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- + \ No newline at end of file diff --git a/previews/PR27/algorithms.html b/previews/PR27/algorithms.html index a7c8710..b1f1eaa 100644 --- a/previews/PR27/algorithms.html +++ b/previews/PR27/algorithms.html @@ -5,19 +5,19 @@ Algorithms | SwarmMakie.jl - - + + - + - - - + + + -
SwarmMakie.jl

Appearance

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
+    
SwarmMakie.jl
Appearance
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)
@@ -25,8 +25,8 @@
 jitter_plots = getproperty.(ax_plots[2, :], :plot)
 setproperty!.(jitter_plots, :markersize, 7)
 setproperty!.(jitter_plots, :alpha, 0.3)
-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/previews/PR27/api.html b/previews/PR27/api.html index a95f1e9..078d147 100644 --- a/previews/PR27/api.html +++ b/previews/PR27/api.html @@ -5,19 +5,19 @@ API Reference | SwarmMakie.jl - - + + - + - - - + + + -
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()
@@ -56,8 +56,8 @@
   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/previews/PR27/assets/algorithms.md.CH3wLhtN.js b/previews/PR27/assets/algorithms.md.BtlfFAuT.js similarity index 96% rename from previews/PR27/assets/algorithms.md.CH3wLhtN.js rename to previews/PR27/assets/algorithms.md.BtlfFAuT.js index 4db2004..242f0e4 100644 --- a/previews/PR27/assets/algorithms.md.CH3wLhtN.js +++ b/previews/PR27/assets/algorithms.md.BtlfFAuT.js @@ -1,4 +1,4 @@ -import{_ as n,c as r,a5 as e,j as s,a,G as l,B as h,o as p}from"./chunks/framework.3NaDR-yp.js";const o="/SwarmMakie.jl/previews/PR27/assets/hlntquo.DXCk5H2R.png",B=JSON.parse('{"title":"Algorithms","description":"","frontmatter":{},"headers":[],"relativePath":"algorithms.md","filePath":"algorithms.md","lastUpdated":null}'),k={name:"algorithms.md"},d={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},m={class:"jldocstring custom-block",open:""},E={class:"jldocstring custom-block",open:""},c={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""};function u(b,i,w,f,F,C){const t=h("Badge");return p(),r("div",null,[i[19]||(i[19]=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 n,c as r,a5 as e,j as s,a,G as l,B as h,o as p}from"./chunks/framework.BlUcFYL6.js";const o="/SwarmMakie.jl/previews/PR27/assets/tznydkl.BKBP3MGz.png",B=JSON.parse('{"title":"Algorithms","description":"","frontmatter":{},"headers":[],"relativePath":"algorithms.md","filePath":"algorithms.md","lastUpdated":null}'),k={name:"algorithms.md"},d={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},m={class:"jldocstring custom-block",open:""},E={class:"jldocstring custom-block",open:""},c={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""};function u(b,i,w,f,F,C){const t=h("Badge");return p(),r("div",null,[i[19]||(i[19]=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)
@@ -6,4 +6,4 @@ import{_ as n,c as r,a5 as e,j as s,a,G as l,B as h,o as p}from"./chunks/framewo
 jitter_plots = getproperty.(ax_plots[2, :], :plot)
 setproperty!.(jitter_plots, :markersize, 7)
 setproperty!.(jitter_plots, :alpha, 0.3)
-fig
Documentation ​
',9)),s("details",d,[s("summary",null,[i[0]||(i[0]=s("a",{id:"SwarmMakie.SimpleBeeswarm-algorithms",href:"#SwarmMakie.SimpleBeeswarm-algorithms"},[s("span",{class:"jlbinding"},"SwarmMakie.SimpleBeeswarm")],-1)),i[1]||(i[1]=a()),l(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),i[2]||(i[2]=e('
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
',4))]),s("details",g,[s("summary",null,[i[3]||(i[3]=s("a",{id:"SwarmMakie.WilkinsonBeeswarm-algorithms",href:"#SwarmMakie.WilkinsonBeeswarm-algorithms"},[s("span",{class:"jlbinding"},"SwarmMakie.WilkinsonBeeswarm")],-1)),i[4]||(i[4]=a()),l(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),i[5]||(i[5]=e('
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
',5))]),s("details",m,[s("summary",null,[i[6]||(i[6]=s("a",{id:"SwarmMakie.NoBeeswarm-algorithms",href:"#SwarmMakie.NoBeeswarm-algorithms"},[s("span",{class:"jlbinding"},"SwarmMakie.NoBeeswarm")],-1)),i[7]||(i[7]=a()),l(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),i[8]||(i[8]=s("p",null,[a("A simple no-op algorithm, which causes the scatter plot to be drawn as if you called "),s("code",null,"scatter"),a(" and not "),s("code",null,"beeswarm"),a(".")],-1)),i[9]||(i[9]=s("p",null,[s("a",{href:"https://github.com/MakieOrg/SwarmMakie.jl/blob/e63d6d8fd51a9b1ad17e53be04d404abe05a317f/src/recipe.jl#L53",target:"_blank",rel:"noreferrer"},"source")],-1))]),i[20]||(i[20]=s("p",null,[a("Code exists for the algorithm suggested by Michael Borregaard in "),s("a",{href:"https://github.com/JuliaPlots/StatsPlots.jl/pull/61#issuecomment-328853342",target:"_blank",rel:"noreferrer"},"this StatsPlots.jl PR"),a(", but it is currently nonfunctional. If you'd like to take a crack at getting it working, please do!")],-1)),s("details",E,[s("summary",null,[i[10]||(i[10]=s("a",{id:"SwarmMakie.UniformJitter-algorithms",href:"#SwarmMakie.UniformJitter-algorithms"},[s("span",{class:"jlbinding"},"SwarmMakie.UniformJitter")],-1)),i[11]||(i[11]=a()),l(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),i[12]||(i[12]=e('
julia
UniformJitter(; jitter_width = 1.0)
A jitter algorithm that uses a uniform distribution to create the jitter.
source
',3))]),s("details",c,[s("summary",null,[i[13]||(i[13]=s("a",{id:"SwarmMakie.PseudorandomJitter-algorithms",href:"#SwarmMakie.PseudorandomJitter-algorithms"},[s("span",{class:"jlbinding"},"SwarmMakie.PseudorandomJitter")],-1)),i[14]||(i[14]=a()),l(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),i[15]||(i[15]=e('
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
',3))]),s("details",y,[s("summary",null,[i[16]||(i[16]=s("a",{id:"SwarmMakie.QuasirandomJitter-algorithms",href:"#SwarmMakie.QuasirandomJitter-algorithms"},[s("span",{class:"jlbinding"},"SwarmMakie.QuasirandomJitter")],-1)),i[17]||(i[17]=a()),l(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),i[18]||(i[18]=e('
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
',3))]),i[21]||(i[21]=e('
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!
',5))])}const S=n(k,[["render",u]]);export{B as __pageData,S as default};
+fig

Documentation ​

',9)),s("details",d,[s("summary",null,[i[0]||(i[0]=s("a",{id:"SwarmMakie.SimpleBeeswarm-algorithms",href:"#SwarmMakie.SimpleBeeswarm-algorithms"},[s("span",{class:"jlbinding"},"SwarmMakie.SimpleBeeswarm")],-1)),i[1]||(i[1]=a()),l(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),i[2]||(i[2]=e('
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

',4))]),s("details",g,[s("summary",null,[i[3]||(i[3]=s("a",{id:"SwarmMakie.WilkinsonBeeswarm-algorithms",href:"#SwarmMakie.WilkinsonBeeswarm-algorithms"},[s("span",{class:"jlbinding"},"SwarmMakie.WilkinsonBeeswarm")],-1)),i[4]||(i[4]=a()),l(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),i[5]||(i[5]=e('
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

',5))]),s("details",m,[s("summary",null,[i[6]||(i[6]=s("a",{id:"SwarmMakie.NoBeeswarm-algorithms",href:"#SwarmMakie.NoBeeswarm-algorithms"},[s("span",{class:"jlbinding"},"SwarmMakie.NoBeeswarm")],-1)),i[7]||(i[7]=a()),l(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),i[8]||(i[8]=s("p",null,[a("A simple no-op algorithm, which causes the scatter plot to be drawn as if you called "),s("code",null,"scatter"),a(" and not "),s("code",null,"beeswarm"),a(".")],-1)),i[9]||(i[9]=s("p",null,[s("a",{href:"https://github.com/MakieOrg/SwarmMakie.jl/blob/471bba40ff1c5a9c3745644ed7a01e32991de712/src/recipe.jl#L53",target:"_blank",rel:"noreferrer"},"source")],-1))]),i[20]||(i[20]=s("p",null,[a("Code exists for the algorithm suggested by Michael Borregaard in "),s("a",{href:"https://github.com/JuliaPlots/StatsPlots.jl/pull/61#issuecomment-328853342",target:"_blank",rel:"noreferrer"},"this StatsPlots.jl PR"),a(", but it is currently nonfunctional. If you'd like to take a crack at getting it working, please do!")],-1)),s("details",E,[s("summary",null,[i[10]||(i[10]=s("a",{id:"SwarmMakie.UniformJitter-algorithms",href:"#SwarmMakie.UniformJitter-algorithms"},[s("span",{class:"jlbinding"},"SwarmMakie.UniformJitter")],-1)),i[11]||(i[11]=a()),l(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),i[12]||(i[12]=e('
julia
UniformJitter(; jitter_width = 1.0)

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

source

',3))]),s("details",c,[s("summary",null,[i[13]||(i[13]=s("a",{id:"SwarmMakie.PseudorandomJitter-algorithms",href:"#SwarmMakie.PseudorandomJitter-algorithms"},[s("span",{class:"jlbinding"},"SwarmMakie.PseudorandomJitter")],-1)),i[14]||(i[14]=a()),l(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),i[15]||(i[15]=e('
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

',3))]),s("details",y,[s("summary",null,[i[16]||(i[16]=s("a",{id:"SwarmMakie.QuasirandomJitter-algorithms",href:"#SwarmMakie.QuasirandomJitter-algorithms"},[s("span",{class:"jlbinding"},"SwarmMakie.QuasirandomJitter")],-1)),i[17]||(i[17]=a()),l(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),i[18]||(i[18]=e('
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

',3))]),i[21]||(i[21]=e('

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!

',5))])}const S=n(k,[["render",u]]);export{B as __pageData,S as default}; diff --git a/previews/PR27/assets/algorithms.md.CH3wLhtN.lean.js b/previews/PR27/assets/algorithms.md.BtlfFAuT.lean.js similarity index 96% rename from previews/PR27/assets/algorithms.md.CH3wLhtN.lean.js rename to previews/PR27/assets/algorithms.md.BtlfFAuT.lean.js index 4db2004..242f0e4 100644 --- a/previews/PR27/assets/algorithms.md.CH3wLhtN.lean.js +++ b/previews/PR27/assets/algorithms.md.BtlfFAuT.lean.js @@ -1,4 +1,4 @@ -import{_ as n,c as r,a5 as e,j as s,a,G as l,B as h,o as p}from"./chunks/framework.3NaDR-yp.js";const o="/SwarmMakie.jl/previews/PR27/assets/hlntquo.DXCk5H2R.png",B=JSON.parse('{"title":"Algorithms","description":"","frontmatter":{},"headers":[],"relativePath":"algorithms.md","filePath":"algorithms.md","lastUpdated":null}'),k={name:"algorithms.md"},d={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},m={class:"jldocstring custom-block",open:""},E={class:"jldocstring custom-block",open:""},c={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""};function u(b,i,w,f,F,C){const t=h("Badge");return p(),r("div",null,[i[19]||(i[19]=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 n,c as r,a5 as e,j as s,a,G as l,B as h,o as p}from"./chunks/framework.BlUcFYL6.js";const o="/SwarmMakie.jl/previews/PR27/assets/tznydkl.BKBP3MGz.png",B=JSON.parse('{"title":"Algorithms","description":"","frontmatter":{},"headers":[],"relativePath":"algorithms.md","filePath":"algorithms.md","lastUpdated":null}'),k={name:"algorithms.md"},d={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},m={class:"jldocstring custom-block",open:""},E={class:"jldocstring custom-block",open:""},c={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""};function u(b,i,w,f,F,C){const t=h("Badge");return p(),r("div",null,[i[19]||(i[19]=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)
@@ -6,4 +6,4 @@ import{_ as n,c as r,a5 as e,j as s,a,G as l,B as h,o as p}from"./chunks/framewo
 jitter_plots = getproperty.(ax_plots[2, :], :plot)
 setproperty!.(jitter_plots, :markersize, 7)
 setproperty!.(jitter_plots, :alpha, 0.3)
-fig
Documentation ​
',9)),s("details",d,[s("summary",null,[i[0]||(i[0]=s("a",{id:"SwarmMakie.SimpleBeeswarm-algorithms",href:"#SwarmMakie.SimpleBeeswarm-algorithms"},[s("span",{class:"jlbinding"},"SwarmMakie.SimpleBeeswarm")],-1)),i[1]||(i[1]=a()),l(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),i[2]||(i[2]=e('
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
',4))]),s("details",g,[s("summary",null,[i[3]||(i[3]=s("a",{id:"SwarmMakie.WilkinsonBeeswarm-algorithms",href:"#SwarmMakie.WilkinsonBeeswarm-algorithms"},[s("span",{class:"jlbinding"},"SwarmMakie.WilkinsonBeeswarm")],-1)),i[4]||(i[4]=a()),l(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),i[5]||(i[5]=e('
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
',5))]),s("details",m,[s("summary",null,[i[6]||(i[6]=s("a",{id:"SwarmMakie.NoBeeswarm-algorithms",href:"#SwarmMakie.NoBeeswarm-algorithms"},[s("span",{class:"jlbinding"},"SwarmMakie.NoBeeswarm")],-1)),i[7]||(i[7]=a()),l(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),i[8]||(i[8]=s("p",null,[a("A simple no-op algorithm, which causes the scatter plot to be drawn as if you called "),s("code",null,"scatter"),a(" and not "),s("code",null,"beeswarm"),a(".")],-1)),i[9]||(i[9]=s("p",null,[s("a",{href:"https://github.com/MakieOrg/SwarmMakie.jl/blob/e63d6d8fd51a9b1ad17e53be04d404abe05a317f/src/recipe.jl#L53",target:"_blank",rel:"noreferrer"},"source")],-1))]),i[20]||(i[20]=s("p",null,[a("Code exists for the algorithm suggested by Michael Borregaard in "),s("a",{href:"https://github.com/JuliaPlots/StatsPlots.jl/pull/61#issuecomment-328853342",target:"_blank",rel:"noreferrer"},"this StatsPlots.jl PR"),a(", but it is currently nonfunctional. If you'd like to take a crack at getting it working, please do!")],-1)),s("details",E,[s("summary",null,[i[10]||(i[10]=s("a",{id:"SwarmMakie.UniformJitter-algorithms",href:"#SwarmMakie.UniformJitter-algorithms"},[s("span",{class:"jlbinding"},"SwarmMakie.UniformJitter")],-1)),i[11]||(i[11]=a()),l(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),i[12]||(i[12]=e('
julia
UniformJitter(; jitter_width = 1.0)
A jitter algorithm that uses a uniform distribution to create the jitter.
source
',3))]),s("details",c,[s("summary",null,[i[13]||(i[13]=s("a",{id:"SwarmMakie.PseudorandomJitter-algorithms",href:"#SwarmMakie.PseudorandomJitter-algorithms"},[s("span",{class:"jlbinding"},"SwarmMakie.PseudorandomJitter")],-1)),i[14]||(i[14]=a()),l(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),i[15]||(i[15]=e('
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
',3))]),s("details",y,[s("summary",null,[i[16]||(i[16]=s("a",{id:"SwarmMakie.QuasirandomJitter-algorithms",href:"#SwarmMakie.QuasirandomJitter-algorithms"},[s("span",{class:"jlbinding"},"SwarmMakie.QuasirandomJitter")],-1)),i[17]||(i[17]=a()),l(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),i[18]||(i[18]=e('
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
',3))]),i[21]||(i[21]=e('
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!
',5))])}const S=n(k,[["render",u]]);export{B as __pageData,S as default};
+fig

