From 897b4fc2fcecd4a56a52a89e34386e78ecce936b Mon Sep 17 00:00:00 2001 From: "Documenter.jl" Date: Mon, 16 Dec 2024 17:37:08 +0000 Subject: [PATCH] build based on 6322582 --- dev/.documenter/index.md | 10 +++++----- dev/.documenter/vignettes/ahvfugp.png | Bin 118583 -> 0 bytes dev/.documenter/vignettes/entropize.md | 2 +- dev/.documenter/vignettes/jndzxmv.png | Bin 0 -> 50516 bytes dev/.documenter/vignettes/miqllmb.png | Bin 0 -> 133756 bytes dev/.documenter/vignettes/overview.md | 2 +- dev/.documenter/vignettes/zqpmxhf.png | Bin 55374 -> 0 bytes dev/final_site/404.html | 4 ++-- dev/final_site/assets/ahvfugp.Bmlmx36h.png | Bin 118583 -> 0 bytes .../{app.B_LX3KeA.js => app.CtROifXg.js} | 2 +- .../chunks/@localSearchIndexroot.BO4QhwZ2.js | 1 + .../chunks/@localSearchIndexroot.DHsgUhP4.js | 1 - ...VjZ6nxU.js => VPLocalSearchBox.JcDdg4E9.js} | 2 +- .../{theme.37LDVK5e.js => theme.DHVoDm5j.js} | 4 ++-- ...dex.md.Dtie0iOP.js => index.md.ZbH1van7.js} | 2 +- ...e0iOP.lean.js => index.md.ZbH1van7.lean.js} | 2 +- dev/final_site/assets/jndzxmv.DEUroPh5.png | Bin 0 -> 50516 bytes dev/final_site/assets/miqllmb.DELNajMy.png | Bin 0 -> 133756 bytes ...l.js => vignettes_entropize.md.D8bK3dtX.js} | 2 +- ...=> vignettes_entropize.md.D8bK3dtX.lean.js} | 2 +- ...nA.js => vignettes_overview.md.vrxwa-Ce.js} | 2 +- ... => vignettes_overview.md.vrxwa-Ce.lean.js} | 2 +- dev/final_site/assets/zqpmxhf.CKZI-JsR.png | Bin 55374 -> 0 bytes dev/final_site/bibliography.html | 6 +++--- dev/final_site/hashmap.json | 2 +- dev/final_site/index.html | 10 +++++----- dev/final_site/vignettes/entropize.html | 10 +++++----- dev/final_site/vignettes/overview.html | 10 +++++----- dev/final_site/vignettes/uniqueness.html | 6 +++--- 29 files changed, 42 insertions(+), 42 deletions(-) delete mode 100644 dev/.documenter/vignettes/ahvfugp.png create mode 100644 dev/.documenter/vignettes/jndzxmv.png create mode 100644 dev/.documenter/vignettes/miqllmb.png delete mode 100644 dev/.documenter/vignettes/zqpmxhf.png delete mode 100644 dev/final_site/assets/ahvfugp.Bmlmx36h.png rename dev/final_site/assets/{app.B_LX3KeA.js => app.CtROifXg.js} (95%) create mode 100644 dev/final_site/assets/chunks/@localSearchIndexroot.BO4QhwZ2.js delete mode 100644 dev/final_site/assets/chunks/@localSearchIndexroot.DHsgUhP4.js rename dev/final_site/assets/chunks/{VPLocalSearchBox.DVjZ6nxU.js => VPLocalSearchBox.JcDdg4E9.js} (99%) rename dev/final_site/assets/chunks/{theme.37LDVK5e.js => theme.DHVoDm5j.js} (99%) rename dev/final_site/assets/{index.md.Dtie0iOP.js => index.md.ZbH1van7.js} (94%) rename dev/final_site/assets/{index.md.Dtie0iOP.lean.js => index.md.ZbH1van7.lean.js} (94%) create mode 100644 dev/final_site/assets/jndzxmv.DEUroPh5.png create mode 100644 dev/final_site/assets/miqllmb.DELNajMy.png rename dev/final_site/assets/{vignettes_entropize.md.yNwRqwtl.js => vignettes_entropize.md.D8bK3dtX.js} (95%) rename dev/final_site/assets/{vignettes_entropize.md.yNwRqwtl.lean.js => vignettes_entropize.md.D8bK3dtX.lean.js} (95%) rename dev/final_site/assets/{vignettes_overview.md.Dv7wORnA.js => vignettes_overview.md.vrxwa-Ce.js} (98%) rename dev/final_site/assets/{vignettes_overview.md.Dv7wORnA.lean.js => vignettes_overview.md.vrxwa-Ce.lean.js} (98%) delete mode 100644 dev/final_site/assets/zqpmxhf.CKZI-JsR.png diff --git a/dev/.documenter/index.md b/dev/.documenter/index.md index fc17b92..1f66d47 100644 --- a/dev/.documenter/index.md +++ b/dev/.documenter/index.md @@ -23,7 +23,7 @@ abstract type BONSampler end A `BONSampler` is any algorithm for proposing a set of sampling locations. -[source](https://github.com/PoisotLab/BiodiversityObservationNetworks.jl/blob/3585107e90ff73c0aadb83235747780d8004ba93/src/types.jl#L1-L5) +[source](https://github.com/PoisotLab/BiodiversityObservationNetworks.jl/blob/6322582496bd794d91f70bf1ce5deae9ccf32e28/src/types.jl#L1-L5) @@ -80,7 +80,7 @@ BalancedAcceptance A `BONSeeder` that uses Balanced-Acceptance Sampling (Van-dem-Bates et al. 2017 https://doi.org/10.1111/2041-210X.13003) -[source](https://github.com/PoisotLab/BiodiversityObservationNetworks.jl/blob/3585107e90ff73c0aadb83235747780d8004ba93/src/balancedacceptance.jl#L1-L6) +[source](https://github.com/PoisotLab/BiodiversityObservationNetworks.jl/blob/6322582496bd794d91f70bf1ce5deae9ccf32e28/src/balancedacceptance.jl#L1-L6) @@ -105,7 +105,7 @@ Uniqueness A `BONSampler` -[source](https://github.com/PoisotLab/BiodiversityObservationNetworks.jl/blob/3585107e90ff73c0aadb83235747780d8004ba93/src/uniqueness.jl#L1-L5) +[source](https://github.com/PoisotLab/BiodiversityObservationNetworks.jl/blob/6322582496bd794d91f70bf1ce5deae9ccf32e28/src/uniqueness.jl#L1-L5) @@ -132,7 +132,7 @@ This function turns a matrix `A` (storing measurement values) into pixel-wise en Pixel-wise entropy is determined by measuring the empirical probability of randomly picking a value in the matrix that is either lower or higher than the pixel value. The entropy of both these probabilities are calculated using the -p×log(2,p) formula. The entropy of the pixel is the _sum_ of the two entropies, so that it is close to 1 for values close to the median, and close to 0 for values close to the extreme of the distribution. -[source](https://github.com/PoisotLab/BiodiversityObservationNetworks.jl/blob/3585107e90ff73c0aadb83235747780d8004ba93/src/entropize.jl#L1-L13) +[source](https://github.com/PoisotLab/BiodiversityObservationNetworks.jl/blob/6322582496bd794d91f70bf1ce5deae9ccf32e28/src/entropize.jl#L1-L13) @@ -149,7 +149,7 @@ entropize(A::Matrix{Number}) Allocation version of `entropize!`. -[source](https://github.com/PoisotLab/BiodiversityObservationNetworks.jl/blob/3585107e90ff73c0aadb83235747780d8004ba93/src/entropize.jl#L30-L34) +[source](https://github.com/PoisotLab/BiodiversityObservationNetworks.jl/blob/6322582496bd794d91f70bf1ce5deae9ccf32e28/src/entropize.jl#L30-L34) diff --git a/dev/.documenter/vignettes/ahvfugp.png b/dev/.documenter/vignettes/ahvfugp.png deleted file mode 100644 index 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``` -![](ahvfugp.png) +![](miqllmb.png) Using the `entropize` function will convert these values into entropy at the pixel scale: diff --git a/dev/.documenter/vignettes/jndzxmv.png b/dev/.documenter/vignettes/jndzxmv.png new file mode 100644 index 0000000000000000000000000000000000000000..79849fd339ae7481828e95d85ecabe1b19281f9e GIT binary patch literal 50516 zcmc$`Wmr_-_xF#qAYDpGcY}mUhmhL2u*cmJRC|K6ciM^*RK?`QBcq;QBcr= zu~C6fqzFvQfCHwPsjsK?I&-?8{*-)ssEW3_a?aNagyD=Fx%SxN* ziJ}FS>+4%PT+#q7jwdNE1zjC{vV0AumubV)FNZp=-P$Cdi~jMJx*MOht*Du@GCsRQ zJev|Mf(-!AhJtcR(l$!<&za|YK6-TE>~pG_9QMN}LfyfH4@aBG$Mg@!2~rZdhhq^t zOYp-{Q|Ar#BjA`_@Qv`HzcOn4|GziTJ#swKypd{~>A4!wepS-$lFUAMly$eAb$6L{ ze|fO-lNGpvL1E;y*SIM*NPrpC>T|y2e}5a)`EGMKquFC$>dzNJzga5$hYq&e1j1mM za9a0^gOvmgsm3wulk3~fw;R&;$I{D(F&+y(`>i84KaYpS_iatD=Y})HzZ%~q8#>I^ zSdA0^YlI(-4CVu1f#=JCT+ZJkFXkkIr+l<&n)S}CXBpP`GMf1(MbZw`h;mRo?E zK6F8(ChWQQ)*ssY3qgwKUweJdcGx4$Adro=>%Hd9OrNv8MW3zFoW*93wAqLM^4MQ; zoBziUMEAdT+-mUV83 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--git a/dev/.documenter/vignettes/overview.md b/dev/.documenter/vignettes/overview.md index 9738cd6..079a5ee 100644 --- a/dev/.documenter/vignettes/overview.md +++ b/dev/.documenter/vignettes/overview.md @@ -17,7 +17,7 @@ U = rand(MidpointDisplacement(0.5), (100, 100)) heatmap(U) ``` -![](zqpmxhf.png) +![](jndzxmv.png) In practice, this uncertainty matrix is likely to be derived from an application of the hyper-parameters optimization step, which is detailed in other vignettes. diff --git a/dev/.documenter/vignettes/zqpmxhf.png b/dev/.documenter/vignettes/zqpmxhf.png deleted file mode 100644 index 295ab492db089a6c9a8d386631fcea9b0c2e4f62..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 55374 zcmc$_cR1Vc+xV?jv_(}_snwR2mNsUn-PWoSs`lP{i#@9?VpCNj_6TCtCbrrVJNB*y zvG)v5eD2@<{Po<=bKKw0A5R>IE4<&w`5xD~uJe3dp&ymxC~q^|CL$uDlz%V%iHPXN zFCrr1Z#S9H>A1uI^L?^h=sAA;@>V%E$3d)f3!UHpzt8s&%%$heBveH7Kw=d#??_AWxw6HG%>%^{iYM4 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custom-block",open:""};function k(m,e,v,f,y,j){const o=l("Badge");return d(),r("div",null,[e[15]||(e[15]=t('

