePath":"source/recipe.md","filePath":"source/recipe.md","lastUpdated":null}'),t={name:"source/recipe.md"};function p(l,s,k,e,r,d){return h(),a("div",null,s[0]||(s[0]=[n(`
Beeswarm recipe ​
julia
export beeswarm, beeswarm!
+import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.Bx3vxiYB.js";const g=JSON.parse('{"title":"Beeswarm recipe","description":"","frontmatter":{},"headers":[],"relativePath":"source/recipe.md","filePath":"source/recipe.md","lastUpdated":null}'),t={name:"source/recipe.md"};function p(l,s,k,e,r,d){return h(),a("div",null,s[0]||(s[0]=[n(`
Beeswarm recipe ​
julia
export beeswarm, beeswarm!
 
 export NoBeeswarm
In this file, we define the Beeswarm recipe.
julia
"""
     beeswarm(x, y)
diff --git a/dev/assets/source_recipe.md.prk7WZ6I.lean.js b/dev/assets/source_recipe.md.CZDLR43C.lean.js
similarity index 99%
rename from dev/assets/source_recipe.md.prk7WZ6I.lean.js
rename to dev/assets/source_recipe.md.CZDLR43C.lean.js
index b0bcc8c..a24675e 100644
--- a/dev/assets/source_recipe.md.prk7WZ6I.lean.js
+++ b/dev/assets/source_recipe.md.CZDLR43C.lean.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.CLWLLiKQ.js";const g=JSON.parse('{"title":"Beeswarm recipe","description":"","frontmatter":{},"headers":[],"relativePath":"source/recipe.md","filePath":"source/recipe.md","lastUpdated":null}'),t={name:"source/recipe.md"};function p(l,s,k,e,r,d){return h(),a("div",null,s[0]||(s[0]=[n(`
Beeswarm recipe ​
julia
export beeswarm, beeswarm!
+import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.Bx3vxiYB.js";const g=JSON.parse('{"title":"Beeswarm recipe","description":"","frontmatter":{},"headers":[],"relativePath":"source/recipe.md","filePath":"source/recipe.md","lastUpdated":null}'),t={name:"source/recipe.md"};function p(l,s,k,e,r,d){return h(),a("div",null,s[0]||(s[0]=[n(`
Beeswarm recipe ​
julia
export beeswarm, beeswarm!
 
 export NoBeeswarm
In this file, we define the Beeswarm recipe.
julia
"""
     beeswarm(x, y)
diff --git a/dev/assets/tryhbsf.B8wH5TFm.png b/dev/assets/sppjqhf.B8wH5TFm.png
similarity index 100%
rename from dev/assets/tryhbsf.B8wH5TFm.png
rename to dev/assets/sppjqhf.B8wH5TFm.png
diff --git a/dev/assets/szomdpo.BZxsO4NP.png b/dev/assets/szomdpo.BZxsO4NP.png
deleted file mode 100644
index 594327b..0000000
Binary files a/dev/assets/szomdpo.BZxsO4NP.png and /dev/null differ
diff --git a/dev/assets/xwbzrjj.CxvVHO24.png b/dev/assets/xwbzrjj.CxvVHO24.png
deleted file mode 100644
index 186eae1..0000000
Binary files a/dev/assets/xwbzrjj.CxvVHO24.png and /dev/null differ
diff --git a/dev/assets/vpufkag.B5ScXlxt.png b/dev/assets/zdammed.B5ScXlxt.png
similarity index 100%
rename from dev/assets/vpufkag.B5ScXlxt.png
rename to dev/assets/zdammed.B5ScXlxt.png
diff --git a/dev/assets/zogshfk.OgIUaXoz.png b/dev/assets/zogshfk.OgIUaXoz.png
deleted file mode 100644
index 82c3ebc..0000000
Binary files a/dev/assets/zogshfk.OgIUaXoz.png and /dev/null differ
diff --git a/dev/examples/examples.html b/dev/examples/examples.html
index 3200418..2764f23 100644
--- a/dev/examples/examples.html
+++ b/dev/examples/examples.html
@@ -8,11 +8,11 @@
     
     
     
-    
+    
     
-    
-    
-    
+    
+    
+    
     
     
     