Documentation ​

',9)),s("details",d,[s("summary",null,[i[0]||(i[0]=s("a",{id:"SwarmMakie.SimpleBeeswarm-algorithms",href:"#SwarmMakie.SimpleBeeswarm-algorithms"},[s("span",{class:"jlbinding"},"SwarmMakie.SimpleBeeswarm")],-1)),i[1]||(i[1]=a()),l(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),i[2]||(i[2]=e('
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

',4))]),s("details",g,[s("summary",null,[i[3]||(i[3]=s("a",{id:"SwarmMakie.WilkinsonBeeswarm-algorithms",href:"#SwarmMakie.WilkinsonBeeswarm-algorithms"},[s("span",{class:"jlbinding"},"SwarmMakie.WilkinsonBeeswarm")],-1)),i[4]||(i[4]=a()),l(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),i[5]||(i[5]=e('
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

',5))]),s("details",m,[s("summary",null,[i[6]||(i[6]=s("a",{id:"SwarmMakie.NoBeeswarm-algorithms",href:"#SwarmMakie.NoBeeswarm-algorithms"},[s("span",{class:"jlbinding"},"SwarmMakie.NoBeeswarm")],-1)),i[7]||(i[7]=a()),l(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),i[8]||(i[8]=s("p",null,[a("A simple no-op algorithm, which causes the scatter plot to be drawn as if you called "),s("code",null,"scatter"),a(" and not "),s("code",null,"beeswarm"),a(".")],-1)),i[9]||(i[9]=s("p",null,[s("a",{href:"https://github.com/MakieOrg/SwarmMakie.jl/blob/471bba40ff1c5a9c3745644ed7a01e32991de712/src/recipe.jl#L53",target:"_blank",rel:"noreferrer"},"source")],-1))]),i[20]||(i[20]=s("p",null,[a("Code exists for the algorithm suggested by Michael Borregaard in "),s("a",{href:"https://github.com/JuliaPlots/StatsPlots.jl/pull/61#issuecomment-328853342",target:"_blank",rel:"noreferrer"},"this StatsPlots.jl PR"),a(", but it is currently nonfunctional. If you'd like to take a crack at getting it working, please do!")],-1)),s("details",E,[s("summary",null,[i[10]||(i[10]=s("a",{id:"SwarmMakie.UniformJitter-algorithms",href:"#SwarmMakie.UniformJitter-algorithms"},[s("span",{class:"jlbinding"},"SwarmMakie.UniformJitter")],-1)),i[11]||(i[11]=a()),l(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),i[12]||(i[12]=e('
julia
UniformJitter(; jitter_width = 1.0)

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

source

',3))]),s("details",c,[s("summary",null,[i[13]||(i[13]=s("a",{id:"SwarmMakie.PseudorandomJitter-algorithms",href:"#SwarmMakie.PseudorandomJitter-algorithms"},[s("span",{class:"jlbinding"},"SwarmMakie.PseudorandomJitter")],-1)),i[14]||(i[14]=a()),l(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),i[15]||(i[15]=e('
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

',3))]),s("details",y,[s("summary",null,[i[16]||(i[16]=s("a",{id:"SwarmMakie.QuasirandomJitter-algorithms",href:"#SwarmMakie.QuasirandomJitter-algorithms"},[s("span",{class:"jlbinding"},"SwarmMakie.QuasirandomJitter")],-1)),i[17]||(i[17]=a()),l(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),i[18]||(i[18]=e('
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

',3))]),i[21]||(i[21]=e('

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!

',5))])}const S=n(k,[["render",u]]);export{B as __pageData,S as default}; diff --git a/previews/PR27/assets/api.md.DRTMKKNK.js b/previews/PR27/assets/api.md.BIHl2OFe.js similarity index 92% rename from previews/PR27/assets/api.md.DRTMKKNK.js rename to previews/PR27/assets/api.md.BIHl2OFe.js index caee8a0..28189ec 100644 --- a/previews/PR27/assets/api.md.DRTMKKNK.js +++ b/previews/PR27/assets/api.md.BIHl2OFe.js @@ -1,4 +1,4 @@ -import{_ as l,c as r,a5 as n,j as e,a as s,G as t,B as o,o as p}from"./chunks/framework.3NaDR-yp.js";const T=JSON.parse('{"title":"API Reference","description":"","frontmatter":{},"headers":[],"relativePath":"api.md","filePath":"api.md","lastUpdated":null}'),d={name:"api.md"},h={class:"jldocstring custom-block",open:""},c={class:"jldocstring custom-block",open:""},k={class:"jldocstring custom-block",open:""},m={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},u={class:"jldocstring custom-block",open:""},b={class:"jldocstring custom-block",open:""},w={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},f={class:"jldocstring custom-block",open:""},j={class:"jldocstring custom-block",open:""},M={class:"jldocstring custom-block",open:""};function S(E,a,v,C,B,A){const i=o("Badge");return p(),r("div",null,[a[39]||(a[39]=n('

API Reference ​

',2)),e("details",h,[e("summary",null,[a[0]||(a[0]=e("a",{id:"SwarmMakie.JitterAlgorithm",href:"#SwarmMakie.JitterAlgorithm"},[e("span",{class:"jlbinding"},"SwarmMakie.JitterAlgorithm")],-1)),a[1]||(a[1]=s()),t(i,{type:"info",class:"jlObjectType jlType",text:"Type"})]),a[2]||(a[2]=e("p",null,"The abstract type for jitter algorithms, which are markersize-agnostic.",-1)),a[3]||(a[3]=e("p",null,[e("a",{href:"https://github.com/MakieOrg/SwarmMakie.jl/blob/e63d6d8fd51a9b1ad17e53be04d404abe05a317f/src/algorithms/jitter.jl#L27",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",c,[e("summary",null,[a[4]||(a[4]=e("a",{id:"SwarmMakie.NoBeeswarm",href:"#SwarmMakie.NoBeeswarm"},[e("span",{class:"jlbinding"},"SwarmMakie.NoBeeswarm")],-1)),a[5]||(a[5]=s()),t(i,{type:"info",class:"jlObjectType jlType",text:"Type"})]),a[6]||(a[6]=e("p",null,[s("A simple no-op algorithm, which causes the scatter plot to be drawn as if you called "),e("code",null,"scatter"),s(" and not "),e("code",null,"beeswarm"),s(".")],-1)),a[7]||(a[7]=e("p",null,[e("a",{href:"https://github.com/MakieOrg/SwarmMakie.jl/blob/e63d6d8fd51a9b1ad17e53be04d404abe05a317f/src/recipe.jl#L53",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",k,[e("summary",null,[a[8]||(a[8]=e("a",{id:"SwarmMakie.PseudorandomJitter",href:"#SwarmMakie.PseudorandomJitter"},[e("span",{class:"jlbinding"},"SwarmMakie.PseudorandomJitter")],-1)),a[9]||(a[9]=s()),t(i,{type:"info",class:"jlObjectType jlType",text:"Type"})]),a[10]||(a[10]=n('
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

',3))]),e("details",m,[e("summary",null,[a[11]||(a[11]=e("a",{id:"SwarmMakie.QuasirandomJitter",href:"#SwarmMakie.QuasirandomJitter"},[e("span",{class:"jlbinding"},"SwarmMakie.QuasirandomJitter")],-1)),a[12]||(a[12]=s()),t(i,{type:"info",class:"jlObjectType jlType",text:"Type"})]),a[13]||(a[13]=n('
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

',3))]),e("details",g,[e("summary",null,[a[14]||(a[14]=e("a",{id:"SwarmMakie.SeabornBeeswarm",href:"#SwarmMakie.SeabornBeeswarm"},[e("span",{class:"jlbinding"},"SwarmMakie.SeabornBeeswarm")],-1)),a[15]||(a[15]=s()),t(i,{type:"info",class:"jlObjectType jlType",text:"Type"})]),a[16]||(a[16]=n('
julia
SeabornBeeswarm()

A beeswarm algorithm based on the seaborn Python package.

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

source

',4))]),e("details",u,[e("summary",null,[a[17]||(a[17]=e("a",{id:"SwarmMakie.SimpleBeeswarm",href:"#SwarmMakie.SimpleBeeswarm"},[e("span",{class:"jlbinding"},"SwarmMakie.SimpleBeeswarm")],-1)),a[18]||(a[18]=s()),t(i,{type:"info",class:"jlObjectType jlType",text:"Type"})]),a[19]||(a[19]=n('
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

',4))]),e("details",b,[e("summary",null,[a[20]||(a[20]=e("a",{id:"SwarmMakie.UniformJitter",href:"#SwarmMakie.UniformJitter"},[e("span",{class:"jlbinding"},"SwarmMakie.UniformJitter")],-1)),a[21]||(a[21]=s()),t(i,{type:"info",class:"jlObjectType jlType",text:"Type"})]),a[22]||(a[22]=n('
julia
UniformJitter(; jitter_width = 1.0)

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

source

',3))]),e("details",w,[e("summary",null,[a[23]||(a[23]=e("a",{id:"SwarmMakie.WilkinsonBeeswarm",href:"#SwarmMakie.WilkinsonBeeswarm"},[e("span",{class:"jlbinding"},"SwarmMakie.WilkinsonBeeswarm")],-1)),a[24]||(a[24]=s()),t(i,{type:"info",class:"jlObjectType jlType",text:"Type"})]),a[25]||(a[25]=n('
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