The purpose of this package is to provide a high-level, extensible, modular interface to the selection of sampling point for biodiversity processes in space. It is based around a collection of types representing point selection algorithms, used to select the most informative sampling points based on raster data. Specifically, many algorithms work from a layer indicating entropy of a model based prediction at each location.

This package is in development

The BiodiversityObservationNetworks.jl package is currently under development. The API is not expected to change a lot, but it may change in order to facilitate the integration of new features.

High-level types ​

',4)),i("details",c,[i("summary",null,[e[0]||(e[0]=i("a",{id:"BiodiversityObservationNetworks.BONSampler",href:"#BiodiversityObservationNetworks.BONSampler"},[i("span",{class:"jlbinding"},"BiodiversityObservationNetworks.BONSampler")],-1)),e[1]||(e[1]=s()),a(o,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[2]||(e[2]=t('
julia
abstract type BONSampler end

A BONSampler is any algorithm for proposing a set of sampling locations.

source

',3))]),e[16]||(e[16]=t('

Missing docstring.

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

Missing docstring.

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

Seeder and refiner functions ​

Missing docstring.

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

Missing docstring.

Missing docstring for seed!. Check Documenter's build log for details.

Missing docstring.

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

Missing docstring.

Missing docstring for refine!. Check Documenter's build log for details.

