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this.mark=(t,s)=>(e.mark(t,s),this),this.markRegExp=(t,s)=>(e.markRegExp(t,s),this),this.markRanges=(t,s)=>(e.markRanges(t,s),this),this.unmark=t=>(e.unmark(t),this),this}function ke(a,e,t,s){function n(r){return r instanceof t?r:new t(function(i){i(r)})}return new(t||(t=Promise))(function(r,i){function o(h){try{c(s.next(h))}catch(m){i(m)}}function l(h){try{c(s.throw(h))}catch(m){i(m)}}function c(h){h.done?r(h.value):n(h.value).then(o,l)}c((s=s.apply(a,[])).next())})}const js="ENTRIES",_t="KEYS",St="VALUES",D="";class De{constructor(e,t){const s=e._tree,n=Array.from(s.keys());this.set=e,this._type=t,this._path=n.length>0?[{node:s,keys:n}]:[]}next(){const e=this.dive();return this.backtrack(),e}dive(){if(this._path.length===0)return{done:!0,value:void 0};const{node:e,keys:t}=le(this._path);if(le(t)===D)return{done:!1,value:this.result()};const s=e.get(le(t));return this._path.push({node:s,keys:Array.from(s.keys())}),this.dive()}backtrack(){if(this._path.length===0)return;const 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n===void 0&&s.set(D,n=t()),n}values(){return new De(this,St)}[Symbol.iterator](){return this.entries()}static from(e){const t=new X;for(const[s,n]of e)t.set(s,n);return t}static fromObject(e){return X.from(Object.entries(e))}}const Re=(a,e,t=[])=>{if(e.length===0||a==null)return[a,t];for(const s of a.keys())if(s!==D&&e.startsWith(s))return t.push([a,s]),Re(a.get(s),e.slice(s.length),t);return t.push([a,e]),Re(void 0,"",t)},Ke=(a,e)=>{if(e.length===0||a==null)return a;for(const t of a.keys())if(t!==D&&e.startsWith(t))return Ke(a.get(t),e.slice(t.length))},ze=(a,e)=>{const t=e.length;e:for(let s=0;a&&s{const[t,s]=Re(a,e);if(t!==void 0){if(t.delete(D),t.size===0)Tt(s);else if(t.size===1){const[n,r]=t.entries().next().value;It(s,n,r)}}},Tt=a=>{if(a.length===0)return;const[e,t]=qe(a);if(e.delete(t),e.size===0)Tt(a.slice(0,-1));else if(e.size===1){const[s,n]=e.entries().next().value;s!==D&&It(a.slice(0,-1),s,n)}},It=(a,e,t)=>{if(a.length===0)return;const[s,n]=qe(a);s.set(n+e,t),s.delete(n)},qe=a=>a[a.length-1],Ge="or",kt="and",Bs="and_not";class ue{constructor(e){if((e==null?void 0:e.fields)==null)throw new Error('MiniSearch: option "fields" must be provided');const t=e.autoVacuum==null||e.autoVacuum===!0?Ve:e.autoVacuum;this._options=Object.assign(Object.assign(Object.assign({},je),e),{autoVacuum:t,searchOptions:Object.assign(Object.assign({},ht),e.searchOptions||{}),autoSuggestOptions:Object.assign(Object.assign({},qs),e.autoSuggestOptions||{})}),this._index=new X,this._documentCount=0,this._documentIds=new Map,this._idToShortId=new Map,this._fieldIds={},this._fieldLength=new Map,this._avgFieldLength=[],this._nextId=0,this._storedFields=new Map,this._dirtCount=0,this._currentVacuum=null,this._enqueuedVacuum=null,this._enqueuedVacuumConditions=Ue,this.addFields(this._options.fields)}add(e){const{extractField:t,tokenize:s,processTerm:n,fields:r,idField:i}=this._options,o=t(e,i);if(o==null)throw new Error(`MiniSearch: document does not have ID field "${i}"`);if(this._idToShortId.has(o))throw new Error(`MiniSearch: duplicate ID ${o}`);const l=this.addDocumentId(o);this.saveStoredFields(l,e);for(const c of r){const h=t(e,c);if(h==null)continue;const m=s(h.toString(),c),f=this._fieldIds[c],b=new Set(m).size;this.addFieldLength(l,f,this._documentCount-1,b);for(const y of m){const x=n(y,c);if(Array.isArray(x))for(const w of x)this.addTerm(f,l,w);else x&&this.addTerm(f,l,x)}}}addAll(e){for(const t of e)this.add(t)}addAllAsync(e,t={}){const{chunkSize:s=10}=t,n={chunk:[],promise:Promise.resolve()},{chunk:r,promise:i}=e.reduce(({chunk:o,promise:l},c,h)=>(o.push(c),(h+1)%s===0?{chunk:[],promise:l.then(()=>new 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Omit the argument to remove all documents.");this._index=new X,this._documentCount=0,this._documentIds=new Map,this._idToShortId=new Map,this._fieldLength=new Map,this._avgFieldLength=[],this._storedFields=new Map,this._nextId=0}}discard(e){const t=this._idToShortId.get(e);if(t==null)throw new Error(`MiniSearch: cannot discard document with ID ${e}: it is not in the index`);this._idToShortId.delete(e),this._documentIds.delete(t),this._storedFields.delete(t),(this._fieldLength.get(t)||[]).forEach((s,n)=>{this.removeFieldLength(t,n,this._documentCount,s)}),this._fieldLength.delete(t),this._documentCount-=1,this._dirtCount+=1,this.maybeAutoVacuum()}maybeAutoVacuum(){if(this._options.autoVacuum===!1)return;const{minDirtFactor:e,minDirtCount:t,batchSize:s,batchWait:n}=this._options.autoVacuum;this.conditionalVacuum({batchSize:s,batchWait:n},{minDirtCount:t,minDirtFactor:e})}discardAll(e){const t=this._options.autoVacuum;try{this._options.autoVacuum=!1;for(const s of e)this.discard(s)}finally{this._options.autoVacuum=t}this.maybeAutoVacuum()}replace(e){const{idField:t,extractField:s}=this._options,n=s(e,t);this.discard(n),this.add(e)}vacuum(e={}){return this.conditionalVacuum(e)}conditionalVacuum(e,t){return this._currentVacuum?(this._enqueuedVacuumConditions=this._enqueuedVacuumConditions&&t,this._enqueuedVacuum!=null?this._enqueuedVacuum:(this._enqueuedVacuum=this._currentVacuum.then(()=>{const s=this._enqueuedVacuumConditions;return this._enqueuedVacuumConditions=Ue,this.performVacuuming(e,s)}),this._enqueuedVacuum)):this.vacuumConditionsMet(t)===!1?Promise.resolve():(this._currentVacuum=this.performVacuuming(e),this._currentVacuum)}performVacuuming(e,t){return ke(this,void 0,void 0,function*(){const s=this._dirtCount;if(this.vacuumConditionsMet(t)){const n=e.batchSize||Je.batchSize,r=e.batchWait||Je.batchWait;let i=1;for(const[o,l]of this._index){for(const[c,h]of l)for(const[m]of h)this._documentIds.has(m)||(h.size<=1?l.delete(c):h.delete(m));this._index.get(o).size===0&&this._index.delete(o),i%n===0&&(yield new Promise(c=>setTimeout(c,r))),i+=1}this._dirtCount-=s}yield null,this._currentVacuum=this._enqueuedVacuum,this._enqueuedVacuum=null})}vacuumConditionsMet(e){if(e==null)return!0;let{minDirtCount:t,minDirtFactor:s}=e;return t=t||Ve.minDirtCount,s=s||Ve.minDirtFactor,this.dirtCount>=t&&this.dirtFactor>=s}get isVacuuming(){return this._currentVacuum!