',5))]),e("details",y,[e("summary",null,[a[26]||(a[26]=e("a",{id:"SwarmMakie.beeswarm-Tuple",href:"#SwarmMakie.beeswarm-Tuple"},[e("span",{class:"jlbinding"},"SwarmMakie.beeswarm")],-1)),a[27]||(a[27]=s()),t(i,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),a[28]||(a[28]=n(`
julia
beeswarm(x, y)
+import{_ as l,c as r,a5 as n,j as e,a as s,G as t,B as o,o as p}from"./chunks/framework.BlUcFYL6.js";const T=JSON.parse('{"title":"API Reference","description":"","frontmatter":{},"headers":[],"relativePath":"api.md","filePath":"api.md","lastUpdated":null}'),d={name:"api.md"},c={class:"jldocstring custom-block",open:""},h={class:"jldocstring custom-block",open:""},k={class:"jldocstring custom-block",open:""},m={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},u={class:"jldocstring custom-block",open:""},b={class:"jldocstring custom-block",open:""},w={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},f={class:"jldocstring custom-block",open:""},j={class:"jldocstring custom-block",open:""},M={class:"jldocstring custom-block",open:""};function S(E,a,v,C,B,A){const i=o("Badge");return p(),r("div",null,[a[39]||(a[39]=n('
API Reference ​
',2)),e("details",c,[e("summary",null,[a[0]||(a[0]=e("a",{id:"SwarmMakie.JitterAlgorithm",href:"#SwarmMakie.JitterAlgorithm"},[e("span",{class:"jlbinding"},"SwarmMakie.JitterAlgorithm")],-1)),a[1]||(a[1]=s()),t(i,{type:"info",class:"jlObjectType jlType",text:"Type"})]),a[2]||(a[2]=e("p",null,"The abstract type for jitter algorithms, which are markersize-agnostic.",-1)),a[3]||(a[3]=e("p",null,[e("a",{href:"https://github.com/MakieOrg/SwarmMakie.jl/blob/471bba40ff1c5a9c3745644ed7a01e32991de712/src/algorithms/jitter.jl#L27",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",h,[e("summary",null,[a[4]||(a[4]=e("a",{id:"SwarmMakie.NoBeeswarm",href:"#SwarmMakie.NoBeeswarm"},[e("span",{class:"jlbinding"},"SwarmMakie.NoBeeswarm")],-1)),a[5]||(a[5]=s()),t(i,{type:"info",class:"jlObjectType jlType",text:"Type"})]),a[6]||(a[6]=e("p",null,[s("A simple no-op algorithm, which causes the scatter plot to be drawn as if you called "),e("code",null,"scatter"),s(" and not "),e("code",null,"beeswarm"),s(".")],-1)),a[7]||(a[7]=e("p",null,[e("a",{href:"https://github.com/MakieOrg/SwarmMakie.jl/blob/471bba40ff1c5a9c3745644ed7a01e32991de712/src/recipe.jl#L53",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",k,[e("summary",null,[a[8]||(a[8]=e("a",{id:"SwarmMakie.PseudorandomJitter",href:"#SwarmMakie.PseudorandomJitter"},[e("span",{class:"jlbinding"},"SwarmMakie.PseudorandomJitter")],-1)),a[9]||(a[9]=s()),t(i,{type:"info",class:"jlObjectType jlType",text:"Type"})]),a[10]||(a[10]=n('
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
',3))]),e("details",m,[e("summary",null,[a[11]||(a[11]=e("a",{id:"SwarmMakie.QuasirandomJitter",href:"#SwarmMakie.QuasirandomJitter"},[e("span",{class:"jlbinding"},"SwarmMakie.QuasirandomJitter")],-1)),a[12]||(a[12]=s()),t(i,{type:"info",class:"jlObjectType jlType",text:"Type"})]),a[13]||(a[13]=n('
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
',3))]),e("details",g,[e("summary",null,[a[14]||(a[14]=e("a",{id:"SwarmMakie.SeabornBeeswarm",href:"#SwarmMakie.SeabornBeeswarm"},[e("span",{class:"jlbinding"},"SwarmMakie.SeabornBeeswarm")],-1)),a[15]||(a[15]=s()),t(i,{type:"info",class:"jlObjectType jlType",text:"Type"})]),a[16]||(a[16]=n('
julia
SeabornBeeswarm()
A beeswarm algorithm based on the seaborn Python package.
More adaptive to marker size than SimpleBeeswarm, but takes longer to compute.
source
',4))]),e("details",u,[e("summary",null,[a[17]||(a[17]=e("a",{id:"SwarmMakie.SimpleBeeswarm",href:"#SwarmMakie.SimpleBeeswarm"},[e("span",{class:"jlbinding"},"SwarmMakie.SimpleBeeswarm")],-1)),a[18]||(a[18]=s()),t(i,{type:"info",class:"jlObjectType jlType",text:"Type"})]),a[19]||(a[19]=n('
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
',4))]),e("details",b,[e("summary",null,[a[20]||(a[20]=e("a",{id:"SwarmMakie.UniformJitter",href:"#SwarmMakie.UniformJitter"},[e("span",{class:"jlbinding"},"SwarmMakie.UniformJitter")],-1)),a[21]||(a[21]=s()),t(i,{type:"info",class:"jlObjectType jlType",text:"Type"})]),a[22]||(a[22]=n('
julia
UniformJitter(; jitter_width = 1.0)
A jitter algorithm that uses a uniform distribution to create the jitter.
source
',3))]),e("details",w,[e("summary",null,[a[23]||(a[23]=e("a",{id:"SwarmMakie.WilkinsonBeeswarm",href:"#SwarmMakie.WilkinsonBeeswarm"},[e("span",{class:"jlbinding"},"SwarmMakie.WilkinsonBeeswarm")],-1)),a[24]||(a[24]=s()),t(i,{type:"info",class:"jlObjectType jlType",text:"Type"})]),a[25]||(a[25]=n('
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
',5))]),e("details",y,[e("summary",null,[a[26]||(a[26]=e("a",{id:"SwarmMakie.beeswarm-Tuple",href:"#SwarmMakie.beeswarm-Tuple"},[e("span",{class:"jlbinding"},"SwarmMakie.beeswarm")],-1)),a[27]||(a[27]=s()),t(i,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),a[28]||(a[28]=n(`
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 l,c as r,a5 as n,j as e,a as s,G as t,B as o,o as p}from"./chunks/fr
   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
`,11))]),e("details",f,[e("summary",null,[a[29]||(a[29]=e("a",{id:"SwarmMakie.could_overlap-Tuple{Point2, Any, AbstractVector{<:Point2}, AbstractVector}",href:"#SwarmMakie.could_overlap-Tuple{Point2, Any, AbstractVector{<:Point2}, AbstractVector}"},[e("span",{class:"jlbinding"},"SwarmMakie.could_overlap")],-1)),a[30]||(a[30]=s()),t(i,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),a[31]||(a[31]=n('
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
',3))]),e("details",j,[e("summary",null,[a[32]||(a[32]=e("a",{id:"SwarmMakie.first_non_overlapping_candidate-NTuple{8, Any}",href:"#SwarmMakie.first_non_overlapping_candidate-NTuple{8, Any}"},[e("span",{class:"jlbinding"},"SwarmMakie.first_non_overlapping_candidate")],-1)),a[33]||(a[33]=s()),t(i,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),a[34]||(a[34]=n('
julia
Returns `(position::Point2f, idx::Int)`
source
',2))]),e("details",M,[e("summary",null,[a[35]||(a[35]=e("a",{id:"SwarmMakie.position_candidates-NTuple{6, Any}",href:"#SwarmMakie.position_candidates-NTuple{6, Any}"},[e("span",{class:"jlbinding"},"SwarmMakie.position_candidates")],-1)),a[36]||(a[36]=s()),t(i,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),a[37]||(a[37]=e("p",null,[s("Returns "),e("code",null,"(positions::Vector{Point2f}, idxs::Vector{Int})"),s(".")],-1)),a[38]||(a[38]=e("p",null,[e("a",{href:"https://github.com/MakieOrg/SwarmMakie.jl/blob/e63d6d8fd51a9b1ad17e53be04d404abe05a317f/src/algorithms/seaborn.jl#L94-L96",target:"_blank",rel:"noreferrer"},"source")],-1))])])}const x=l(d,[["render",S]]);export{T as __pageData,x as default};
+beeswarm(ones(100), randn(100); color = rand(RGBf, 100))

source

`,11))]),e("details",f,[e("summary",null,[a[29]||(a[29]=e("a",{id:"SwarmMakie.could_overlap-Tuple{Point2, Any, AbstractVector{<:Point2}, AbstractVector}",href:"#SwarmMakie.could_overlap-Tuple{Point2, Any, AbstractVector{<:Point2}, AbstractVector}"},[e("span",{class:"jlbinding"},"SwarmMakie.could_overlap")],-1)),a[30]||(a[30]=s()),t(i,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),a[31]||(a[31]=n('
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

',3))]),e("details",j,[e("summary",null,[a[32]||(a[32]=e("a",{id:"SwarmMakie.first_non_overlapping_candidate-NTuple{8, Any}",href:"#SwarmMakie.first_non_overlapping_candidate-NTuple{8, Any}"},[e("span",{class:"jlbinding"},"SwarmMakie.first_non_overlapping_candidate")],-1)),a[33]||(a[33]=s()),t(i,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),a[34]||(a[34]=n('
julia
Returns `(position::Point2f, idx::Int)`

source

',2))]),e("details",M,[e("summary",null,[a[35]||(a[35]=e("a",{id:"SwarmMakie.position_candidates-NTuple{6, Any}",href:"#SwarmMakie.position_candidates-NTuple{6, Any}"},[e("span",{class:"jlbinding"},"SwarmMakie.position_candidates")],-1)),a[36]||(a[36]=s()),t(i,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),a[37]||(a[37]=e("p",null,[s("Returns "),e("code",null,"(positions::Vector{Point2f}, idxs::Vector{Int})"),s(".")],-1)),a[38]||(a[38]=e("p",null,[e("a",{href:"https://github.com/MakieOrg/SwarmMakie.jl/blob/471bba40ff1c5a9c3745644ed7a01e32991de712/src/algorithms/seaborn.jl#L94-L96",target:"_blank",rel:"noreferrer"},"source")],-1))])])}const x=l(d,[["render",S]]);export{T as __pageData,x as default}; diff --git a/previews/PR27/assets/api.md.DRTMKKNK.lean.js b/previews/PR27/assets/api.md.BIHl2OFe.lean.js similarity index 92% rename from previews/PR27/assets/api.md.DRTMKKNK.lean.js rename to previews/PR27/assets/api.md.BIHl2OFe.lean.js index caee8a0..28189ec 100644 --- a/previews/PR27/assets/api.md.DRTMKKNK.lean.js +++ b/previews/PR27/assets/api.md.BIHl2OFe.lean.js @@ -1,4 +1,4 @@ -import{_ as l,c as r,a5 as n,j as e,a as s,G as t,B as o,o as p}from"./chunks/framework.3NaDR-yp.js";const T=JSON.parse('{"title":"API Reference","description":"","frontmatter":{},"headers":[],"relativePath":"api.md","filePath":"api.md","lastUpdated":null}'),d={name:"api.md"},h={class:"jldocstring custom-block",open:""},c={class:"jldocstring custom-block",open:""},k={class:"jldocstring custom-block",open:""},m={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},u={class:"jldocstring custom-block",open:""},b={class:"jldocstring custom-block",open:""},w={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},f={class:"jldocstring custom-block",open:""},j={class:"jldocstring custom-block",open:""},M={class:"jldocstring custom-block",open:""};function S(E,a,v,C,B,A){const i=o("Badge");return p(),r("div",null,[a[39]||(a[39]=n('

API Reference ​

',2)),e("details",h,[e("summary",null,[a[0]||(a[0]=e("a",{id:"SwarmMakie.JitterAlgorithm",href:"#SwarmMakie.JitterAlgorithm"},[e("span",{class:"jlbinding"},"SwarmMakie.JitterAlgorithm")],-1)),a[1]||(a[1]=s()),t(i,{type:"info",class:"jlObjectType jlType",text:"Type"})]),a[2]||(a[2]=e("p",null,"The abstract type for jitter algorithms, which are markersize-agnostic.",-1)),a[3]||(a[3]=e("p",null,[e("a",{href:"https://github.com/MakieOrg/SwarmMakie.jl/blob/e63d6d8fd51a9b1ad17e53be04d404abe05a317f/src/algorithms/jitter.jl#L27",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",c,[e("summary",null,[a[4]||(a[4]=e("a",{id:"SwarmMakie.NoBeeswarm",href:"#SwarmMakie.NoBeeswarm"},[e("span",{class:"jlbinding"},"SwarmMakie.NoBeeswarm")],-1)),a[5]||(a[5]=s()),t(i,{type:"info",class:"jlObjectType jlType",text:"Type"})]),a[6]||(a[6]=e("p",null,[s("A simple no-op algorithm, which causes the scatter plot to be drawn as if you called "),e("code",null,"scatter"),s(" and not "),e("code",null,"beeswarm"),s(".")],-1)),a[7]||(a[7]=e("p",null,[e("a",{href:"https://github.com/MakieOrg/SwarmMakie.jl/blob/e63d6d8fd51a9b1ad17e53be04d404abe05a317f/src/recipe.jl#L53",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",k,[e("summary",null,[a[8]||(a[8]=e("a",{id:"SwarmMakie.PseudorandomJitter",href:"#SwarmMakie.PseudorandomJitter"},[e("span",{class:"jlbinding"},"SwarmMakie.PseudorandomJitter")],-1)),a[9]||(a[9]=s()),t(i,{type:"info",class:"jlObjectType jlType",text:"Type"})]),a[10]||(a[10]=n('
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

',3))]),e("details",m,[e("summary",null,[a[11]||(a[11]=e("a",{id:"SwarmMakie.QuasirandomJitter",href:"#SwarmMakie.QuasirandomJitter"},[e("span",{class:"jlbinding"},"SwarmMakie.QuasirandomJitter")],-1)),a[12]||(a[12]=s()),t(i,{type:"info",class:"jlObjectType jlType",text:"Type"})]),a[13]||(a[13]=n('
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

',3))]),e("details",g,[e("summary",null,[a[14]||(a[14]=e("a",{id:"SwarmMakie.SeabornBeeswarm",href:"#SwarmMakie.SeabornBeeswarm"},[e("span",{class:"jlbinding"},"SwarmMakie.SeabornBeeswarm")],-1)),a[15]||(a[15]=s()),t(i,{type:"info",class:"jlObjectType jlType",text:"Type"})]),a[16]||(a[16]=n('
julia
SeabornBeeswarm()

A beeswarm algorithm based on the seaborn Python package.

