From bfbca27525c799784b7bc91bffab07e7df937b17 Mon Sep 17 00:00:00 2001 From: "Documenter.jl" Date: Sun, 10 Mar 2024 22:07:42 +0000 Subject: [PATCH] build based on 501ed23 --- dev/404.html | 4 +- dev/algorithms.html | 10 +- dev/api.html | 12 +-- ....DRCJHfq3.js => algorithms.md.v-_GJxoX.js} | 2 +- ...lean.js => algorithms.md.v-_GJxoX.lean.js} | 0 ...{api.md.B5Kj4H6I.js => api.md.BM2uvZ2j.js} | 4 +- ...Kj4H6I.lean.js => api.md.BM2uvZ2j.lean.js} | 2 +- .../{app.gJ71ZNgn.js => app.AQHurBFt.js} | 2 +- dev/assets/aympomb.Cd879Nlj.png | Bin 121848 -> 0 bytes .../chunks/@localSearchIndexroot.DUJ5u3_9.js | 1 - .../chunks/@localSearchIndexroot.gUI4qgog.js | 1 + ...RL5jYv.js => VPLocalSearchBox.Y6T6dHZ7.js} | 2 +- .../{theme.CfGuI44w.js => theme.DlApXHcL.js} | 4 +- ...wgbv.DcQ1VF4U.png => ckwndaw.DcQ1VF4U.png} | Bin ...md.BjwbFmYi.js => examples.md.Xiq9LrlA.js} | 8 +- ...i.lean.js => examples.md.Xiq9LrlA.lean.js} | 2 +- ...xncu.Efqwolsi.png => gwbxcsx.Efqwolsi.png} | Bin dev/assets/ialjgew.Cd9ZjDYa.png | Bin 0 -> 125123 bytes ...LN9cl7G.js => introduction.md.CUHnHsKA.js} | 2 +- ...an.js => introduction.md.CUHnHsKA.lean.js} | 2 +- ...iuuz._gTVJm3g.png => jovtkco._gTVJm3g.png} | Bin dev/assets/jxwjnpf.DNpkuEgi.png | Bin 140167 -> 0 bytes ...njtw.BDepz78g.png => nhcrzug.BDepz78g.png} | Bin dev/assets/oxdxrvx.DGyCKkfu.png | Bin 0 -> 43526 bytes ...VV.js => source_SwarmMakie.md.BzVkK5sT.js} | 7 +- ... => source_SwarmMakie.md.BzVkK5sT.lean.js} | 2 +- ...source_algorithms_wilkinson.md.DoVlTOzM.js | 36 ------- ...e_algorithms_wilkinson.md.DoVlTOzM.lean.js | 1 - ...source_algorithms_wilkinson.md.IBf6tx2D.js | 94 +++++++++++++++++ ...e_algorithms_wilkinson.md.IBf6tx2D.lean.js | 1 + dev/assets/todjytk.bbVSmzS1.png | Bin 44160 -> 0 bytes dev/assets/xmvrqdo.CVGJu_20.png | Bin 0 -> 141517 bytes dev/examples.html | 16 +-- dev/hashmap.json | 2 +- dev/index.html | 6 +- dev/introduction.html | 12 +-- dev/source/SwarmMakie.html | 11 +- dev/source/algorithms/mkborregaard.html | 6 +- dev/source/algorithms/seaborn.html | 6 +- dev/source/algorithms/simple.html | 6 +- dev/source/algorithms/wilkinson.html | 98 ++++++++++++++---- dev/source/recipe.html | 6 +- 42 files changed, 243 insertions(+), 125 deletions(-) rename dev/assets/{algorithms.md.DRCJHfq3.js => algorithms.md.v-_GJxoX.js} (95%) rename dev/assets/{algorithms.md.DRCJHfq3.lean.js => algorithms.md.v-_GJxoX.lean.js} (100%) rename dev/assets/{api.md.B5Kj4H6I.js => api.md.BM2uvZ2j.js} (87%) rename dev/assets/{api.md.B5Kj4H6I.lean.js => api.md.BM2uvZ2j.lean.js} (66%) rename dev/assets/{app.gJ71ZNgn.js => app.AQHurBFt.js} (95%) delete mode 100644 dev/assets/aympomb.Cd879Nlj.png delete mode 100644 dev/assets/chunks/@localSearchIndexroot.DUJ5u3_9.js create mode 100644 dev/assets/chunks/@localSearchIndexroot.gUI4qgog.js rename dev/assets/chunks/{VPLocalSearchBox.BFRL5jYv.js => VPLocalSearchBox.Y6T6dHZ7.js} (99%) rename dev/assets/chunks/{theme.CfGuI44w.js => theme.DlApXHcL.js} (99%) rename dev/assets/{rldwgbv.DcQ1VF4U.png => ckwndaw.DcQ1VF4U.png} (100%) rename dev/assets/{examples.md.BjwbFmYi.js => examples.md.Xiq9LrlA.js} (98%) rename dev/assets/{examples.md.BjwbFmYi.lean.js => examples.md.Xiq9LrlA.lean.js} (60%) rename dev/assets/{otixncu.Efqwolsi.png => gwbxcsx.Efqwolsi.png} (100%) create mode 100644 dev/assets/ialjgew.Cd9ZjDYa.png rename dev/assets/{introduction.md.BLN9cl7G.js => introduction.md.CUHnHsKA.js} (98%) rename dev/assets/{introduction.md.BLN9cl7G.lean.js => introduction.md.CUHnHsKA.lean.js} (74%) rename dev/assets/{zgriuuz._gTVJm3g.png => jovtkco._gTVJm3g.png} (100%) delete mode 100644 dev/assets/jxwjnpf.DNpkuEgi.png rename dev/assets/{sppnjtw.BDepz78g.png => nhcrzug.BDepz78g.png} (100%) create mode 100644 dev/assets/oxdxrvx.DGyCKkfu.png rename dev/assets/{source_SwarmMakie.md.zR4D0qVV.js => source_SwarmMakie.md.BzVkK5sT.js} (92%) rename dev/assets/{source_SwarmMakie.md.zR4D0qVV.lean.js => source_SwarmMakie.md.BzVkK5sT.lean.js} (69%) delete mode 100644 dev/assets/source_algorithms_wilkinson.md.DoVlTOzM.js delete mode 100644 dev/assets/source_algorithms_wilkinson.md.DoVlTOzM.lean.js create mode 100644 dev/assets/source_algorithms_wilkinson.md.IBf6tx2D.js create mode 100644 dev/assets/source_algorithms_wilkinson.md.IBf6tx2D.lean.js delete mode 100644 dev/assets/todjytk.bbVSmzS1.png create mode 100644 dev/assets/xmvrqdo.CVGJu_20.png diff --git a/dev/404.html b/dev/404.html index e80ff28..b55d66b 100644 --- a/dev/404.html +++ b/dev/404.html @@ -8,7 +8,7 @@ - + @@ -16,7 +16,7 @@
SwarmMakie.jl