=null}get dirtCount(){return this._dirtCount}get dirtFactor(){return this._dirtCount/(1+this._documentCount+this._dirtCount)}has(e){return this._idToShortId.has(e)}getStoredFields(e){const t=this._idToShortId.get(e);if(t!=null)return this._storedFields.get(t)}search(e,t={}){const{searchOptions:s}=this._options,n=Object.assign(Object.assign({},s),t),r=this.executeQuery(e,t),i=[];for(const[o,{score:l,terms:c,match:h}]of r){const m=c.length||1,f={id:this._documentIds.get(o),score:l*m,terms:Object.keys(h),queryTerms:c,match:h};Object.assign(f,this._storedFields.get(o)),(n.filter==null||n.filter(f))&&i.push(f)}return e===ue.wildcard&&n.boostDocument==null||i.sort(pt),i}autoSuggest(e,t={}){t=Object.assign(Object.assign({},this._options.autoSuggestOptions),t);const s=new Map;for(const{score:r,terms:i}of this.search(e,t)){const o=i.join(" "),l=s.get(o);l!=null?(l.score+=r,l.count+=1):s.set(o,{score:r,terms:i,count:1})}const n=[];for(const[r,{score:i,terms:o,count:l}]of s)n.push({suggestion:r,terms:o,score:i/l});return n.sort(pt),n}get documentCount(){return this._documentCount}get termCount(){return this._index.size}static loadJSON(e,t){if(t==null)throw new Error("MiniSearch: loadJSON should be given the same options used when serializing the index");return this.loadJS(JSON.parse(e),t)}static loadJSONAsync(e,t){return ke(this,void 0,void 0,function*(){if(t==null)throw new Error("MiniSearch: loadJSON should be given the same options used when serializing the index");return this.loadJSAsync(JSON.parse(e),t)})}static getDefault(e){if(je.hasOwnProperty(e))return Pe(je,e);throw new Error(`MiniSearch: unknown option "${e}"`)}static loadJS(e,t){const{index:s,documentIds:n,fieldLength:r,storedFields:i,serializationVersion:o}=e,l=this.instantiateMiniSearch(e,t);l._documentIds=Te(n),l._fieldLength=Te(r),l._storedFields=Te(i);for(const[c,h]of l._documentIds)l._idToShortId.set(h,c);for(const[c,h]of s){const m=new Map;for(const f of Object.keys(h)){let b=h[f];o===1&&(b=b.ds),m.set(parseInt(f,10),Te(b))}l._index.set(c,m)}return l}static loadJSAsync(e,t){return ke(this,void 0,void 0,function*(){const{index:s,documentIds:n,fieldLength:r,storedFields:i,serializationVersion:o}=e,l=this.instantiateMiniSearch(e,t);l._documentIds=yield Ie(n),l._fieldLength=yield Ie(r),l._storedFields=yield Ie(i);for(const[h,m]of l._documentIds)l._idToShortId.set(m,h);let c=0;for(const[h,m]of s){const f=new Map;for(const b of Object.keys(m)){let y=m[b];o===1&&(y=y.ds),f.set(parseInt(b,10),yield Ie(y))}++c%1e3===0&&(yield Nt(0)),l._index.set(h,f)}return l})}static instantiateMiniSearch(e,t){const{documentCount:s,nextId:n,fieldIds:r,averageFieldLength:i,dirtCount:o,serializationVersion:l}=e;if(l!==1&&l!==2)throw new Error("MiniSearch: cannot deserialize an index created with an incompatible version");const c=new ue(t);return c._documentCount=s,c._nextId=n,c._idToShortId=new Map,c._fieldIds=r,c._avgFieldLength=i,c._dirtCount=o||0,c._index=new X,c}executeQuery(e,t={}){if(e===ue.wildcard)return this.executeWildcardQuery(t);if(typeof e!="string"){const f=Object.assign(Object.assign(Object.assign({},t),e),{queries:void 0}),b=e.queries.map(y=>this.executeQuery(y,f));return this.combineResults(b,f.combineWith)}const{tokenize:s,processTerm:n,searchOptions:r}=this._options,i=Object.assign(Object.assign({tokenize:s,processTerm:n},r),t),{tokenize:o,processTerm:l}=i,m=o(e).flatMap(f=>l(f)).filter(f=>!!f).map(Us(i)).map(f=>this.executeQuerySpec(f,i));return this.combineResults(m,i.combineWith)}executeQuerySpec(e,t){const s=Object.assign(Object.assign({},this._options.searchOptions),t),n=(s.fields||this._options.fields).reduce((x,w)=>Object.assign(Object.assign({},x),{[w]:Pe(s.boost,w)||1}),{}),{boostDocument:r,weights:i,maxFuzzy:o,bm25:l}=s,{fuzzy:c,prefix:h}=Object.assign(Object.assign({},ht.weights),i),m=this._index.get(e.term),f=this.termResults(e.term,e.term,1,e.termBoost,m,n,r,l);let b,y;if(e.prefix&&(b=this._index.atPrefix(e.term)),e.fuzzy){const x=e.fuzzy===!0?.2:e.fuzzy,w=x<1?Math.min(o,Math.round(e.term.length*x)):x;w&&(y=this._index.fuzzyGet(e.term,w))}if(b)for(const[x,w]of b){const R=x.length-e.term.length;if(!R)continue;y==null||y.delete(x);const A=h*x.length/(x.length+.3*R);this.termResults(e.term,x,A,e.termBoost,w,n,r,l,f)}if(y)for(const x of y.keys()){const[w,R]=y.get(x);if(!R)continue;const A=c*x.length/(x.length+R);this.termResults(e.term,x,A,e.termBoost,w,n,r,l,f)}return f}executeWildcardQuery(e){const t=new Map,s=Object.assign(Object.assign({},this._options.searchOptions),e);for(const[n,r]of this._documentIds){const i=s.boostDocument?s.boostDocument(r,"",this._storedFields.get(n)):1;t.set(n,{score:i,terms:[],match:{}})}return t}combineResults(e,t=Ge){if(e.length===0)return new Map;const s=t.toLowerCase(),n=Ws[s];if(!n)throw new Error(`Invalid combination operator: ${t}`);return e.reduce(n)||new Map}toJSON(){const e=[];for(const[t,s]of this._index){const n={};for(const[r,i]of s)n[r]=Object.fromEntries(i);e.push([t,n])}return{documentCount:this._documentCount,nextId:this._nextId,documentIds:Object.fromEntries(this._documentIds),fieldIds:this._fieldIds,fieldLength:Object.fromEntries(this._fieldLength),averageFieldLength:this._avgFieldLength,storedFields:Object.fromEntries(this._storedFields),dirtCount:this._dirtCount,index:e,serializationVersion:2}}termResults(e,t,s,n,r,i,o,l,c=new Map){if(r==null)return c;for(const h of Object.keys(i)){const m=i[h],f=this._fieldIds[h],b=r.get(f);if(b==null)continue;let y=b.size;const x=this._avgFieldLength[f];for(const w of b.keys()){if(!this._documentIds.has(w)){this.removeTerm(f,w,t),y-=1;continue}const R=o?o(this._documentIds.get(w),t,this._storedFields.get(w)):1;if(!R)continue;const A=b.get(w),J=this._fieldLength.get(w)[f],Q=Js(A,y,this._documentCount,J,x,l),W=s*n*m*R*Q,V=c.get(w);if(V){V.score+=W,Gs(V.terms,e);const $=Pe(V.match,t);$?$.push(h):V.match[t]=[h]}else c.set(w,{score:W,terms:[e],match:{[t]:[h]}})}}return 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new file mode 100644 index 0000000..6d8d9fc --- /dev/null +++ b/previews/PR72/assets/vignettes_entropize.md.CK-iFMSh.js @@ -0,0 +1,119 @@ +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