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

source

',4))]),e("details",u,[e("summary",null,[a[17]||(a[17]=e("a",{id:"SwarmMakie.SimpleBeeswarm",href:"#SwarmMakie.SimpleBeeswarm"},[e("span",{class:"jlbinding"},"SwarmMakie.SimpleBeeswarm")],-1)),a[18]||(a[18]=s()),t(i,{type:"info",class:"jlObjectType jlType",text:"Type"})]),a[19]||(a[19]=n('
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

',4))]),e("details",b,[e("summary",null,[a[20]||(a[20]=e("a",{id:"SwarmMakie.UniformJitter",href:"#SwarmMakie.UniformJitter"},[e("span",{class:"jlbinding"},"SwarmMakie.UniformJitter")],-1)),a[21]||(a[21]=s()),t(i,{type:"info",class:"jlObjectType jlType",text:"Type"})]),a[22]||(a[22]=n('
julia
UniformJitter(; jitter_width = 1.0)

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

source

',3))]),e("details",w,[e("summary",null,[a[23]||(a[23]=e("a",{id:"SwarmMakie.WilkinsonBeeswarm",href:"#SwarmMakie.WilkinsonBeeswarm"},[e("span",{class:"jlbinding"},"SwarmMakie.WilkinsonBeeswarm")],-1)),a[24]||(a[24]=s()),t(i,{type:"info",class:"jlObjectType jlType",text:"Type"})]),a[25]||(a[25]=n('
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

',5))]),e("details",y,[e("summary",null,[a[26]||(a[26]=e("a",{id:"SwarmMakie.beeswarm-Tuple",href:"#SwarmMakie.beeswarm-Tuple"},[e("span",{class:"jlbinding"},"SwarmMakie.beeswarm")],-1)),a[27]||(a[27]=s()),t(i,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),a[28]||(a[28]=n(`
julia
beeswarm(x, y)
+import{_ as l,c as r,a5 as n,j as e,a as s,G as t,B as o,o as p}from"./chunks/framework.BlUcFYL6.js";const T=JSON.parse('{"title":"API Reference","description":"","frontmatter":{},"headers":[],"relativePath":"api.md","filePath":"api.md","lastUpdated":null}'),d={name:"api.md"},c={class:"jldocstring custom-block",open:""},h={class:"jldocstring custom-block",open:""},k={class:"jldocstring custom-block",open:""},m={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},u={class:"jldocstring custom-block",open:""},b={class:"jldocstring custom-block",open:""},w={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},f={class:"jldocstring custom-block",open:""},j={class:"jldocstring custom-block",open:""},M={class:"jldocstring custom-block",open:""};function S(E,a,v,C,B,A){const i=o("Badge");return p(),r("div",null,[a[39]||(a[39]=n('
API Reference ​
',2)),e("details",c,[e("summary",null,[a[0]||(a[0]=e("a",{id:"SwarmMakie.JitterAlgorithm",href:"#SwarmMakie.JitterAlgorithm"},[e("span",{class:"jlbinding"},"SwarmMakie.JitterAlgorithm")],-1)),a[1]||(a[1]=s()),t(i,{type:"info",class:"jlObjectType jlType",text:"Type"})]),a[2]||(a[2]=e("p",null,"The abstract type for jitter algorithms, which are markersize-agnostic.",-1)),a[3]||(a[3]=e("p",null,[e("a",{href:"https://github.com/MakieOrg/SwarmMakie.jl/blob/471bba40ff1c5a9c3745644ed7a01e32991de712/src/algorithms/jitter.jl#L27",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",h,[e("summary",null,[a[4]||(a[4]=e("a",{id:"SwarmMakie.NoBeeswarm",href:"#SwarmMakie.NoBeeswarm"},[e("span",{class:"jlbinding"},"SwarmMakie.NoBeeswarm")],-1)),a[5]||(a[5]=s()),t(i,{type:"info",class:"jlObjectType jlType",text:"Type"})]),a[6]||(a[6]=e("p",null,[s("A simple no-op algorithm, which causes the scatter plot to be drawn as if you called "),e("code",null,"scatter"),s(" and not "),e("code",null,"beeswarm"),s(".")],-1)),a[7]||(a[7]=e("p",null,[e("a",{href:"https://github.com/MakieOrg/SwarmMakie.jl/blob/471bba40ff1c5a9c3745644ed7a01e32991de712/src/recipe.jl#L53",target:"_blank",rel:"noreferrer"},"source")],-1))]),e("details",k,[e("summary",null,[a[8]||(a[8]=e("a",{id:"SwarmMakie.PseudorandomJitter",href:"#SwarmMakie.PseudorandomJitter"},[e("span",{class:"jlbinding"},"SwarmMakie.PseudorandomJitter")],-1)),a[9]||(a[9]=s()),t(i,{type:"info",class:"jlObjectType jlType",text:"Type"})]),a[10]||(a[10]=n('
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
',3))]),e("details",m,[e("summary",null,[a[11]||(a[11]=e("a",{id:"SwarmMakie.QuasirandomJitter",href:"#SwarmMakie.QuasirandomJitter"},[e("span",{class:"jlbinding"},"SwarmMakie.QuasirandomJitter")],-1)),a[12]||(a[12]=s()),t(i,{type:"info",class:"jlObjectType jlType",text:"Type"})]),a[13]||(a[13]=n('
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
',3))]),e("details",g,[e("summary",null,[a[14]||(a[14]=e("a",{id:"SwarmMakie.SeabornBeeswarm",href:"#SwarmMakie.SeabornBeeswarm"},[e("span",{class:"jlbinding"},"SwarmMakie.SeabornBeeswarm")],-1)),a[15]||(a[15]=s()),t(i,{type:"info",class:"jlObjectType jlType",text:"Type"})]),a[16]||(a[16]=n('
julia
SeabornBeeswarm()
A beeswarm algorithm based on the seaborn Python package.
More adaptive to marker size than SimpleBeeswarm, but takes longer to compute.
source
',4))]),e("details",u,[e("summary",null,[a[17]||(a[17]=e("a",{id:"SwarmMakie.SimpleBeeswarm",href:"#SwarmMakie.SimpleBeeswarm"},[e("span",{class:"jlbinding"},"SwarmMakie.SimpleBeeswarm")],-1)),a[18]||(a[18]=s()),t(i,{type:"info",class:"jlObjectType jlType",text:"Type"})]),a[19]||(a[19]=n('
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
',4))]),e("details",b,[e("summary",null,[a[20]||(a[20]=e("a",{id:"SwarmMakie.UniformJitter",href:"#SwarmMakie.UniformJitter"},[e("span",{class:"jlbinding"},"SwarmMakie.UniformJitter")],-1)),a[21]||(a[21]=s()),t(i,{type:"info",class:"jlObjectType jlType",text:"Type"})]),a[22]||(a[22]=n('
julia
UniformJitter(; jitter_width = 1.0)
A jitter algorithm that uses a uniform distribution to create the jitter.
source
',3))]),e("details",w,[e("summary",null,[a[23]||(a[23]=e("a",{id:"SwarmMakie.WilkinsonBeeswarm",href:"#SwarmMakie.WilkinsonBeeswarm"},[e("span",{class:"jlbinding"},"SwarmMakie.WilkinsonBeeswarm")],-1)),a[24]||(a[24]=s()),t(i,{type:"info",class:"jlObjectType jlType",text:"Type"})]),a[25]||(a[25]=n('
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
',5))]),e("details",y,[e("summary",null,[a[26]||(a[26]=e("a",{id:"SwarmMakie.beeswarm-Tuple",href:"#SwarmMakie.beeswarm-Tuple"},[e("span",{class:"jlbinding"},"SwarmMakie.beeswarm")],-1)),a[27]||(a[27]=s()),t(i,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),a[28]||(a[28]=n(`
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 l,c as r,a5 as n,j as e,a as s,G as t,B as o,o as p}from"./chunks/fr
   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
`,11))]),e("details",f,[e("summary",null,[a[29]||(a[29]=e("a",{id:"SwarmMakie.could_overlap-Tuple{Point2, Any, AbstractVector{<:Point2}, AbstractVector}",href:"#SwarmMakie.could_overlap-Tuple{Point2, Any, AbstractVector{<:Point2}, AbstractVector}"},[e("span",{class:"jlbinding"},"SwarmMakie.could_overlap")],-1)),a[30]||(a[30]=s()),t(i,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),a[31]||(a[31]=n('
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
',3))]),e("details",j,[e("summary",null,[a[32]||(a[32]=e("a",{id:"SwarmMakie.first_non_overlapping_candidate-NTuple{8, Any}",href:"#SwarmMakie.first_non_overlapping_candidate-NTuple{8, Any}"},[e("span",{class:"jlbinding"},"SwarmMakie.first_non_overlapping_candidate")],-1)),a[33]||(a[33]=s()),t(i,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),a[34]||(a[34]=n('
julia
Returns `(position::Point2f, idx::Int)`
source
',2))]),e("details",M,[e("summary",null,[a[35]||(a[35]=e("a",{id:"SwarmMakie.position_candidates-NTuple{6, Any}",href:"#SwarmMakie.position_candidates-NTuple{6, Any}"},[e("span",{class:"jlbinding"},"SwarmMakie.position_candidates")],-1)),a[36]||(a[36]=s()),t(i,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),a[37]||(a[37]=e("p",null,[s("Returns "),e("code",null,"(positions::Vector{Point2f}, idxs::Vector{Int})"),s(".")],-1)),a[38]||(a[38]=e("p",null,[e("a",{href:"https://github.com/MakieOrg/SwarmMakie.jl/blob/e63d6d8fd51a9b1ad17e53be04d404abe05a317f/src/algorithms/seaborn.jl#L94-L96",target:"_blank",rel:"noreferrer"},"source")],-1))])])}const x=l(d,[["render",S]]);export{T as __pageData,x as default};
+beeswarm(ones(100), randn(100); color = rand(RGBf, 100))

source

`,11))]),e("details",f,[e("summary",null,[a[29]||(a[29]=e("a",{id:"SwarmMakie.could_overlap-Tuple{Point2, Any, AbstractVector{<:Point2}, AbstractVector}",href:"#SwarmMakie.could_overlap-Tuple{Point2, Any, AbstractVector{<:Point2}, AbstractVector}"},[e("span",{class:"jlbinding"},"SwarmMakie.could_overlap")],-1)),a[30]||(a[30]=s()),t(i,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),a[31]||(a[31]=n('
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

',3))]),e("details",j,[e("summary",null,[a[32]||(a[32]=e("a",{id:"SwarmMakie.first_non_overlapping_candidate-NTuple{8, Any}",href:"#SwarmMakie.first_non_overlapping_candidate-NTuple{8, Any}"},[e("span",{class:"jlbinding"},"SwarmMakie.first_non_overlapping_candidate")],-1)),a[33]||(a[33]=s()),t(i,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),a[34]||(a[34]=n('
julia
Returns `(position::Point2f, idx::Int)`