Appearance

404

PAGE NOT FOUND

But if you don't change your direction, and if you keep looking, you may end up where you are heading.
Take me home
- + \ No newline at end of file diff --git a/dev/algorithms.html b/dev/algorithms.html index f01d828..14c8072 100644 --- a/dev/algorithms.html +++ b/dev/algorithms.html @@ -8,18 +8,18 @@ - + - - + + -
SwarmMakie.jl

Appearance

Algorithms ​

SwarmMakie aims to offer several beeswarm algorithms, which give different results.

Currently, it only offers the SimpleBeeswarm algorithm, which is inspired by Matplotlib and Seaborn, and a no-op NoBeeswarm struct which simply decomposes back to the original scatter plot.

# SwarmMakie.SimpleBeeswarm — Type.
julia
SimpleBeeswarm()

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

source


Missing docstring.

Missing docstring for NoBeeswarm. Check Documenter's build log for details.

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!

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.

- +
SwarmMakie.jl

Appearance

Algorithms ​

SwarmMakie aims to offer several beeswarm algorithms, which give different results.

Currently, it only offers the SimpleBeeswarm algorithm, which is inspired by Matplotlib and Seaborn, and a no-op NoBeeswarm struct which simply decomposes back to the original scatter plot.

# SwarmMakie.SimpleBeeswarm — Type.
julia
SimpleBeeswarm()

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

source


Missing docstring.

Missing docstring for NoBeeswarm. Check Documenter's build log for details.

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!

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.

+ \ No newline at end of file diff --git a/dev/api.html b/dev/api.html index a7c9dd8..dc1d0cf 100644 --- a/dev/api.html +++ b/dev/api.html @@ -8,17 +8,17 @@ - + - - + + -
SwarmMakie.jl

Appearance

API Reference ​

# 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.

source


# SwarmMakie.WilkinsonBeeswarm — Type.
julia
WilkinsonBeeswarm()

A simple implementation like Matplotlib's algorithm.

source


# SwarmMakie.beeswarm — Method.
julia
beeswarm(x, y)
+    
SwarmMakie.jl
Appearance
API Reference ​
# 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.
source

# SwarmMakie.WilkinsonBeeswarm — Type. 
julia
WilkinsonBeeswarm()
A simple implementation like Matplotlib's algorithm.
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).
Arguments
Available attributes and their defaults for Plot{SwarmMakie.beeswarm} are:
  algorithm         SimpleBeeswarm()
   alpha             1.0
   color             :black
@@ -50,8 +50,8 @@
   transparency      false
   uv_offset_width   (0.0, 0.0, 0.0, 0.0)
   visible           true
Example
julia
using Makie, SwarmMakie
-beeswarm(ones(100), randn(100); color = rand(RGBf, 100))
source

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

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

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


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

source


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

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

source


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

source


# SwarmMakie.position_candidates — Method.

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

source


+ \ No newline at end of file diff --git a/dev/assets/algorithms.md.DRCJHfq3.js b/dev/assets/algorithms.md.v-_GJxoX.js similarity index 95% rename from dev/assets/algorithms.md.DRCJHfq3.js rename to dev/assets/algorithms.md.v-_GJxoX.js index 2a29709..b7e37d9 100644 --- a/dev/assets/algorithms.md.DRCJHfq3.js +++ b/dev/assets/algorithms.md.v-_GJxoX.js @@ -1 +1 @@ -import{_ as e,c as a,o as i,a7 as t}from"./chunks/framework.CtKH--HK.js";const u=JSON.parse('{"title":"Algorithms","description":"","frontmatter":{},"headers":[],"relativePath":"algorithms.md","filePath":"algorithms.md","lastUpdated":null}'),s={name:"algorithms.md"},o=t('

Algorithms ​

SwarmMakie aims to offer several beeswarm algorithms, which give different results.

Currently, it only offers the SimpleBeeswarm algorithm, which is inspired by Matplotlib and Seaborn, and a no-op NoBeeswarm struct which simply decomposes back to the original scatter plot.

# SwarmMakie.SimpleBeeswarm — Type.
julia
SimpleBeeswarm()

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

source


Missing docstring.

Missing docstring for NoBeeswarm. Check Documenter's build log for details.

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!

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.

',11),r=[o];function l(d,n,c,h,m,p){return i(),a("div",null,r)}const b=e(s,[["render",l]]);export{u as __pageData,b as default}; +import{_ as e,c as a,o as i,a7 as t}from"./chunks/framework.CtKH--HK.js";const u=JSON.parse('{"title":"Algorithms","description":"","frontmatter":{},"headers":[],"relativePath":"algorithms.md","filePath":"algorithms.md","lastUpdated":null}'),s={name:"algorithms.md"},o=t('

Algorithms ​

SwarmMakie aims to offer several beeswarm algorithms, which give different results.

Currently, it only offers the SimpleBeeswarm algorithm, which is inspired by Matplotlib and Seaborn, and a no-op NoBeeswarm struct which simply decomposes back to the original scatter plot.

# SwarmMakie.SimpleBeeswarm — Type.
julia
SimpleBeeswarm()

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

source


Missing docstring.

Missing docstring for NoBeeswarm. Check Documenter's build log for details.

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!

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.