@@ -21,7 +21,7 @@
     
SwarmMakie.jl
Appearance
Examples ​
Multiple variables with colors ​
julia
using CairoMakie, SwarmMakie
 xs = rand(1:4, 500)
 ys = randn(500) .+ xs
-fig, ax, plt = beeswarm(xs, ys, color = xs, colormap = :isoluminant_cm_70_c39_n256)
Now, we can also pass categorical colormaps:
julia
plt.colormap[] = Makie.Categorical(Makie.wong_colors()[1:4])
Categorical(ColorTypes.RGBA{Float32}[RGBA{Float32}(0.0f0,0.44705883f0,0.69803923f0,1.0f0), RGBA{Float32}(0.9019608f0,0.62352943f0,0.0f0,1.0f0), RGBA{Float32}(0.0f0,0.61960787f0,0.4509804f0,1.0f0), RGBA{Float32}(0.8f0,0.4745098f0,0.654902f0,1.0f0)])
and the figure is:
julia
fig
Palmer Penguins ​
julia
using AlgebraOfGraphics, CairoMakie, SwarmMakie
+fig, ax, plt = beeswarm(xs, ys, color = xs, colormap = :isoluminant_cm_70_c39_n256)
Now, we can also pass categorical colormaps:
julia
plt.colormap[] = Makie.Categorical(Makie.wong_colors()[1:4])
Categorical(ColorTypes.RGBA{Float32}[RGBA{Float32}(0.0f0,0.44705883f0,0.69803923f0,1.0f0), RGBA{Float32}(0.9019608f0,0.62352943f0,0.0f0,1.0f0), RGBA{Float32}(0.0f0,0.61960787f0,0.4509804f0,1.0f0), RGBA{Float32}(0.8f0,0.4745098f0,0.654902f0,1.0f0)])
and the figure is:
julia
fig
Palmer Penguins ​
julia
using AlgebraOfGraphics, CairoMakie, SwarmMakie
 using PalmerPenguins, DataFrames
 
 penguins = dropmissing(DataFrame(PalmerPenguins.load()))
@@ -30,7 +30,7 @@
 Makie.update_state_before_display!(f.figure)
 Makie.update_state_before_display!(f.figure)
 Makie.update_state_before_display!(f.figure)
-f
julia
using CairoMakie, SwarmMakie
+f
julia
using CairoMakie, SwarmMakie
 using RDatasets, DataFrames
 iris = dataset("datasets", "iris")
 f, a, p = beeswarm(
@@ -48,7 +48,7 @@
 Makie.update_state_before_display!(f)
 Makie.update_state_before_display!(f)
 Makie.update_state_before_display!(f)
-f
Wilkinson's dot histogram ​
julia
using CairoMakie, SwarmMakie
+f
Wilkinson's dot histogram ​
julia
using CairoMakie, SwarmMakie
 using RDatasets, DataFrames
 mtcars = dataset("datasets", "mtcars")
 
@@ -60,8 +60,8 @@
 )
 p.side = :both
 p.direction = :x
-f
Note that to use side != :both, you will have to set the limits of the axis explicitly.
This page was generated using Literate.jl.

-    
+f
Note that to use side != :both, you will have to set the limits of the axis explicitly.
This page was generated using Literate.jl.

+    
     
   
 
\ No newline at end of file
diff --git a/dev/examples/scales.html b/dev/examples/scales.html
index 1ca3da7..06acc93 100644
--- a/dev/examples/scales.html
+++ b/dev/examples/scales.html
@@ -8,27 +8,47 @@
     
     
     
-    
+    
     
-    
-    
-    
+    
+    
+    
     
     
     
   
   
-    
SwarmMakie.jl
Appearance
Nonlinear beeswarm plots ​
Beeswarm plots can be plotted in any combination of xscale and yscale.
Specifically, beeswarm plots are correct in any separable transform - basically, any transform in which the x and y coordinates are independent of each other. This excludes most geographic transformations, but includes any transformation you can make using xscale and yscale in a Makie Axis.
julia
using SwarmMakie, CairoMakie
-data = randn(75) .+ 3
-fig = Figure()
-ax1 = Axis(fig[1, 1]; title = "No transform")
-beeswarm!(ax1, ones(75), data)
-ax2 = Axis(fig[1, 2]; title = "Log y axis", yscale = log10)
-beeswarm!(ax2, ones(75), data)
-fig
Note how the shape of the beeswarm is transformed in the left plot, because of the log scale.
julia
ax2.xscale = Makie.pseudolog10
-ax2.title = "Log x and y axes"
-fig

