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zsYmvW8HZ76A`^##SXqTT&3Myym;hV%OiWAw_^fx?98zapOY;z#ONW0E4-m38xdL|&4%&Z5u(Q!nFnmD{^Yl{^ zwVOp5*)##+$H4cXJ|-sSum4@|SH*&IZ8H7?e)pyTVGO5obslxxIJ6!JJgn5z)NJSY zf}r?{+H_EWz^0W%IHA>ca9(SD4q`wX4&rzJ?v1n!KxgIdXg7K00C$(@HOokSK!q!QTy2UM|_@#%QuIuM>)QS$NpewwZlrww-$0> z*apS!j{ycdj-`WQ(D?lUFz2`sz&p-hj&fN=bN?rd4(j#h9*EK!ow?)NT%ETke?3*V z)x&=C?k~Ay$O6Z?)-)OoTimQq?*UnbJ5h>8#37u`Wzg2N<@r}h?I?EwxST!eJQ-qF zi7wG|XfC?GvwBl2cRXe6B!YR=VSH`6q^2jOVnQ52x&1 z0L}M48Dt5=-x|#nCSzhihH7I*;c;EU78H(maYr19Q} z-vovj(sFva9vkcMr?TUEZ|}TFmGzp%m^rZ-&_tS*`d7=Y;}Gx7S;fz=aJfIIcFFYIpZIVorUXGM0*9RR9x1d^4H|rmE zVbI)8=ib)=wgA#z7sP^n0FU__1TN0K^Hpy8X#)NJQTo5X6~wgs0jRF&C#oE}DVz7@ zudqfy$^hf#7=X-l&)_udxXllvzTOsl5E2lWo1R7{SNUz840EmNF?|C#qxG<5A7DZM zqxK77VPRrU-I=8&UtsK&Mu<^}Yz_McVyHwMI`7W^Y6QIoJ6T3TAZWk&1ts$F^(}Y^ z@&Y>Y9*Bs5^0PKPgjKek$gxhi|NO`Qf$7fM#NX2of9E~_Y3uplXGj0-tmeP0Wc`<& j$A2y1|Nr`ky@yA~p&cLHbrUQ84w9^-()&trqu~DoAtp&c diff --git a/previews/PR72/assets/vignettes_entropize.md.CK-iFMSh.js b/previews/PR72/assets/vignettes_entropize.md.B5cvWAQI.js similarity index 99% rename from previews/PR72/assets/vignettes_entropize.md.CK-iFMSh.js rename to previews/PR72/assets/vignettes_entropize.md.B5cvWAQI.js index 6d8d9fc..0f08640 100644 --- a/previews/PR72/assets/vignettes_entropize.md.CK-iFMSh.js +++ b/previews/PR72/assets/vignettes_entropize.md.B5cvWAQI.js @@ -1,4 +1,4 @@ -import{_ as n,c as a,a5 as e,o as p}from"./chunks/framework.CuKCF6Tm.js";const i="/BiodiversityObservationNetworks.jl/previews/PR72/assets/tjaiqow.DMkIsKS-.png",m=JSON.parse('{"title":"Getting the entropy matrix","description":"","frontmatter":{},"headers":[],"relativePath":"vignettes/entropize.md","filePath":"vignettes/entropize.md","lastUpdated":null}'),l={name:"vignettes/entropize.md"};function t(r,s,o,c,u,d){return p(),a("div",null,s[0]||(s[0]=[e(`

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 n,c as a,a5 as e,o as p}from"./chunks/framework.CuKCF6Tm.js";const i="/BiodiversityObservationNetworks.jl/previews/PR72/assets/phwltws.BAe7_IJO.png",h=JSON.parse('{"title":"Getting the entropy matrix","description":"","frontmatter":{},"headers":[],"relativePath":"vignettes/entropize.md","filePath":"vignettes/entropize.md","lastUpdated":null}'),l={name:"vignettes/entropize.md"};function t(r,s,o,c,u,d){return p(),a("div",null,s[0]||(s[0]=[e(`

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
[ Info: Loading NeutralLandscapes support for SimpleSDMLayers.jl...
 ┌ Warning: Error requiring \`SpeciesDistributionToolkit\` from \`NeutralLandscapes\`
@@ -116,4 +116,4 @@ import{_ as n,c as a,a5 as e,o as p}from"./chunks/framework.CuKCF6Tm.js";const i
 └ @ Requires ~/.julia/packages/Requires/Z8rfN/src/require.jl:51