',11),r=[o];function l(d,n,c,h,m,p){return i(),a("div",null,r)}const b=e(s,[["render",l]]);export{u as __pageData,b as default}; diff --git a/dev/assets/algorithms.md.DRCJHfq3.lean.js b/dev/assets/algorithms.md.v-_GJxoX.lean.js similarity index 100% rename from dev/assets/algorithms.md.DRCJHfq3.lean.js rename to dev/assets/algorithms.md.v-_GJxoX.lean.js diff --git a/dev/assets/api.md.B5Kj4H6I.js b/dev/assets/api.md.BM2uvZ2j.js similarity index 87% rename from dev/assets/api.md.B5Kj4H6I.js rename to dev/assets/api.md.BM2uvZ2j.js index 859f76c..8b1e708 100644 --- a/dev/assets/api.md.B5Kj4H6I.js +++ b/dev/assets/api.md.BM2uvZ2j.js @@ -1,4 +1,4 @@ -import{_ as a,c as s,o as e,a7 as i}from"./chunks/framework.CtKH--HK.js";const m=JSON.parse('{"title":"API Reference","description":"","frontmatter":{},"headers":[],"relativePath":"api.md","filePath":"api.md","lastUpdated":null}'),n={name:"api.md"},r=i(`

API Reference ​

# 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.

source


# SwarmMakie.WilkinsonBeeswarm — Type.
julia
WilkinsonBeeswarm()

A simple implementation like Matplotlib's algorithm.

source


# SwarmMakie.beeswarm — Method.
julia
beeswarm(x, y)
+import{_ as a,c as e,o as s,a7 as i}from"./chunks/framework.CtKH--HK.js";const m=JSON.parse('{"title":"API Reference","description":"","frontmatter":{},"headers":[],"relativePath":"api.md","filePath":"api.md","lastUpdated":null}'),n={name:"api.md"},r=i(`
API Reference ​
# 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.
source

# SwarmMakie.WilkinsonBeeswarm — Type. 
julia
WilkinsonBeeswarm()
A simple implementation like Matplotlib's algorithm.
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).
Arguments
Available attributes and their defaults for Plot{SwarmMakie.beeswarm} are:
  algorithm         SimpleBeeswarm()
   alpha             1.0
   color             :black
@@ -30,4 +30,4 @@ import{_ as a,c as s,o as e,a7 as i}from"./chunks/framework.CtKH--HK.js";const m
   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

`,16),l=[r];function p(t,o,d,c,h,k){return e(),s("div",null,l)}const u=a(n,[["render",p]]);export{m as __pageData,u as default};
+beeswarm(ones(100), randn(100); color = rand(RGBf, 100))

source


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

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

source


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

source


# SwarmMakie.position_candidates — Method.

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

source


`,16),l=[r];function p(t,o,d,c,h,k){return s(),e("div",null,l)}const u=a(n,[["render",p]]);export{m as __pageData,u as default}; diff --git a/dev/assets/api.md.B5Kj4H6I.lean.js b/dev/assets/api.md.BM2uvZ2j.lean.js similarity index 66% rename from dev/assets/api.md.B5Kj4H6I.lean.js rename to dev/assets/api.md.BM2uvZ2j.lean.js index da9a14a..5fa1ad4 100644 --- a/dev/assets/api.md.B5Kj4H6I.lean.js +++ b/dev/assets/api.md.BM2uvZ2j.lean.js @@ -1 +1 @@ -import{_ as a,c as s,o as e,a7 as i}from"./chunks/framework.CtKH--HK.js";const m=JSON.parse('{"title":"API Reference","description":"","frontmatter":{},"headers":[],"relativePath":"api.md","filePath":"api.md","lastUpdated":null}'),n={name:"api.md"},r=i("",16),l=[r];function p(t,o,d,c,h,k){return e(),s("div",null,l)}const u=a(n,[["render",p]]);export{m as __pageData,u as default}; +import{_ as a,c as e,o as s,a7 as i}from"./chunks/framework.CtKH--HK.js";const m=JSON.parse('{"title":"API Reference","description":"","frontmatter":{},"headers":[],"relativePath":"api.md","filePath":"api.md","lastUpdated":null}'),n={name:"api.md"},r=i("",16),l=[r];function p(t,o,d,c,h,k){return s(),e("div",null,l)}const u=a(n,[["render",p]]);export{m as __pageData,u as default}; diff --git a/dev/assets/app.gJ71ZNgn.js b/dev/assets/app.AQHurBFt.js similarity index 95% rename from dev/assets/app.gJ71ZNgn.js rename to dev/assets/app.AQHurBFt.js index e19e3bb..c71ab50 100644 --- a/dev/assets/app.gJ71ZNgn.js +++ b/dev/assets/app.AQHurBFt.js @@ -4,4 +4,4 @@ function __vite__mapDeps(indexes) { } return indexes.map((i) => __vite__mapDeps.viteFileDeps[i]) } -import{j as o,a8 as p,a9 as u,aa as l,ab as c,ac as f,ad as d,ae as m,af as h,ag as g,ah as A,Y as P,d as _,u as v,l as R,z as w,ai as y,aj as C,ak as E,a6 as b}from"./chunks/framework.CtKH--HK.js";import{R as T}from"./chunks/theme.CfGuI44w.js";function i(e){if(e.extends){const a=i(e.extends);return{...a,...e,async 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k="/SwarmMakie.jl/dev/assets/aympomb.Cd879Nlj.png",n="/SwarmMakie.jl/dev/assets/jxwjnpf.DNpkuEgi.png",l="/SwarmMakie.jl/dev/assets/sppnjtw.BDepz78g.png",p="/SwarmMakie.jl/dev/assets/zgriuuz._gTVJm3g.png",t="/SwarmMakie.jl/dev/assets/rldwgbv.DcQ1VF4U.png",u=JSON.parse('{"title":"Examples","description":"","frontmatter":{},"headers":[],"relativePath":"examples.md","filePath":"examples.md","lastUpdated":null}'),e={name:"examples.md"},E=h(`