Seeder algorithms ​

',8)),i("details",h,[i("summary",null,[e[3]||(e[3]=i("a",{id:"BiodiversityObservationNetworks.BalancedAcceptance",href:"#BiodiversityObservationNetworks.BalancedAcceptance"},[i("span",{class:"jlbinding"},"BiodiversityObservationNetworks.BalancedAcceptance")],-1)),e[4]||(e[4]=s()),a(o,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[5]||(e[5]=t('
julia
BalancedAcceptance

A BONSeeder that uses Balanced-Acceptance Sampling (Van-dem-Bates et al. 2017 https://doi.org/10.1111/2041-210X.13003)

source

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julia
Uniqueness

A BONSampler

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julia
entropize!(U::Matrix{AbstractFloat}, A::Matrix{Number})

This function turns a matrix A (storing measurement values) into pixel-wise entropy values, stored in a matrix U (that is previously allocated).

Pixel-wise entropy is determined by measuring the empirical probability of randomly picking a value in the matrix that is either lower or higher than the pixel value. The entropy of both these probabilities are calculated using the -p×log(2,p) formula. The entropy of the pixel is the sum of the two entropies, so that it is close to 1 for values close to the median, and close to 0 for values close to the extreme of the distribution.

source

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julia
entropize(A::Matrix{Number})

Allocation version of entropize!.

source

',3))])])}const N=n(p,[["render",k]]);export{w as __pageData,N as default}; +import{_ as n,c as r,a5 as t,j as i,a as s,G as a,B as l,o as d}from"./chunks/framework.BdiZ026L.js";const w=JSON.parse('{"title":"BiodiversityObservationNetworks.jl","description":"","frontmatter":{},"headers":[],"relativePath":"index.md","filePath":"index.md","lastUpdated":null}'),p={name:"index.md"},c={class:"jldocstring custom-block",open:""},h={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},b={class:"jldocstring custom-block",open:""},u={class:"jldocstring custom-block",open:""};function k(m,e,v,f,y,j){const o=l("Badge");return d(),r("div",null,[e[15]||(e[15]=t('

BiodiversityObservationNetworks.jl ​

The purpose of this package is to provide a high-level, extensible, modular interface to the selection of sampling point for biodiversity processes in space. It is based around a collection of types representing point selection algorithms, used to select the most informative sampling points based on raster data. Specifically, many algorithms work from a layer indicating entropy of a model based prediction at each location.

This package is in development

The BiodiversityObservationNetworks.jl package is currently under development. The API is not expected to change a lot, but it may change in order to facilitate the integration of new features.

High-level types ​

',4)),i("details",c,[i("summary",null,[e[0]||(e[0]=i("a",{id:"BiodiversityObservationNetworks.BONSampler",href:"#BiodiversityObservationNetworks.BONSampler"},[i("span",{class:"jlbinding"},"BiodiversityObservationNetworks.BONSampler")],-1)),e[1]||(e[1]=s()),a(o,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[2]||(e[2]=t('
julia
abstract type BONSampler end

A BONSampler is any algorithm for proposing a set of sampling locations.

source

',3))]),e[16]||(e[16]=t('

Missing docstring.

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

Missing docstring.

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

Seeder and refiner functions ​

Missing docstring.

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

Missing docstring.

Missing docstring for seed!. Check Documenter's build log for details.

Missing docstring.

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

Missing docstring.

Missing docstring for refine!. Check Documenter's build log for details.

Seeder algorithms ​

',8)),i("details",h,[i("summary",null,[e[3]||(e[3]=i("a",{id:"BiodiversityObservationNetworks.BalancedAcceptance",href:"#BiodiversityObservationNetworks.BalancedAcceptance"},[i("span",{class:"jlbinding"},"BiodiversityObservationNetworks.BalancedAcceptance")],-1)),e[4]||(e[4]=s()),a(o,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[5]||(e[5]=t('
julia
BalancedAcceptance

A BONSeeder that uses Balanced-Acceptance Sampling (Van-dem-Bates et al. 2017 https://doi.org/10.1111/2041-210X.13003)

source

',3))]),e[17]||(e[17]=i("h2",{id:"Refiner-algorithms",tabindex:"-1"},[s("Refiner algorithms "),i("a",{class:"header-anchor",href:"#Refiner-algorithms","aria-label":'Permalink to "Refiner algorithms {#Refiner-algorithms}"'},"​")],-1)),e[18]||(e[18]=i("div",{class:"warning custom-block"},[i("p",{class:"custom-block-title"},"Missing docstring."),i("p",null,[s("Missing docstring for "),i("code",null,"AdaptiveSpatial"),s(". Check Documenter's build log for details.")])],-1)),i("details",g,[i("summary",null,[e[6]||(e[6]=i("a",{id:"BiodiversityObservationNetworks.Uniqueness",href:"#BiodiversityObservationNetworks.Uniqueness"},[i("span",{class:"jlbinding"},"BiodiversityObservationNetworks.Uniqueness")],-1)),e[7]||(e[7]=s()),a(o,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[8]||(e[8]=t('
julia
Uniqueness

A BONSampler

source

',3))]),e[19]||(e[19]=i("h2",{id:"Helper-functions",tabindex:"-1"},[s("Helper functions "),i("a",{class:"header-anchor",href:"#Helper-functions","aria-label":'Permalink to "Helper functions {#Helper-functions}"'},"​")],-1)),e[20]||(e[20]=i("div",{class:"warning custom-block"},[i("p",{class:"custom-block-title"},"Missing docstring."),i("p",null,[s("Missing docstring for "),i("code",null,"squish"),s(". Check Documenter's build log for details.")])],-1)),i("details",b,[i("summary",null,[e[9]||(e[9]=i("a",{id:"BiodiversityObservationNetworks.entropize!",href:"#BiodiversityObservationNetworks.entropize!"},[i("span",{class:"jlbinding"},"BiodiversityObservationNetworks.entropize!")],-1)),e[10]||(e[10]=s()),a(o,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[11]||(e[11]=t('
julia
entropize!(U::Matrix{AbstractFloat}, A::Matrix{Number})

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source

',4))]),i("details",u,[i("summary",null,[e[12]||(e[12]=i("a",{id:"BiodiversityObservationNetworks.entropize",href:"#BiodiversityObservationNetworks.entropize"},[i("span",{class:"jlbinding"},"BiodiversityObservationNetworks.entropize")],-1)),e[13]||(e[13]=s()),a(o,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[14]||(e[14]=t('
julia
entropize(A::Matrix{Number})