-    
+    
SwarmMakie.jl
Appearance
Nonlinear beeswarm plots ​
Beeswarm plots can be plotted in any combination of xscale and yscale.
Specifically, beeswarm plots are correct in any separable transform - basically, any transform in which the x and y coordinates are independent of each other. This excludes most geographic transformations, but includes any transformation you can make using xscale and yscale in a Makie Axis.
@example
import CairoMakie # hide
+CairoMakie.activate!() # hide
+import Main.MakieDocsHelpers4 # hide
+var"#result" = begin # hide
+using SwarmMakie, CairoMakie
+data = randn(75) .+ 3
+fig = Figure()
+ax1 = Axis(fig[1, 1]; title = "No transform")
+beeswarm!(ax1, ones(75), data)
+ax2 = Axis(fig[1, 2]; title = "Log y axis", yscale = log10)
+beeswarm!(ax2, ones(75), data)
+fig
+end # hide
+if var"#result" isa Makie.FigureLike # hide
+    MakieDocsHelpers4.AsMIME(MIME"image/png"(), var"#result") # hide
+else # hide
+    var"#result" # hide
+end # hide
Note how the shape of the beeswarm is transformed in the left plot, because of the log scale.
@example
import CairoMakie # hide
+CairoMakie.activate!() # hide
+import Main.MakieDocsHelpers4 # hide
+var"#result" = begin # hide
+ax2.xscale = Makie.pseudolog10
+ax2.title = "Log x and y axes"
+fig
+end # hide
+if var"#result" isa Makie.FigureLike # hide
+    MakieDocsHelpers4.AsMIME(MIME"image/png"(), var"#result") # hide
+else # hide
+    var"#result" # hide
+end # hide

+    
     
   
 
\ No newline at end of file
diff --git a/dev/examples/unconventional.html b/dev/examples/unconventional.html
index 2e04c9c..a943e05 100644
--- a/dev/examples/unconventional.html
+++ b/dev/examples/unconventional.html
@@ -8,11 +8,11 @@
     
     
     
-    
+    
     
-    
-    
-    
+    
+    
+    
     
     
     
@@ -114,7 +114,7 @@
     bms.pool.levels,
     "Benchmark";
 )
-f
Benchmarks colored by language ​
julia
f, a, p = beeswarm(
+f
Benchmarks colored by language ​
julia
f, a, p = beeswarm(
     bms.refs, benchmarks.normtime;
     color = langs.refs,
     colormap = Makie.Colors.distinguishable_colors(13),#:Set1_9,
@@ -145,7 +145,7 @@
     langs.pool.levels,
     "Benchmark";
 )
-f
Custom markers ​
julia
# Get logos for programming languages
+f
Custom markers ​
julia
# Get logos for programming languages
 
 using Rsvg
 using CairoMakie
@@ -193,8 +193,8 @@
     langs.pool.levels,
     "Language";
 )
-f

-    
+f

+    
     
   
 
\ No newline at end of file
diff --git a/dev/gutters.html b/dev/gutters.html
index bb8abca..576e9cf 100644
--- a/dev/gutters.html
+++ b/dev/gutters.html
@@ -8,11 +8,11 @@
     
     
     
-    
+    
     
-    
-    
-    
+    
+    
+    
     
     
     