Examples ​

Multiple variables with colors ​

julia
using CairoMakie, SwarmMakie
+import{_ as s,c as i,o as a,a7 as h}from"./chunks/framework.CtKH--HK.js";const k="/SwarmMakie.jl/dev/assets/ialjgew.Cd9ZjDYa.png",n="/SwarmMakie.jl/dev/assets/xmvrqdo.CVGJu_20.png",l="/SwarmMakie.jl/dev/assets/nhcrzug.BDepz78g.png",t="/SwarmMakie.jl/dev/assets/jovtkco._gTVJm3g.png",p="/SwarmMakie.jl/dev/assets/ckwndaw.DcQ1VF4U.png",u=JSON.parse('{"title":"Examples","description":"","frontmatter":{},"headers":[],"relativePath":"examples.md","filePath":"examples.md","lastUpdated":null}'),e={name:"examples.md"},E=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])
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])
and the figure is:
julia
fig
Palmer Penguins ​
julia
using AlgebraOfGraphics, CairoMakie, SwarmMakie
 using PalmerPenguins, DataFrames
 
 penguins = dropmissing(DataFrame(PalmerPenguins.load()))
@@ -28,7 +28,7 @@ import{_ as s,c as i,o as a,a7 as h}from"./chunks/framework.CtKH--HK.js";const k
 Makie.update_state_before_display!(f)
 Makie.update_state_before_display!(f)
 Makie.update_state_before_display!(f)
-f
Wilkinson's dot histogram ​
julia
using CairoMakie, SwarmMakie
+f
Wilkinson's dot histogram ​
julia
using CairoMakie, SwarmMakie
 using RDatasets, DataFrames
 mtcars = dataset("datasets", "mtcars")
 
@@ -40,4 +40,4 @@ import{_ as s,c as i,o as a,a7 as h}from"./chunks/framework.CtKH--HK.js";const k
 )
 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.
',22),r=[E];function g(d,y,F,o,C,c){return a(),i("div",null,r)}const B=s(e,[["render",g]]);export{u as __pageData,B as default};
+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.

',22),r=[E];function g(d,y,F,o,C,c){return a(),i("div",null,r)}const B=s(e,[["render",g]]);export{u as __pageData,B as default}; diff --git a/dev/assets/examples.md.BjwbFmYi.lean.js b/dev/assets/examples.md.Xiq9LrlA.lean.js similarity index 60% rename from dev/assets/examples.md.BjwbFmYi.lean.js rename to dev/assets/examples.md.Xiq9LrlA.lean.js index a221429..9ce2727 100644 --- a/dev/assets/examples.md.BjwbFmYi.lean.js +++ b/dev/assets/examples.md.Xiq9LrlA.lean.js @@ -1 +1 @@ -import{_ as s,c as i,o as a,a7 as h}from"./chunks/framework.CtKH--HK.js";const 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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 s,c as i,o as a,a7 as t}from"./chunks/framework.CtKH--HK.js";const h="/SwarmMakie.jl/dev/assets/oxdxrvx.DGyCKkfu.png",e="/SwarmMakie.jl/dev/assets/gwbxcsx.Efqwolsi.png",F=JSON.parse('{"title":"SwarmMakie","description":"","frontmatter":{},"headers":[],"relativePath":"introduction.md","filePath":"introduction.md","lastUpdated":null}'),n={name:"introduction.md"},k=t(`
SwarmMakie ​
SwarmMakie implements beeswarm or swarm plots in Makie. These are scatter plots which are categorical (or singular) in the x-axis, where the markers are nudged so that each marker is visible and avoids overlap.
The main entry point to the package is the beeswarm recipe, which takes the same arguments as Makie's scatter plots, and transforms them into a beautiful beeswarm plot!
Being a Makie recipe, you can also use this with AlgebraOfGraphics.
Quick start ​
Here's a quick example to get you started:
julia
using CairoMakie, SwarmMakie
 xs = rand(1:3, 40)
 ys = randn(40)
 f, a, p = scatter(xs, ys; color = xs, axis = (; title = "Scatter plot"))
diff --git a/dev/assets/introduction.md.BLN9cl7G.lean.js b/dev/assets/introduction.md.CUHnHsKA.lean.js
similarity index 74%
rename from dev/assets/introduction.md.BLN9cl7G.lean.js
rename to dev/assets/introduction.md.CUHnHsKA.lean.js
index bc1cdd8..d3a1a9f 100644
--- a/dev/assets/introduction.md.BLN9cl7G.lean.js
+++ b/dev/assets/introduction.md.CUHnHsKA.lean.js
@@ -1 +1 @@
-import{_ as s,c as i,o as a,a7 as t}from"./chunks/framework.CtKH--HK.js";const h="/SwarmMakie.jl/dev/assets/todjytk.bbVSmzS1.png",e="/SwarmMakie.jl/dev/assets/otixncu.Efqwolsi.png",F=JSON.parse('{"title":"SwarmMakie","description":"","frontmatter":{},"headers":[],"relativePath":"introduction.md","filePath":"introduction.md","lastUpdated":null}'),n={name:"introduction.md"},k=t("",12),l=[k];function p(r,E,d,g,o,c){return a(),i("div",null,l)}const u=s(n,[["render",p]]);export{F as __pageData,u as default};
+import{_ as s,c as i,o as a,a7 as t}from"./chunks/framework.CtKH--HK.js";const h="/SwarmMakie.jl/dev/assets/oxdxrvx.DGyCKkfu.png",e="/SwarmMakie.jl/dev/assets/gwbxcsx.Efqwolsi.png",F=JSON.parse('{"title":"SwarmMakie","description":"","frontmatter":{},"headers":[],"relativePath":"introduction.md","filePath":"introduction.md","lastUpdated":null}'),n={name:"introduction.md"},k=t("",12),l=[k];function p(r,E,d,g,o,c){return a(),i("div",null,l)}const u=s(n,[["render",p]]);export{F as __pageData,u as default};
diff --git a/dev/assets/zgriuuz._gTVJm3g.png b/dev/assets/jovtkco._gTVJm3g.png
similarity index 100%
rename from dev/assets/zgriuuz._gTVJm3g.png
rename to dev/assets/jovtkco._gTVJm3g.png
diff --git a/dev/assets/jxwjnpf.DNpkuEgi.png b/dev/assets/jxwjnpf.DNpkuEgi.png
deleted file mode 100644
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zzKY61n|#iPjMSwarmMakie.jl ​
julia
module SwarmMakie
+import{_ as s,c as i,o as a,a7 as n}from"./chunks/framework.CtKH--HK.js";const g=JSON.parse('{"title":"SwarmMakie.jl","description":"","frontmatter":{},"headers":[],"relativePath":"source/SwarmMakie.md","filePath":"source/SwarmMakie.md","lastUpdated":null}'),e={name:"source/SwarmMakie.md"},l=n(`
SwarmMakie.jl ​
julia
module SwarmMakie
 
 using Makie
 using Random
 import StatsBase:
     Histogram,
-    fit
+    fit,
+    UnitWeights
 
 include("recipe.jl")
 include("algorithms/simple.jl")
include("algorithms/mkborregaard.jl")
julia
include("algorithms/seaborn.jl")
 include("algorithms/wilkinson.jl")
 