source

',2))]),e("details",M,[e("summary",null,[a[35]||(a[35]=e("a",{id:"SwarmMakie.position_candidates-NTuple{6, Any}",href:"#SwarmMakie.position_candidates-NTuple{6, Any}"},[e("span",{class:"jlbinding"},"SwarmMakie.position_candidates")],-1)),a[36]||(a[36]=s()),t(i,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),a[37]||(a[37]=e("p",null,[s("Returns "),e("code",null,"(positions::Vector{Point2f}, idxs::Vector{Int})"),s(".")],-1)),a[38]||(a[38]=e("p",null,[e("a",{href:"https://github.com/MakieOrg/SwarmMakie.jl/blob/471bba40ff1c5a9c3745644ed7a01e32991de712/src/algorithms/seaborn.jl#L94-L96",target:"_blank",rel:"noreferrer"},"source")],-1))])])}const x=l(d,[["render",S]]);export{T as __pageData,x as default}; diff --git a/previews/PR27/assets/app.B4CAhVK-.js b/previews/PR27/assets/app.DuHyeY2E.js similarity index 90% rename from previews/PR27/assets/app.B4CAhVK-.js rename to previews/PR27/assets/app.DuHyeY2E.js index 65cccac..7d4bc48 100644 --- a/previews/PR27/assets/app.B4CAhVK-.js 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Map,this._dirtCount=0,this._currentVacuum=null,this._enqueuedVacuum=null,this._enqueuedVacuumConditions=We,this.addFields(this._options.fields)}add(e){const{extractField:t,tokenize:s,processTerm:n,fields:r,idField:i}=this._options,o=t(e,i);if(o==null)throw new Error(`MiniSearch: document does not have ID field "${i}"`);if(this._idToShortId.has(o))throw new Error(`MiniSearch: duplicate ID ${o}`);const l=this.addDocumentId(o);this.saveStoredFields(l,e);for(const c of r){const h=t(e,c);if(h==null)continue;const v=s(h.toString(),c),p=this._fieldIds[c],b=new Set(v).size;this.addFieldLength(l,p,this._documentCount-1,b);for(const y of v){const x=n(y,c);if(Array.isArray(x))for(const w of x)this.addTerm(p,l,w);else x&&this.addTerm(p,l,x)}}}addAll(e){for(const t of e)this.add(t)}addAllAsync(e,t={}){const{chunkSize:s=10}=t,n={chunk:[],promise:Promise.resolve()},{chunk:r,promise:i}=e.reduce(({chunk:o,promise:l},c,h)=>(o.push(c),(h+1)%s===0?{chunk:[],promise:l.then(()=>new 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e)this.discard(s)}finally{this._options.autoVacuum=t}this.maybeAutoVacuum()}replace(e){const{idField:t,extractField:s}=this._options,n=s(e,t);this.discard(n),this.add(e)}vacuum(e={}){return this.conditionalVacuum(e)}conditionalVacuum(e,t){return this._currentVacuum?(this._enqueuedVacuumConditions=this._enqueuedVacuumConditions&&t,this._enqueuedVacuum!=null?this._enqueuedVacuum:(this._enqueuedVacuum=this._currentVacuum.then(()=>{const s=this._enqueuedVacuumConditions;return this._enqueuedVacuumConditions=We,this.performVacuuming(e,s)}),this._enqueuedVacuum)):this.vacuumConditionsMet(t)===!1?Promise.resolve():(this._currentVacuum=this.performVacuuming(e),this._currentVacuum)}performVacuuming(e,t){return Te(this,void 0,void 0,function*(){const s=this._dirtCount;if(this.vacuumConditionsMet(t)){const n=e.batchSize||Be.batchSize,r=e.batchWait||Be.batchWait;let i=1;for(const[o,l]of this._index){for(const[c,h]of l)for(const[v]of h)this._documentIds.has(v)||(h.size<=1?l.delete(c):h.delete(v));this._index.get(o).size===0&&this._index.delete(o),i%n===0&&(yield new Promise(c=>setTimeout(c,r))),i+=1}this._dirtCount-=s}yield null,this._currentVacuum=this._enqueuedVacuum,this._enqueuedVacuum=null})}vacuumConditionsMet(e){if(e==null)return!0;let{minDirtCount:t,minDirtFactor:s}=e;return t=t||Pe.minDirtCount,s=s||Pe.minDirtFactor,this.dirtCount>=t&&this.dirtFactor>=s}get isVacuuming(){return this._currentVacuum!=null}get dirtCount(){return this._dirtCount}get dirtFactor(){return this._dirtCount/(1+this._documentCount+this._dirtCount)}has(e){return this._idToShortId.has(e)}getStoredFields(e){const t=this._idToShortId.get(e);if(t!=null)return this._storedFields.get(t)}search(e,t={}){const s=this.executeQuery(e,t),n=[];for(const[r,{score:i,terms:o,match:l}]of s){const c=o.length||1,h={id:this._documentIds.get(r),score:i*c,terms:Object.keys(l),queryTerms:o,match:l};Object.assign(h,this._storedFields.get(r)),(t.filter==null||t.filter(h))&&n.push(h)}return e===le.wildcard&&t.boostDocument==null&&this._options.searchOptions.boostDocument==null||n.sort(ft),n}autoSuggest(e,t={}){t=Object.assign(Object.assign({},this._options.autoSuggestOptions),t);const s=new Map;for(const{score:r,terms:i}of this.search(e,t)){const o=i.join(" "),l=s.get(o);l!=null?(l.score+=r,l.count+=1):s.set(o,{score:r,terms:i,count:1})}const n=[];for(const[r,{score:i,terms:o,count:l}]of s)n.push({suggestion:r,terms:o,score:i/l});return n.sort(ft),n}get documentCount(){return this._documentCount}get termCount(){return this._index.size}static loadJSON(e,t){if(t==null)throw new Error("MiniSearch: loadJSON should be given the same options used when serializing the index");return this.loadJS(JSON.parse(e),t)}static loadJSONAsync(e,t){return Te(this,void 0,void 0,function*(){if(t==null)throw 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c=0;for(const[h,v]of s){const p=new Map;for(const b of Object.keys(v)){let y=v[b];o===1&&(y=y.ds),p.set(parseInt(b,10),yield Ee(y))}++c%1e3===0&&(yield kt(0)),l._index.set(h,p)}return l})}static instantiateMiniSearch(e,t){const{documentCount:s,nextId:n,fieldIds:r,averageFieldLength:i,dirtCount:o,serializationVersion:l}=e;if(l!==1&&l!==2)throw new Error("MiniSearch: cannot deserialize an index created with an incompatible version");const c=new le(t);return c._documentCount=s,c._nextId=n,c._idToShortId=new Map,c._fieldIds=r,c._avgFieldLength=i,c._dirtCount=o||0,c._index=new Z,c}executeQuery(e,t={}){if(e===le.wildcard)return this.executeWildcardQuery(t);if(typeof e!="string"){const p=Object.assign(Object.assign(Object.assign({},t),e),{queries:void 0}),b=e.queries.map(y=>this.executeQuery(y,p));return this.combineResults(b,p.combineWith)}const{tokenize:s,processTerm:n,searchOptions:r}=this._options,i=Object.assign(Object.assign({tokenize:s,processTerm:n},r),t),{tokenize:o,processTerm:l}=i,v=o(e).flatMap(p=>l(p)).filter(p=>!!p).map(Vs(i)).map(p=>this.executeQuerySpec(p,i));return this.combineResults(v,i.combineWith)}executeQuerySpec(e,t){const s=Object.assign(Object.assign({},this._options.searchOptions),t),n=(s.fields||this._options.fields).reduce((x,w)=>Object.assign(Object.assign({},x),{[w]:De(s.boost,w)||1}),{}),{boostDocument:r,weights:i,maxFuzzy:o,bm25:l}=s,{fuzzy:c,prefix:h}=Object.assign(Object.assign({},dt.weights),i),v=this._index.get(e.term),p=this.termResults(e.term,e.term,1,e.termBoost,v,n,r,l);let b,y;if(e.prefix&&(b=this._index.atPrefix(e.term)),e.fuzzy){const x=e.fuzzy===!0?.2:e.fuzzy,w=x<1?Math.min(o,Math.round(e.term.length*x)):x;w&&(y=this._index.fuzzyGet(e.term,w))}if(b)for(const[x,w]of b){const O=x.length-e.term.length;if(!O)continue;y==null||y.delete(x);const R=h*x.length/(x.length+.3*O);this.termResults(e.term,x,R,e.termBoost,w,n,r,l,p)}if(y)for(const x of y.keys()){const[w,O]=y.get(x);if(!O)continue;const R=c*x.length/(x.length+O);this.termResults(e.term,x,R,e.termBoost,w,n,r,l,p)}return p}executeWildcardQuery(e){const t=new Map,s=Object.assign(Object.assign({},this._options.searchOptions),e);for(const[n,r]of this._documentIds){const i=s.boostDocument?s.boostDocument(r,"",this._storedFields.get(n)):1;t.set(n,{score:i,terms:[],match:{}})}return t}combineResults(e,t=Je){if(e.length===0)return new Map;const s=t.toLowerCase(),n=zs[s];if(!n)throw new Error(`Invalid combination operator: ${t}`);return e.reduce(n)||new Map}toJSON(){const e=[];for(const[t,s]of this._index){const n={};for(const[r,i]of s)n[r]=Object.fromEntries(i);e.push([t,n])}return{documentCount:this._documentCount,nextId:this._nextId,documentIds:Object.fromEntries(this._documentIds),fieldIds:this._fieldIds,fieldLength:Object.fromEntries(this._fieldLength),averageFieldLength:this._avgFieldLength,storedFields:Object.fromEntries(this._storedFields),dirtCount:this._dirtCount,index:e,serializationVersion:2}}termResults(e,t,s,n,r,i,o,l,c=new Map){if(r==null)return c;for(const h of Object.keys(i)){const v=i[h],p=this._fieldIds[h],b=r.get(p);if(b==null)continue;let y=b.size;const x=this._avgFieldLength[p];for(const w of b.keys()){if(!this._documentIds.has(w)){this.removeTerm(p,w,t),y-=1;continue}const O=o?o(this._documentIds.get(w),t,this._storedFields.get(w)):1;if(!O)continue;const R=b.get(w),K=this._fieldLength.get(w)[p],G=js(R,y,this._documentCount,K,x,l),W=s*n*v*O*G,V=c.get(w);if(V){V.score+=W,Bs(V.terms,e);const $=De(V.match,t);$?$.push(h):V.match[t]=[h]}else c.set(w,{score:W,terms:[e],match:{[t]:[h]}})}}return c}addTerm(e,t,s){const n=this._index.fetch(s,pt);let r=n.get(e);if(r==null)r=new Map,r.set(t,1),n.set(e,r);else{const i=r.get(t);r.set(t,(i||0)+1)}}removeTerm(e,t,s){if(!this._index.has(s)){this.warnDocumentChanged(t,e,s);return}const n=this._index.fetch(s,pt),r=n.get(e);r==null||r.get(t)==null?this.warnDocumentChanged(t,e,s):r.get(t)<=1?r.size<=1?n.delete(e):r.delete(t):r.set(t,r.get(t)-1),this._index.get(s).size===0&&this._index.delete(s)}warnDocumentChanged(e,t,s){for(const n of Object.keys(this._fieldIds))if(this._fieldIds[n]===t){this._options.logger("warn",`MiniSearch: document with ID ${this._documentIds.get(e)} has changed before removal: term "${s}" was not present in field "${n}". 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Map,this._dirtCount=0,this._currentVacuum=null,this._enqueuedVacuum=null,this._enqueuedVacuumConditions=Ke,this.addFields(this._options.fields)}add(e){const{extractField:t,tokenize:s,processTerm:n,fields:r,idField:i}=this._options,o=t(e,i);if(o==null)throw new Error(`MiniSearch: document does not have ID field "${i}"`);if(this._idToShortId.has(o))throw new Error(`MiniSearch: duplicate ID ${o}`);const l=this.addDocumentId(o);this.saveStoredFields(l,e);for(const c of r){const h=t(e,c);if(h==null)continue;const v=s(h.toString(),c),p=this._fieldIds[c],b=new Set(v).size;this.addFieldLength(l,p,this._documentCount-1,b);for(const y of v){const x=n(y,c);if(Array.isArray(x))for(const w of x)this.addTerm(p,l,w);else x&&this.addTerm(p,l,x)}}}addAll(e){for(const t of e)this.add(t)}addAllAsync(e,t={}){const{chunkSize:s=10}=t,n={chunk:[],promise:Promise.resolve()},{chunk:r,promise:i}=e.reduce(({chunk:o,promise:l},c,h)=>(o.push(c),(h+1)%s===0?{chunk:[],promise:l.then(()=>new 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e)this.discard(s)}finally{this._options.autoVacuum=t}this.maybeAutoVacuum()}replace(e){const{idField:t,extractField:s}=this._options,n=s(e,t);this.discard(n),this.add(e)}vacuum(e={}){return this.conditionalVacuum(e)}conditionalVacuum(e,t){return this._currentVacuum?(this._enqueuedVacuumConditions=this._enqueuedVacuumConditions&&t,this._enqueuedVacuum!=null?this._enqueuedVacuum:(this._enqueuedVacuum=this._currentVacuum.then(()=>{const s=this._enqueuedVacuumConditions;return this._enqueuedVacuumConditions=Ke,this.performVacuuming(e,s)}),this._enqueuedVacuum)):this.vacuumConditionsMet(t)===!1?Promise.resolve():(this._currentVacuum=this.performVacuuming(e),this._currentVacuum)}performVacuuming(e,t){return Ie(this,void 0,void 0,function*(){const s=this._dirtCount;if(this.vacuumConditionsMet(t)){const n=e.batchSize||We.batchSize,r=e.batchWait||We.batchWait;let i=1;for(const[o,l]of this._index){for(const[c,h]of l)for(const[v]of h)this._documentIds.has(v)||(h.size<=1?l.delete(c):h.delete(v));this._index.get(o).size===0&&this._index.delete(o),i%n===0&&(yield new Promise(c=>setTimeout(c,r))),i+=1}this._dirtCount-=s}yield null,this._currentVacuum=this._enqueuedVacuum,this._enqueuedVacuum=null})}vacuumConditionsMet(e){if(e==null)return!0;let{minDirtCount:t,minDirtFactor:s}=e;return t=t||je.minDirtCount,s=s||je.minDirtFactor,this.dirtCount>=t&&this.dirtFactor>=s}get isVacuuming(){return this._currentVacuum!=null}get dirtCount(){return this._dirtCount}get dirtFactor(){return this._dirtCount/(1+this._documentCount+this._dirtCount)}has(e){return this._idToShortId.has(e)}getStoredFields(e){const t=this._idToShortId.get(e);if(t!=null)return this._storedFields.get(t)}search(e,t={}){const s=this.executeQuery(e,t),n=[];for(const[r,{score:i,terms:o,match:l}]of s){const c=o.length||1,h={id:this._documentIds.get(r),score:i*c,terms:Object.keys(l),queryTerms:o,match:l};Object.assign(h,this._storedFields.get(r)),(t.filter==null||t.filter(h))&&n.push(h)}return e===ce.wildcard&&t.boostDocument==null&&this._options.searchOptions.boostDocument==null||n.sort(ft),n}autoSuggest(e,t={}){t=Object.assign(Object.assign({},this._options.autoSuggestOptions),t);const s=new Map;for(const{score:r,terms:i}of this.search(e,t)){const o=i.join(" "),l=s.get(o);l!=null?(l.score+=r,l.count+=1):s.set(o,{score:r,terms:i,count:1})}const n=[];for(const[r,{score:i,terms:o,count:l}]of s)n.push({suggestion:r,terms:o,score:i/l});return n.sort(ft),n}get documentCount(){return this._documentCount}get termCount(){return this._index.size}static loadJSON(e,t){if(t==null)throw new Error("MiniSearch: loadJSON should be given the same options used when serializing the index");return this.loadJS(JSON.parse(e),t)}static loadJSONAsync(e,t){return Ie(this,void 0,void 0,function*(){if(t==null)throw 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c=0;for(const[h,v]of s){const p=new Map;for(const b of Object.keys(v)){let y=v[b];o===1&&(y=y.ds),p.set(parseInt(b,10),yield Te(y))}++c%1e3===0&&(yield kt(0)),l._index.set(h,p)}return l})}static instantiateMiniSearch(e,t){const{documentCount:s,nextId:n,fieldIds:r,averageFieldLength:i,dirtCount:o,serializationVersion:l}=e;if(l!==1&&l!==2)throw new Error("MiniSearch: cannot deserialize an index created with an incompatible version");const c=new ce(t);return c._documentCount=s,c._nextId=n,c._idToShortId=new Map,c._fieldIds=r,c._avgFieldLength=i,c._dirtCount=o||0,c._index=new Z,c}executeQuery(e,t={}){if(e===ce.wildcard)return this.executeWildcardQuery(t);if(typeof e!="string"){const p=Object.assign(Object.assign(Object.assign({},t),e),{queries:void 0}),b=e.queries.map(y=>this.executeQuery(y,p));return this.combineResults(b,p.combineWith)}const{tokenize:s,processTerm:n,searchOptions:r}=this._options,i=Object.assign(Object.assign({tokenize:s,processTerm:n},r),t),{tokenize:o,processTerm:l}=i,v=o(e).flatMap(p=>l(p)).filter(p=>!!p).map(Vs(i)).map(p=>this.executeQuerySpec(p,i));return this.combineResults(v,i.combineWith)}executeQuerySpec(e,t){const s=Object.assign(Object.assign({},this._options.searchOptions),t),n=(s.fields||this._options.fields).reduce((x,w)=>Object.assign(Object.assign({},x),{[w]:ze(s.boost,w)||1}),{}),{boostDocument:r,weights:i,maxFuzzy:o,bm25:l}=s,{fuzzy:c,prefix:h}=Object.assign(Object.assign({},dt.weights),i),v=this._index.get(e.term),p=this.termResults(e.term,e.term,1,e.termBoost,v,n,r,l);let b,y;if(e.prefix&&(b=this._index.atPrefix(e.term)),e.fuzzy){const x=e.fuzzy===!0?.2:e.fuzzy,w=x<1?Math.min(o,Math.round(e.term.length*x)):x;w&&(y=this._index.fuzzyGet(e.term,w))}if(b)for(const[x,w]of b){const O=x.length-e.term.length;if(!O)continue;y==null||y.delete(x);const 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s)n[r]=Object.fromEntries(i);e.push([t,n])}return{documentCount:this._documentCount,nextId:this._nextId,documentIds:Object.fromEntries(this._documentIds),fieldIds:this._fieldIds,fieldLength:Object.fromEntries(this._fieldLength),averageFieldLength:this._avgFieldLength,storedFields:Object.fromEntries(this._storedFields),dirtCount:this._dirtCount,index:e,serializationVersion:2}}termResults(e,t,s,n,r,i,o,l,c=new Map){if(r==null)return c;for(const h of Object.keys(i)){const v=i[h],p=this._fieldIds[h],b=r.get(p);if(b==null)continue;let y=b.size;const x=this._avgFieldLength[p];for(const w of b.keys()){if(!this._documentIds.has(w)){this.removeTerm(p,w,t),y-=1;continue}const O=o?o(this._documentIds.get(w),t,this._storedFields.get(w)):1;if(!O)continue;const R=b.get(w),K=this._fieldLength.get(w)[p],G=js(R,y,this._documentCount,K,x,l),W=s*n*v*O*G,V=c.get(w);if(V){V.score+=W,Bs(V.terms,e);const $=ze(V.match,t);$?$.push(h):V.match[t]=[h]}else c.set(w,{score:W,terms:[e],match:{[t]:[h]}})}}return c}addTerm(e,t,s){const n=this._index.fetch(s,pt);let r=n.get(e);if(r==null)r=new Map,r.set(t,1),n.set(e,r);else{const i=r.get(t);r.set(t,(i||0)+1)}}removeTerm(e,t,s){if(!this._index.has(s)){this.warnDocumentChanged(t,e,s);return}const n=this._index.fetch(s,pt),r=n.get(e);r==null||r.get(t)==null?this.warnDocumentChanged(t,e,s):r.get(t)<=1?r.size<=1?n.delete(e):r.delete(t):r.set(t,r.get(t)-1),this._index.get(s).size===0&&this._index.delete(s)}warnDocumentChanged(e,t,s){for(const n of Object.keys(this._fieldIds))if(this._fieldIds[n]===t){this._options.logger("warn",`MiniSearch: document with ID ${this._documentIds.get(e)} has changed before removal: term "${s}" was not present in field "${n}". 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Examples ​