Allocation version of entropize!.

source

',3))])])}const N=n(p,[["render",k]]);export{w as __pageData,N as default}; diff --git a/dev/final_site/assets/index.md.Dtie0iOP.lean.js b/dev/final_site/assets/index.md.ZbH1van7.lean.js similarity index 94% rename from dev/final_site/assets/index.md.Dtie0iOP.lean.js rename to dev/final_site/assets/index.md.ZbH1van7.lean.js index 8569fdd..c460977 100644 --- a/dev/final_site/assets/index.md.Dtie0iOP.lean.js +++ b/dev/final_site/assets/index.md.ZbH1van7.lean.js @@ -1 +1 @@ -import{_ as n,c as r,a5 as t,j as i,a as s,G as a,B as l,o as d}from"./chunks/framework.BdiZ026L.js";const w=JSON.parse('{"title":"BiodiversityObservationNetworks.jl","description":"","frontmatter":{},"headers":[],"relativePath":"index.md","filePath":"index.md","lastUpdated":null}'),p={name:"index.md"},c={class:"jldocstring custom-block",open:""},h={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},b={class:"jldocstring custom-block",open:""},u={class:"jldocstring custom-block",open:""};function k(m,e,v,f,y,j){const o=l("Badge");return d(),r("div",null,[e[15]||(e[15]=t('

BiodiversityObservationNetworks.jl ​

The purpose of this package is to provide a high-level, extensible, modular interface to the selection of sampling point for biodiversity processes in space. It is based around a collection of types representing point selection algorithms, used to select the most informative sampling points based on raster data. Specifically, many algorithms work from a layer indicating entropy of a model based prediction at each location.

This package is in development

The BiodiversityObservationNetworks.jl package is currently under development. The API is not expected to change a lot, but it may change in order to facilitate the integration of new features.

High-level types ​

',4)),i("details",c,[i("summary",null,[e[0]||(e[0]=i("a",{id:"BiodiversityObservationNetworks.BONSampler",href:"#BiodiversityObservationNetworks.BONSampler"},[i("span",{class:"jlbinding"},"BiodiversityObservationNetworks.BONSampler")],-1)),e[1]||(e[1]=s()),a(o,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[2]||(e[2]=t('
julia
abstract type BONSampler end

A BONSampler is any algorithm for proposing a set of sampling locations.

source

',3))]),e[16]||(e[16]=t('

Missing docstring.

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

Missing docstring.

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

Seeder and refiner functions ​

Missing docstring.

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

Missing docstring.

Missing docstring for seed!. Check Documenter's build log for details.

Missing docstring.

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

Missing docstring.

Missing docstring for refine!. Check Documenter's build log for details.

Seeder algorithms ​

',8)),i("details",h,[i("summary",null,[e[3]||(e[3]=i("a",{id:"BiodiversityObservationNetworks.BalancedAcceptance",href:"#BiodiversityObservationNetworks.BalancedAcceptance"},[i("span",{class:"jlbinding"},"BiodiversityObservationNetworks.BalancedAcceptance")],-1)),e[4]||(e[4]=s()),a(o,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[5]||(e[5]=t('
julia
BalancedAcceptance

A BONSeeder that uses Balanced-Acceptance Sampling (Van-dem-Bates et al. 2017 https://doi.org/10.1111/2041-210X.13003)

source

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julia
Uniqueness

A BONSampler

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julia
entropize!(U::Matrix{AbstractFloat}, A::Matrix{Number})

This function turns a matrix A (storing measurement values) into pixel-wise entropy values, stored in a matrix U (that is previously allocated).

Pixel-wise entropy is determined by measuring the empirical probability of randomly picking a value in the matrix that is either lower or higher than the pixel value. The entropy of both these probabilities are calculated using the -p×log(2,p) formula. The entropy of the pixel is the sum of the two entropies, so that it is close to 1 for values close to the median, and close to 0 for values close to the extreme of the distribution.

source

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julia
entropize(A::Matrix{Number})

Allocation version of entropize!.

source

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BiodiversityObservationNetworks.jl ​

The purpose of this package is to provide a high-level, extensible, modular interface to the selection of sampling point for biodiversity processes in space. It is based around a collection of types representing point selection algorithms, used to select the most informative sampling points based on raster data. Specifically, many algorithms work from a layer indicating entropy of a model based prediction at each location.

This package is in development

The BiodiversityObservationNetworks.jl package is currently under development. The API is not expected to change a lot, but it may change in order to facilitate the integration of new features.

High-level types ​

',4)),i("details",c,[i("summary",null,[e[0]||(e[0]=i("a",{id:"BiodiversityObservationNetworks.BONSampler",href:"#BiodiversityObservationNetworks.BONSampler"},[i("span",{class:"jlbinding"},"BiodiversityObservationNetworks.BONSampler")],-1)),e[1]||(e[1]=s()),a(o,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[2]||(e[2]=t('
julia
abstract type BONSampler end

A BONSampler is any algorithm for proposing a set of sampling locations.

source

',3))]),e[16]||(e[16]=t('

Missing docstring.

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

Missing docstring.

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

Seeder and refiner functions ​

Missing docstring.

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

Missing docstring.

Missing docstring for seed!. Check Documenter's build log for details.

Missing docstring.

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

Missing docstring.

Missing docstring for refine!. Check Documenter's build log for details.

Seeder algorithms ​

',8)),i("details",h,[i("summary",null,[e[3]||(e[3]=i("a",{id:"BiodiversityObservationNetworks.BalancedAcceptance",href:"#BiodiversityObservationNetworks.BalancedAcceptance"},[i("span",{class:"jlbinding"},"BiodiversityObservationNetworks.BalancedAcceptance")],-1)),e[4]||(e[4]=s()),a(o,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[5]||(e[5]=t('
julia
BalancedAcceptance

A BONSeeder that uses Balanced-Acceptance Sampling (Van-dem-Bates et al. 2017 https://doi.org/10.1111/2041-210X.13003)

source

',3))]),e[17]||(e[17]=i("h2",{id:"Refiner-algorithms",tabindex:"-1"},[s("Refiner algorithms "),i("a",{class:"header-anchor",href:"#Refiner-algorithms","aria-label":'Permalink to "Refiner algorithms {#Refiner-algorithms}"'},"​")],-1)),e[18]||(e[18]=i("div",{class:"warning custom-block"},[i("p",{class:"custom-block-title"},"Missing docstring."),i("p",null,[s("Missing docstring for "),i("code",null,"AdaptiveSpatial"),s(". Check Documenter's build log for details.")])],-1)),i("details",g,[i("summary",null,[e[6]||(e[6]=i("a",{id:"BiodiversityObservationNetworks.Uniqueness",href:"#BiodiversityObservationNetworks.Uniqueness"},[i("span",{class:"jlbinding"},"BiodiversityObservationNetworks.Uniqueness")],-1)),e[7]||(e[7]=s()),a(o,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[8]||(e[8]=t('
julia
Uniqueness

A BONSampler

source

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julia
entropize!(U::Matrix{AbstractFloat}, A::Matrix{Number})

This function turns a matrix A (storing measurement values) into pixel-wise entropy values, stored in a matrix U (that is previously allocated).