-end
This page was generated using Literate.jl.
`,6),t=[l];function p(h,k,r,d,o,E){return i(),a("div",null,t)}const u=s(e,[["render",p]]);export{g as __pageData,u as default};
+end
This page was generated using Literate.jl.
`,6),t=[l];function p(h,k,r,d,o,E){return a(),i("div",null,t)}const u=s(e,[["render",p]]);export{g as __pageData,u as default};
diff --git a/dev/assets/source_SwarmMakie.md.zR4D0qVV.lean.js b/dev/assets/source_SwarmMakie.md.BzVkK5sT.lean.js
similarity index 69%
rename from dev/assets/source_SwarmMakie.md.zR4D0qVV.lean.js
rename to dev/assets/source_SwarmMakie.md.BzVkK5sT.lean.js
index 1acaca6..75b5a53 100644
--- a/dev/assets/source_SwarmMakie.md.zR4D0qVV.lean.js
+++ b/dev/assets/source_SwarmMakie.md.BzVkK5sT.lean.js
@@ -1 +1 @@
-import{_ as s,c as a,o as i,a7 as n}from"./chunks/framework.CtKH--HK.js";const g=JSON.parse('{"title":"SwarmMakie.jl","description":"","frontmatter":{},"headers":[],"relativePath":"source/SwarmMakie.md","filePath":"source/SwarmMakie.md","lastUpdated":null}'),e={name:"source/SwarmMakie.md"},l=n("",6),t=[l];function p(h,k,r,d,o,E){return i(),a("div",null,t)}const u=s(e,[["render",p]]);export{g as __pageData,u as default};
+import{_ as s,c as i,o as a,a7 as n}from"./chunks/framework.CtKH--HK.js";const g=JSON.parse('{"title":"SwarmMakie.jl","description":"","frontmatter":{},"headers":[],"relativePath":"source/SwarmMakie.md","filePath":"source/SwarmMakie.md","lastUpdated":null}'),e={name:"source/SwarmMakie.md"},l=n("",6),t=[l];function p(h,k,r,d,o,E){return a(),i("div",null,t)}const u=s(e,[["render",p]]);export{g as __pageData,u as default};
diff --git a/dev/assets/source_algorithms_wilkinson.md.DoVlTOzM.js b/dev/assets/source_algorithms_wilkinson.md.DoVlTOzM.js
deleted file mode 100644
index 8020c6c..0000000
--- a/dev/assets/source_algorithms_wilkinson.md.DoVlTOzM.js
+++ /dev/null
@@ -1,36 +0,0 @@
-import{_ as s,c as i,o as a,a7 as n}from"./chunks/framework.CtKH--HK.js";const y=JSON.parse('{"title":"Wilkinson beeswarm","description":"","frontmatter":{},"headers":[],"relativePath":"source/algorithms/wilkinson.md","filePath":"source/algorithms/wilkinson.md","lastUpdated":null}'),l={name:"source/algorithms/wilkinson.md"},t=n(`
Wilkinson beeswarm ​
julia
export WilkinsonBeeswarm
This is a beeswarm implementation as described in Leland Wilkinson's original paper on dot plots
julia
"""
-    WilkinsonBeeswarm()
-
-A simple implementation like Matplotlib's algorithm.
-"""
-struct WilkinsonBeeswarm <: BeeswarmAlgorithm
-end
-
-function calculate!(buffer::AbstractVector{<: Point2}, alg::WilkinsonBeeswarm, positions::AbstractVector{<: Point2}, markersize, side::Symbol)
-    #=
-
-    Initial sketch of code credited to Frederic Freyer and Julius Krumbiegel.
-    Reference here: https://github.com/MakieOrg/Makie.jl/issues/1950
-
-    =#
-
-    new_positions = Float32[]
-    for pos in positions
-        push!(new_positions, pos[2])
-    end
-    h = fit(Histogram, new_positions; nbins = markersize)
-    xs = 1:length(h.weights)
-
-    dot_per_bin = round.(Int, h.weights ./ markersize)
-
-    points = Point2f[]
-    for x in xs
-        for dot in 1:dot_per_bin[x]
-            point = Point2f(x, dot)
-            push!(points, point)
-        end
-    end
-
-    return points
-
-end
This page was generated using Literate.jl.
`,6),h=[t];function p(k,e,r,d,E,g){return a(),i("div",null,h)}const c=s(l,[["render",p]]);export{y as __pageData,c as default};
diff --git a/dev/assets/source_algorithms_wilkinson.md.DoVlTOzM.lean.js b/dev/assets/source_algorithms_wilkinson.md.DoVlTOzM.lean.js
deleted file mode 100644
index b903782..0000000
--- a/dev/assets/source_algorithms_wilkinson.md.DoVlTOzM.lean.js
+++ /dev/null
@@ -1 +0,0 @@
-import{_ as s,c as i,o as a,a7 as n}from"./chunks/framework.CtKH--HK.js";const y=JSON.parse('{"title":"Wilkinson beeswarm","description":"","frontmatter":{},"headers":[],"relativePath":"source/algorithms/wilkinson.md","filePath":"source/algorithms/wilkinson.md","lastUpdated":null}'),l={name:"source/algorithms/wilkinson.md"},t=n("",6),h=[t];function p(k,e,r,d,E,g){return a(),i("div",null,h)}const c=s(l,[["render",p]]);export{y as __pageData,c as default};
diff --git a/dev/assets/source_algorithms_wilkinson.md.IBf6tx2D.js b/dev/assets/source_algorithms_wilkinson.md.IBf6tx2D.js
new file mode 100644
index 0000000..7208812
--- /dev/null
+++ b/dev/assets/source_algorithms_wilkinson.md.IBf6tx2D.js
@@ -0,0 +1,94 @@
+import{_ as s,c as i,o as a,a7 as n}from"./chunks/framework.CtKH--HK.js";const o=JSON.parse('{"title":"Wilkinson beeswarm","description":"","frontmatter":{},"headers":[],"relativePath":"source/algorithms/wilkinson.md","filePath":"source/algorithms/wilkinson.md","lastUpdated":null}'),h={name:"source/algorithms/wilkinson.md"},l=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 simple implementation like Matplotlib's algorithm.
+"""
+struct WilkinsonBeeswarm <: BeeswarmAlgorithm
+end
The calculate! function ​
The calculate! function is the main function that is called by the beeswarm! recipe to calculate the positions of the beeswarm.
It accepts points in pixelspace and marker size through positions, and sets the elements of buffer to the correct positions in pixelspace.
This decreases the amount of memory allocation, as the buffer is preallocated.