Multiple variables with colors ​

julia
using CairoMakie, SwarmMakie
+import{_ as i,c as a,a5 as h,o as n}from"./chunks/framework.BlUcFYL6.js";const k="/SwarmMakie.jl/previews/PR27/assets/pvlbpfy.BVo4yzAQ.png",l="/SwarmMakie.jl/previews/PR27/assets/enwmxeh.DGFAEXiT.png",t="/SwarmMakie.jl/previews/PR27/assets/qwpcgoi.DmbDKian.png",p="/SwarmMakie.jl/previews/PR27/assets/dwbfkxy.Bi9EHqHJ.png",e="/SwarmMakie.jl/previews/PR27/assets/usvxxlj.C9Xb5qEe.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
diff --git a/previews/PR27/assets/examples_examples.md.sNb7dMTS.lean.js b/previews/PR27/assets/examples_examples.md.BSWj1S9U.lean.js
similarity index 98%
rename from previews/PR27/assets/examples_examples.md.sNb7dMTS.lean.js
rename to previews/PR27/assets/examples_examples.md.BSWj1S9U.lean.js
index 3edec12..447c555 100644
--- a/previews/PR27/assets/examples_examples.md.sNb7dMTS.lean.js
+++ b/previews/PR27/assets/examples_examples.md.BSWj1S9U.lean.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a5 as h,o as n}from"./chunks/framework.3NaDR-yp.js";const k="/SwarmMakie.jl/previews/PR27/assets/vbsomlm.BuyPW54g.png",l="/SwarmMakie.jl/previews/PR27/assets/dcsdxti.BH-By5fc.png",t="/SwarmMakie.jl/previews/PR27/assets/vnzrrst.DmbDKian.png",p="/SwarmMakie.jl/previews/PR27/assets/ryhhdrr.Bi9EHqHJ.png",e="/SwarmMakie.jl/previews/PR27/assets/qahuljq.C9Xb5qEe.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
+import{_ as i,c as a,a5 as h,o as n}from"./chunks/framework.BlUcFYL6.js";const k="/SwarmMakie.jl/previews/PR27/assets/pvlbpfy.BVo4yzAQ.png",l="/SwarmMakie.jl/previews/PR27/assets/enwmxeh.DGFAEXiT.png",t="/SwarmMakie.jl/previews/PR27/assets/qwpcgoi.DmbDKian.png",p="/SwarmMakie.jl/previews/PR27/assets/dwbfkxy.Bi9EHqHJ.png",e="/SwarmMakie.jl/previews/PR27/assets/usvxxlj.C9Xb5qEe.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
diff --git a/previews/PR27/assets/examples_scales.md.B34Gtc84.js b/previews/PR27/assets/examples_scales.md.B34Gtc84.js
deleted file mode 100644
index 7f1a7bd..0000000
--- a/previews/PR27/assets/examples_scales.md.B34Gtc84.js
+++ /dev/null
@@ -1,10 +0,0 @@
-import{_ as i,c as a,a5 as n,o as e}from"./chunks/framework.3NaDR-yp.js";const t="/SwarmMakie.jl/previews/PR27/assets/yykgwxn.Lwo-risk.png",l="/SwarmMakie.jl/previews/PR27/assets/yhqjort.DdKDRCCk.png",y=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,E,d,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 c=i(h,[["render",p]]);export{y as __pageData,c as default};
diff --git a/previews/PR27/assets/examples_scales.md.B34Gtc84.lean.js b/previews/PR27/assets/examples_scales.md.B34Gtc84.lean.js
deleted file mode 100644
index 7f1a7bd..0000000
--- a/previews/PR27/assets/examples_scales.md.B34Gtc84.lean.js
+++ /dev/null
@@ -1,10 +0,0 @@
-import{_ as i,c as a,a5 as n,o as e}from"./chunks/framework.3NaDR-yp.js";const t="/SwarmMakie.jl/previews/PR27/assets/yykgwxn.Lwo-risk.png",l="/SwarmMakie.jl/previews/PR27/assets/yhqjort.DdKDRCCk.png",y=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,E,d,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 c=i(h,[["render",p]]);export{y as __pageData,c as default};
diff --git a/previews/PR27/assets/examples_scales.md.DOrpoqmO.js b/previews/PR27/assets/examples_scales.md.DOrpoqmO.js
new file mode 100644
index 0000000..0d6e675
--- /dev/null
+++ b/previews/PR27/assets/examples_scales.md.DOrpoqmO.js
@@ -0,0 +1,30 @@
+import{_ as s,c as n,a5 as e,o as p}from"./chunks/framework.BlUcFYL6.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/previews/PR27/assets/examples_scales.md.DOrpoqmO.lean.js b/previews/PR27/assets/examples_scales.md.DOrpoqmO.lean.js
new file mode 100644
index 0000000..0d6e675
--- /dev/null
+++ b/previews/PR27/assets/examples_scales.md.DOrpoqmO.lean.js
@@ -0,0 +1,30 @@
+import{_ as s,c as n,a5 as e,o as p}from"./chunks/framework.BlUcFYL6.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/previews/PR27/assets/examples_unconventional.md.BF6sASGH.js b/previews/PR27/assets/examples_unconventional.md.BtZHLrKq.js
similarity index 99%
rename from previews/PR27/assets/examples_unconventional.md.BF6sASGH.js
rename to previews/PR27/assets/examples_unconventional.md.BtZHLrKq.js
index 662d672..10158b1 100644
--- a/previews/PR27/assets/examples_unconventional.md.BF6sASGH.js
+++ b/previews/PR27/assets/examples_unconventional.md.BtZHLrKq.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.3NaDR-yp.js";const k="/SwarmMakie.jl/previews/PR27/assets/gymcoja.BYH6Yu1P.png",l="/SwarmMakie.jl/previews/PR27/assets/xvmjehf.V8-0WaAh.png",p="/SwarmMakie.jl/previews/PR27/assets/dgupicq.DChJehoU.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.BlUcFYL6.js";const k="/SwarmMakie.jl/previews/PR27/assets/jfedtvr.BYH6Yu1P.png",l="/SwarmMakie.jl/previews/PR27/assets/hoggfml.V8-0WaAh.png",p="/SwarmMakie.jl/previews/PR27/assets/rinrdgn.GVthIZnS.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/previews/PR27/assets/examples_unconventional.md.BF6sASGH.lean.js b/previews/PR27/assets/examples_unconventional.md.BtZHLrKq.lean.js
similarity index 99%
rename from previews/PR27/assets/examples_unconventional.md.BF6sASGH.lean.js
rename to previews/PR27/assets/examples_unconventional.md.BtZHLrKq.lean.js
index 662d672..10158b1 100644
--- a/previews/PR27/assets/examples_unconventional.md.BF6sASGH.lean.js
+++ b/previews/PR27/assets/examples_unconventional.md.BtZHLrKq.lean.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.3NaDR-yp.js";const k="/SwarmMakie.jl/previews/PR27/assets/gymcoja.BYH6Yu1P.png",l="/SwarmMakie.jl/previews/PR27/assets/xvmjehf.V8-0WaAh.png",p="/SwarmMakie.jl/previews/PR27/assets/dgupicq.DChJehoU.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.BlUcFYL6.js";const k="/SwarmMakie.jl/previews/PR27/assets/jfedtvr.BYH6Yu1P.png",l="/SwarmMakie.jl/previews/PR27/assets/hoggfml.V8-0WaAh.png",p="/SwarmMakie.jl/previews/PR27/assets/rinrdgn.GVthIZnS.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/previews/PR27/assets/gutters.md.D8Su6OSx.js b/previews/PR27/assets/gutters.md.BydtjO5O.js
similarity index 96%
rename from previews/PR27/assets/gutters.md.D8Su6OSx.js
rename to previews/PR27/assets/gutters.md.BydtjO5O.js
index a89bb49..238a84c 100644
--- a/previews/PR27/assets/gutters.md.D8Su6OSx.js
+++ b/previews/PR27/assets/gutters.md.BydtjO5O.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a5 as t,o as e}from"./chunks/framework.3NaDR-yp.js";const n="/SwarmMakie.jl/previews/PR27/assets/dcznqmi.PrNyDGTk.svg",h="/SwarmMakie.jl/previews/PR27/assets/paafstf.DFCZcexu.png",c=JSON.parse('{"title":"Gutters","description":"","frontmatter":{},"headers":[],"relativePath":"gutters.md","filePath":"gutters.md","lastUpdated":null}'),l={name:"gutters.md"};function p(k,s,r,d,o,g){return e(),a("div",null,s[0]||(s[0]=[t(`
Gutters ​
julia
using SwarmMakie, CairoMakie
+import{_ as i,c as a,a5 as t,o as e}from"./chunks/framework.BlUcFYL6.js";const n="/SwarmMakie.jl/previews/PR27/assets/uqhhjgx.Aa7D3T8W.svg",h="/SwarmMakie.jl/previews/PR27/assets/ehqnwbt.CzHxqQBj.png",c=JSON.parse('{"title":"Gutters","description":"","frontmatter":{},"headers":[],"relativePath":"gutters.md","filePath":"gutters.md","lastUpdated":null}'),l={name:"gutters.md"};function p(k,s,r,d,o,g){return e(),a("div",null,s[0]||(s[0]=[t(`
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(
diff --git a/previews/PR27/assets/gutters.md.D8Su6OSx.lean.js b/previews/PR27/assets/gutters.md.BydtjO5O.lean.js
similarity index 96%
rename from previews/PR27/assets/gutters.md.D8Su6OSx.lean.js
rename to previews/PR27/assets/gutters.md.BydtjO5O.lean.js
index a89bb49..238a84c 100644
--- a/previews/PR27/assets/gutters.md.D8Su6OSx.lean.js
+++ b/previews/PR27/assets/gutters.md.BydtjO5O.lean.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a5 as t,o as e}from"./chunks/framework.3NaDR-yp.js";const n="/SwarmMakie.jl/previews/PR27/assets/dcznqmi.PrNyDGTk.svg",h="/SwarmMakie.jl/previews/PR27/assets/paafstf.DFCZcexu.png",c=JSON.parse('{"title":"Gutters","description":"","frontmatter":{},"headers":[],"relativePath":"gutters.md","filePath":"gutters.md","lastUpdated":null}'),l={name:"gutters.md"};function p(k,s,r,d,o,g){return e(),a("div",null,s[0]||(s[0]=[t(`
Gutters ​
julia
using SwarmMakie, CairoMakie
+import{_ as i,c as a,a5 as t,o as e}from"./chunks/framework.BlUcFYL6.js";const n="/SwarmMakie.jl/previews/PR27/assets/uqhhjgx.Aa7D3T8W.svg",h="/SwarmMakie.jl/previews/PR27/assets/ehqnwbt.CzHxqQBj.png",c=JSON.parse('{"title":"Gutters","description":"","frontmatter":{},"headers":[],"relativePath":"gutters.md","filePath":"gutters.md","lastUpdated":null}'),l={name:"gutters.md"};function p(k,s,r,d,o,g){return e(),a("div",null,s[0]||(s[0]=[t(`
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(
diff --git a/previews/PR27/assets/hlntquo.DXCk5H2R.png b/previews/PR27/assets/hlntquo.DXCk5H2R.png
deleted file mode 100644
index 4a43eb58548dc8ade56db1471de2c893f4bb330b..0000000000000000000000000000000000000000
GIT binary patch
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diff --git a/previews/PR27/assets/xvmjehf.V8-0WaAh.png b/previews/PR27/assets/hoggfml.V8-0WaAh.png
similarity index 100%
rename from previews/PR27/assets/xvmjehf.V8-0WaAh.png
rename to previews/PR27/assets/hoggfml.V8-0WaAh.png
diff --git a/previews/PR27/assets/index.md.45hJHmLp.js b/previews/PR27/assets/index.md.DBRi-Clc.js
similarity index 97%
rename from previews/PR27/assets/index.md.45hJHmLp.js
rename to previews/PR27/assets/index.md.DBRi-Clc.js
index 39662a8..1d23ba3 100644
--- a/previews/PR27/assets/index.md.45hJHmLp.js
+++ b/previews/PR27/assets/index.md.DBRi-Clc.js
@@ -1,4 +1,4 @@
-import{_ as s,c as a,a5 as e,o as t}from"./chunks/framework.3NaDR-yp.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.BlUcFYL6.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/previews/PR27/assets/index.md.45hJHmLp.lean.js b/previews/PR27/assets/index.md.DBRi-Clc.lean.js
similarity index 97%
rename from previews/PR27/assets/index.md.45hJHmLp.lean.js
rename to previews/PR27/assets/index.md.DBRi-Clc.lean.js
index 39662a8..1d23ba3 100644
--- a/previews/PR27/assets/index.md.45hJHmLp.lean.js
+++ b/previews/PR27/assets/index.md.DBRi-Clc.lean.js
@@ -1,4 +1,4 @@
-import{_ as s,c as a,a5 as e,o as t}from"./chunks/framework.3NaDR-yp.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.BlUcFYL6.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/previews/PR27/assets/introduction.md.C_96zFxC.js b/previews/PR27/assets/introduction.md.BIv-Zlrr.js
similarity index 97%
rename from previews/PR27/assets/introduction.md.C_96zFxC.js
rename to previews/PR27/assets/introduction.md.BIv-Zlrr.js
index 7fa125e..f7ddac8 100644
--- a/previews/PR27/assets/introduction.md.C_96zFxC.js