Pixel-wise entropy is determined by measuring the empirical probability of randomly picking a value in the matrix that is either lower or higher than the pixel value. The entropy of both these probabilities are calculated using the -p×log(2,p) formula. The entropy of the pixel is the sum of the two entropies, so that it is close to 1 for values close to the median, and close to 0 for values close to the extreme of the distribution.

source

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julia
entropize(A::Matrix{Number})

Allocation version of entropize!.

source

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--git a/dev/final_site/assets/vignettes_entropize.md.yNwRqwtl.js b/dev/final_site/assets/vignettes_entropize.md.D8bK3dtX.js similarity index 95% rename from dev/final_site/assets/vignettes_entropize.md.yNwRqwtl.js rename to dev/final_site/assets/vignettes_entropize.md.D8bK3dtX.js index e15daa5..69e8760 100644 --- a/dev/final_site/assets/vignettes_entropize.md.yNwRqwtl.js +++ b/dev/final_site/assets/vignettes_entropize.md.D8bK3dtX.js @@ -1,4 +1,4 @@ -import{_ as i,c as e,a5 as t,o as a}from"./chunks/framework.BdiZ026L.js";const n="/BiodiversityObservationNetworks.jl/assets/ahvfugp.Bmlmx36h.png",g=JSON.parse('{"title":"Getting the entropy matrix","description":"","frontmatter":{},"headers":[],"relativePath":"vignettes/entropize.md","filePath":"vignettes/entropize.md","lastUpdated":null}'),p={name:"vignettes/entropize.md"};function l(o,s,h,r,d,c){return a(),e("div",null,s[0]||(s[0]=[t(`

Getting the entropy matrix ​

For some applications, we want to place points to capture the maximum amount of information, which is to say that we want to sample a balance of entropy values, as opposed to absolute values. In this vignette, we will walk through an example using the entropize function to convert raw data to entropy values.