It is vital that the order of points in buffer is the same as in positions, since all of the other attributes (like color, size, etc.) are indexed by the order of the points in positions.
This is why we've essentially reimplemented the histogram here, as opposed to using it from StatsBase.
julia
function calculate!(buffer::AbstractVector{<: Point2}, alg::WilkinsonBeeswarm, positions::AbstractVector{<: Point2}, markersize, side::Symbol)
+    @info "Calculating..."
Here, we need to find each unique x-value, which indicates a different group or category.
julia
    xs = first.(positions)
+    # Once we've found this, then we can calculate
+    # the beeswarm for each group.
+    for x_val in unique(xs)
+        group = findall(==(x_val), xs)
+        wilkinson_kernel!(view(buffer, group), view(positions, group), markersize, side)
+    end
+
+end
The kernel ​
The wilkinson_kernel! function is the main function that calculates the beeswarm for a single group.
It accepts points in pixelspace and marker size through positions and markersize, and sets the elements of buffer to the correct positions in pixelspace.
julia
function wilkinson_kernel!(buffer, positions, markersize, side::Symbol)
+    #=
+
+    Initial sketch of code credited to Frederic Freyer and Julius Krumbiegel.
+    Reference here: https://github.com/MakieOrg/Makie.jl/issues/1950
+
+    =#
+
+    # This code calculates some parameters
+    # like the minimum and maximum y-values,
+    # and the number of bins.
+    ys = last.(positions)
+    ymin, ymax = extrema(ys)
+    Δy = ymax - ymin
+    nbins = round(Int, Δy / markersize)
+
+    # This will be used for us to push to, and is probably one of
+    # two allocation sources in the code!
+    bin_idxs = [Int[] for _ in 1:nbins]
+
+    #=
+    ### Binning y-values
+
+    We now need to bin the y-values into \`nbins\` bins.
+
+    This is done by looping through each y-value, calculating
+    the index of the bin it should be in, and then pushing the index
+    to the relevant vector in \`bin_idxs\`.
+
+    Since the bins are all uniform, we don't actually need to materialize
+    anything!
+    =#
+
+    for (i, y) in enumerate(ys)
+        current_index = round(Int, (y - ymin)/markersize)
+        if current_index == 0
+            current_index = 1
+        elseif current_index > nbins
+            current_index = nbins
+        end
+
+        push!(bin_idxs[current_index], i)
+    end
+
+    #=
+    ### Calculating positions
+
+    Now that we have the indices of the points in each bin, we can calculate the positions.
+    We force the points to dodge each other by \`markersize\`.
+    =#
+    for (i, idxs) in enumerate(bin_idxs)
+        isempty(idxs) && continue
+        # This is the center of the bin cell
+        current_y = ymin + (i - 1) * markersize + markersize/2
+        idxs_by_position = @view idxs[sortperm(last.(@view positions[idxs]))]
+
+        if side == :both
+            # Split the bin in two parts, evenly split.
+            # One will go left and one will go right.
+            a = idxs_by_position[begin:2:end]
+            b = idxs_by_position[(begin+1):2:end]
+            # Update the buffer array.
+            buffer[a] .= Point2f.(
+                ((1:length(a))) .* markersize .- markersize/2 .+ first.(view(positions, a)),
+                current_y
+                )
+            buffer[b] .= Point2f.(
+                ((1:length(b))) .* (-markersize) .+ markersize/2 .+ first.(view(positions, b)),
+                current_y
+                )
+        elseif side == :left
+            # Update the buffer array.
+            buffer[idxs_by_position] .= Point2f.(((1:length(idxs_by_position))) .* markersize .- markersize/2 .+ first.(view(positions, idxs_by_position)), current_y)
+        elseif side == :right
+            # Update the buffer array.
+            buffer[idxs_by_position] .= Point2f.(((1:length(idxs_by_position))) .* (-markersize) .+ markersize/2 .+ first.(view(positions, idxs_by_position)), current_y)
+        end
+    end
+end
This page was generated using Literate.jl.
`,23),t=[l];function e(k,p,r,d,E,g){return a(),i("div",null,t)}const c=s(h,[["render",e]]);export{o as __pageData,c as default};
diff --git a/dev/assets/source_algorithms_wilkinson.md.IBf6tx2D.lean.js b/dev/assets/source_algorithms_wilkinson.md.IBf6tx2D.lean.js
new file mode 100644
index 0000000..90c0fe3
--- /dev/null
+++ b/dev/assets/source_algorithms_wilkinson.md.IBf6tx2D.lean.js
@@ -0,0 +1 @@
+import{_ as s,c as i,o as a,a7 as n}from"./chunks/framework.CtKH--HK.js";const o=JSON.parse('{"title":"Wilkinson beeswarm","description":"","frontmatter":{},"headers":[],"relativePath":"source/algorithms/wilkinson.md","filePath":"source/algorithms/wilkinson.md","lastUpdated":null}'),h={name:"source/algorithms/wilkinson.md"},l=n("",23),t=[l];function e(k,p,r,d,E,g){return a(),i("div",null,t)}const c=s(h,[["render",e]]);export{o as __pageData,c as default};
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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])
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])
and the figure is:
julia
fig
Palmer Penguins ​
julia
using AlgebraOfGraphics, CairoMakie, SwarmMakie
 using PalmerPenguins, DataFrames
 