+++ b/previews/PR27/assets/introduction.md.BIv-Zlrr.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a5 as t,o as e}from"./chunks/framework.3NaDR-yp.js";const h="/SwarmMakie.jl/previews/PR27/assets/ufkgtrw.4mJRyZ7l.png",n="/SwarmMakie.jl/previews/PR27/assets/zoatsev.BnSlljpp.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.BlUcFYL6.js";const h="/SwarmMakie.jl/previews/PR27/assets/kxmadac.D6k83ke7.png",n="/SwarmMakie.jl/previews/PR27/assets/tuvsxfg.BnSlljpp.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/previews/PR27/assets/introduction.md.C_96zFxC.lean.js b/previews/PR27/assets/introduction.md.BIv-Zlrr.lean.js
similarity index 97%
rename from previews/PR27/assets/introduction.md.C_96zFxC.lean.js
rename to previews/PR27/assets/introduction.md.BIv-Zlrr.lean.js
index 7fa125e..f7ddac8 100644
--- a/previews/PR27/assets/introduction.md.C_96zFxC.lean.js
+++ b/previews/PR27/assets/introduction.md.BIv-Zlrr.lean.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a5 as t,o as e}from"./chunks/framework.3NaDR-yp.js";const h="/SwarmMakie.jl/previews/PR27/assets/ufkgtrw.4mJRyZ7l.png",n="/SwarmMakie.jl/previews/PR27/assets/zoatsev.BnSlljpp.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.BlUcFYL6.js";const h="/SwarmMakie.jl/previews/PR27/assets/kxmadac.D6k83ke7.png",n="/SwarmMakie.jl/previews/PR27/assets/tuvsxfg.BnSlljpp.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/previews/PR27/assets/gymcoja.BYH6Yu1P.png b/previews/PR27/assets/jfedtvr.BYH6Yu1P.png
similarity index 100%
rename from previews/PR27/assets/gymcoja.BYH6Yu1P.png
rename to previews/PR27/assets/jfedtvr.BYH6Yu1P.png
diff --git a/previews/PR27/assets/kxmadac.D6k83ke7.png b/previews/PR27/assets/kxmadac.D6k83ke7.png
new file mode 100644
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lHQQ(v5^1*a-u}A;ou#SV-tUSwarmMakie.jl ​
julia
module SwarmMakie
+import{_ as i,c as a,a5 as n,o as l}from"./chunks/framework.BlUcFYL6.js";const g=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/previews/PR27/assets/source_SwarmMakie.md.CYotaraR.lean.js b/previews/PR27/assets/source_SwarmMakie.md.DoC0DsWn.lean.js
similarity index 97%
rename from previews/PR27/assets/source_SwarmMakie.md.CYotaraR.lean.js
rename to previews/PR27/assets/source_SwarmMakie.md.DoC0DsWn.lean.js
index b6e0f9d..2b00a95 100644
--- a/previews/PR27/assets/source_SwarmMakie.md.CYotaraR.lean.js
+++ b/previews/PR27/assets/source_SwarmMakie.md.DoC0DsWn.lean.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a5 as n,o as l}from"./chunks/framework.3NaDR-yp.js";const g=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.BlUcFYL6.js";const g=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/previews/PR27/assets/source_algorithms_jitter.md.3an8LnIn.js b/previews/PR27/assets/source_algorithms_jitter.md.KA-6KTWA.js
similarity index 99%
rename from previews/PR27/assets/source_algorithms_jitter.md.3an8LnIn.js
rename to previews/PR27/assets/source_algorithms_jitter.md.KA-6KTWA.js
index 6a44fae..a5b3913 100644
--- a/previews/PR27/assets/source_algorithms_jitter.md.3an8LnIn.js
+++ b/previews/PR27/assets/source_algorithms_jitter.md.KA-6KTWA.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a5 as t,o as n}from"./chunks/framework.3NaDR-yp.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.BlUcFYL6.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/previews/PR27/assets/source_algorithms_jitter.md.3an8LnIn.lean.js b/previews/PR27/assets/source_algorithms_jitter.md.KA-6KTWA.lean.js
similarity index 99%
rename from previews/PR27/assets/source_algorithms_jitter.md.3an8LnIn.lean.js
rename to previews/PR27/assets/source_algorithms_jitter.md.KA-6KTWA.lean.js
index 6a44fae..a5b3913 100644
--- a/previews/PR27/assets/source_algorithms_jitter.md.3an8LnIn.lean.js
+++ b/previews/PR27/assets/source_algorithms_jitter.md.KA-6KTWA.lean.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a5 as t,o as n}from"./chunks/framework.3NaDR-yp.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.BlUcFYL6.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/previews/PR27/assets/source_algorithms_mkborregaard.md.CClWkSMe.js b/previews/PR27/assets/source_algorithms_mkborregaard.md.BW7o9Glm.js
similarity index 99%
rename from previews/PR27/assets/source_algorithms_mkborregaard.md.CClWkSMe.js
rename to previews/PR27/assets/source_algorithms_mkborregaard.md.BW7o9Glm.js
index 0ae6f47..3e9bf33 100644
--- a/previews/PR27/assets/source_algorithms_mkborregaard.md.CClWkSMe.js
+++ b/previews/PR27/assets/source_algorithms_mkborregaard.md.BW7o9Glm.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.3NaDR-yp.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.BlUcFYL6.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/previews/PR27/assets/source_algorithms_mkborregaard.md.CClWkSMe.lean.js b/previews/PR27/assets/source_algorithms_mkborregaard.md.BW7o9Glm.lean.js
similarity index 99%
rename from previews/PR27/assets/source_algorithms_mkborregaard.md.CClWkSMe.lean.js
rename to previews/PR27/assets/source_algorithms_mkborregaard.md.BW7o9Glm.lean.js
index 0ae6f47..3e9bf33 100644
--- a/previews/PR27/assets/source_algorithms_mkborregaard.md.CClWkSMe.lean.js
+++ b/previews/PR27/assets/source_algorithms_mkborregaard.md.BW7o9Glm.lean.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.3NaDR-yp.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.BlUcFYL6.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/previews/PR27/assets/source_algorithms_seaborn.md.CMhi_Bxj.js b/previews/PR27/assets/source_algorithms_seaborn.md.Dyxm848b.js
similarity index 99%
rename from previews/PR27/assets/source_algorithms_seaborn.md.CMhi_Bxj.js
rename to previews/PR27/assets/source_algorithms_seaborn.md.Dyxm848b.js
index 437b3f0..0ddeab5 100644
--- a/previews/PR27/assets/source_algorithms_seaborn.md.CMhi_Bxj.js
+++ b/previews/PR27/assets/source_algorithms_seaborn.md.Dyxm848b.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.3NaDR-yp.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.BlUcFYL6.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/previews/PR27/assets/source_algorithms_seaborn.md.CMhi_Bxj.lean.js b/previews/PR27/assets/source_algorithms_seaborn.md.Dyxm848b.lean.js
similarity index 99%
rename from previews/PR27/assets/source_algorithms_seaborn.md.CMhi_Bxj.lean.js
rename to previews/PR27/assets/source_algorithms_seaborn.md.Dyxm848b.lean.js
index 437b3f0..0ddeab5 100644
--- a/previews/PR27/assets/source_algorithms_seaborn.md.CMhi_Bxj.lean.js
+++ b/previews/PR27/assets/source_algorithms_seaborn.md.Dyxm848b.lean.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.3NaDR-yp.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.BlUcFYL6.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/previews/PR27/assets/source_algorithms_simple.md.CENw21kQ.js b/previews/PR27/assets/source_algorithms_simple.md.BQh78-JT.js
similarity index 99%
rename from previews/PR27/assets/source_algorithms_simple.md.CENw21kQ.js
rename to previews/PR27/assets/source_algorithms_simple.md.BQh78-JT.js
index 9cd3572..5ad0e75 100644
--- a/previews/PR27/assets/source_algorithms_simple.md.CENw21kQ.js
+++ b/previews/PR27/assets/source_algorithms_simple.md.BQh78-JT.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.3NaDR-yp.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.BlUcFYL6.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/previews/PR27/assets/source_algorithms_simple.md.CENw21kQ.lean.js b/previews/PR27/assets/source_algorithms_simple.md.BQh78-JT.lean.js
similarity index 99%
rename from previews/PR27/assets/source_algorithms_simple.md.CENw21kQ.lean.js
rename to previews/PR27/assets/source_algorithms_simple.md.BQh78-JT.lean.js
index 9cd3572..5ad0e75 100644
--- a/previews/PR27/assets/source_algorithms_simple.md.CENw21kQ.lean.js
+++ b/previews/PR27/assets/source_algorithms_simple.md.BQh78-JT.lean.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.3NaDR-yp.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.BlUcFYL6.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/previews/PR27/assets/source_algorithms_simple2.md.rbXXkVLN.js b/previews/PR27/assets/source_algorithms_simple2.md.Brdq-nLt.js
similarity index 99%
rename from previews/PR27/assets/source_algorithms_simple2.md.rbXXkVLN.js
rename to previews/PR27/assets/source_algorithms_simple2.md.Brdq-nLt.js
index 828844d..4cdcc37 100644
--- a/previews/PR27/assets/source_algorithms_simple2.md.rbXXkVLN.js
+++ b/previews/PR27/assets/source_algorithms_simple2.md.Brdq-nLt.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.3NaDR-yp.js";const g=JSON.parse('{"title":"","description":"","frontmatter":{},"headers":[],"relativePath":"source/algorithms/simple2.md","filePath":"source/algorithms/simple2.md","lastUpdated":null}'),l={name:"source/algorithms/simple2.md"};function t(k,s,p,e,r,E){return h(),a("div",null,s[0]||(s[0]=[n(`
julia
struct SimpleBeeswarm2 <: BeeswarmAlgorithm
+import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.BlUcFYL6.js";const g=JSON.parse('{"title":"","description":"","frontmatter":{},"headers":[],"relativePath":"source/algorithms/simple2.md","filePath":"source/algorithms/simple2.md","lastUpdated":null}'),l={name:"source/algorithms/simple2.md"};function t(k,s,p,e,r,E){return h(),a("div",null,s[0]||(s[0]=[n(`
julia
struct SimpleBeeswarm2 <: BeeswarmAlgorithm
 end
 
 
diff --git a/previews/PR27/assets/source_algorithms_simple2.md.rbXXkVLN.lean.js b/previews/PR27/assets/source_algorithms_simple2.md.Brdq-nLt.lean.js
similarity index 99%
rename from previews/PR27/assets/source_algorithms_simple2.md.rbXXkVLN.lean.js
rename to previews/PR27/assets/source_algorithms_simple2.md.Brdq-nLt.lean.js
index 828844d..4cdcc37 100644
--- a/previews/PR27/assets/source_algorithms_simple2.md.rbXXkVLN.lean.js
+++ b/previews/PR27/assets/source_algorithms_simple2.md.Brdq-nLt.lean.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.3NaDR-yp.js";const g=JSON.parse('{"title":"","description":"","frontmatter":{},"headers":[],"relativePath":"source/algorithms/simple2.md","filePath":"source/algorithms/simple2.md","lastUpdated":null}'),l={name:"source/algorithms/simple2.md"};function t(k,s,p,e,r,E){return h(),a("div",null,s[0]||(s[0]=[n(`
julia
struct SimpleBeeswarm2 <: BeeswarmAlgorithm
+import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.BlUcFYL6.js";const g=JSON.parse('{"title":"","description":"","frontmatter":{},"headers":[],"relativePath":"source/algorithms/simple2.md","filePath":"source/algorithms/simple2.md","lastUpdated":null}'),l={name:"source/algorithms/simple2.md"};function t(k,s,p,e,r,E){return h(),a("div",null,s[0]||(s[0]=[n(`
julia
struct SimpleBeeswarm2 <: BeeswarmAlgorithm
 end
 