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)
 locations =
-    seed(BalancedAcceptance(; numsites = 100, uncertainty=U))
`,10)]))}const h=n(l,[["render",t]]);export{m as __pageData,h as default}; + seed(BalancedAcceptance(; numsites = 100, uncertainty=U))
`,10)]))}const m=n(l,[["render",t]]);export{h as __pageData,m as default}; diff --git a/previews/PR72/assets/vignettes_entropize.md.CK-iFMSh.lean.js b/previews/PR72/assets/vignettes_entropize.md.B5cvWAQI.lean.js similarity index 99% rename from previews/PR72/assets/vignettes_entropize.md.CK-iFMSh.lean.js rename to previews/PR72/assets/vignettes_entropize.md.B5cvWAQI.lean.js index 6d8d9fc..0f08640 100644 --- a/previews/PR72/assets/vignettes_entropize.md.CK-iFMSh.lean.js +++ b/previews/PR72/assets/vignettes_entropize.md.B5cvWAQI.lean.js @@ -1,4 +1,4 @@ -import{_ as n,c as a,a5 as e,o as p}from"./chunks/framework.CuKCF6Tm.js";const i="/BiodiversityObservationNetworks.jl/previews/PR72/assets/tjaiqow.DMkIsKS-.png",m=JSON.parse('{"title":"Getting the entropy matrix","description":"","frontmatter":{},"headers":[],"relativePath":"vignettes/entropize.md","filePath":"vignettes/entropize.md","lastUpdated":null}'),l={name:"vignettes/entropize.md"};function t(r,s,o,c,u,d){return p(),a("div",null,s[0]||(s[0]=[e(`

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 n,c as a,a5 as e,o as p}from"./chunks/framework.CuKCF6Tm.js";const i="/BiodiversityObservationNetworks.jl/previews/PR72/assets/phwltws.BAe7_IJO.png",h=JSON.parse('{"title":"Getting the entropy matrix","description":"","frontmatter":{},"headers":[],"relativePath":"vignettes/entropize.md","filePath":"vignettes/entropize.md","lastUpdated":null}'),l={name:"vignettes/entropize.md"};function t(r,s,o,c,u,d){return p(),a("div",null,s[0]||(s[0]=[e(`

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
[ Info: Loading NeutralLandscapes support for SimpleSDMLayers.jl...
 ┌ Warning: Error requiring \`SpeciesDistributionToolkit\` from \`NeutralLandscapes\`
@@ -116,4 +116,4 @@ import{_ as n,c as a,a5 as e,o as p}from"./chunks/framework.CuKCF6Tm.js";const i
 └ @ Requires ~/.julia/packages/Requires/Z8rfN/src/require.jl:51

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)
 locations =
-    seed(BalancedAcceptance(; numsites = 100, uncertainty=U))
`,10)]))}const h=n(l,[["render",t]]);export{m as __pageData,h as default}; + seed(BalancedAcceptance(; numsites = 100, uncertainty=U))
`,10)]))}const m=n(l,[["render",t]]);export{h as __pageData,m as default}; diff --git a/previews/PR72/assets/vignettes_overview.md.CChV6Jfe.js b/previews/PR72/assets/vignettes_overview.md.jmpOXKS-.js similarity index 99% rename from previews/PR72/assets/vignettes_overview.md.CChV6Jfe.js rename to previews/PR72/assets/vignettes_overview.md.jmpOXKS-.js index 5df890c..5cf7a5e 100644 --- a/previews/PR72/assets/vignettes_overview.md.CChV6Jfe.js +++ b/previews/PR72/assets/vignettes_overview.md.jmpOXKS-.js @@ -1,4 +1,4 @@ -import{_ as s,c as a,a5 as i,o as t}from"./chunks/framework.CuKCF6Tm.js";const n="/BiodiversityObservationNetworks.jl/previews/PR72/assets/erywfvu.DJhouA8V.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.CuKCF6Tm.js";const n="/BiodiversityObservationNetworks.jl/previews/PR72/assets/wsbqyts.Dw8HSdCv.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/previews/PR72/assets/vignettes_overview.md.CChV6Jfe.lean.js b/previews/PR72/assets/vignettes_overview.md.jmpOXKS-.lean.js
similarity index 99%
rename from previews/PR72/assets/vignettes_overview.md.CChV6Jfe.lean.js
rename to previews/PR72/assets/vignettes_overview.md.jmpOXKS-.lean.js
index 5df890c..5cf7a5e 100644
--- a/previews/PR72/assets/vignettes_overview.md.CChV6Jfe.lean.js
+++ b/previews/PR72/assets/vignettes_overview.md.jmpOXKS-.lean.js
@@ -1,4 +1,4 @@
-import{_ as s,c as a,a5 as i,o as t}from"./chunks/framework.CuKCF6Tm.js";const n="/BiodiversityObservationNetworks.jl/previews/PR72/assets/erywfvu.DJhouA8V.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.CuKCF6Tm.js";const n="/BiodiversityObservationNetworks.jl/previews/PR72/assets/wsbqyts.Dw8HSdCv.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/previews/PR72/assets/wsbqyts.Dw8HSdCv.png b/previews/PR72/assets/wsbqyts.Dw8HSdCv.png
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Skip to content

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.

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.

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References


Bibliography

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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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()
- + \ No newline at end of file diff --git a/previews/PR72/vignettes/uniqueness.html b/previews/PR72/vignettes/uniqueness.html index 0e81d6e..497256e 100644 --- a/previews/PR72/vignettes/uniqueness.html +++ b/previews/PR72/vignettes/uniqueness.html @@ -32,7 +32,7 @@ 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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