 penguins = dropmissing(DataFrame(PalmerPenguins.load()))
@@ -30,7 +30,7 @@
 Makie.update_state_before_display!(f.figure)
 Makie.update_state_before_display!(f.figure)
 Makie.update_state_before_display!(f.figure)
-f
julia
using CairoMakie, SwarmMakie
+f
julia
using CairoMakie, SwarmMakie
 using RDatasets, DataFrames
 iris = dataset("datasets", "iris")
 f, a, p = beeswarm(
@@ -48,7 +48,7 @@
 Makie.update_state_before_display!(f)
 Makie.update_state_before_display!(f)
 Makie.update_state_before_display!(f)
-f
Wilkinson's dot histogram ​
julia
using CairoMakie, SwarmMakie
+f
Wilkinson's dot histogram ​
julia
using CairoMakie, SwarmMakie
 using RDatasets, DataFrames
 mtcars = dataset("datasets", "mtcars")
 
@@ -60,8 +60,8 @@
 )
 p.side = :both
 p.direction = :x
-f
Note that to use side != :both, you will have to set the limits of the axis explicitly.
This page was generated using Literate.jl.

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zU;^@{Zu+i!8_<4+8+p6DqJjaRhxx)r)r$@$2N%o#wg@8oAdnO%;d4F$
z-)Xkm+}`5%%lW{x{;OgY`A&INmH5Def~KY>2>5Zp`o4KstR*q^T?G3?326!+o1X)#
zWB8ujXFg^x^HU4eTgQk+VB^n3jpa0?(kx~m8bAl=1arZN@_^f
     
     
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+    
     
     
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-    
+    
+    
     
     
     
@@ -21,7 +21,7 @@
     
Note that to use side != :both, you will have to set the limits of the axis explicitly.
This page was generated using Literate.jl.
+ \ No newline at end of file diff --git a/dev/hashmap.json b/dev/hashmap.json index bcf49b5..b22d4cf 100644 --- a/dev/hashmap.json +++ b/dev/hashmap.json @@ -1 +1 @@ -{"api.md":"B5Kj4H6I","introduction.md":"BLN9cl7G","index.md":"Ch-Sv6rX","source_recipe.md":"B-9q_tCq","examples.md":"BjwbFmYi","algorithms.md":"DRCJHfq3","source_algorithms_mkborregaard.md":"BP8ufsX3","source_algorithms_wilkinson.md":"DoVlTOzM","source_swarmmakie.md":"zR4D0qVV","source_algorithms_seaborn.md":"MippC_oW","source_algorithms_simple.md":"2zEkqpu_"} +{"source_algorithms_wilkinson.md":"IBf6tx2D","introduction.md":"CUHnHsKA","index.md":"Ch-Sv6rX","examples.md":"Xiq9LrlA","source_algorithms_mkborregaard.md":"BP8ufsX3","source_recipe.md":"B-9q_tCq","source_algorithms_simple.md":"2zEkqpu_","api.md":"BM2uvZ2j","algorithms.md":"v-_GJxoX","source_swarmmakie.md":"BzVkK5sT","source_algorithms_seaborn.md":"MippC_oW"} diff --git a/dev/index.html b/dev/index.html index 1bd7d02..2884cbf 100644 --- a/dev/index.html +++ b/dev/index.html @@ -8,10 +8,10 @@ - + - + @@ -19,7 +19,7 @@ - + \ No newline at end of file diff --git a/dev/introduction.html b/dev/introduction.html index 31317b0..71bc4c1 100644 --- a/dev/introduction.html +++ b/dev/introduction.html @@ -8,11 +8,11 @@ - + - - + + @@ -23,7 +23,7 @@ ys = randn(40) f, a, p = scatter(xs, ys; color = xs, axis = (; title = "Scatter plot")) beeswarm(f[1, 2], xs, ys; color = xs, algorithm = SimpleBeeswarm(), axis = (; title = "Beeswarm plot")) -f

Use with AlgebraOfGraphics.jl ​

As a Makie recipe, beeswarm also composes with AlgebraOfGraphics!

julia
using AlgebraOfGraphics, CairoMakie, SwarmMakie
+f

Use with AlgebraOfGraphics.jl ​

As a Makie recipe, beeswarm also composes with AlgebraOfGraphics!

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

- +f

+ \ No newline at end of file diff --git a/dev/source/SwarmMakie.html b/dev/source/SwarmMakie.html index 3b9574a..6782d98 100644 --- a/dev/source/SwarmMakie.html +++ b/dev/source/SwarmMakie.html @@ -8,11 +8,11 @@ - + - - + + @@ -24,14 +24,15 @@ using Random import StatsBase: Histogram, - fit + fit, + UnitWeights include("recipe.jl") include("algorithms/simple.jl")

include("algorithms/mkborregaard.jl")

julia
include("algorithms/seaborn.jl")
 include("algorithms/wilkinson.jl")
 
 end

This page was generated using Literate.jl.

- + \ No newline at end of file diff --git a/dev/source/algorithms/mkborregaard.html b/dev/source/algorithms/mkborregaard.html index 85581d4..0d6d57a 100644 --- a/dev/source/algorithms/mkborregaard.html +++ b/dev/source/algorithms/mkborregaard.html @@ -8,10 +8,10 @@ - + - + @@ -123,7 +123,7 @@ x,y = SwarmMakie.beeswarm_coords(collect(iris[!, :SepalLength]), :both, 7) Makie.scatter(x,y, color = iris[!, :Species].refs, markersize = 7, axis = (; aspect = DataAspect()))

This page was generated using Literate.jl.

- + \ No newline at end of file diff --git a/dev/source/algorithms/seaborn.html b/dev/source/algorithms/seaborn.html index 6fd70a6..e780066 100644 --- a/dev/source/algorithms/seaborn.html +++ b/dev/source/algorithms/seaborn.html @@ -8,10 +8,10 @@ - + - + @@ -161,7 +161,7 @@ """ end end

This page was generated using Literate.jl.