@@ -20,13 +20,13 @@
   
     
SwarmMakie.jl
Appearance
Gutters ​
julia
using SwarmMakie, CairoMakie
 xs = rand(1:10, 2000)
-beeswarm(xs, rand(2000); gutter = 0.3, color = xs)
Gutters are a threshold on how far the beeswarm plot can extend from the category point, in data space.
They are off by default, but can be set by passing gutter::Float64 as a keyword argument or by setting the gutter attribute of the plot. You can turn off guttering by setting gutter = nothing, which is the default.
A nice gutter size to avoid overlap in neighboring categories ranges between 0.5 and 0.3 (the latter shown in the example above).
Examples ​
julia
using SwarmMakie, CairoMakie
+beeswarm(xs, rand(2000); gutter = 0.3, color = xs)
Gutters are a threshold on how far the beeswarm plot can extend from the category point, in data space.
They are off by default, but can be set by passing gutter::Float64 as a keyword argument or by setting the gutter attribute of the plot. You can turn off guttering by setting gutter = nothing, which is the default.
A nice gutter size to avoid overlap in neighboring categories ranges between 0.5 and 0.3 (the latter shown in the example above).
Examples ​
julia
using SwarmMakie, CairoMakie
 f, a, p = beeswarm(
     rand(1:3, 300), randn(300);
     color = rand(RGBAf, 300), markersize = 20, algorithm = SimpleBeeswarm()
 )
-p.gutter = 0.5
0.5
Note the warning messages printed here! These can be helpful to diagnose when your data is moving too far out of the gutter, but you can turn them off by passing gutter_threshold = false or setting the gutter_threshold to a higher value (must be an Int and >0).
julia
f
Gutters work with all beeswarm plots.
Implementation ​
Gutters are implemented through the gutterize! function, and you can overload gutter handling for your own beeswarm type by defining a new dispatch for your algorithm.

-    
+p.gutter = 0.5
0.5
Note the warning messages printed here! These can be helpful to diagnose when your data is moving too far out of the gutter, but you can turn them off by passing gutter_threshold = false or setting the gutter_threshold to a higher value (must be an Int and >0).
julia
f
Gutters work with all beeswarm plots.
Implementation ​
Gutters are implemented through the gutterize! function, and you can overload gutter handling for your own beeswarm type by defining a new dispatch for your algorithm.

+    
     
   
 
\ No newline at end of file
diff --git a/dev/hashmap.json b/dev/hashmap.json
index 06c52ea..f53de13 100644
--- a/dev/hashmap.json
+++ b/dev/hashmap.json
@@ -1 +1 @@
-{"algorithms.md":"Dx4XOb6s","api.md":"COLbOG1V","examples_examples.md":"YYHIoRWe","examples_scales.md":"DaaSMAdS","examples_unconventional.md":"MMAyDr8z","gutters.md":"quAAtSD6","index.md":"D309uB29","introduction.md":"BY6rcP0y","source_algorithms_jitter.md":"CwVkr6fT","source_algorithms_mkborregaard.md":"CskHhlIR","source_algorithms_seaborn.md":"BHYLmSO1","source_algorithms_simple.md":"H5dL6DhE","source_algorithms_wilkinson.md":"_K56EWAL","source_recipe.md":"prk7WZ6I","source_swarmmakie.md":"B36at8SY"}
+{"algorithms.md":"_5FBLjFM","api.md":"03JrFq4R","examples_examples.md":"CLWQhraz","examples_scales.md":"CNGQ6RBo","examples_unconventional.md":"BSnBm1_2","gutters.md":"BWh_I-Hj","index.md":"Dvnn5-Dd","introduction.md":"BhTeXRCX","source_algorithms_jitter.md":"BpG3cFsL","source_algorithms_mkborregaard.md":"BjI3XOVC","source_algorithms_seaborn.md":"BT7ccLLi","source_algorithms_simple.md":"DZ0L0gdY","source_algorithms_wilkinson.md":"BxECk7D0","source_recipe.md":"CZDLR43C","source_swarmmakie.md":"B3s4EFqA"}
diff --git a/dev/index.html b/dev/index.html
index a3f55bc..163aa73 100644
--- a/dev/index.html
+++ b/dev/index.html
@@ -8,11 +8,11 @@
     
     
     
-    
+    
     
-    
-    
-    
+    
+    
+    
     
     
     
@@ -24,7 +24,7 @@
 xs = rand(1:4, 500)
 ys = randn(500)
 beeswarm(xs, ys; color = xs)
Beeswarm with multiple categories
- + \ No newline at end of file diff --git a/dev/introduction.html b/dev/introduction.html index 0255956..8d20225 100644 --- a/dev/introduction.html +++ b/dev/introduction.html @@ -8,11 +8,11 @@ - + - - - + + + @@ -23,7 +23,7 @@ ys = randn(40) f, a, p = scatter(xs, ys; color = xs, axis = (; title = "Scatter plot")) beeswarm(f[1, 2], xs, ys; color = xs, algorithm = SimpleBeeswarm(), axis = (; title = "Beeswarm plot")) -f

Use with AlgebraOfGraphics.jl ​

As a Makie recipe, beeswarm also composes with AlgebraOfGraphics!