julia
using BiodiversityObservationNetworks
+import{_ as i,c as e,a5 as t,o as a}from"./chunks/framework.BdiZ026L.js";const n="/BiodiversityObservationNetworks.jl/assets/miqllmb.DELNajMy.png",g=JSON.parse('{"title":"Getting the entropy matrix","description":"","frontmatter":{},"headers":[],"relativePath":"vignettes/entropize.md","filePath":"vignettes/entropize.md","lastUpdated":null}'),p={name:"vignettes/entropize.md"};function l(o,s,r,h,d,c){return a(),e("div",null,s[0]||(s[0]=[t(`
Getting the entropy matrix ​
For some applications, we want to place points to capture the maximum amount of information, which is to say that we want to sample a balance of entropy values, as opposed to absolute values. In this vignette, we will walk through an example using the entropize function to convert raw data to entropy values.
julia
using BiodiversityObservationNetworks
 using NeutralLandscapes
 using CairoMakie
Entropy is problem-specific
The solution presented in this vignette is a least-assumption solution based on the empirical values given in a matrix of measurements. In a lot of situations, this is not the entropy that you want. For example, if your pixels are storing probabilities of Bernoulli events, you can directly use the entropy of the events in the entropy matrix.
We start by generating a random matrix of measurements:
julia
measurements = rand(MidpointDisplacement(), (200, 200)) .* 100
 heatmap(measurements)
Using the entropize function will convert these values into entropy at the pixel scale:
@example
U = entropize(measurements)
diff --git a/dev/final_site/assets/vignettes_entropize.md.yNwRqwtl.lean.js b/dev/final_site/assets/vignettes_entropize.md.D8bK3dtX.lean.js
similarity index 95%
rename from dev/final_site/assets/vignettes_entropize.md.yNwRqwtl.lean.js
rename to dev/final_site/assets/vignettes_entropize.md.D8bK3dtX.lean.js
index e15daa5..69e8760 100644
--- a/dev/final_site/assets/vignettes_entropize.md.yNwRqwtl.lean.js
+++ b/dev/final_site/assets/vignettes_entropize.md.D8bK3dtX.lean.js
@@ -1,4 +1,4 @@
-import{_ as i,c as e,a5 as t,o as a}from"./chunks/framework.BdiZ026L.js";const n="/BiodiversityObservationNetworks.jl/assets/ahvfugp.Bmlmx36h.png",g=JSON.parse('{"title":"Getting the entropy matrix","description":"","frontmatter":{},"headers":[],"relativePath":"vignettes/entropize.md","filePath":"vignettes/entropize.md","lastUpdated":null}'),p={name:"vignettes/entropize.md"};function l(o,s,h,r,d,c){return a(),e("div",null,s[0]||(s[0]=[t(`
Getting the entropy matrix ​
For some applications, we want to place points to capture the maximum amount of information, which is to say that we want to sample a balance of entropy values, as opposed to absolute values. In this vignette, we will walk through an example using the entropize function to convert raw data to entropy values.
julia
using BiodiversityObservationNetworks
+import{_ as i,c as e,a5 as t,o as a}from"./chunks/framework.BdiZ026L.js";const n="/BiodiversityObservationNetworks.jl/assets/miqllmb.DELNajMy.png",g=JSON.parse('{"title":"Getting the entropy matrix","description":"","frontmatter":{},"headers":[],"relativePath":"vignettes/entropize.md","filePath":"vignettes/entropize.md","lastUpdated":null}'),p={name:"vignettes/entropize.md"};function l(o,s,r,h,d,c){return a(),e("div",null,s[0]||(s[0]=[t(`
Getting the entropy matrix ​
For some applications, we want to place points to capture the maximum amount of information, which is to say that we want to sample a balance of entropy values, as opposed to absolute values. In this vignette, we will walk through an example using the entropize function to convert raw data to entropy values.
julia
using BiodiversityObservationNetworks
 using NeutralLandscapes
 using CairoMakie
Entropy is problem-specific
The solution presented in this vignette is a least-assumption solution based on the empirical values given in a matrix of measurements. In a lot of situations, this is not the entropy that you want. For example, if your pixels are storing probabilities of Bernoulli events, you can directly use the entropy of the events in the entropy matrix.
We start by generating a random matrix of measurements:
julia
measurements = rand(MidpointDisplacement(), (200, 200)) .* 100
 heatmap(measurements)
Using the entropize function will convert these values into entropy at the pixel scale:
@example
U = entropize(measurements)
diff --git a/dev/final_site/assets/vignettes_overview.md.Dv7wORnA.js b/dev/final_site/assets/vignettes_overview.md.vrxwa-Ce.js
similarity index 98%
rename from dev/final_site/assets/vignettes_overview.md.Dv7wORnA.js
rename to dev/final_site/assets/vignettes_overview.md.vrxwa-Ce.js
index d6bbcbe..9d02e1d 100644
--- a/dev/final_site/assets/vignettes_overview.md.Dv7wORnA.js
+++ b/dev/final_site/assets/vignettes_overview.md.vrxwa-Ce.js
@@ -1,4 +1,4 @@
-import{_ as s,c as a,a5 as i,o as t}from"./chunks/framework.BdiZ026L.js";const n="/BiodiversityObservationNetworks.jl/assets/zqpmxhf.CKZI-JsR.png",u=JSON.parse('{"title":"An introduction to BiodiversityObservationNetworks","description":"","frontmatter":{},"headers":[],"relativePath":"vignettes/overview.md","filePath":"vignettes/overview.md","lastUpdated":null}'),o={name:"vignettes/overview.md"};function p(l,e,r,d,h,c){return t(),a("div",null,e[0]||(e[0]=[i(`
An introduction to BiodiversityObservationNetworks ​
In this vignette, we will walk through the basic functionalities of the package, by generating a random uncertainty matrix, and then using a seeder and a refiner to decide which locations should be sampled in order to gain more insights about the process generating this entropy.
julia
using BiodiversityObservationNetworks
+import{_ as s,c as a,a5 as i,o as t}from"./chunks/framework.BdiZ026L.js";const n="/BiodiversityObservationNetworks.jl/assets/jndzxmv.DEUroPh5.png",u=JSON.parse('{"title":"An introduction to BiodiversityObservationNetworks","description":"","frontmatter":{},"headers":[],"relativePath":"vignettes/overview.md","filePath":"vignettes/overview.md","lastUpdated":null}'),o={name:"vignettes/overview.md"};function p(l,e,r,d,h,c){return t(),a("div",null,e[0]||(e[0]=[i(`
An introduction to BiodiversityObservationNetworks ​
In this vignette, we will walk through the basic functionalities of the package, by generating a random uncertainty matrix, and then using a seeder and a refiner to decide which locations should be sampled in order to gain more insights about the process generating this entropy.
julia
using BiodiversityObservationNetworks
 using NeutralLandscapes
 using CairoMakie
In order to simplify the process, we will use the NeutralLandscapes package to generate a 100×100 pixels landscape, where each cell represents the entropy (or information content) in a unit we can sample:
julia
U = rand(MidpointDisplacement(0.5), (100, 100))
 heatmap(U)
In practice, this uncertainty matrix is likely to be derived from an application of the hyper-parameters optimization step, which is detailed in other vignettes.
The first step of defining a series of locations to sample is to use a BONSeeder, which will generate a number of relatively coarse proposals that cover the entire landscape, and have a balanced distribution in space. We do so using the BalancedAcceptance sampler, which can be tweaked to capture more (or less) uncertainty. To start with, we will extract 200 candidate points, i.e. 200 possible locations which will then be refined.
@example
candidates = seed(BalancedAcceptance(; numsites = 200));
We can have a look at the first five points:
@example
candidates[1:5]
The positions of locations to sample are given as a vector of CartesianIndex, which are coordinates in the uncertainty matrix. Once we have generated a candidate proposal, we can further refine it using a BONRefiner – in this case, AdaptiveSpatial, which performs adaptive spatial sampling (maximizing the distribution of entropy while minimizing spatial auto-correlation).
@example
locations = refine(candidates, AdaptiveSpatial(; numsites = 50, uncertainty=U))
diff --git a/dev/final_site/assets/vignettes_overview.md.Dv7wORnA.lean.js b/dev/final_site/assets/vignettes_overview.md.vrxwa-Ce.lean.js
similarity index 98%
rename from dev/final_site/assets/vignettes_overview.md.Dv7wORnA.lean.js
rename to dev/final_site/assets/vignettes_overview.md.vrxwa-Ce.lean.js
index d6bbcbe..9d02e1d 100644
--- a/dev/final_site/assets/vignettes_overview.md.Dv7wORnA.lean.js
+++ b/dev/final_site/assets/vignettes_overview.md.vrxwa-Ce.lean.js
@@ -1,4 +1,4 @@
-import{_ as s,c as a,a5 as i,o as t}from"./chunks/framework.BdiZ026L.js";const n="/BiodiversityObservationNetworks.jl/assets/zqpmxhf.CKZI-JsR.png",u=JSON.parse('{"title":"An introduction to BiodiversityObservationNetworks","description":"","frontmatter":{},"headers":[],"relativePath":"vignettes/overview.md","filePath":"vignettes/overview.md","lastUpdated":null}'),o={name:"vignettes/overview.md"};function p(l,e,r,d,h,c){return t(),a("div",null,e[0]||(e[0]=[i(`
An introduction to BiodiversityObservationNetworks ​
In this vignette, we will walk through the basic functionalities of the package, by generating a random uncertainty matrix, and then using a seeder and a refiner to decide which locations should be sampled in order to gain more insights about the process generating this entropy.
julia
using BiodiversityObservationNetworks
+import{_ as s,c as a,a5 as i,o as t}from"./chunks/framework.BdiZ026L.js";const n="/BiodiversityObservationNetworks.jl/assets/jndzxmv.DEUroPh5.png",u=JSON.parse('{"title":"An introduction to BiodiversityObservationNetworks","description":"","frontmatter":{},"headers":[],"relativePath":"vignettes/overview.md","filePath":"vignettes/overview.md","lastUpdated":null}'),o={name:"vignettes/overview.md"};function p(l,e,r,d,h,c){return t(),a("div",null,e[0]||(e[0]=[i(`
An introduction to BiodiversityObservationNetworks ​
In this vignette, we will walk through the basic functionalities of the package, by generating a random uncertainty matrix, and then using a seeder and a refiner to decide which locations should be sampled in order to gain more insights about the process generating this entropy.
julia
using BiodiversityObservationNetworks
 using NeutralLandscapes
 using CairoMakie
In order to simplify the process, we will use the NeutralLandscapes package to generate a 100×100 pixels landscape, where each cell represents the entropy (or information content) in a unit we can sample:
julia
U = rand(MidpointDisplacement(0.5), (100, 100))
 heatmap(U)
In practice, this uncertainty matrix is likely to be derived from an application of the hyper-parameters optimization step, which is detailed in other vignettes.
The first step of defining a series of locations to sample is to use a BONSeeder, which will generate a number of relatively coarse proposals that cover the entire landscape, and have a balanced distribution in space. We do so using the BalancedAcceptance sampler, which can be tweaked to capture more (or less) uncertainty. To start with, we will extract 200 candidate points, i.e. 200 possible locations which will then be refined.
@example
candidates = seed(BalancedAcceptance(; numsites = 200));
We can have a look at the first five points:
@example
candidates[1:5]
The positions of locations to sample are given as a vector of CartesianIndex, which are coordinates in the uncertainty matrix. Once we have generated a candidate proposal, we can further refine it using a BONRefiner – in this case, AdaptiveSpatial, which performs adaptive spatial sampling (maximizing the distribution of entropy while minimizing spatial auto-correlation).
@example
locations = refine(candidates, AdaptiveSpatial(; numsites = 50, uncertainty=U))
diff --git a/dev/final_site/assets/zqpmxhf.CKZI-JsR.png b/dev/final_site/assets/zqpmxhf.CKZI-JsR.png
deleted file mode 100644
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BiodiversityObservationNetwork.jl
Appearance
References ​
Bibliography ​