+using NeutralLandscapes
+using CairoMakie
[ Info: Loading NeutralLandscapes support for SimpleSDMLayers.jl...
+┌ Warning: Error requiring \`SpeciesDistributionToolkit\` from \`NeutralLandscapes\`
+│   exception =
+│    LoadError: UndefVarError: \`SimpleSDMLayer\` not defined in \`SimpleSDMLayers\`
+│    Suggestion: check for spelling errors or missing imports.
+│    Stacktrace:
+│      [1] getproperty(x::Module, f::Symbol)
+│        @ Base ./Base.jl:42
+│      [2] top-level scope
+│        @ ~/.julia/packages/NeutralLandscapes/KXn74/src/integrations/simplesdmlayers.jl:3
+│      [3] include(mod::Module, _path::String)
+│        @ Base ./Base.jl:557
+│      [4] include(x::String)
+│        @ NeutralLandscapes ~/.julia/packages/NeutralLandscapes/KXn74/src/NeutralLandscapes.jl:1
+│      [5] top-level scope
+│        @ none:1
+│      [6] eval
+│        @ ./boot.jl:430 [inlined]
+│      [7] eval
+│        @ ~/.julia/packages/NeutralLandscapes/KXn74/src/NeutralLandscapes.jl:1 [inlined]
+│      [8] (::NeutralLandscapes.var"#60#63")()
+│        @ NeutralLandscapes ~/.julia/packages/Requires/Z8rfN/src/require.jl:101
+│      [9] macro expansion
+│        @ timing.jl:421 [inlined]
+│     [10] err(f::Any, listener::Module, modname::String, file::String, line::Any)
+│        @ Requires ~/.julia/packages/Requires/Z8rfN/src/require.jl:47
+│     [11] (::NeutralLandscapes.var"#59#62")()
+│        @ NeutralLandscapes ~/.julia/packages/Requires/Z8rfN/src/require.jl:100
+│     [12] withpath(f::Any, path::String)
+│        @ Requires ~/.julia/packages/Requires/Z8rfN/src/require.jl:37
+│     [13] (::NeutralLandscapes.var"#58#61")()
+│        @ NeutralLandscapes ~/.julia/packages/Requires/Z8rfN/src/require.jl:99
+│     [14] listenpkg(f::Any, pkg::Base.PkgId)
+│        @ Requires ~/.julia/packages/Requires/Z8rfN/src/require.jl:20
+│     [15] macro expansion
+│        @ ~/.julia/packages/Requires/Z8rfN/src/require.jl:98 [inlined]
+│     [16] __init__()
+│        @ NeutralLandscapes ~/.julia/packages/NeutralLandscapes/KXn74/src/NeutralLandscapes.jl:60
+│     [17] run_module_init(mod::Module, i::Int64)
+│        @ Base ./loading.jl:1378
+│     [18] register_restored_modules(sv::Core.SimpleVector, pkg::Base.PkgId, path::String)
+│        @ Base ./loading.jl:1366
+│     [19] _include_from_serialized(pkg::Base.PkgId, path::String, ocachepath::String, depmods::Vector{Any}, ignore_native::Nothing; register::Bool)
+│        @ Base ./loading.jl:1254
+│     [20] _include_from_serialized (repeats 2 times)
+│        @ ./loading.jl:1210 [inlined]
+│     [21] _require_search_from_serialized(pkg::Base.PkgId, sourcepath::String, build_id::UInt128, stalecheck::Bool; reasons::Dict{String, Int64}, DEPOT_PATH::Vector{String})
+│        @ Base ./loading.jl:2057
+│     [22] _require(pkg::Base.PkgId, env::String)
+│        @ Base ./loading.jl:2527
+│     [23] __require_prelocked(uuidkey::Base.PkgId, env::String)
+│        @ Base ./loading.jl:2388
+│     [24] #invoke_in_world#3
+│        @ ./essentials.jl:1089 [inlined]
+│     [25] invoke_in_world
+│        @ ./essentials.jl:1086 [inlined]
+│     [26] _require_prelocked(uuidkey::Base.PkgId, env::String)
+│        @ Base ./loading.jl:2375
+│     [27] macro expansion
+│        @ ./loading.jl:2314 [inlined]
+│     [28] macro expansion
+│        @ ./lock.jl:273 [inlined]
+│     [29] __require(into::Module, mod::Symbol)
+│        @ Base ./loading.jl:2271
+│     [30] #invoke_in_world#3
+│        @ ./essentials.jl:1089 [inlined]
+│     [31] invoke_in_world
+│        @ ./essentials.jl:1086 [inlined]
+│     [32] require(into::Module, mod::Symbol)
+│        @ Base ./loading.jl:2260
+│     [33] eval
+│        @ ./boot.jl:430 [inlined]
+│     [34] #60
+│        @ ~/.julia/packages/Documenter/Bs999/src/expander_pipeline.jl:803 [inlined]
+│     [35] cd(f::Documenter.var"#60#62"{Module, Expr}, dir::String)
+│        @ Base.Filesystem ./file.jl:112
+│     [36] (::Documenter.var"#59#61"{Documenter.Page, Module, Expr})()
+│        @ Documenter ~/.julia/packages/Documenter/Bs999/src/expander_pipeline.jl:802
+│     [37] (::IOCapture.var"#5#9"{DataType, Documenter.var"#59#61"{Documenter.Page, Module, Expr}, IOContext{Base.PipeEndpoint}, IOContext{Base.PipeEndpoint}, Base.PipeEndpoint, Base.PipeEndpoint})()
+│        @ IOCapture ~/.julia/packages/IOCapture/Y5rEA/src/IOCapture.jl:170
+│     [38] with_logstate(f::IOCapture.var"#5#9"{DataType, Documenter.var"#59#61"{Documenter.Page, Module, Expr}, IOContext{Base.PipeEndpoint}, IOContext{Base.PipeEndpoint}, Base.PipeEndpoint, Base.PipeEndpoint}, logstate::Base.CoreLogging.LogState)
+│        @ Base.CoreLogging ./logging/logging.jl:522
+│     [39] with_logger(f::Function, logger::Base.CoreLogging.ConsoleLogger)
+│        @ Base.CoreLogging ./logging/logging.jl:632
+│     [40] capture(f::Documenter.var"#59#61"{Documenter.Page, Module, Expr}; rethrow::Type, color::Bool, passthrough::Bool, capture_buffer::IOBuffer, io_context::Vector{Any})