julia
using AlgebraOfGraphics, CairoMakie, SwarmMakie
+f

Use with AlgebraOfGraphics.jl ​

As a Makie recipe, beeswarm also composes with AlgebraOfGraphics!

julia
using AlgebraOfGraphics, CairoMakie, SwarmMakie
 using RDatasets, DataFrames
 iris = dataset("datasets", "iris")
 
@@ -32,8 +32,8 @@
     visual(Beeswarm) |> draw
 Makie.update_state_before_display!(f.figure)
 Makie.update_state_before_display!(f.figure)
-f

Tips and tricks ​

If your beeswarms are overlapping, or extending outside the axis area, try decreasing markersize. You can do this by setting plot.markersize = 6 for example, and then re-displaying the figure.

Generally, the algorithm takes a few iterations of calling Makie.update_state_before_display!(figure) to settle in a good configuration. We are working to fix this.

- +f

Tips and tricks ​

If your beeswarms are overlapping, or extending outside the axis area, try decreasing markersize. You can do this by setting plot.markersize = 6 for example, and then re-displaying the figure.

Generally, the algorithm takes a few iterations of calling Makie.update_state_before_display!(figure) to settle in a good configuration. We are working to fix this.

+ \ No newline at end of file diff --git a/dev/source/SwarmMakie.html b/dev/source/SwarmMakie.html index 40e6e6d..8350eb4 100644 --- a/dev/source/SwarmMakie.html +++ b/dev/source/SwarmMakie.html @@ -8,11 +8,11 @@ - + - - - + + + @@ -36,7 +36,7 @@ include("algorithms/jitter.jl") end

This page was generated using Literate.jl.

- + \ No newline at end of file diff --git a/dev/source/algorithms/jitter.html b/dev/source/algorithms/jitter.html index 8f6595d..e787619 100644 --- a/dev/source/algorithms/jitter.html +++ b/dev/source/algorithms/jitter.html @@ -8,11 +8,11 @@ - + - - - + + + @@ -95,7 +95,7 @@ return jitter end

This page was generated using Literate.jl.

- + \ No newline at end of file diff --git a/dev/source/algorithms/mkborregaard.html b/dev/source/algorithms/mkborregaard.html index 14e2daa..4b08a52 100644 --- a/dev/source/algorithms/mkborregaard.html +++ b/dev/source/algorithms/mkborregaard.html @@ -8,11 +8,11 @@ - + - - - + + + @@ -123,7 +123,7 @@ x,y = SwarmMakie.beeswarm_coords(collect(iris[!, :SepalLength]), :both, 7) Makie.scatter(x,y, color = iris[!, :Species].refs, markersize = 7, axis = (; aspect = DataAspect()))

This page was generated using Literate.jl.

- + \ No newline at end of file diff --git a/dev/source/algorithms/seaborn.html b/dev/source/algorithms/seaborn.html index c911d76..d4aeb7d 100644 --- a/dev/source/algorithms/seaborn.html +++ b/dev/source/algorithms/seaborn.html @@ -8,11 +8,11 @@ - + - - - + + + @@ -161,7 +161,7 @@ """ end end

This page was generated using Literate.jl.

- + \ No newline at end of file diff --git a/dev/source/algorithms/simple.html b/dev/source/algorithms/simple.html index bca29a4..d4d61ef 100644 --- a/dev/source/algorithms/simple.html +++ b/dev/source/algorithms/simple.html @@ -8,11 +8,11 @@ - + - - - + + + @@ -90,7 +90,7 @@ end return xs end

This page was generated using Literate.jl.

- + \ No newline at end of file diff --git a/dev/source/algorithms/wilkinson.html b/dev/source/algorithms/wilkinson.html index ffb6aaa..52726c7 100644 --- a/dev/source/algorithms/wilkinson.html +++ b/dev/source/algorithms/wilkinson.html @@ -8,11 +8,11 @@ - + - - - + + + @@ -94,7 +94,7 @@ end end end

This page was generated using Literate.jl.

- + \ No newline at end of file diff --git a/dev/source/recipe.html b/dev/source/recipe.html index eafc983..25a9266 100644 --- a/dev/source/recipe.html +++ b/dev/source/recipe.html @@ -8,11 +8,11 @@ - + - - - + + + @@ -131,7 +131,7 @@ end end end

This page was generated using Literate.jl.

- + \ No newline at end of file