 
diff --git a/previews/PR27/assets/source_algorithms_wilkinson.md.Dl9pjUVc.js b/previews/PR27/assets/source_algorithms_wilkinson.md.ZSfvWZ-O.js
similarity index 99%
rename from previews/PR27/assets/source_algorithms_wilkinson.md.Dl9pjUVc.js
rename to previews/PR27/assets/source_algorithms_wilkinson.md.ZSfvWZ-O.js
index a6e9552..1536d09 100644
--- a/previews/PR27/assets/source_algorithms_wilkinson.md.Dl9pjUVc.js
+++ b/previews/PR27/assets/source_algorithms_wilkinson.md.ZSfvWZ-O.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.3NaDR-yp.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.BlUcFYL6.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/previews/PR27/assets/source_algorithms_wilkinson.md.Dl9pjUVc.lean.js b/previews/PR27/assets/source_algorithms_wilkinson.md.ZSfvWZ-O.lean.js
similarity index 99%
rename from previews/PR27/assets/source_algorithms_wilkinson.md.Dl9pjUVc.lean.js
rename to previews/PR27/assets/source_algorithms_wilkinson.md.ZSfvWZ-O.lean.js
index a6e9552..1536d09 100644
--- a/previews/PR27/assets/source_algorithms_wilkinson.md.Dl9pjUVc.lean.js
+++ b/previews/PR27/assets/source_algorithms_wilkinson.md.ZSfvWZ-O.lean.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.3NaDR-yp.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.BlUcFYL6.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/previews/PR27/assets/source_recipe.md.f1G8L5h4.js b/previews/PR27/assets/source_recipe.md.CYW5jnZh.js
similarity index 99%
rename from previews/PR27/assets/source_recipe.md.f1G8L5h4.js
rename to previews/PR27/assets/source_recipe.md.CYW5jnZh.js
index ff6f524..a51de1a 100644
--- a/previews/PR27/assets/source_recipe.md.f1G8L5h4.js
+++ b/previews/PR27/assets/source_recipe.md.CYW5jnZh.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.3NaDR-yp.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(k,s,l,e,r,E){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.BlUcFYL6.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(k,s,l,e,r,E){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/previews/PR27/assets/source_recipe.md.f1G8L5h4.lean.js b/previews/PR27/assets/source_recipe.md.CYW5jnZh.lean.js
similarity index 99%
rename from previews/PR27/assets/source_recipe.md.f1G8L5h4.lean.js
rename to previews/PR27/assets/source_recipe.md.CYW5jnZh.lean.js
index ff6f524..a51de1a 100644
--- a/previews/PR27/assets/source_recipe.md.f1G8L5h4.lean.js
+++ b/previews/PR27/assets/source_recipe.md.CYW5jnZh.lean.js
@@ -1,4 +1,4 @@
-import{_ as i,c as a,a5 as n,o as h}from"./chunks/framework.3NaDR-yp.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(k,s,l,e,r,E){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.BlUcFYL6.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(k,s,l,e,r,E){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/previews/PR27/assets/style.D3ycwdoG.css b/previews/PR27/assets/style.BB_nTKhY.css
similarity index 95%
rename from previews/PR27/assets/style.D3ycwdoG.css
rename to previews/PR27/assets/style.BB_nTKhY.css
index d3c09a5..de5d83b 100644
--- a/previews/PR27/assets/style.D3ycwdoG.css
+++ b/previews/PR27/assets/style.BB_nTKhY.css
@@ -1 +1 @@
-@import"https://fonts.googleapis.com/css?family=Space+Mono:regular,italic,700,700italic";@import"https://fonts.googleapis.com/css?family=Space+Grotesk:regular,italic,700,700italic";@font-face{font-family:Inter;font-style:normal;font-weight:100 900;font-display:swap;src:url(/SwarmMakie.jl/previews/PR27/assets/inter-roman-cyrillic-ext.BBPuwvHQ.woff2) format("woff2");unicode-range:U+0460-052F,U+1C80-1C88,U+20B4,U+2DE0-2DFF,U+A640-A69F,U+FE2E-FE2F}@font-face{font-family:Inter;font-style:normal;font-weight:100 900;font-display:swap;src:url(/SwarmMakie.jl/previews/PR27/assets/inter-roman-cyrillic.C5lxZ8CY.woff2) format("woff2");unicode-range:U+0301,U+0400-045F,U+0490-0491,U+04B0-04B1,U+2116}@font-face{font-family:Inter;font-style:normal;font-weight:100 900;font-display:swap;src:url(/SwarmMakie.jl/previews/PR27/assets/inter-roman-greek-ext.CqjqNYQ-.woff2) format("woff2");unicode-range:U+1F00-1FFF}@font-face{font-family:Inter;font-style:normal;font-weight:100 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SwarmMakie.jl
Appearance
Examples ​
Multiple variables with colors ​
julia
using CairoMakie, SwarmMakie
+    
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()))
 
 f = data(penguins) * mapping(:species, :bill_depth_mm, color=:sex) * visual(Beeswarm) |> draw
-f
julia
using CairoMakie, SwarmMakie
+f
julia
using CairoMakie, SwarmMakie
 using RDatasets, DataFrames
 iris = dataset("datasets", "iris")
 f, a, p = beeswarm(
@@ -40,7 +40,7 @@
 a.scene.backgroundcolor[] = RGBAf(1,1,1,0)
 hidedecorations!(a)
 hidespines!(a)
-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")
 
@@ -52,8 +52,8 @@
 )
 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/previews/PR27/examples/scales.html b/previews/PR27/examples/scales.html
index 27ef056..992abef 100644
--- a/previews/PR27/examples/scales.html
+++ b/previews/PR27/examples/scales.html
@@ -5,29 +5,49 @@
     
     Nonlinear beeswarm plots | SwarmMakie.jl
     
-    
-    
+    
+    
     
-    
+    
     
-    
-    
-    
+    
+    
+    
     
     
   
   
-    
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

+    
     
   
 
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diff --git a/previews/PR27/examples/unconventional.html b/previews/PR27/examples/unconventional.html
index 5355035..89be047 100644
--- a/previews/PR27/examples/unconventional.html
+++ b/previews/PR27/examples/unconventional.html
@@ -5,19 +5,19 @@
     
     Unconventional swarm plots | SwarmMakie.jl
     
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SwarmMakie.jl
Appearance
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
+    
SwarmMakie.jl
Appearance
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
@@ -113,7 +113,7 @@
     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,
@@ -144,7 +144,7 @@
     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
@@ -192,8 +192,8 @@
     langs.pool.levels,
     "Language";
 )
-f

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+f

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diff --git a/previews/PR27/gutters.html b/previews/PR27/gutters.html
index 0b8dda2..4fc8a22 100644
--- a/previews/PR27/gutters.html
+++ b/previews/PR27/gutters.html
@@ -5,27 +5,27 @@
     
     Gutters | SwarmMakie.jl
     
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SwarmMakie.jl
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Gutters ​
julia
using SwarmMakie, CairoMakie
+    
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/previews/PR27/hashmap.json b/previews/PR27/hashmap.json
index 47fdc2d..2e376d1 100644
--- a/previews/PR27/hashmap.json
+++ b/previews/PR27/hashmap.json
@@ -1 +1 @@
-{"algorithms.md":"CH3wLhtN","api.md":"DRTMKKNK","examples_examples.md":"sNb7dMTS","examples_scales.md":"B34Gtc84","examples_unconventional.md":"BF6sASGH","gutters.md":"D8Su6OSx","index.md":"45hJHmLp","introduction.md":"C_96zFxC","source_algorithms_jitter.md":"3an8LnIn","source_algorithms_mkborregaard.md":"CClWkSMe","source_algorithms_seaborn.md":"CMhi_Bxj","source_algorithms_simple.md":"CENw21kQ","source_algorithms_simple2.md":"rbXXkVLN","source_algorithms_wilkinson.md":"Dl9pjUVc","source_recipe.md":"f1G8L5h4","source_swarmmakie.md":"CYotaraR"}
+{"algorithms.md":"BtlfFAuT","api.md":"BIHl2OFe","examples_examples.md":"BSWj1S9U","examples_scales.md":"DOrpoqmO","examples_unconventional.md":"BtZHLrKq","gutters.md":"BydtjO5O","index.md":"DBRi-Clc","introduction.md":"BIv-Zlrr","source_algorithms_jitter.md":"KA-6KTWA","source_algorithms_mkborregaard.md":"BW7o9Glm","source_algorithms_seaborn.md":"Dyxm848b","source_algorithms_simple.md":"BQh78-JT","source_algorithms_simple2.md":"Brdq-nLt","source_algorithms_wilkinson.md":"ZSfvWZ-O","source_recipe.md":"CYW5jnZh","source_swarmmakie.md":"DoC0DsWn"}
diff --git a/previews/PR27/index.html b/previews/PR27/index.html
index c8ecd26..a7e3379 100644
--- a/previews/PR27/index.html
+++ b/previews/PR27/index.html
@@ -5,25 +5,25 @@
     
     What is SwarmMakie.jl? | SwarmMakie.jl
     
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SwarmMakie.jl
Appearance
SwarmMakie.jl
Beeswarm plots for Makie.jl
Introduction
View on Github
API Reference
SwarmMakie
Julia code
Pure Julia code
Fast, understandable, extensible functions
Literate programming
Documented source code with examples!
Full integration with Makie
Use any Makie.jl scatter input!
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
+    
SwarmMakie.jl
Appearance
SwarmMakie.jl
Beeswarm plots for Makie.jl
Introduction
View on Github
API Reference
SwarmMakie
Julia code
Pure Julia code
Fast, understandable, extensible functions
Literate programming
Documented source code with examples!
Full integration with Makie
Use any Makie.jl scatter input!
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)
 ys = randn(500)
 beeswarm(xs, ys; color = xs)
Beeswarm with multiple categories

-    
+    
     
   
 
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diff --git a/previews/PR27/introduction.html b/previews/PR27/introduction.html
index f0f7cd8..0e81864 100644
--- a/previews/PR27/introduction.html
+++ b/previews/PR27/introduction.html
@@ -5,32 +5,32 @@
     
     SwarmMakie | SwarmMakie.jl
     
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SwarmMakie.jl
Appearance
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
+    
SwarmMakie.jl
Appearance
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"))
 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")
 
 f = data(iris) *
     mapping(:Species, :SepalLength; color = :Species) *
     visual(Beeswarm) |> draw
-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.

-    
+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.

+    
     
   
 
\ No newline at end of file
diff --git a/previews/PR27/source/SwarmMakie.html b/previews/PR27/source/SwarmMakie.html
index 2f658a7..3504976 100644
--- a/previews/PR27/source/SwarmMakie.html
+++ b/previews/PR27/source/SwarmMakie.html
@@ -5,19 +5,19 @@
     
     SwarmMakie.jl
     
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SwarmMakie.jl
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SwarmMakie.jl ​
julia
module SwarmMakie
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diff --git a/previews/PR27/source/algorithms/jitter.html b/previews/PR27/source/algorithms/jitter.html
index f5675ac..04cc2c7 100644
--- a/previews/PR27/source/algorithms/jitter.html
+++ b/previews/PR27/source/algorithms/jitter.html
@@ -5,19 +5,19 @@
     
     Jitter algorithms | SwarmMakie.jl
     
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SwarmMakie.jl
Appearance
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)
+    
SwarmMakie.jl
Appearance
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
@@ -94,7 +94,7 @@
 
     return jitter
 end
This page was generated using Literate.jl.

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diff --git a/previews/PR27/source/algorithms/mkborregaard.html b/previews/PR27/source/algorithms/mkborregaard.html
index cf5113b..96b6443 100644
--- a/previews/PR27/source/algorithms/mkborregaard.html
+++ b/previews/PR27/source/algorithms/mkborregaard.html
@@ -5,19 +5,19 @@
     
     Michael Borregaard beeswarm | SwarmMakie.jl
     
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SwarmMakie.jl
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Michael Borregaard beeswarm ​
julia
export MKBorregaardBeeswarm
+    
SwarmMakie.jl
Appearance
Michael Borregaard beeswarm ​
julia
export MKBorregaardBeeswarm
 
 struct MKBorregaardBeeswarm <: BeeswarmAlgorithm end
 
@@ -122,7 +122,7 @@
 
 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.

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diff --git a/previews/PR27/source/algorithms/seaborn.html b/previews/PR27/source/algorithms/seaborn.html
index 9f92fa7..273090c 100644
--- a/previews/PR27/source/algorithms/seaborn.html
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     Seaborn algorithm | SwarmMakie.jl
     
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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.
+    
SwarmMakie.jl
Appearance
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:
 
@@ -160,7 +160,7 @@
         """
     end
 end
This page was generated using Literate.jl.

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diff --git a/previews/PR27/source/algorithms/simple.html b/previews/PR27/source/algorithms/simple.html
index 2aee1c7..a6c0358 100644
--- a/previews/PR27/source/algorithms/simple.html
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@@ -5,19 +5,19 @@
     
     Simple beeswarm | SwarmMakie.jl
     
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Simple beeswarm ​
julia
export SimpleBeeswarm
This is a simple beeswarm implementation as used in Matplotlib.
julia
"""
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SwarmMakie.jl
Appearance
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
@@ -89,7 +89,7 @@
     end
     return xs
 end
This page was generated using Literate.jl.

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diff --git a/previews/PR27/source/algorithms/simple2.html b/previews/PR27/source/algorithms/simple2.html
index d60965d..4a3c818 100644
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     SwarmMakie.jl
     
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julia
struct SimpleBeeswarm2 <: BeeswarmAlgorithm
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diff --git a/previews/PR27/source/algorithms/wilkinson.html b/previews/PR27/source/algorithms/wilkinson.html
index 92352ca..3524995 100644
--- a/previews/PR27/source/algorithms/wilkinson.html
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     Wilkinson beeswarm | SwarmMakie.jl
     
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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
"""
+    
SwarmMakie.jl
Appearance
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.
@@ -93,7 +93,7 @@
         end
     end
 end
This page was generated using Literate.jl.

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diff --git a/previews/PR27/source/recipe.html b/previews/PR27/source/recipe.html
index 69eb801..1d2d8d6 100644
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     Beeswarm recipe | SwarmMakie.jl
     
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Beeswarm recipe ​
julia
export beeswarm, beeswarm!
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+++ b/previews/PR27/examples/examples.html
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