+│        @ IOCapture ~/.julia/packages/IOCapture/Y5rEA/src/IOCapture.jl:167
+│     [41] runner(::Type{Documenter.Expanders.ExampleBlocks}, node::MarkdownAST.Node{Nothing}, page::Documenter.Page, doc::Documenter.Document)
+│        @ Documenter ~/.julia/packages/Documenter/Bs999/src/expander_pipeline.jl:801
+│     [42] dispatch(::Type{Documenter.Expanders.ExpanderPipeline}, ::MarkdownAST.Node{Nothing}, ::Vararg{Any})
+│        @ Documenter.Selectors ~/.julia/packages/Documenter/Bs999/src/utilities/Selectors.jl:170
+│     [43] expand(doc::Documenter.Document)
+│        @ Documenter ~/.julia/packages/Documenter/Bs999/src/expander_pipeline.jl:22
+│     [44] runner(::Type{Documenter.Builder.ExpandTemplates}, doc::Documenter.Document)
+│        @ Documenter ~/.julia/packages/Documenter/Bs999/src/builder_pipeline.jl:224
+│     [45] dispatch(::Type{Documenter.Builder.DocumentPipeline}, x::Documenter.Document)
+│        @ Documenter.Selectors ~/.julia/packages/Documenter/Bs999/src/utilities/Selectors.jl:170
+│     [46] #89
+│        @ ~/.julia/packages/Documenter/Bs999/src/makedocs.jl:273 [inlined]
+│     [47] withenv(::Documenter.var"#89#91"{Documenter.Document}, ::Pair{String, Nothing}, ::Vararg{Pair{String, Nothing}})
+│        @ Base ./env.jl:265
+│     [48] #88
+│        @ ~/.julia/packages/Documenter/Bs999/src/makedocs.jl:272 [inlined]
+│     [49] cd(f::Documenter.var"#88#90"{Documenter.Document}, dir::String)
+│        @ Base.Filesystem ./file.jl:112
+│     [50] makedocs(; debug::Bool, format::MarkdownVitepress, kwargs::@Kwargs{sitename::String, authors::String, modules::Vector{Module}, warnonly::Bool, plugins::Vector{CitationBibliography}})
+│        @ Documenter ~/.julia/packages/Documenter/Bs999/src/makedocs.jl:271
+│     [51] top-level scope
+│        @ ~/work/BiodiversityObservationNetworks.jl/BiodiversityObservationNetworks.jl/docs/make.jl:10
+│     [52] include(mod::Module, _path::String)
+│        @ Base ./Base.jl:557
+│     [53] exec_options(opts::Base.JLOptions)
+│        @ Base ./client.jl:323
+│    in expression starting at /home/runner/.julia/packages/NeutralLandscapes/KXn74/src/integrations/simplesdmlayers.jl:3
+└ @ 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}; diff --git a/previews/PR72/assets/vignettes_entropize.md.CK-iFMSh.lean.js b/previews/PR72/assets/vignettes_entropize.md.CK-iFMSh.lean.js new file mode 100644 index 0000000..6d8d9fc --- /dev/null +++ b/previews/PR72/assets/vignettes_entropize.md.CK-iFMSh.lean.js @@ -0,0 +1,119 @@ +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
+using NeutralLandscapes
+using CairoMakie
[ Info: Loading NeutralLandscapes support for SimpleSDMLayers.jl...
+┌ Warning: Error requiring \`SpeciesDistributionToolkit\` from \`NeutralLandscapes\`
+│   exception =
+│    LoadError: UndefVarError: \`SimpleSDMLayer\` not defined in \`SimpleSDMLayers\`
+│    Suggestion: check for spelling errors or missing imports.
+│    Stacktrace:
+│      [1] getproperty(x::Module, f::Symbol)
+│        @ Base ./Base.jl:42
+│      [2] top-level scope
+│        @ ~/.julia/packages/NeutralLandscapes/KXn74/src/integrations/simplesdmlayers.jl:3
+│      [3] include(mod::Module, _path::String)
+│        @ Base ./Base.jl:557
+│      [4] include(x::String)
+│        @ NeutralLandscapes ~/.julia/packages/NeutralLandscapes/KXn74/src/NeutralLandscapes.jl:1
+│      [5] top-level scope
+│        @ none:1
+│      [6] eval
+│        @ ./boot.jl:430 [inlined]
+│      [7] eval
+│        @ ~/.julia/packages/NeutralLandscapes/KXn74/src/NeutralLandscapes.jl:1 [inlined]
+│      [8] (::NeutralLandscapes.var"#60#63")()
+│        @ NeutralLandscapes ~/.julia/packages/Requires/Z8rfN/src/require.jl:101
+│      [9] macro expansion
+│        @ timing.jl:421 [inlined]
+│     [10] err(f::Any, listener::Module, modname::String, file::String, line::Any)
+│        @ Requires ~/.julia/packages/Requires/Z8rfN/src/require.jl:47
+│     [11] (::NeutralLandscapes.var"#59#62")()
+│        @ NeutralLandscapes ~/.julia/packages/Requires/Z8rfN/src/require.jl:100
+│     [12] withpath(f::Any, path::String)
+│        @ Requires ~/.julia/packages/Requires/Z8rfN/src/require.jl:37
+│     [13] (::NeutralLandscapes.var"#58#61")()
+│        @ NeutralLandscapes ~/.julia/packages/Requires/Z8rfN/src/require.jl:99
+│     [14] listenpkg(f::Any, pkg::Base.PkgId)
+│        @ Requires ~/.julia/packages/Requires/Z8rfN/src/require.jl:20
+│     [15] macro expansion
+│        @ ~/.julia/packages/Requires/Z8rfN/src/require.jl:98 [inlined]
+│     [16] __init__()
+│        @ NeutralLandscapes ~/.julia/packages/NeutralLandscapes/KXn74/src/NeutralLandscapes.jl:60
+│     [17] run_module_init(mod::Module, i::Int64)
+│        @ Base ./loading.jl:1378
+│     [18] register_restored_modules(sv::Core.SimpleVector, pkg::Base.PkgId, path::String)
+│        @ Base ./loading.jl:1366
+│     [19] _include_from_serialized(pkg::Base.PkgId, path::String, ocachepath::String, depmods::Vector{Any}, ignore_native::Nothing; register::Bool)
+│        @ Base ./loading.jl:1254
+│     [20] _include_from_serialized (repeats 2 times)
+│        @ ./loading.jl:1210 [inlined]
+│     [21] _require_search_from_serialized(pkg::Base.PkgId, sourcepath::String, build_id::UInt128, stalecheck::Bool; reasons::Dict{String, Int64}, DEPOT_PATH::Vector{String})
+│        @ Base ./loading.jl:2057
+│     [22] _require(pkg::Base.PkgId, env::String)
+│        @ Base ./loading.jl:2527
+│     [23] __require_prelocked(uuidkey::Base.PkgId, env::String)
+│        @ Base ./loading.jl:2388
+│     [24] #invoke_in_world#3
+│        @ ./essentials.jl:1089 [inlined]
+│     [25] invoke_in_world
+│        @ ./essentials.jl:1086 [inlined]
+│     [26] _require_prelocked(uuidkey::Base.PkgId, env::String)
+│        @ Base ./loading.jl:2375
+│     [27] macro expansion
+│        @ ./loading.jl:2314 [inlined]
+│     [28] macro expansion
+│        @ ./lock.jl:273 [inlined]
+│     [29] __require(into::Module, mod::Symbol)
+│        @ Base ./loading.jl:2271
+│     [30] #invoke_in_world#3
+│        @ ./essentials.jl:1089 [inlined]
+│     [31] invoke_in_world
+│        @ ./essentials.jl:1086 [inlined]
+│     [32] require(into::Module, mod::Symbol)
+│        @ Base ./loading.jl:2260
+│     [33] eval
+│        @ ./boot.jl:430 [inlined]
+│     [34] #60
+│        @ ~/.julia/packages/Documenter/Bs999/src/expander_pipeline.jl:803 [inlined]
+│     [35] cd(f::Documenter.var"#60#62"{Module, Expr}, dir::String)
+│        @ Base.Filesystem ./file.jl:112
+│     [36] (::Documenter.var"#59#61"{Documenter.Page, Module, Expr})()
+│        @ Documenter ~/.julia/packages/Documenter/Bs999/src/expander_pipeline.jl:802
+│     [37] (::IOCapture.var"#5#9"{DataType, Documenter.var"#59#61"{Documenter.Page, Module, Expr}, IOContext{Base.PipeEndpoint}, IOContext{Base.PipeEndpoint}, Base.PipeEndpoint, Base.PipeEndpoint})()
+│        @ IOCapture ~/.julia/packages/IOCapture/Y5rEA/src/IOCapture.jl:170
+│     [38] with_logstate(f::IOCapture.var"#5#9"{DataType, Documenter.var"#59#61"{Documenter.Page, Module, Expr}, IOContext{Base.PipeEndpoint}, IOContext{Base.PipeEndpoint}, Base.PipeEndpoint, Base.PipeEndpoint}, logstate::Base.CoreLogging.LogState)
+│        @ Base.CoreLogging ./logging/logging.jl:522
+│     [39] with_logger(f::Function, logger::Base.CoreLogging.ConsoleLogger)
+│        @ Base.CoreLogging ./logging/logging.jl:632
+│     [40] capture(f::Documenter.var"#59#61"{Documenter.Page, Module, Expr}; rethrow::Type, color::Bool, passthrough::Bool, capture_buffer::IOBuffer, io_context::Vector{Any})
+│        @ IOCapture ~/.julia/packages/IOCapture/Y5rEA/src/IOCapture.jl:167
+│     [41] runner(::Type{Documenter.Expanders.ExampleBlocks}, node::MarkdownAST.Node{Nothing}, page::Documenter.Page, doc::Documenter.Document)
+│        @ Documenter ~/.julia/packages/Documenter/Bs999/src/expander_pipeline.jl:801
+│     [42] dispatch(::Type{Documenter.Expanders.ExpanderPipeline}, ::MarkdownAST.Node{Nothing}, ::Vararg{Any})
+│        @ Documenter.Selectors ~/.julia/packages/Documenter/Bs999/src/utilities/Selectors.jl:170
+│     [43] expand(doc::Documenter.Document)
+│        @ Documenter ~/.julia/packages/Documenter/Bs999/src/expander_pipeline.jl:22
+│     [44] runner(::Type{Documenter.Builder.ExpandTemplates}, doc::Documenter.Document)
+│        @ Documenter ~/.julia/packages/Documenter/Bs999/src/builder_pipeline.jl:224
+│     [45] dispatch(::Type{Documenter.Builder.DocumentPipeline}, x::Documenter.Document)
+│        @ Documenter.Selectors ~/.julia/packages/Documenter/Bs999/src/utilities/Selectors.jl:170
+│     [46] #89
+│        @ ~/.julia/packages/Documenter/Bs999/src/makedocs.jl:273 [inlined]
+│     [47] withenv(::Documenter.var"#89#91"{Documenter.Document}, ::Pair{String, Nothing}, ::Vararg{Pair{String, Nothing}})
+│        @ Base ./env.jl:265
+│     [48] #88
+│        @ ~/.julia/packages/Documenter/Bs999/src/makedocs.jl:272 [inlined]
+│     [49] cd(f::Documenter.var"#88#90"{Documenter.Document}, dir::String)
+│        @ Base.Filesystem ./file.jl:112
+│     [50] makedocs(; debug::Bool, format::MarkdownVitepress, kwargs::@Kwargs{sitename::String, authors::String, modules::Vector{Module}, warnonly::Bool, plugins::Vector{CitationBibliography}})
+│        @ Documenter ~/.julia/packages/Documenter/Bs999/src/makedocs.jl:271
+│     [51] top-level scope
+│        @ ~/work/BiodiversityObservationNetworks.jl/BiodiversityObservationNetworks.jl/docs/make.jl:10
+│     [52] include(mod::Module, _path::String)
+│        @ Base ./Base.jl:557
+│     [53] exec_options(opts::Base.JLOptions)
+│        @ Base ./client.jl:323
+│    in expression starting at /home/runner/.julia/packages/NeutralLandscapes/KXn74/src/integrations/simplesdmlayers.jl:3
+└ @ 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}; diff --git a/previews/PR72/assets/vignettes_overview.md.CChV6Jfe.js b/previews/PR72/assets/vignettes_overview.md.CChV6Jfe.js new file mode 100644 index 0000000..5df890c --- /dev/null +++ b/previews/PR72/assets/vignettes_overview.md.CChV6Jfe.js @@ -0,0 +1,9 @@ +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
+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))
+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()
`,18)]))}const k=s(o,[["render",p]]);export{u as __pageData,k as default}; diff --git a/previews/PR72/assets/vignettes_overview.md.CChV6Jfe.lean.js b/previews/PR72/assets/vignettes_overview.md.CChV6Jfe.lean.js new file mode 100644 index 0000000..5df890c --- /dev/null +++ b/previews/PR72/assets/vignettes_overview.md.CChV6Jfe.lean.js @@ -0,0 +1,9 @@ +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
+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))
+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()
`,18)]))}const k=s(o,[["render",p]]);export{u as __pageData,k as default}; diff --git a/previews/PR72/assets/vignettes_uniqueness.md.BxV-u-oH.js b/previews/PR72/assets/vignettes_uniqueness.md.BxV-u-oH.js new file mode 100644 index 0000000..684ea47 --- /dev/null +++ b/previews/PR72/assets/vignettes_uniqueness.md.BxV-u-oH.js @@ -0,0 +1,14 @@ +import{_ as a,c as s,a5 as n,o as t}from"./chunks/framework.CuKCF6Tm.js";const h=JSON.parse('{"title":"Selecting environmentally unique locations","description":"","frontmatter":{},"headers":[],"relativePath":"vignettes/uniqueness.md","filePath":"vignettes/uniqueness.md","lastUpdated":null}'),i={name:"vignettes/uniqueness.md"};function l(o,e,p,c,d,r){return t(),s("div",null,e[0]||(e[0]=[n(`

Selecting environmentally unique locations ​

For some applications, we want to sample a set of locations that cover a broad range of values in environment space. Another way to rephrase this problem is to say we want to find the set of points with the least covariance in their environmental values.

To do this, we use a BONRefiner called Uniqueness. We'll start by loading the required packages.

@example
using BiodiversityObservationNetworks
+using SpeciesDistributionToolkit
+using StatsBase
+using NeutralLandscapes
+using CairoMakie

Consider setting your SDMLAYERS_PATH

When accessing data using SimpleSDMDatasets.jl, it is best to set the SDM_LAYERSPATH environmental variable to tell SimpleSDMDatasets.jl where to download data. This can be done by setting ENV["SDMLAYERS_PATH"] = "/home/user/Data/" or similar in the ~/.julia/etc/julia/startup.jl file. (Note this will be different depending on where julia is installed.)

@example
bbox = (left=-83.0, bottom=46.4, right=-55.2, top=63.7);
+temp, precip, elevation = 
+    convert(Float32, SimpleSDMPredictor(RasterData(WorldClim2, AverageTemperature); bbox...)),
+    convert(Float32, SimpleSDMPredictor(RasterData(WorldClim2, Precipitation); bbox...)),
+    convert(Float32, SimpleSDMPredictor(RasterData(WorldClim2, Elevation); bbox...));

Now we'll use the stack function to combine our four environmental layers into a single, 3-dimensional array, which we'll pass to our Uniqueness refiner.

@example
layers = BiodiversityObservationNetworks.stack([temp,precip,elevation]);
@example
uncert = rand(MidpointDisplacement(0.8), size(temp), mask=temp);
+heatmap(uncert)

Now we'll get a set of candidate points from a BalancedAcceptance seeder that has no bias toward higher uncertainty values.

@example
candpts = seed(BalancedAcceptance(numsites=100));

Now we'll refine our 100 candidate points down to the 30 most environmentally unique.

@example
finalpts = refine(candpts, Uniqueness(;numsites=30, layers=layers))
+heatmap(uncert)
+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()
`,13)]))}const g=a(i,[["render",l]]);export{h as __pageData,g as default}; diff --git a/previews/PR72/assets/vignettes_uniqueness.md.BxV-u-oH.lean.js b/previews/PR72/assets/vignettes_uniqueness.md.BxV-u-oH.lean.js new file mode 100644 index 0000000..684ea47 --- /dev/null +++ b/previews/PR72/assets/vignettes_uniqueness.md.BxV-u-oH.lean.js @@ -0,0 +1,14 @@ +import{_ as a,c as s,a5 as n,o as t}from"./chunks/framework.CuKCF6Tm.js";const h=JSON.parse('{"title":"Selecting environmentally unique locations","description":"","frontmatter":{},"headers":[],"relativePath":"vignettes/uniqueness.md","filePath":"vignettes/uniqueness.md","lastUpdated":null}'),i={name:"vignettes/uniqueness.md"};function l(o,e,p,c,d,r){return t(),s("div",null,e[0]||(e[0]=[n(`

Selecting environmentally unique locations ​

For some applications, we want to sample a set of locations that cover a broad range of values in environment space. Another way to rephrase this problem is to say we want to find the set of points with the least covariance in their environmental values.

To do this, we use a BONRefiner called Uniqueness. We'll start by loading the required packages.

@example
using BiodiversityObservationNetworks
+using SpeciesDistributionToolkit
+using StatsBase
+using NeutralLandscapes
+using CairoMakie

Consider setting your SDMLAYERS_PATH

When accessing data using SimpleSDMDatasets.jl, it is best to set the SDM_LAYERSPATH environmental variable to tell SimpleSDMDatasets.jl where to download data. This can be done by setting ENV["SDMLAYERS_PATH"] = "/home/user/Data/" or similar in the ~/.julia/etc/julia/startup.jl file. (Note this will be different depending on where julia is installed.)

@example
bbox = (left=-83.0, bottom=46.4, right=-55.2, top=63.7);
+temp, precip, elevation = 
+    convert(Float32, SimpleSDMPredictor(RasterData(WorldClim2, AverageTemperature); bbox...)),
+    convert(Float32, SimpleSDMPredictor(RasterData(WorldClim2, Precipitation); bbox...)),
+    convert(Float32, SimpleSDMPredictor(RasterData(WorldClim2, Elevation); bbox...));

Now we'll use the stack function to combine our four environmental layers into a single, 3-dimensional array, which we'll pass to our Uniqueness refiner.

@example
layers = BiodiversityObservationNetworks.stack([temp,precip,elevation]);
@example
uncert = rand(MidpointDisplacement(0.8), size(temp), mask=temp);
+heatmap(uncert)

Now we'll get a set of candidate points from a BalancedAcceptance seeder that has no bias toward higher uncertainty values.

@example
candpts = seed(BalancedAcceptance(numsites=100));

Now we'll refine our 100 candidate points down to the 30 most environmentally unique.

@example
finalpts = refine(candpts, Uniqueness(;numsites=30, layers=layers))
+heatmap(uncert)
+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()
`,13)]))}const g=a(i,[["render",l]]);export{h as __pageData,g as default}; diff --git a/previews/PR72/bibliography.html b/previews/PR72/bibliography.html new file mode 100644 index 0000000..70a3c74 --- /dev/null +++ b/previews/PR72/bibliography.html @@ -0,0 +1,25 @@ + + + + + + References | BiodiversityObservationNetworks.jl + + + + + + + + + + + + + + +
BiodiversityObservationNetworks.jl

Appearance

References ​

Bibliography ​

+ + + + \ No newline at end of file diff --git a/previews/PR72/hashmap.json b/previews/PR72/hashmap.json new file mode 100644 index 0000000..a285834 --- /dev/null +++ b/previews/PR72/hashmap.json @@ -0,0 +1 @@ +{"bibliography.md":"lp9eY1zx","index.md":"CtPQDIDR","vignettes_entropize.md":"CK-iFMSh","vignettes_overview.md":"CChV6Jfe","vignettes_uniqueness.md":"BxV-u-oH"} diff --git a/previews/PR72/index.html b/previews/PR72/index.html new file mode 100644 index 0000000..7eabe62 --- /dev/null +++ b/previews/PR72/index.html @@ -0,0 +1,25 @@ + + + + + + BiodiversityObservationNetworks.jl + + + + + + + + + + + + + + +
BiodiversityObservationNetworks.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.

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.

+ + + + \ No newline at end of file diff --git a/previews/PR72/logo.png b/previews/PR72/logo.png new file mode 100644 index 0000000..8e8ff8c Binary files /dev/null and b/previews/PR72/logo.png differ diff --git a/previews/PR72/siteinfo.js b/previews/PR72/siteinfo.js new file mode 100644 index 0000000..3f8ba2c --- /dev/null +++ b/previews/PR72/siteinfo.js @@ -0,0 +1 @@ +var DOCUMENTER_CURRENT_VERSION = "previews/PR72"; diff --git a/previews/PR72/vignettes/entropize.html b/previews/PR72/vignettes/entropize.html new file mode 100644 index 0000000..bad4b91 --- /dev/null +++ b/previews/PR72/vignettes/entropize.html @@ -0,0 +1,143 @@ + + + + + + Getting the entropy matrix | BiodiversityObservationNetworks.jl + + + + + + + + + + + + + + +
BiodiversityObservationNetworks.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
[ Info: Loading NeutralLandscapes support for SimpleSDMLayers.jl...
+┌ Warning: Error requiring `SpeciesDistributionToolkit` from `NeutralLandscapes`
+│   exception =
+│    LoadError: UndefVarError: `SimpleSDMLayer` not defined in `SimpleSDMLayers`
+│    Suggestion: check for spelling errors or missing imports.
+│    Stacktrace:
+│      [1] getproperty(x::Module, f::Symbol)
+│        @ Base ./Base.jl:42
+│      [2] top-level scope
+│        @ ~/.julia/packages/NeutralLandscapes/KXn74/src/integrations/simplesdmlayers.jl:3
+│      [3] include(mod::Module, _path::String)
+│        @ Base ./Base.jl:557
+│      [4] include(x::String)
+│        @ NeutralLandscapes ~/.julia/packages/NeutralLandscapes/KXn74/src/NeutralLandscapes.jl:1
+│      [5] top-level scope
+│        @ none:1
+│      [6] eval
+│        @ ./boot.jl:430 [inlined]
+│      [7] eval
+│        @ ~/.julia/packages/NeutralLandscapes/KXn74/src/NeutralLandscapes.jl:1 [inlined]
+│      [8] (::NeutralLandscapes.var"#60#63")()
+│        @ NeutralLandscapes ~/.julia/packages/Requires/Z8rfN/src/require.jl:101
+│      [9] macro expansion
+│        @ timing.jl:421 [inlined]
+│     [10] err(f::Any, listener::Module, modname::String, file::String, line::Any)
+│        @ Requires ~/.julia/packages/Requires/Z8rfN/src/require.jl:47
+│     [11] (::NeutralLandscapes.var"#59#62")()
+│        @ NeutralLandscapes ~/.julia/packages/Requires/Z8rfN/src/require.jl:100
+│     [12] withpath(f::Any, path::String)
+│        @ Requires ~/.julia/packages/Requires/Z8rfN/src/require.jl:37
+│     [13] (::NeutralLandscapes.var"#58#61")()
+│        @ NeutralLandscapes ~/.julia/packages/Requires/Z8rfN/src/require.jl:99
+│     [14] listenpkg(f::Any, pkg::Base.PkgId)
+│        @ Requires ~/.julia/packages/Requires/Z8rfN/src/require.jl:20
+│     [15] macro expansion
+│        @ ~/.julia/packages/Requires/Z8rfN/src/require.jl:98 [inlined]
+│     [16] __init__()
+│        @ NeutralLandscapes ~/.julia/packages/NeutralLandscapes/KXn74/src/NeutralLandscapes.jl:60
+│     [17] run_module_init(mod::Module, i::Int64)
+│        @ Base ./loading.jl:1378
+│     [18] register_restored_modules(sv::Core.SimpleVector, pkg::Base.PkgId, path::String)
+│        @ Base ./loading.jl:1366
+│     [19] _include_from_serialized(pkg::Base.PkgId, path::String, ocachepath::String, depmods::Vector{Any}, ignore_native::Nothing; register::Bool)
+│        @ Base ./loading.jl:1254
+│     [20] _include_from_serialized (repeats 2 times)
+│        @ ./loading.jl:1210 [inlined]
+│     [21] _require_search_from_serialized(pkg::Base.PkgId, sourcepath::String, build_id::UInt128, stalecheck::Bool; reasons::Dict{String, Int64}, DEPOT_PATH::Vector{String})
+│        @ Base ./loading.jl:2057
+│     [22] _require(pkg::Base.PkgId, env::String)
+│        @ Base ./loading.jl:2527
+│     [23] __require_prelocked(uuidkey::Base.PkgId, env::String)
+│        @ Base ./loading.jl:2388
+│     [24] #invoke_in_world#3
+│        @ ./essentials.jl:1089 [inlined]
+│     [25] invoke_in_world
+│        @ ./essentials.jl:1086 [inlined]
+│     [26] _require_prelocked(uuidkey::Base.PkgId, env::String)
+│        @ Base ./loading.jl:2375
+│     [27] macro expansion
+│        @ ./loading.jl:2314 [inlined]
+│     [28] macro expansion
+│        @ ./lock.jl:273 [inlined]
+│     [29] __require(into::Module, mod::Symbol)
+│        @ Base ./loading.jl:2271
+│     [30] #invoke_in_world#3
+│        @ ./essentials.jl:1089 [inlined]
+│     [31] invoke_in_world
+│        @ ./essentials.jl:1086 [inlined]
+│     [32] require(into::Module, mod::Symbol)
+│        @ Base ./loading.jl:2260
+│     [33] eval
+│        @ ./boot.jl:430 [inlined]
+│     [34] #60
+│        @ ~/.julia/packages/Documenter/Bs999/src/expander_pipeline.jl:803 [inlined]
+│     [35] cd(f::Documenter.var"#60#62"{Module, Expr}, dir::String)
+│        @ Base.Filesystem ./file.jl:112
+│     [36] (::Documenter.var"#59#61"{Documenter.Page, Module, Expr})()
+│        @ Documenter ~/.julia/packages/Documenter/Bs999/src/expander_pipeline.jl:802
+│     [37] (::IOCapture.var"#5#9"{DataType, Documenter.var"#59#61"{Documenter.Page, Module, Expr}, IOContext{Base.PipeEndpoint}, IOContext{Base.PipeEndpoint}, Base.PipeEndpoint, Base.PipeEndpoint})()
+│        @ IOCapture ~/.julia/packages/IOCapture/Y5rEA/src/IOCapture.jl:170
+│     [38] with_logstate(f::IOCapture.var"#5#9"{DataType, Documenter.var"#59#61"{Documenter.Page, Module, Expr}, IOContext{Base.PipeEndpoint}, IOContext{Base.PipeEndpoint}, Base.PipeEndpoint, Base.PipeEndpoint}, logstate::Base.CoreLogging.LogState)
+│        @ Base.CoreLogging ./logging/logging.jl:522
+│     [39] with_logger(f::Function, logger::Base.CoreLogging.ConsoleLogger)
+│        @ Base.CoreLogging ./logging/logging.jl:632
+│     [40] capture(f::Documenter.var"#59#61"{Documenter.Page, Module, Expr}; rethrow::Type, color::Bool, passthrough::Bool, capture_buffer::IOBuffer, io_context::Vector{Any})
+│        @ IOCapture ~/.julia/packages/IOCapture/Y5rEA/src/IOCapture.jl:167
+│     [41] runner(::Type{Documenter.Expanders.ExampleBlocks}, node::MarkdownAST.Node{Nothing}, page::Documenter.Page, doc::Documenter.Document)
+│        @ Documenter ~/.julia/packages/Documenter/Bs999/src/expander_pipeline.jl:801
+│     [42] dispatch(::Type{Documenter.Expanders.ExpanderPipeline}, ::MarkdownAST.Node{Nothing}, ::Vararg{Any})
+│        @ Documenter.Selectors ~/.julia/packages/Documenter/Bs999/src/utilities/Selectors.jl:170
+│     [43] expand(doc::Documenter.Document)
+│        @ Documenter ~/.julia/packages/Documenter/Bs999/src/expander_pipeline.jl:22
+│     [44] runner(::Type{Documenter.Builder.ExpandTemplates}, doc::Documenter.Document)
+│        @ Documenter ~/.julia/packages/Documenter/Bs999/src/builder_pipeline.jl:224
+│     [45] dispatch(::Type{Documenter.Builder.DocumentPipeline}, x::Documenter.Document)
+│        @ Documenter.Selectors ~/.julia/packages/Documenter/Bs999/src/utilities/Selectors.jl:170
+│     [46] #89
+│        @ ~/.julia/packages/Documenter/Bs999/src/makedocs.jl:273 [inlined]
+│     [47] withenv(::Documenter.var"#89#91"{Documenter.Document}, ::Pair{String, Nothing}, ::Vararg{Pair{String, Nothing}})
+│        @ Base ./env.jl:265
+│     [48] #88
+│        @ ~/.julia/packages/Documenter/Bs999/src/makedocs.jl:272 [inlined]
+│     [49] cd(f::Documenter.var"#88#90"{Documenter.Document}, dir::String)
+│        @ Base.Filesystem ./file.jl:112
+│     [50] makedocs(; debug::Bool, format::MarkdownVitepress, kwargs::@Kwargs{sitename::String, authors::String, modules::Vector{Module}, warnonly::Bool, plugins::Vector{CitationBibliography}})
+│        @ Documenter ~/.julia/packages/Documenter/Bs999/src/makedocs.jl:271
+│     [51] top-level scope
+│        @ ~/work/BiodiversityObservationNetworks.jl/BiodiversityObservationNetworks.jl/docs/make.jl:10
+│     [52] include(mod::Module, _path::String)
+│        @ Base ./Base.jl:557
+│     [53] exec_options(opts::Base.JLOptions)
+│        @ Base ./client.jl:323
+│    in expression starting at /home/runner/.julia/packages/NeutralLandscapes/KXn74/src/integrations/simplesdmlayers.jl:3
+└ @ 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))
+ + + + \ No newline at end of file diff --git a/previews/PR72/vignettes/overview.html b/previews/PR72/vignettes/overview.html new file mode 100644 index 0000000..66ca23e --- /dev/null +++ b/previews/PR72/vignettes/overview.html @@ -0,0 +1,33 @@ + + + + + + An introduction to BiodiversityObservationNetworks | BiodiversityObservationNetworks.jl + + + + + + + + + + + + + + +
BiodiversityObservationNetworks.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))
+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 new file mode 100644 index 0000000..0e81d6e --- /dev/null +++ b/previews/PR72/vignettes/uniqueness.html @@ -0,0 +1,38 @@ + + + + + + Selecting environmentally unique locations | BiodiversityObservationNetworks.jl + + + + + + + + + + + + + + +
BiodiversityObservationNetworks.jl

Appearance

Selecting environmentally unique locations ​

For some applications, we want to sample a set of locations that cover a broad range of values in environment space. Another way to rephrase this problem is to say we want to find the set of points with the least covariance in their environmental values.

To do this, we use a BONRefiner called Uniqueness. We'll start by loading the required packages.

@example
using BiodiversityObservationNetworks
+using SpeciesDistributionToolkit
+using StatsBase
+using NeutralLandscapes
+using CairoMakie

Consider setting your SDMLAYERS_PATH

When accessing data using SimpleSDMDatasets.jl, it is best to set the SDM_LAYERSPATH environmental variable to tell SimpleSDMDatasets.jl where to download data. This can be done by setting ENV["SDMLAYERS_PATH"] = "/home/user/Data/" or similar in the ~/.julia/etc/julia/startup.jl file. (Note this will be different depending on where julia is installed.)

@example
bbox = (left=-83.0, bottom=46.4, right=-55.2, top=63.7);
+temp, precip, elevation = 
+    convert(Float32, SimpleSDMPredictor(RasterData(WorldClim2, AverageTemperature); bbox...)),
+    convert(Float32, SimpleSDMPredictor(RasterData(WorldClim2, Precipitation); bbox...)),
+    convert(Float32, SimpleSDMPredictor(RasterData(WorldClim2, Elevation); bbox...));

Now we'll use the stack function to combine our four environmental layers into a single, 3-dimensional array, which we'll pass to our Uniqueness refiner.

@example
layers = BiodiversityObservationNetworks.stack([temp,precip,elevation]);
@example
uncert = rand(MidpointDisplacement(0.8), size(temp), mask=temp);
+heatmap(uncert)

Now we'll get a set of candidate points from a BalancedAcceptance seeder that has no bias toward higher uncertainty values.

@example
candpts = seed(BalancedAcceptance(numsites=100));

Now we'll refine our 100 candidate points down to the 30 most environmentally unique.

@example
finalpts = refine(candpts, Uniqueness(;numsites=30, layers=layers))
+heatmap(uncert)
+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()
+ + + + \ No newline at end of file diff --git a/previews/PR72/vp-icons.css b/previews/PR72/vp-icons.css new file mode 100644 index 0000000..ddc5bd8 --- /dev/null +++ b/previews/PR72/vp-icons.css @@ -0,0 +1 @@ +.vpi-social-github{--icon:url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 24 24' width='24' height='24'%3E%3Cpath fill='black' d='M12 .297c-6.63 0-12 5.373-12 12c0 5.303 3.438 9.8 8.205 11.385c.6.113.82-.258.82-.577c0-.285-.01-1.04-.015-2.04c-3.338.724-4.042-1.61-4.042-1.61C4.422 18.07 3.633 17.7 3.633 17.7c-1.087-.744.084-.729.084-.729c1.205.084 1.838 1.236 1.838 1.236c1.07 1.835 2.809 1.305 3.495.998c.108-.776.417-1.305.76-1.605c-2.665-.3-5.466-1.332-5.466-5.93c0-1.31.465-2.38 1.235-3.22c-.135-.303-.54-1.523.105-3.176c0 0 1.005-.322 3.3 1.23c.96-.267 1.98-.399 3-.405c1.02.006 2.04.138 3 .405c2.28-1.552 3.285-1.23 3.285-1.23c.645 1.653.24 2.873.12 3.176c.765.84 1.23 1.91 1.23 3.22c0 4.61-2.805 5.625-5.475 5.92c.42.36.81 1.096.81 2.22c0 1.606-.015 2.896-.015 3.286c0 .315.21.69.825.57C20.565 22.092 24 17.592 24 12.297c0-6.627-5.373-12-12-12'/%3E%3C/svg%3E")} \ No newline at end of file