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-{"bibliography.md":"DJ6rgR3y","index.md":"Dtie0iOP","vignettes_entropize.md":"yNwRqwtl","vignettes_overview.md":"Dv7wORnA","vignettes_uniqueness.md":"BE8Uzeyu"}
+{"bibliography.md":"DJ6rgR3y","index.md":"ZbH1van7","vignettes_entropize.md":"D8bK3dtX","vignettes_overview.md":"vrxwa-Ce","vignettes_uniqueness.md":"BE8Uzeyu"}
diff --git a/dev/final_site/index.html b/dev/final_site/index.html
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BiodiversityObservationNetwork.jl
Appearance
BiodiversityObservationNetworks.jl ​
The purpose of this package is to provide a high-level, extensible, modular interface to the selection of sampling point for biodiversity processes in space. It is based around a collection of types representing point selection algorithms, used to select the most informative sampling points based on raster data. Specifically, many algorithms work from a layer indicating entropy of a model based prediction at each location.
This package is in development
The BiodiversityObservationNetworks.jl package is currently under development. The API is not expected to change a lot, but it may change in order to facilitate the integration of new features.
High-level types ​
BiodiversityObservationNetworks.BONSampler Type
julia
abstract type BONSampler end
A BONSampler is any algorithm for proposing a set of sampling locations.
source
Missing docstring.
Missing docstring for BONSeeder. Check Documenter's build log for details.
Missing docstring.
Missing docstring for BONRefiner. Check Documenter's build log for details.
Seeder and refiner functions ​
Missing docstring.
Missing docstring for seed. Check Documenter's build log for details.
Missing docstring.
Missing docstring for seed!. Check Documenter's build log for details.
Missing docstring.
Missing docstring for refine. Check Documenter's build log for details.
Missing docstring.
Missing docstring for refine!. Check Documenter's build log for details.
Seeder algorithms ​
BiodiversityObservationNetworks.BalancedAcceptance Type
julia
BalancedAcceptance
A BONSeeder that uses Balanced-Acceptance Sampling (Van-dem-Bates et al. 2017 https://doi.org/10.1111/2041-210X.13003)
source
Refiner algorithms ​
Missing docstring.
Missing docstring for AdaptiveSpatial. Check Documenter's build log for details.
BiodiversityObservationNetworks.Uniqueness Type
julia
Uniqueness
A BONSampler
source
Helper functions ​
Missing docstring.
Missing docstring for squish. Check Documenter's build log for details.
BiodiversityObservationNetworks.entropize! Function
julia
entropize!(U::Matrix{AbstractFloat}, A::Matrix{Number})
This function turns a matrix A (storing measurement values) into pixel-wise entropy values, stored in a matrix U (that is previously allocated).
Pixel-wise entropy is determined by measuring the empirical probability of randomly picking a value in the matrix that is either lower or higher than the pixel value. The entropy of both these probabilities are calculated using the -p×log(2,p) formula. The entropy of the pixel is the sum of the two entropies, so that it is close to 1 for values close to the median, and close to 0 for values close to the extreme of the distribution.
source
BiodiversityObservationNetworks.entropize Function
julia
entropize(A::Matrix{Number})
Allocation version of entropize!.
source