- + \ No newline at end of file diff --git a/dev/source/algorithms/simple.html b/dev/source/algorithms/simple.html index a73d8a8..e10c5a2 100644 --- a/dev/source/algorithms/simple.html +++ b/dev/source/algorithms/simple.html @@ -8,10 +8,10 @@ - + - + @@ -82,7 +82,7 @@ end return xs end

This page was generated using Literate.jl.

- + \ No newline at end of file diff --git a/dev/source/algorithms/wilkinson.html b/dev/source/algorithms/wilkinson.html index 39523ca..2dc2873 100644 --- a/dev/source/algorithms/wilkinson.html +++ b/dev/source/algorithms/wilkinson.html @@ -8,25 +8,32 @@ - + - - + + -
SwarmMakie.jl

Appearance

Wilkinson beeswarm ​

julia
export WilkinsonBeeswarm

This is a beeswarm implementation as described in Leland Wilkinson's original paper on dot plots

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 simple implementation like Matplotlib's algorithm.
 """
 struct WilkinsonBeeswarm <: BeeswarmAlgorithm
-end
+end
The calculate! function ​
The calculate! function is the main function that is called by the beeswarm! recipe to calculate the positions of the beeswarm.
It accepts points in pixelspace and marker size through positions, and sets the elements of buffer to the correct positions in pixelspace.
This decreases the amount of memory allocation, as the buffer is preallocated.
It is vital that the order of points in buffer is the same as in positions, since all of the other attributes (like color, size, etc.) are indexed by the order of the points in positions.
This is why we've essentially reimplemented the histogram here, as opposed to using it from StatsBase.
julia
function calculate!(buffer::AbstractVector{<: Point2}, alg::WilkinsonBeeswarm, positions::AbstractVector{<: Point2}, markersize, side::Symbol)
+    @info "Calculating..."
Here, we need to find each unique x-value, which indicates a different group or category.
julia
    xs = first.(positions)
+    # Once we've found this, then we can calculate
+    # the beeswarm for each group.
+    for x_val in unique(xs)
+        group = findall(==(x_val), xs)
+        wilkinson_kernel!(view(buffer, group), view(positions, group), markersize, side)
+    end
 
-function calculate!(buffer::AbstractVector{<: Point2}, alg::WilkinsonBeeswarm, positions::AbstractVector{<: Point2}, markersize, side::Symbol)
+end
The kernel ​
The wilkinson_kernel! function is the main function that calculates the beeswarm for a single group.
It accepts points in pixelspace and marker size through positions and markersize, and sets the elements of buffer to the correct positions in pixelspace.
julia
function wilkinson_kernel!(buffer, positions, markersize, side::Symbol)
     #=
 
     Initial sketch of code credited to Frederic Freyer and Julius Krumbiegel.
@@ -34,27 +41,78 @@
 
     =#
 
-    new_positions = Float32[]
-    for pos in positions
-        push!(new_positions, pos[2])
-    end
-    h = fit(Histogram, new_positions; nbins = markersize)
-    xs = 1:length(h.weights)
+    # This code calculates some parameters
+    # like the minimum and maximum y-values,
+    # and the number of bins.
+    ys = last.(positions)
+    ymin, ymax = extrema(ys)
+    Δy = ymax - ymin
+    nbins = round(Int, Δy / markersize)
+
+    # This will be used for us to push to, and is probably one of
+    # two allocation sources in the code!
+    bin_idxs = [Int[] for _ in 1:nbins]
+
+    #=
+    ### Binning y-values
+
+    We now need to bin the y-values into `nbins` bins.
+
+    This is done by looping through each y-value, calculating
+    the index of the bin it should be in, and then pushing the index
+    to the relevant vector in `bin_idxs`.
 
-    dot_per_bin = round.(Int, h.weights ./ markersize)
+    Since the bins are all uniform, we don't actually need to materialize
+    anything!
+    =#
 
-    points = Point2f[]
-    for x in xs
-        for dot in 1:dot_per_bin[x]
-            point = Point2f(x, dot)
-            push!(points, point)
+    for (i, y) in enumerate(ys)
+        current_index = round(Int, (y - ymin)/markersize)
+        if current_index == 0
+            current_index = 1
+        elseif current_index > nbins
+            current_index = nbins
         end
+
+        push!(bin_idxs[current_index], i)
     end
 
-    return points
+    #=
+    ### Calculating positions
 
+    Now that we have the indices of the points in each bin, we can calculate the positions.
+    We force the points to dodge each other by `markersize`.
+    =#
+    for (i, idxs) in enumerate(bin_idxs)
+        isempty(idxs) && continue
+        # This is the center of the bin cell
+        current_y = ymin + (i - 1) * markersize + markersize/2
+        idxs_by_position = @view idxs[sortperm(last.(@view positions[idxs]))]
+
+        if side == :both
+            # Split the bin in two parts, evenly split.
+            # One will go left and one will go right.
+            a = idxs_by_position[begin:2:end]
+            b = idxs_by_position[(begin+1):2:end]
+            # Update the buffer array.
+            buffer[a] .= Point2f.(
+                ((1:length(a))) .* markersize .- markersize/2 .+ first.(view(positions, a)),
+                current_y
+                )
+            buffer[b] .= Point2f.(
+                ((1:length(b))) .* (-markersize) .+ markersize/2 .+ first.(view(positions, b)),
+                current_y
+                )
+        elseif side == :left
+            # Update the buffer array.
+            buffer[idxs_by_position] .= Point2f.(((1:length(idxs_by_position))) .* markersize .- markersize/2 .+ first.(view(positions, idxs_by_position)), current_y)
+        elseif side == :right
+            # Update the buffer array.
+            buffer[idxs_by_position] .= Point2f.(((1:length(idxs_by_position))) .* (-markersize) .+ markersize/2 .+ first.(view(positions, idxs_by_position)), current_y)
+        end
+    end
 end
This page was generated using Literate.jl.

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diff --git a/dev/source/recipe.html b/dev/source/recipe.html
index 98b9c87..6618af0 100644
--- a/dev/source/recipe.html
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@@ -105,7 +105,7 @@
     notify(should_update_based_on_zoom)
     return
 end

This page was generated using Literate.jl.

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