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BiodiversityObservationNetwork.jl
Appearance
BiodiversityObservationNetworks.jl ​
The purpose of this package is to provide a high-level, extensible, modular interface to the selection of sampling point for biodiversity processes in space. It is based around a collection of types representing point selection algorithms, used to select the most informative sampling points based on raster data. Specifically, many algorithms work from a layer indicating entropy of a model based prediction at each location.
This package is in development
The BiodiversityObservationNetworks.jl package is currently under development. The API is not expected to change a lot, but it may change in order to facilitate the integration of new features.
High-level types ​
BiodiversityObservationNetworks.BONSampler Type
julia
abstract type BONSampler end
A BONSampler is any algorithm for proposing a set of sampling locations.
source
Missing docstring.
Missing docstring for BONSeeder. Check Documenter's build log for details.
Missing docstring.
Missing docstring for BONRefiner. Check Documenter's build log for details.
Seeder and refiner functions ​
Missing docstring.
Missing docstring for seed. Check Documenter's build log for details.
Missing docstring.
Missing docstring for seed!. Check Documenter's build log for details.
Missing docstring.
Missing docstring for refine. Check Documenter's build log for details.
Missing docstring.
Missing docstring for refine!. Check Documenter's build log for details.
Seeder algorithms ​
BiodiversityObservationNetworks.BalancedAcceptance Type
julia
BalancedAcceptance
A BONSeeder that uses Balanced-Acceptance Sampling (Van-dem-Bates et al. 2017 https://doi.org/10.1111/2041-210X.13003)
source
Refiner algorithms ​
Missing docstring.
Missing docstring for AdaptiveSpatial. Check Documenter's build log for details.
BiodiversityObservationNetworks.Uniqueness Type
julia
Uniqueness
A BONSampler
source
Helper functions ​
Missing docstring.
Missing docstring for squish. Check Documenter's build log for details.
BiodiversityObservationNetworks.entropize! Function
julia
entropize!(U::Matrix{AbstractFloat}, A::Matrix{Number})
This function turns a matrix A (storing measurement values) into pixel-wise entropy values, stored in a matrix U (that is previously allocated).
Pixel-wise entropy is determined by measuring the empirical probability of randomly picking a value in the matrix that is either lower or higher than the pixel value. The entropy of both these probabilities are calculated using the -p×log(2,p) formula. The entropy of the pixel is the sum of the two entropies, so that it is close to 1 for values close to the median, and close to 0 for values close to the extreme of the distribution.
source
BiodiversityObservationNetworks.entropize Function
julia
entropize(A::Matrix{Number})
Allocation version of entropize!.
source

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BiodiversityObservationNetwork.jl
Appearance
Getting the entropy matrix ​
For some applications, we want to place points to capture the maximum amount of information, which is to say that we want to sample a balance of entropy values, as opposed to absolute values. In this vignette, we will walk through an example using the entropize function to convert raw data to entropy values.
julia
using BiodiversityObservationNetworks
 using NeutralLandscapes
 using CairoMakie
Entropy is problem-specific
The solution presented in this vignette is a least-assumption solution based on the empirical values given in a matrix of measurements. In a lot of situations, this is not the entropy that you want. For example, if your pixels are storing probabilities of Bernoulli events, you can directly use the entropy of the events in the entropy matrix.
We start by generating a random matrix of measurements:
julia
measurements = rand(MidpointDisplacement(), (200, 200)) .* 100
-heatmap(measurements)
Using the entropize function will convert these values into entropy at the pixel scale:
@example
U = entropize(measurements)
+heatmap(measurements)
Using the entropize function will convert these values into entropy at the pixel scale:
@example
U = entropize(measurements)
 locations =
     seed(BalancedAcceptance(; numsites = 100, uncertainty=U))

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BiodiversityObservationNetwork.jl
Appearance
An introduction to BiodiversityObservationNetworks ​
In this vignette, we will walk through the basic functionalities of the package, by generating a random uncertainty matrix, and then using a seeder and a refiner to decide which locations should be sampled in order to gain more insights about the process generating this entropy.
julia
using BiodiversityObservationNetworks
 using NeutralLandscapes
 using CairoMakie
In order to simplify the process, we will use the NeutralLandscapes package to generate a 100×100 pixels landscape, where each cell represents the entropy (or information content) in a unit we can sample:
julia
U = rand(MidpointDisplacement(0.5), (100, 100))
-heatmap(U)
In practice, this uncertainty matrix is likely to be derived from an application of the hyper-parameters optimization step, which is detailed in other vignettes.
The first step of defining a series of locations to sample is to use a BONSeeder, which will generate a number of relatively coarse proposals that cover the entire landscape, and have a balanced distribution in space. We do so using the BalancedAcceptance sampler, which can be tweaked to capture more (or less) uncertainty. To start with, we will extract 200 candidate points, i.e. 200 possible locations which will then be refined.
@example
candidates = seed(BalancedAcceptance(; numsites = 200));
We can have a look at the first five points:
@example
candidates[1:5]
The positions of locations to sample are given as a vector of CartesianIndex, which are coordinates in the uncertainty matrix. Once we have generated a candidate proposal, we can further refine it using a BONRefiner – in this case, AdaptiveSpatial, which performs adaptive spatial sampling (maximizing the distribution of entropy while minimizing spatial auto-correlation).
@example
locations = refine(candidates, AdaptiveSpatial(; numsites = 50, uncertainty=U))
+heatmap(U)
In practice, this uncertainty matrix is likely to be derived from an application of the hyper-parameters optimization step, which is detailed in other vignettes.
The first step of defining a series of locations to sample is to use a BONSeeder, which will generate a number of relatively coarse proposals that cover the entire landscape, and have a balanced distribution in space. We do so using the BalancedAcceptance sampler, which can be tweaked to capture more (or less) uncertainty. To start with, we will extract 200 candidate points, i.e. 200 possible locations which will then be refined.
@example
candidates = seed(BalancedAcceptance(; numsites = 200));
We can have a look at the first five points:
@example
candidates[1:5]
The positions of locations to sample are given as a vector of CartesianIndex, which are coordinates in the uncertainty matrix. Once we have generated a candidate proposal, we can further refine it using a BONRefiner – in this case, AdaptiveSpatial, which performs adaptive spatial sampling (maximizing the distribution of entropy while minimizing spatial auto-correlation).
@example
locations = refine(candidates, AdaptiveSpatial(; numsites = 50, uncertainty=U))
 locations[1:5]
The reason we start from a candidate set of points is that some algorithms struggle with full landscapes, and work much better with a sub-sample of them. There is no hard rule (or no heuristic) to get a sense for how many points should be generated at the seeding step, and so experimentation is a must!
The previous code examples used a version of the seed and refine functions that is very useful if you want to change arguments between steps, or examine the content of the candidate pool of points. In addition to this syntax, both functions have a curried version that allows chaining them together using pipes (|>):
@example
locations =
     seed(BalancedAcceptance(; numsites = 200)) |>
     refine(AdaptiveSpatial(; numsites = 50, uncertainty=U))
This works because seed and refine have curried versions that can be used directly in a pipeline. Proposed sampling locations can then be overlayed onto the original uncertainty matrix:
@example
plt = heatmap(U)
 scatter!(plt, [x[1] for x in locations], [x[2] for x in locations])
 current_figure()

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 scatter!([p[1] for p in candpts], [p[2] for p in candpts], color=:white)
 scatter!([p[1] for p in finalpts], [p[2] for p in finalpts], color=:dodgerblue, msc=:white)
 current_figure()
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BiodiversityObservationNetworks.jl ​